地理科学进展  2016 , 35 (3): 304-319 https://doi.org/10.18306/dlkxjz.2016.03.005

研究综述

植物物候遥感监测精度影响因素研究综述

范德芹1, 赵学胜1, 朱文泉2*, 郑周涛2

1. 中国矿业大学(北京)地球科学与测绘工程学院,北京 100083
2. 北京师范大学资源学院,北京 100875

Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data

FAN Deqin1, ZHAO Xuesheng1, ZHU Wenquan2*, ZHENG Zhoutao2

1. College of Geosciences and Survey Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China
2. College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China

通讯作者:  朱文泉(1975-),男,湖南永兴人,博士,教授,主要从事植被与生态遥感研究,E-mail: zhuwq75@bnu.edu.cn

收稿日期: 2015-06-25

接受日期:  2015-08-25

网络出版日期:  2016-03-25

版权声明:  2016 地理科学进展 《地理科学进展》杂志 版权所有

基金资助:  国家自然科学基金项目(41371389,41171306)

作者简介:

作者简介:范德芹(1982-),女,内蒙古呼伦贝尔人,博士后,主要从事遥感数据处理研究,E-mail: kinly129@163.com

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摘要

基于植物物候的遥感监测对于研究植被对气候变化的响应具有重要的科学价值。本文在阐述植物物候遥感监测原理及其通用技术流程的基础上,分别从植被类型及其所处的地理条件、遥感数据源及其预处理、植物物候遥感识别方法和植物物候遥感监测结果评价4个方面分析了影响植物物候遥感监测精度的因素,并针对当前研究中存在的不足,探讨了提高植物物候遥感监测精度的可行性途径,即建立高分辨率的近地面遥感定点观测及数据共享网络,发展普适性更强的卫星遥感时序数据去噪及植被指数曲线重建方法,寻求稳定性更高的植物物候期遥感识别方法,探索综合运用地面观测、遥感监测与模型模拟实现物候观测空间尺度拓展的可能性。

关键词: 植物物候 ; 遥感 ; 植被指数 ; 时间序列 ; 精度 ; 影响因素 ; 综述

Abstract

Monitoring plant phenology with remote sensing data has important scientific value for studying the response of vegetation to climate change. A comprehensive analysis on the influencing factors of accuracy of plant phenology estimation based on principles and general technical processes of remote sensing application in vegetation monitoring was carried out by taking into account the following four aspects: the specific vegetation type and its geographical conditions; remote sensing data and pre-processing; techniques used to identify plant phenometrics; and evaluation of satellite-derived plant phenometrics. Potential methods for improving the accuracy of plant phenology monitoring are thoroughly discussed. These include: building high-resolution near-surface sensor-derived phenology observation and sharing network; developing universally applicable methods for noise removal of satellite remote sensing time-series data and reconstruction of vegetation index curves; searching more stable methods to estimate plant phenology; and exploring the possibility of synthesizing ground-based observation, remote sensing monitoring, and model simulation to achieve the spatial scaling-up of phenometrics.

Keywords: plant phenology ; remote sensing ; vegetation index ; time series ; accuracy ; influencing factor ; review

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范德芹, 赵学胜, 朱文泉, 郑周涛. 植物物候遥感监测精度影响因素研究综述[J]. , 2016, 35(3): 304-319 https://doi.org/10.18306/dlkxjz.2016.03.005

FAN Deqin, ZHAO Xuesheng, ZHU Wenquan, ZHENG Zhoutao. Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data[J]. 地理科学进展, 2016, 35(3): 304-319 https://doi.org/10.18306/dlkxjz.2016.03.005

1 引言

物候是动植物发育或生活周期随季节变化的现象,如植物的发芽、开花、落叶,动物的迁移、繁殖等(Rathcke et al, 1985)。植物物候对气候变化的响应表明,长时间、大尺度的物候观测结果可在时空上反映气候变化(Peñuelas et al, 2002; Walther et al, 2002),尤其是温度的变化(Myneni et al, 1997)。20世纪90年代中期以来,随着全球气温升高,植物物候已经发生了变化(Schwartz et al, 2006; Zhu, Tian et al, 2012),这种变化同时导致了群落和生态系统结构、植被生产力、土壤—植被—大气系统之间能量和水分交换的变化(Piao et al, 2008; Dragoni et al, 2011);这些变化反过来又会影响和加剧气候变化(Peñuelas et al, 2009)。因此,开展植物物候研究不仅有助于增进植被对气候变化响应的理解,而且对提高气候—植被之间物质与能量交换的模拟精度、准确评估植被生产力与全球碳收支均具有重要意义(Walther et al, 2002)。

植物物候监测主要指通过各种方法和手段来监测植物生长节律的季节性和年际变化。目前,植物物候监测方法主要有地面观测法、遥感监测法、模型模拟法。地面观测方法是传统的物候监测方法,主要用于观测物种在个体水平上的生长节律变化,是针对某一个或多个物种进行的单株、定点观测。地面观测方法对于单株和小区域范围的物候研究具有时间精度高、易于操作等优点,仍然是目前研究群落结构随季节变化的基本方法。但由于植物物候地面观测站点分布不均、覆盖的空间范围有限且缺少广泛的地面观测数据(Schwartz et al, 2006),因此它很难在更大尺度上反映区域的植物物候状况(Menzel et al, 2006)。遥感监测方法主要是根据任何目标物都具有发射、反射和吸收电磁波的性质,利用传感器对地物波谱信息进行记录。由于卫星遥感具有大面积监测地物的特点,使其可以在区域乃至全球尺度,通过监测地表植被指数变化情况,来监测植被的物候变化(Reed et al, 2005)。遥感监测结果在像元尺度上,反映植物群落或生态系统的植被生长状态及变化特征,这与地面观测的植物个体或物种水平的物候现象存在很大差异(Badeck et al, 2004; 陈效逑等, 2009)。虽然目前有很多基于卫星遥感数据进行植物物候识别的方法,但各具优缺点,不同方法往往仅对特定研究区域或特定植被类型有效,不具有普适性(陈效逑等, 2009)。因此,应根据研究区域、植被类型的不同,选择适当的遥感监测方法,并结合地面物候观测数据对其加以参数化和本地化。模型模拟法主要是指在个体和种群水平上通过研究植物生长节律的生理发生机制,建立植物物候模型来研究植物物候的时空变化。目前各物候模型大多是利用地面观测数据在物种水平建立物候模型(Chuine, 2000; Morin et al, 2009),少数采用遥感数据在种群或群落水平构建物候模型(Fan et al, 2015; Xin et al, 2015),通过模型模拟在机理上反映植物内在的生长过程。

植物物候遥感监测结果的精度主要取决于具体的植被类型及其所处的地理条件、遥感数据源及其预处理方法、植物物候遥感识别方法、植物物候遥感监测结果评价等,因此,本文旨在分析遥感监测的各个环节对植物物候提取精度的影响,并探寻提高植物物候遥感监测精度的可行性途径。

2 植物物候遥感监测原理

2.1 植被指数

植物物候遥感监测主要利用时间序列植被指数,如增强型植被指数(Enhanced Vegetation Index, EVI)、归一化差值植被指数(Normalized Difference Vegetation Index, NDVI)等。NDVI是目前应用最广泛的植被指数,可部分消除太阳高度角和大气等观测条件影响的噪声,其公式本身的特点使之对叶绿素敏感(Gao et al, 2000),曲线幅度大,从而能够灵敏地监测植被的长势特征。但其公式在高生物量地区易饱和(Gao et al, 2000),易受植被冠层和土壤背景信息、双向反射等的影响。EVI对植物形态和冠层结构等信息响应敏感,从而有效提高了高生物量地区植被长势的监测精度,同时公式中加入了对背景信息修正的参数,消除了大部分土壤背景信息的影响,采用蓝光波段进行计算以减少大气中气溶胶影响(Huete et al, 2002)。EVI数据合成时优先选择无云干扰且传感器视角小的像元,其数据质量与其他植被指数相比更好。EVI在以上各方面的改进,为定量开展遥感研究提供了更好的基础;但EVI时序数据的曲线幅度较小,可能会影响其对植被绿度的反映;此外,由于对数据进行了大气和土壤背景值的校正(Jiang et al, 2008),因此EVI对土壤背景的噪声不敏感。

植被指数时序数据可以反映植物的生长节律。基于MODIS NDVI数据(空间分辨率为250 m,http://lpdaac.usgs.gov/),植被类型选取依据源于国际地圈生物圈计划(IGBP)的全球植被分类方案,制作了常见植被类型的NDVI时序数据及其遥感识别的物候期示意图(图1),并进行去噪和拟合(双高斯函数)处理。由图1可见,在一年内,不同植被类型的NDVI,其极值、幅度、幅宽和生长周期均存在较大差别,如常绿林的幅宽最大,但一年内的NDVI值变化不大;一年一熟的农作物、落叶阔叶林、草地、混交林、灌木在一年内完成1个生长周期;而在有些地区农作物或将完成2~3个生长周期(一年种植两至三茬)。

图1   常见植被类型的NDVI时序数据及其遥感识别的物候期示意图

Fig.1   NDVI time series for general vegetation types and the derived phenological stages

植被指数时序数据所反映出的植物生长节律可用于植物物候期的识别。如图1a为温带落叶阔叶林的NDVI时序数据曲线(图1中横坐标表示年积日(Day of Year, DOY),指一年从1月1日起累积的天数。例如:每年的1月1日的年积日为1,2月1日的年积日为32,以此类推),根据地面观测的返青期和落叶末期可以在曲线上找到对应的NDVI值,通过分析该NDVI值及其在曲线中的位置则可找出其特征(见3.3节),然后就可依据该特征来识别其他落叶阔叶林的生长季起始和结束日期。图1e为具有旱季—雨季之分的亚热带常绿阔叶林NDVI时序数据曲线,虽然常绿阔叶林无生长季起始与结束之分,但可以从这条曲线上找出雨季来临和结束后的植物生长旺季起始与结束日期。

2.2 通用技术流程

植物物候遥感监测的主要技术环节包括遥感数据预处理和物候期估算。基于MODIS NDVI数据,以落叶阔叶林的返青期和常绿阔叶林的生长旺季起始日期识别为例,阐述植物物候遥感监测的通用技术流程(图2)。从图2可见,落叶阔叶林和常绿阔叶林的NDVI时序数据曲线存在差异,这种差异会影响后面的植物物候期识别结果和精度。由于原始NDVI时序数据存在较多的噪声,因此需对其进行去噪声处理,图2示意了两种不同的去噪声处理方法,不同的去噪方法也会影响NDVI时序数据曲线的特征(如极值、幅度和幅宽等)。对于去噪声处理后的NDVI时序数据,不同的植物物候估算方法也会影响最终的物候期识别结果和精度。由此可见,植物物候遥感监测结果的精度不但取决于具体的植被类型及其所处的地理条件,还与遥感数据源及其预处理方法、植物物候遥感识别方法等单个技术环节以及各技术环节的优化组合有关。因此,要获得高精度的植物物候遥感监测结果,必须针对不同的植被类型及其所处的地理条件,从遥感数据源及其预处理、植物物候遥感识别方法等方面进行有针对性的优化组合设计。

图2   植物物候遥感识别的技术流程

Fig.2   Main technical processes for plant phenology estimating based on remote sensing data

3 影响植物物候遥感监测精度的因素

3.1 植被类型及其所处的地理条件

不同的植被类型以及同一植被类型处于不同的地理条件(如位置、地形、土壤、气候等),其表现的光谱特征存在差异,从而导致植被指数时序曲线也存在差异,造成其最终的物候识别精度也有所不同(White et al, 2009)。

不同植被类型,其物候的遥感识别精度不同,如遥感数据识别的中国东北部森林的展叶期和落叶期的误差在10天左右(侯学会等, 2014),法国落叶阔叶林返青期的误差在8.5天左右(Soudani et al, 2008),青藏高原植被(主要为高寒草甸和高寒草原)返青期的误差达5~30天(Zhang et al, 2013)。遥感识别的河北省冬小麦、东北旱地作物的物候期与实际地面观测的物候期相近(张峰等, 2004; 司文才等, 2011)。此外,不同植被类型的植被覆盖度不同,也会影响遥感监测其物候的精度,如阔叶林的郁闭度较针叶林高,其植被指数受环境影响相对较小;植被信号强烈的高覆盖度草地(如青藏高原的高寒草甸)较植被信号较弱的低覆盖度荒漠草原(如高寒半荒漠植被)更有利于物候期的遥感识别;农作物(如水稻、小麦、玉米、大豆)的光谱特征常常会因农作物类型、生长季、长势状况以及田间管理等不同而有所差别(Van Niel et al, 2004; Hartfield et al, 2013),因此其关键物候期(返青期、移栽期、抽穗期和收获期等)的识别应充分利用农作物的典型季相节律特征(Wardlow et al, 2007; Arvor et al, 2011; 胡琼等, 2015)。

从植被所处的地理条件来看,对于同一种植被类型而言,分布于中纬度(30~60o)比高纬度(60~90o)和低纬度(0~30o)更有利于遥感识别其物候期,一方面是因为中纬度地区的大气水汽含量相对较低(唐仁茂等, 2010),遥感成像时受太阳高度角、卫星扫描角及地球曲率的影响相对较小(张云松等, 2007),得到的遥感数据质量相对较好;另一方面则是因为中纬度地区的植被信号强度适中、季相变化也较为明显,避免了因植被信号太强导致植被指数易饱和或因植被信号太弱从而易受土壤背景等因素的影响。同种植被类型在不同地形条件下,植被反射率还受到太阳高度角、坡度、坡向的影响,一方面,改变了辐照方向,引起入射照度的变化,同时影响植物冠层的形态结构;另一方面,使植物和土壤的相对比例发生了变化(赵英时等, 2003; Matsushita et al, 2007)。因此在地形复杂地区,受地形影响的波段反射率会将误差引入植被指数评估中,导致植被指数不能正确反映植被内部物理机制或生物量的变化(Matsushita et al, 2007; 姚晨等, 2009; Wang et al, 2013)。植被所处地理位置的土壤因受湿度、有机质含量不同等影响,导致表面反射率存在差异,从而影响植被指数的质量,主要是由于太阳光透过植被冠层到达土壤表面,使土壤和植被之间会发生多次散射,土壤表面的不同反射特性影响了植被指数的计算精度(Hill et al, 2011)。此外,不同地区的气候特征也会影响植被指数,如高纬度、高海拔地区的积雪,低纬度地区多云雨雾的天气影响正常植被光谱信息的获取,从而容易干扰植被物候遥感识别(Delbart et al, 2005; White et al, 2009)。

3.2 遥感数据源及其预处理

3.2.1 遥感数据源

卫星遥感数据具有大范围覆盖的优势,可以在种群、群落以及生态系统尺度上监测植物物候的时空变化(Delbart et al, 2006; Piao et al, 2006; Zhang et al, 2013)。基于遥感技术的植物物候监测一般采用时间分辨率较高的植被指数时序数据,如基于NOAA/AVHRR、SPOT-VGT、MODIS、MERIS等传感器的NDVI和EVI时序数据,其原始的时间分辨率为1天,合成后的植被指数数据产品一般为10天、15天或一个月。现有时间序列最长的植被指数时序数据为NOAA/AVHRR GIMMS NDVI 3g数据(Jiang et al, 2013),其时间跨度从1981年6月至今,但其空间分辨率较低(8 km),一个像元往往包含了多种植被类型,虽然可以在生态系统水平上监测植物物候变化,但很难从植物生理机制上对植物物候变化进行解释;MERIS和MODIS的植被指数数据空间分辨率提高到300 m和250 m,比较适合在种群或群落水平上监测植物物候变化,但其时间序列较短(分别自2003年5月和2000年2月之后才有数据)。除以上中低分辨率的遥感数据外,Landsat TM/ETM+/OLI遥感数据由于具有时间跨度长和空间分辨率高的优势也逐步用于植物物候研究(Melaas et al, 2013)。由于受云雨等天气条件的影响,光学遥感存在着数据质量不高的缺陷,而微波遥感则不易受天气条件的影响,从而可获得较高质量的植被数据,例如:Jones等(2012)使用AMSR-E被动微波数据研究植物物候,取得了较好的结果。

目前用于植物物候监测最为广泛的遥感数据为NOAA/AVHRR、SPOT-VGT和MODIS的植被指数时序数据,这些数据虽然具有大范围观测的优势,但因其观测时间相对地面观测来说较短,无法在更长的时间尺度上揭示植物物候变化规律;而且其空间分辨率较低,致使在多种植被类型混合的情况下很难分析植物物候变化的生理机制。

3.2.2 遥感数据常规预处理

卫星遥感数据受传感器、卫星位置和运行状态、卫星观测角、地表起伏、云覆盖情况、水汽含量、气溶胶含量、太阳高度角等因素影响,得到的植被指数时序数据往往包含了大量的噪声,致使植被指数时序数据曲线的季节变化趋势及其蕴涵的物候特征并不明显,因而无法有效提取植物物候信息(于信芳等, 2006)。为消除和减少上述因素的影响,需要对其进行常规的数据预处理,如几何校正、辐射校正(辐射定标和大气校正)等,这是生成空间上可比、时间上一致的植被指数数据集的前提条件。

几何校正的目的是将遥感数据准确地与地球表面的位置一一对应,并使多期数据之间在地理位置上相互匹配。如果几何校正不准确,将使从数据集中获取的植被指数数据受到几何畸变的影响而与实际的地理位置不匹配,致使多期遥感数据的相同像元并不代表相同的地理位置,从而无法反映处于同一地理位置上的植被在时间上的变化,因此,得到精度较高的几何校正遥感数据集对于准确表达确定地理位置的植被生长规律具有重要意义。

大气校正主要是消除遥感影像中由于大气的反射、散射和吸收用引起的误差的过程。大气对植被指数的影响主要体现在其对不同波段辐射的影响,一般对红光波段有增强作用,而对近红外波段有减弱作用(赵英时等, 2003; Hill et al, 2011)。大气效应对NDVI的影响以气溶胶最大,水汽次之,再次为瑞利散射。研究发现,不确定的大气影响所产生的冠层光谱变化有时超过植被自身的变化(Gao et al, 2000; Chappell et al, 2001; Rahman, 2001)。因此,在计算植被指数之前,需要对大气效应进行校正。目前常用的大气校正方法主要包括暗目标像元法、地表实测线性回归法、大气辐射传输模型法等(徐永明等, 2010),其中辐射传输模型法,因其普适性好、精度较高,在近年来得到了广泛的应用。而对于植被而言,二向性反射分布函数(Bidirectional Reflectance Distribution Function, BRDF)模型对于研究植被表面的二向性反射分布特征与植被的结构参数之间的关系具有重要意义(姚延娟等, 2007; Hill et al, 2011; Zhang et al, 2014)。二向性反射不仅有方向性,还依赖于入射方向。植被的辐射、反射、发射与其表面组成物质及冠层结构特征关系密切,不同的植被冠层将入射电磁波向四周散射(吸收除外),会形成不同的散射通量空间分布,反射的方向性是植被冠层结构特征和组成成分波谱特征的函数。因此有必要对遥感监测的植被波段数据进行BRDF的大气校正,从而为植被指数的计算提供有效的数据源。

3.2.3 植被指数去噪预处理

尽管目前大部分已有的植被指数数据产品(如NOAA/AVHRR GIMSS NDVI3g、SPOT-VGT NDVI和MODIS)已经进行了比较严格的常规预处理,并采用最大值合成法(Maximum Value Composite, MVC)(Holben, 1986)或限制视角的最大值合成法(Constrained View Angle-Maximum Value Composite, CVMVC),将多天植被指数时序数据进行了合成,但得到的植被指数时序数据产品仍存在噪声(Huete et al, 2002)。尤其是当合成期一直有云存在时,云会成为对植被指数产品质量影响最大的噪声。因此,在使用这些含有噪声的植被指数数据产品之前,需要对其作进一步的去噪声处理。

目前,对于植被指数时序数据的噪声去除方法主要为滤波法和曲线拟合法两大类。滤波法有最佳指数斜率法(Best Index Slope Extraction, BISE)(Viovy et al, 1992)、均值迭代滤波(Mean-Value Iteration Filter, MVI)(Ma et al, 2006)、S-G(Savitzky-Golay)滤波(Chen et al, 2004)、傅立叶变换(闫慧敏等, 2005)和谐波分析(Harmonic ANalysis of Time Series, HANTS)(Roerink et al, 2000)、变权重滤波(Changing-Weight, CW)(Zhu, Pan et al, 2012)等;曲线拟合法有非对称性高斯函数拟合(Jönsson et al, 2004)、双Logistic函数拟合(Beck et al, 2006)、多项式拟合等。

各种去噪声方法分别被国内外学者应用于全球不同区域和领域的研究,但有关各算法的优劣尚未达成共识(宋春桥等, 2011),基本不存在某种具有明显优势的算法,而是因研究区域、植被覆盖类型的不同特征和应用目的而异。上述各种植被指数时序数据去噪重建方法普遍存在以下问题:①各种方法的预处理效果与研究区域、研究目的相关,需要根据研究经验进行判断。②假设植被指数的时序变化对应于植被的生长过程,各项研究中均采用某一年的数据,根据植被生长周期对时间序列进行分段或对截止频率进行选择,需要预先掌握研究区域的植被覆盖情况及其物候特征。③假设植被指数时序数据中出现的陡升或陡降是受植物生长过程不一致的噪声干扰(Chen et al, 2004),可能会将导致由自然灾害(如森林火灾)或者人为管理(如农作物收割)引起的植被异常作为噪声剔除。④参数设置缺乏客观标准,需根据经验及多次试验进行判断,例如:BISE法需要研究者依据不同气候区和不同植被特征的研究经验,不断调整NDVI变化率的阈值及滑动窗口的大小;均值迭代滤波法需不断调整阈值的设置以避免遗漏有用的最大植被指数信息;S-G滤波是一种基于局域多项式最小二乘法拟合的滤波方法,需根据经验设定滤波窗口宽度和拟合阶次两个基本参数,若窗口宽度设置过小,会降低去噪声效果;若窗口宽度设置过大,会滤波过度,导致失去原始植被曲线特征;若拟合阶次设置过高,可能引入植被曲线的非真实波动;若拟合阶次设置过低,则难以描述植被曲线的细节变化信息。傅里叶变换法对频率分量个数比较敏感,若分量设置过大,可能人为引入噪声,导致曲线局部发生非真实跳动;若分量设置过小,则会导致重建曲线过于光滑,失去细节变化信息(颉继珍等, 2010);HANTS算法需设置误差阈值、频率个数、最大删除点个数以及有效数据范围等参数。⑤各种数据重建方法均不包含专门的噪声检测环节,仅根据经验在重建前进行预处理,将与重建数据偏离较大的原始数据直接作为噪声点去除,噪声定位精度和噪声检测的合理性有待提高,例如:S-G和CW方法根据植被生长特性,首先将NDVI突然降低0.4的点视为噪声,并用噪声点的邻近两点进行线性插值来获得该点的NDVI值,然后再进行S-G或CW滤波(Chen et al, 2004; Zhu, Pan et al, 2012);而BISE等滤波方法则没有数据预处理环节,而是直接根据经验设置好参数后进行滤波。⑥一些曲线拟合法对初值有较强的依赖性,难以获取全局最优解。例如,非对称高斯函数和双逻辑斯蒂函数拟合法,受限于其基函数的强非线性,需通过非线性回归求解待定系数。但若采用牛顿法等经典优化算法,其收敛性随初值的选择不同差异较大,算法鲁棒性不强;若采用单纯形法等群体搜索的算法,在不同的初值条件下可能陷入不同的局部最优解(范德芹等, 2014)。因此,对于此类强非线性函数拟合问题,有必要引入禁忌搜索算法、模拟退火算法和人工神经网络等现代优化算法,来降低初值依赖性,确保获取全局最优解。

3.3 植物物候遥感识别方法

植物物候遥感识别主要是指根据植被指数时序数据来估算植物的物候期,尤其是植物生长季的起始和结束日期。近20多年来,全球已发展了大量利用植被指数时序数据监测植物物候的方法,主要包括阈值法、导数法、滑动平均法和函数拟合法。

阈值法通过对植被指数曲线设置一定阈值来确定植物的生长季起止时间,可进一步细分为固定阈值法(或全局阈值法)(Lloyd, 1990)和动态阈值法(或局部阈值法)(Jönsson et al, 2002)。Lloyd(1990)利用NOAA/AVHRR NDVI数据,设置NDVI等于0.099时作为植被生长季起始的阈值;Fischer(1994)也利用预先设置的阈值来确定生长季的起始和结束日期。固定阈值法对于确定当地植物生长季开始和结束期较为有效,但不能应用于具有不同土地覆盖类型和土壤背景的区域,动态阈值法则不受此限制。如White等(2006)采用1982-2003年的AVHRR NDVI数据,基于动态阈值法识别了加拿大东部地表植物的物候,并预测了短期植物物候。Delbart等(2006)利用SPOT-VGT和NOAA/AVHRR NDVI数据,采用动态阈值法研究了1982-2004年欧亚大陆北部植被返青期的变化,发现1982-1991年植被返青期平均提前了8天,1993-2004年植被返青期平均推迟了3.6天。司文才等(2011)采用2000-2009年SPOT/VEGETATION NDVI数据,采用动态阈值法求取了中国河北省冬小麦的返青期和抽穗期,发现冬小麦物候期总体上呈现由南到北逐渐推迟的空间分布规律。侯学会等(2013)利用2001-2010年SPOT-VGT NDVI数据,基于S-G滤波和动态阈值法,获取了中国北方农牧交错带的植被物候期,探讨了研究区域植被物候期的空间差异和时间变化。许青云等(2014)基于2003-2012年MODIS NDVI数据,采用动态阈值法识别了陕西省农作物的物候期,并在此基础上实现了农作物种植模式和类型的识别。牟敏杰等(2012)选择北美洲72座通量塔观测的净生态系统碳交换(NEE)数据来计算植物物候,并以此作为参考数据,从可行性和准确性两方面对移动平均法、阈值法和函数拟合法三种常用的植物物候识别方法进行了综合评价,结果表明,动态阈值法对植物物候识别的可行性和准确性均最优;其次为Logistic函数拟合法中的一阶导数方法;而固定阈值法对植物物候识别的可行性和准确性均最差。阈值法充分体现了植被指数曲线的主要特征,但其阈值设定受到研究区域、植被类型、植被指数及人为经验等因素的影响。

导数法主要结合其他条件或方法,通过对植被指数曲线求导,将曲线上升速率最大值对应的日期定义为生长季起始日期,将曲线下降速率最大值对应的日期定义为生长季结束日期(White et al, 1997; Balzter et al, 2007)。如Moulin等(1997)通过导数法和经验系数,利用NOAA/AVHRR NDVI 数据估算出了全球植物生长季的开始日期和结束日期。Yu等(2003)提出了含有一组条件阈值的求导方法,估算了中亚东部地区植被的返青期。斜率最大值法对求解只有一个生长季的农作物较为有效,但存在两个生长季的农作物,其提取的第一个生长季的收获期会晚于实际收获期;同时,其对生长季内NDVI变化缓慢的植被生长季起止日期提取效果不好。Balzter等(2007)提出了一种导数法和滑动窗口相结合的“驼背物候算法(Camelback Phenology Algorithm)”,利用NDVI数据,估算了西伯利亚中部和东部植物的生长季起始日期和结束日期。Sakamoto等(2005)将农作物MODIS EVI时序数据曲线的上升速率出现最大值时对应的日期定为返青期;将二阶导数为0且导数由正变负的转折点对应的日期定为收获期。徐岩岩等(2012)基于2008年MODIS EVI数据,采用导数法求取了东北地区水稻的移栽期、抽穗期和成熟期。由于导数法不对误差进行分析,因此,它很难解释监测的植物物候变化是在合理的范围变化,还是存在显著的变化。同时,当植被指数曲线不存在突升和突降时,这种方法很难判断生长季起始日期和结束日期,尤其是当植被指数时序数据存在云污染时更难确定(Hudson et al, 2010)。

滑动平均法利用原始植被指数曲线与其滑动平均曲线的交叉点来判断植物物候期。Reed等(1994)首先提出了延迟滑动平均法(Delayed Moving Average, DMA),利用AVHRR NDVI数据,分别求取了农作物、草地、灌木和森林四种植被的生长季起止日期,发现其与实际观测结果具有较好的一致性。Duchemin等(1999)采用滑动平均法识别了温带落叶林的生长季起止日期。Schwartz等(2002)分别采用季节性NDVI中点法、地表物候模拟法和延迟滑动平均法求取了美国大陆混交林和落叶林植被的生长季起始日期,发现延迟滑动平均法的精度优于另外两种方法。该方法可有效地监测实测数据较少地区的植被生长季。滑动平均法对一年内只有一个生长季的NDVI时间序列数据的计算更稳定、可靠,但对一年内存在多个生长季或对降雨响应明显的地区,识别效果则较差,因为时间间隔的选择可能使第一个返青期无法监测(Hudson et al, 2010);如受春季融雪影响,遥感识别结果可能早于实际返青期(武永峰等, 2008);此外,滑动平均法对滑动窗口的设置较为敏感。

函数拟合法一般是用“S”形的多项式函数、Logistic函数、傅里叶函数和高斯函数等对原始的植被指数时序数据进行拟合,然后基于拟合结果来估算植物物候。例如:Logistic函数拟合法首先对NDVI时间序列数据进行逐年拟合,再以拟合曲线的曲率变化率的极值点来确定植被各物候转换期(Zhang et al, 2003)。Zhang等(2003)首先提出Logistic函数拟合法,并对新英格兰中心附近区域植被的返青期、成熟期、衰落期和休眠期进行了识别。Logistic函数拟合法不需预先定义阈值并进行数据平滑,在一定程度上减少了主观因素的影响,但由于不同植被的实际NDVI时间序列曲线并非理想的S型规则曲线,因此存在拟合不成功的情况,从而导致监测精度的降低(崔凯等, 2012)。谐波分析法利用离散傅里叶变换将NDVI时序数据分成多个不同频率的周期函数之和,再利用谐波特征值谐波特征值(振幅、相位和谐波余项)与地表植被动态特征之间的相关性,提取植物物候信息(张峰等, 2004; 那晓东等, 2007)。林忠辉等(2006)采用1992年NOAA/AVHRR NDVI数据,针对河北省南部地区的几种不同植被类型,首先利用改进的傅立叶算法对其NDVI时序数据进行重构,再采用谐波分析法提取了植被的物候期。Moody等(2001)采用离散的傅里叶分析法估算了美国加利福尼亚南部植物的物候。谐波分析法可以更好的去除NDVI时间序列中噪声的影响,但重建后的曲线可能过于平滑,曲线特征可能出现较大偏离,影响物候特征的判别(那晓东等, 2007)。Jönsson等(2002)基于非对称高斯函数法对非洲植物的生长季开始和结束日期进行了估算。Wang等(2014)基于非对称高斯和双逻辑斯蒂函数法计算了中国东北地区农作物的物候。函数拟合法对函数的初始值依赖性较强,难以获得全局最优解;同时,函数参数的优化受到原始植被时序数据个数的限制,即受到时间分辨率的限制而影响其拟合的精度(Hudson et al, 2010)。

3.4 植物物候遥感监测结果评价

由于卫星遥感数据的空间分辨率相对较低,且受遥感数据本身的质量、数据预处理方法和物候识别方法影响,使得基于遥感数据获取的像元尺度上的植物物候期与地面观测的物种水平上的物候期存在差异,导致对遥感监测的植物物候期的可靠性评价成为难题。目前,大部分研究均采用地面观测的物候数据对遥感识别的物候数据进行验证。例如Fisher等(2006)利用地面物候观测结果对Landsat高分辨率和MODIS粗分辨率物候观测进行验证,量化评价了两种尺度下的植物物候遥感监测精度,发现植物物候遥感监测结果的均值可以反映精细尺度到粗尺度的统计转换,而局地微气候引起的物候空间差异则是造成地面与遥感观测不一致的主要原因;Yu等(2010)利用1982-2006年NOAA AVHRR NDVI数据研究青藏高原植被春季物候时,采用平均绝对误差(Mean Absolute Error, MAE)与均方根误差(Root Mean Square Error, RMSE)评价了地面观测数据与遥感监测结果之间的误差。在缺乏足够的地面物候观测数据时,常清等(2014)根据气象站点的日均温度,采用标准偏差法对植被生长季的遥感识别结果进行了间接验证;Zhang等(2013)利用三种遥感数据源(GIMMS、SPOT VEGETATION、MODIS)识别青藏高原的植被返青期,然后采用趋势线一致法与地面观测的物候数据进行了直观的对比,交互评价了遥感识别的物候期是否合理。

实际上,因观测尺度(单株植物与遥感像元)和观测内容(植物的某一物候事件与植物的光谱响应)上的差异,遥感识别的植物物候期(如返青期、休眠期等)与地面观测到的具体物候事件(如植物发芽、开花等)之间并不存在明确的对应关系(Schwartz et al, 2002; Fisher et al, 2006)。因此,应用地面观测结果来评价遥感监测结果时,不宜比较两者的具体时间点,而应综合比较二者的时空变化趋势是否一致。

4 讨论与展望

随着遥感技术的快速发展,基于遥感观测数据的大面积植物物候监测已逐步成为现实,并针对不同的研究区域及物种,在不同的时空尺度上建立了一系列有针对性的遥感物候监测方法。尽管如此,目前的遥感物候监测方法,还面临着数据分辨率不高、噪声干扰因素较多、物候期识别方法普适性较低、物候研究结果验证不充分等诸多难题,在很大程度上制约了遥感物候监测的深入开展。为推动大面积物候监测研究的持续发展,未来可分别从遥感数据源的质量提升及数据来源拓展、数据处理及分析、物候识别结果验证及物候预测等几个方面开展深入研究,以下分别对各项研究工作进行阐述。

4.1 建立高分辨率的近地面遥感定点观测网络

传统的植物物候数据采集方法是通过人工定点目视观测同时记录植物个体发育的各个物候事件(Sparks et al, 2002),这种方法适用于监测特定的物候期,如植物的发芽、开花、衰败等。遥感监测是利用植被指数在大尺度上监测植物光谱特征的变化。近年来,随着传感器技术和数字图像处理技术的发展,产生了一种监测植物物候的新方法,即近地面遥感监测。近地面遥感中,通过将每个光学传感器安装在离地表相对较近的位置(一般≤50 m)来获取高时间和高空间分辨率的植物生长发育和衰败的光谱特征。它监测的植物物候尺度介于人工个体观测和遥感卫星像元监测之间,这对于各种应用而言是十分有利的。目前常用的近地面方法主要是基于宽波段辐射传感器、窄波段辐射传感器和数码相机的监测。

宽波段辐射传感器可用来测量大致在红光波段和近红外波段的光量子通量密度和总的短波太阳辐射,其监测结果与卫星监测结果相似。窄波段辐射传感器主要是监测特定区域和特定波段的电磁波谱特征,但其费用高于宽波段辐射传感器。这两种传感器输出的都是单一信息,因此它们反映的是一段时间观测的平均值,通常不含空间变化信息(Hilker et al, 2011),不能区分视区范围内不同的对象和个体。数码相机的观测是在同一地点,使用相同拍摄角度和参数,针对同一研究对象、同一生态现象或过程,在不同时段进行拍摄,持续记录区域生态变迁并研究生态变化趋势(Webb et al, 2010),其主要原理是利用数码相机的红、绿、蓝色彩通道或更多通道(6个或更多特定波段),以及图形处理技术,构造与归一化差值植被指数类似的颜色指数,这不仅可在植物群落和生态系统水平上监测植被的优势物种组成结构、植被生长状况和物候、植被生产力等变化,同时随着数码相机时空分辨率的提高,还可监测植被组成成分,如冠层叶绿素、水分含量变化,以及植被冠层的光合速率等数据。因此该方法已成为“近地面”遥感的一种新方法(Richardson, Hollinger et al, 2009)。

目前,基于数码相机的定点重复观测法(Sonnentag et al, 2012)已在全球多个生态系统的物候研究中得到了应用。在单机监测方面,Richardson等(2007)利用数码相机监测了落叶阔叶林的返青期。Ahrends等(2009)采用数码相机监测了温带森林植被的物候,发现其与总初级生产力(GPP)密切相关。Kurc等(2010)利用数码相机监测了石炭酸灌木为优势物种的生态系统,发现其植被的绿度与深层土壤湿度有很强相关性。Migliavacca等(2011)基于数码相机监测了高寒草原的植被物候,发现植被冠层绿度与叶面积指数和冠层光合作用相关性强,可用来优化物候模型。Sonnentag等(2012)利用数码相机监测了森林生态系统的物候,发现可根据RGB颜色建立绿色色度坐标来研究物候。在联网观测方面,有些国家已经建立了数码相机物候观测网络。例如,在美国北部的12个森林中建立了一个类似的观测网络,用于监测植物冠层的展叶期(Richardson, Braswell et al, 2009)。Richardson、Braswell 等(2009)利用数码相机物候观测网对森林物候期进行了监测,发现数码相机物候观测网能够很好地监测春季和秋季植被的物候。Coops等(2012)将数码相机网络观测的物候数据与遥感监测的物候数据进行了分析,发现数码相机观测的植被生长季起止日期较遥感监测数据均提前,但两者有较高的相关性。Graham等(2010)利用北美公共设施的1100多个数码相机观测的物候与遥感监测结果,选取其中30个观测点进行了比较,发现数码相机在噪声较少时对植被春季物候的估算误差与遥感监测的误差相近。

基于数码相机的定点重复观测具有人工观测和遥感监测的双重优点,即它具有一定的空间尺度融合能力,能在野外条件下自动、连续获取高时空分辨率图像(Migliavacca et al, 2011),从而可及时准确地获取植被群落的冠层生长状态(Sonnentag et al, 2012)。该方法已成为监测植物物候变化及其对环境因子响应的一种新手段(Kurc et al, 2010),同时也为植物物候遥感监测结果的地面验证提供了有效途径(Fisher et al, 2006),为建立高分辨率的近地面遥感定点观测网络提供了可能。在进行数码相机定点重复观测时,应注意选择合适的数码相机,并将其安置在合适的位置,但成像传感器的校准和长期的稳定性问题是目前的难题(Richardson, Braswell et al, 2009; Migliavacca et al, 2011; Sonnentag et al, 2012)。因此,未来应充分发挥数码相机的定点重复观测法在植物物候研究中的优势,一方面,需发展像美国和日本那样的网络化、自动化的大范围物候观测系统,如借鉴美国建立国家物候网(https://www.usanpn.org/)的经验,组织多个研究机构、野外观测站和公众共同参与物候观测,在统一的观测网架构及数据模型基础上实现物候数据共享,同时利用各种微型传感器和无线网络,建立可远程控制的、可自动拍摄的小范围物候监测系统,建立高分辨率的近地面遥感定点观测网络;另一方面,将其监测的植物物候信息与地面观测和卫星遥感数据相结合(Hufkens et al, 2012),作为遥感监测和地面监测的桥梁,实现遥感监测数据的地面验证和地面到遥感监测尺度的扩展,填补地面人工观测与区域遥感数据之间在时间和空间上的数据空缺。

4.2 发展合适的卫星遥感时序数据处理方法

(1) 发展有效的植被指数时序数据噪声检测和去除方法。为提高植被指数时序数据预处理方法的普适性,减少对研究经验、研究区域、植被类别、参数设置的依赖,避免对地表覆盖类型的误判,应着力发展不依赖于初值和经验等的有效噪声检测方法。

(2) 发展合适的植被指数时序数据拟合方法。植被时序数据在去噪拟合时应保留其实际生长规律,以便提取到高精度的植物物候期信息。对于曲线拟合去噪声方法来说,可选择合理、稳定的函数集(如高斯函数集),增强对不同植物生长规律的数学表达能力,同时在函数拟合中引入现代优化算法(如模拟退火算法、遗传算法等),以此作为滤波、多项式拟合等传统方法的有益补充,增强对植物物候演化规律的最优描述能力,从数学角度保证植被指数曲线重建结果的稳定性,为物候期识别提供合理、准确的输入条件。

现有的各种拟合方法导致波峰的幅度和位置均可能发生变化,很难保持时序数据的物候特征,这就需要加强对植被指数拟合曲线的评价,不但要从视觉上评价拟合效果是否与原始数据一致(梁守真等, 2011),还需利用RMSE、相关系数等量化指标对拟合效果进行评价。如衡量拟合结果与原始数据之间的偏差(宋春桥等, 2011),利用赤池信息量准则(AIC)、贝叶斯信息准则(BIC)等方法评估拟合后的植被指数时间序列曲线保持原始植被生长季曲线特征的能力(Atkinson et al, 2012)。此外,对于单幅影像,还可采用方差法、局部方差法、去相关法、地学统计量法等(朱博等, 2010)方法,通过信噪比(SNR)来评价数据质量(朱博等, 2010)。可见,对植被指数时序数据拟合结果进行评价,既有利于保证数据拟合精度,也有利于保持植被指数时序数据质量和形状,使其波峰的幅度和位置能够更好、更真实地反映植被的生长状况和物候特征。

4.3 发展植物物候遥感识别方法

目前,各种物候识别方法受植被类型、区域范围、生长周期、植被指数和参数设置等因素影响,其应用均具有一定的局限性,因此,未来应努力发展一些具有广泛普适性和稳定性的物候识别方法。

为提高植物物候遥感识别方法的鲁棒性(普适性),可在稳健的植被曲线重建结果基础上,发展基于植被内在生长特性的返青期估算方法,即物候期识别过程中所需的各项参数均根据其多年的时间演化规律确定,尽量减少或避免引入经验参数,综合发挥现有的阈值法、斜率法、曲率法等各种识别方法的优点,同时规避各自的不足,发展出诸如最大斜率阈值法等耦合识别方法,从而避免对研究经验、植被类型、研究区域的依赖,减少人为因素干扰,提高识别方法在不同地区、不同物种间的通用性。

4.4 建立地面观测与卫星遥感监测之间的转换桥梁

4.4.1 地面观测物候与遥感监测物候之间的内涵差异

地面物候观测针对单株植物或单个物种进行,观测到的物候通常定义为发芽期、展叶期、开花期、衰落期、收获期等,反映的是植株或物种水平的物候变化;而基于卫星遥感所识别的物候期与其存在本质差异,遥感数据描述的是地表景观尺度的光谱反射率信息,在有植被覆盖的区域则反映了像元尺度上植物群落或生态系统的植被生长状况及变化特征(Badeck et al, 2004; 陈效逑等, 2009; Xu et al, 2014),即遥感监测的物候期不是特定的物种的物候事件(如发芽,落叶等),因此,不能用传统的植物物候来定义遥感物候监测结果。许多学者根据各自研究内容对遥感识别的物候给出了不同的定义(Lloyd, 1990; Reed et al, 1994),如Moulin 等(1997)根据一年内NDVI时间序列曲线的高低变化,将植物生长季定义为休眠期、生长期和衰老期,Xin等(2002)在对中国黄淮海平原植物物候进行遥感监测时,将冬小麦的生长季分为返青期、抽穗期和成熟期,将夏玉米的生长季分为出芽期、抽穗期和成熟期。

植物物候遥感监测目前主要是在大尺度上用于确定地表植物生长季的开始和结束日期(Schwartz et al, 2002),这两个物候期与植物的光合作用过程密切相关,即:生长季开始日期对应于区域内植物光合作用的开始日期,结束日期对应于区域内植物光合作用的结束日期,而并非传统意义上基于定点、定株观测定义的单一植物或植株的发芽期和落叶期。区域内最早展叶的植物出现绿色,并不一定表明植物遥感生长季的开始,因为遥感监测的生长季开始日期是区域内多数植物开始展叶的日期,该日期后植被进入衰亡或休眠阶段(武永峰等, 2008)。

4.4.2 地面观测物候与遥感监测物候之间的尺度匹配

由于地面观测主要是在个体水平上通过物候观测网络对物种进行物候监测,而遥感监测则主要是在群落和生态系统水平上通过卫星监测地表植被指数变化,来监测区域植被的物候,因此,若采用地面观测的物候期对遥感监测的物候期进行验证,将产生监测尺度和监测内容上的不匹配,为此需要找到一种方法,将地面个体水平上观测的物候期扩展到遥感群落甚至生态系统尺度上。如基于地面观测的物候数据根据观测站点附近该植被的分布情况进行空间平均或根据生物量、覆盖度、叶面积指数等指标进行加权来确定一定区域范围的地面物候观测值,从而实现地面观测数据从个体水平到群落或生态系统、从点到面(区域)的尺度扩展。在此基础上与相同区域范围的遥感监测的物候数据进行匹配,实现遥感物候监测数据的地面验证。但目前地面个体物候观测的数据有限,一般一个站点只有一个数据,而且遥感监测的植被指数数据分辨率较低,如NOAA AVHRR 的分辨率为8 km,MODIS的分辨率为250 m,采用Landsat TM生成的数据虽达到了30 m,但这对于一个地面观测的单株植物物候数据来说,分辨率远不能满足要求,从而给尺度扩展带来困难。而随着数码相机技术的发展,可考虑将高分辨的数码相机监测作为遥感监测和地面观测植物物候的桥梁,以实现遥感监测数据的地面验证和地面到遥感监测尺度的扩展。

实际上,遥感监测和地面观测本就属于在不同尺度上对植物物候进行的监测,它们各自所反应的植物物候在本质上存在差异,因此,采用地面观测数据对遥感监测结果进行定量精确验证的必要性不强,但为提高遥感监测的可靠性,可采用地面观测的数据对遥感监测结果进行物候时空变化趋势的验证。同时,由于物候模型模拟结果也是一种重要的植物物候监测手段,因此,应将地面、遥感和模型模拟三种监测手段综合起来进行交互验证,以减少对植物物候变化监测的不确定性。

The authors have declared that no competing interests exist.


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[2] 陈效逑, 王林海. 2009.

遥感物候学研究进展

[J]. 地理科学进展, 28(1): 33-40.

https://doi.org/10.11820/dlkxjz.2009.01.005      Magsci      [本文引用: 3]      摘要

<p>植物物候现象是环境条件季节和年际变化最直观、最敏感的生物指示器,其发生时间可以反映陆地生态系 统对气候变化的快速响应。近年来,遥感物候观测因其具有多时相、覆盖范围广、空间连续、时间序列较长等特点, 已成为揭示植被动态对全球气候变化响应与反馈的重要手段。文章在介绍植物物候遥感监测的数据集及其预处理 方法的基础上,从植物物候生长季节的划分、植物物候与气候变化、植物物候与净初级生产量、植物物候与土地覆 盖、植物物候与农作物估产等方面系统阐述了近5 年来国内外遥感物候学研究的重要进展,并针对目前研究中存 在的问题,提出近期遥感物候研究的主要方向:(1)发展一种更具普适性的物候生长季节划分方法;(2)通过开展植物 群落的物候观测和选择合适的尺度转换方法,统一地面与遥感的空间信息;(3)定量分析植物物候变化对人类活动 的响应机制;(4)选择适宜的数学方法和模型,实现各种不同分辨率遥感数据的融合;(5)通过动态模拟,预测植物物 候对未来气候变化的响应。</p>

[Chen X Q, Wang L H.2009.

Progress in remote sensing phenological research

[J]. Progress in Geography, 28(1): 33-40.]

https://doi.org/10.11820/dlkxjz.2009.01.005      Magsci      [本文引用: 3]      摘要

<p>植物物候现象是环境条件季节和年际变化最直观、最敏感的生物指示器,其发生时间可以反映陆地生态系 统对气候变化的快速响应。近年来,遥感物候观测因其具有多时相、覆盖范围广、空间连续、时间序列较长等特点, 已成为揭示植被动态对全球气候变化响应与反馈的重要手段。文章在介绍植物物候遥感监测的数据集及其预处理 方法的基础上,从植物物候生长季节的划分、植物物候与气候变化、植物物候与净初级生产量、植物物候与土地覆 盖、植物物候与农作物估产等方面系统阐述了近5 年来国内外遥感物候学研究的重要进展,并针对目前研究中存 在的问题,提出近期遥感物候研究的主要方向:(1)发展一种更具普适性的物候生长季节划分方法;(2)通过开展植物 群落的物候观测和选择合适的尺度转换方法,统一地面与遥感的空间信息;(3)定量分析植物物候变化对人类活动 的响应机制;(4)选择适宜的数学方法和模型,实现各种不同分辨率遥感数据的融合;(5)通过动态模拟,预测植物物 候对未来气候变化的响应。</p>
[3] 崔凯, 蒙继华, 左廷英. 2012.

遥感作物物候监测方法研究

[J]. 安徽农业科学, 40(10): 6279-6281, 6321.

https://doi.org/10.3969/j.issn.0517-6611.2012.10.201      URL      [本文引用: 1]      摘要

分析了阈值法、Logistic函数拟合法、谐波分析法、滑动平均法、斜率最大值法等目前遥感物候监测方法的优点及存在的不足,并讨论了相应改进措施,对遥感作物物候监测方法进行了展望。

[Cui K, Meng J H, Zuo T Y.2012.

Monitoring of crop phenology with remote sensing

[J]. Journal of Anhui Agricultural Sciences, 40(10): 6279-6281, 6321.]

https://doi.org/10.3969/j.issn.0517-6611.2012.10.201      URL      [本文引用: 1]      摘要

分析了阈值法、Logistic函数拟合法、谐波分析法、滑动平均法、斜率最大值法等目前遥感物候监测方法的优点及存在的不足,并讨论了相应改进措施,对遥感作物物候监测方法进行了展望。
[4] 范德芹, 朱文泉, 潘耀忠, . 2014.

青藏高原小嵩草高寒草甸返青期遥感识别方法筛选

[J]. 遥感学报, 18(5): 1117-1127.

https://doi.org/10.11834/jrs.20143299      URL      [本文引用: 1]      摘要

小嵩草高寒草甸是青藏高原的主要植被类型,研究其返青期识别方法 对于模拟及预测青藏高原植被物候变化具有重要意义.常用的植被返青期遥感识别方法主要是先对遥感植被指数原始时序数据进行拟合去噪声再求取返青期,各种方 法对研究区域、研究经验、参数设置、函数初值设置等有很强的依赖性.为避免返青期识别方法在曲线拟合时对参数初值的依赖性和陷入局部最优解,本文引入了模 拟退火算法对双高斯和双逻辑斯蒂函数进行参数优化,并分别对基于以上两种函数及多项式拟合的植被指数时序曲线进行对比,从而选出最佳拟合方法,最后采用最 大斜率阈值法、动态阈值法和曲率法识别返青期.利用青藏高原小嵩草高寒草甸34个样本点的返青期地面观测数据及相应的8km分辨率的NOAA归一化差值植 被指数(NDVI)时序数据对以上各种组合的返青期遥感识别方案进行了测试,并选取了153个遥感实验点求取了近30年(1982年-2011年)青藏高 原小嵩草高寒草甸的返青期,结果表明:采用双高斯函数拟合的NDVI曲线与原始NDVI时序数据最为接近,在此基础上采用最大斜率阈值法识别的小嵩草高寒 草甸返青期及其变化趋势与地面物候观测结果最为一致;同时发现近30年青藏高原小嵩草高寒草甸的平均返青期主要集中在每年的第120-140天,并且呈逐 年提前趋势,30年来提前了7天.

[Fan D Q, Zhu W Q, Pan Y Z, et al.2014.

Identifying an optimal method for estimating greenup date of Kobresia pygmaea alpine meadow in Qinghai-Tibetan Plateau

[J]. Journal of Remote Sensing, 18(5): 1117-1127.]

https://doi.org/10.11834/jrs.20143299      URL      [本文引用: 1]      摘要

小嵩草高寒草甸是青藏高原的主要植被类型,研究其返青期识别方法 对于模拟及预测青藏高原植被物候变化具有重要意义.常用的植被返青期遥感识别方法主要是先对遥感植被指数原始时序数据进行拟合去噪声再求取返青期,各种方 法对研究区域、研究经验、参数设置、函数初值设置等有很强的依赖性.为避免返青期识别方法在曲线拟合时对参数初值的依赖性和陷入局部最优解,本文引入了模 拟退火算法对双高斯和双逻辑斯蒂函数进行参数优化,并分别对基于以上两种函数及多项式拟合的植被指数时序曲线进行对比,从而选出最佳拟合方法,最后采用最 大斜率阈值法、动态阈值法和曲率法识别返青期.利用青藏高原小嵩草高寒草甸34个样本点的返青期地面观测数据及相应的8km分辨率的NOAA归一化差值植 被指数(NDVI)时序数据对以上各种组合的返青期遥感识别方案进行了测试,并选取了153个遥感实验点求取了近30年(1982年-2011年)青藏高 原小嵩草高寒草甸的返青期,结果表明:采用双高斯函数拟合的NDVI曲线与原始NDVI时序数据最为接近,在此基础上采用最大斜率阈值法识别的小嵩草高寒 草甸返青期及其变化趋势与地面物候观测结果最为一致;同时发现近30年青藏高原小嵩草高寒草甸的平均返青期主要集中在每年的第120-140天,并且呈逐 年提前趋势,30年来提前了7天.
[5] 侯学会, 牛铮, 高帅. 2014.

近十年中国东北森林植被物候遥感监测

[J]. 光谱学与光谱分析, 34(2): 515-519.

[本文引用: 1]     

[Hou X H, Niu Z, Gao S.2014.

Phenology of forest vegetation in northeast of China in ten years using remote sensing

[J]. Spectroscopy and Spectral Analysis, 34(2): 515-519.]

[本文引用: 1]     

[6] 侯学会, 牛铮, 高帅, . 2013.

基于SPOT-VGT NDVI时间序列的农牧交错带植被物候监测

[J]. 农业工程学报, 29(1): 142-150.

https://doi.org/10.3969/j.issn.1002-6819.2013.01.019      URL      摘要

为了分析中国农牧交错带植被典型物候期(生长开始日期,生长结束 日期和生长季长度)的变化趋势,利用2001-2010年 SPOT-VGT NDVI(SPOT-VEGETATION normalized differential vegetation index)数据,基于 Savitzky— Golay滤波和动态阈值法,提取了中国北方农牧交错带植被物候期,探讨研究区植被物候期的空间差异和时间变化.研究表明,农牧交错带植被的生长季一般从 4月中旬到5月下旬开始,9月下旬至10月下旬结束;从西南部到东北部,植被物候表现出明显的空间差异;农田植被物候期与自然植被略有不同;对研究区10 a 物候期线性拟合,得出研究区大部分植被覆盖区域生长季开始日期呈现提前趋势,提前日期大约为1~10 d 左右;除部分地区外,2001-2010年农牧交错带植被生长季结束日期没有明显变化趋势;10 a 间研究区大部分草地生长季延长,也有一部分地区的生长季出现缩短趋势.研究提取结果与已有的相关研究结果较为一致,可为农牧交错带生态环境评价和保护提供 一定的参考.

[Hou X H, Niu Z, Gao S, et al.2013.

Monitoring vegetation phenology in farming-pastoral zone using SPOT-VGT NDVI data

[J]. Transactions of the Chinese Society of Agricultural Engineering, 29(1): 142-150.]

https://doi.org/10.3969/j.issn.1002-6819.2013.01.019      URL      摘要

为了分析中国农牧交错带植被典型物候期(生长开始日期,生长结束 日期和生长季长度)的变化趋势,利用2001-2010年 SPOT-VGT NDVI(SPOT-VEGETATION normalized differential vegetation index)数据,基于 Savitzky— Golay滤波和动态阈值法,提取了中国北方农牧交错带植被物候期,探讨研究区植被物候期的空间差异和时间变化.研究表明,农牧交错带植被的生长季一般从 4月中旬到5月下旬开始,9月下旬至10月下旬结束;从西南部到东北部,植被物候表现出明显的空间差异;农田植被物候期与自然植被略有不同;对研究区10 a 物候期线性拟合,得出研究区大部分植被覆盖区域生长季开始日期呈现提前趋势,提前日期大约为1~10 d 左右;除部分地区外,2001-2010年农牧交错带植被生长季结束日期没有明显变化趋势;10 a 间研究区大部分草地生长季延长,也有一部分地区的生长季出现缩短趋势.研究提取结果与已有的相关研究结果较为一致,可为农牧交错带生态环境评价和保护提供 一定的参考.
[7] 胡琼, 吴文斌, 宋茜, . 2015.

农作物种植结构遥感提取研究进展

[J]. 中国农业科学, 48(10): 1900-1914.

https://doi.org/10.3864/j.issn.0578-1752.2015.10.004      URL      [本文引用: 1]      摘要

农作物种植结构信息对农业生产管理、农业可持续发展及国家粮食安全等具有重要意义。本文中概括了农作物种植结构遥感提取的理论基础,归类了近10年间不同农作物种植结构遥感提取技术方法,重点评述了不同技术方法的特点及应用情况,讨论和展望了未来农作物种植结构遥感提取研究的发展方向。当前,光谱特征、时相特征和空间特征是农作物种植结构遥感提取的三大理论基础。基于单一影像源的种植结构提取方法操作简单,但往往难以获取种植结构“最佳识别期”的遥感影像;基于多时序影像源的种植结构提取方法可以充分利用农作物季相节律特征,成为当前农作物种植结构遥感提取的主流方法。在基于多时序影像源的种植结构提取方法中,多特征参量法较单一特征参量法更适用于农作物种植结构复杂区域,基于多特征参量的统计模型法一定程度上解决了混合像元问题,但模型的鲁棒性有待提高。此外,遥感与统计数据融合的农作物种植结构提取法在国家及全球大尺度的农作物种植结构提取中具有优势,但较低的制图分辨率使得数据产品的区域适宜性较差。未来农作物种植结构遥感提取将以区域“作物一张图”为目标,充分发挥多源数据组合利用的优势,围绕多类型作物同步提取和大范围作物种植结构提取开展深入研究,重点加强遥感数据预处理、特征参量提取和分类器高效选择等关键技术研究,从而提升农作物种植结构遥感提取的时空尺度,满足多方位的农业应用需求。

[Hu Q, Wu W B, Song Q, et al.2015.

Recent progresses in research of crop patterns mapping by using remote sensing

[J]. Scientia Agricultura Sinica, 48(10): 1900-1914.]

https://doi.org/10.3864/j.issn.0578-1752.2015.10.004      URL      [本文引用: 1]      摘要

农作物种植结构信息对农业生产管理、农业可持续发展及国家粮食安全等具有重要意义。本文中概括了农作物种植结构遥感提取的理论基础,归类了近10年间不同农作物种植结构遥感提取技术方法,重点评述了不同技术方法的特点及应用情况,讨论和展望了未来农作物种植结构遥感提取研究的发展方向。当前,光谱特征、时相特征和空间特征是农作物种植结构遥感提取的三大理论基础。基于单一影像源的种植结构提取方法操作简单,但往往难以获取种植结构“最佳识别期”的遥感影像;基于多时序影像源的种植结构提取方法可以充分利用农作物季相节律特征,成为当前农作物种植结构遥感提取的主流方法。在基于多时序影像源的种植结构提取方法中,多特征参量法较单一特征参量法更适用于农作物种植结构复杂区域,基于多特征参量的统计模型法一定程度上解决了混合像元问题,但模型的鲁棒性有待提高。此外,遥感与统计数据融合的农作物种植结构提取法在国家及全球大尺度的农作物种植结构提取中具有优势,但较低的制图分辨率使得数据产品的区域适宜性较差。未来农作物种植结构遥感提取将以区域“作物一张图”为目标,充分发挥多源数据组合利用的优势,围绕多类型作物同步提取和大范围作物种植结构提取开展深入研究,重点加强遥感数据预处理、特征参量提取和分类器高效选择等关键技术研究,从而提升农作物种植结构遥感提取的时空尺度,满足多方位的农业应用需求。
[8] 梁守真, 施平, 邢前国. 2011.

MODIS NDVI时间序列数据的去云算法比较

[J]. 国土资源遥感, (1): 33-36.

https://doi.org/10.6046/gtzyyg.2011.01.06      URL      Magsci      [本文引用: 1]      摘要

&nbsp;受多重因素的影响,MODIS NDVI数据产品中存在着大量的噪声,需要进行去噪重建。针对目前几种常用的NDVI时间序列数据去云方法,如HANTS法、SPLINE插值法以及Savizky-Golay法,以山东省MODIS NDVI时间序列数据(一年的)作为检验数据,从不同角度比较几种算法的去云能力和使用范围。结果表明: SPLINE插值法的去噪效果取决于云掩模数据的质量,但有时会产生异常值; HANTS算法和Savizky-Golay算法会改变几乎所有像元的值,得到一个比较平滑的时间序列曲线,但这两种算法的输入参数没有统一标准,需多次试验才能确定最优参数。

[Liang S Z, Shi P, Xing Q G.2011.

A comparison between the algorithms for removing cloud pixel from MODIS NDVI time series data

[J]. Remote Sensing for Land & Resources, (1): 33-36.]

https://doi.org/10.6046/gtzyyg.2011.01.06      URL      Magsci      [本文引用: 1]      摘要

&nbsp;受多重因素的影响,MODIS NDVI数据产品中存在着大量的噪声,需要进行去噪重建。针对目前几种常用的NDVI时间序列数据去云方法,如HANTS法、SPLINE插值法以及Savizky-Golay法,以山东省MODIS NDVI时间序列数据(一年的)作为检验数据,从不同角度比较几种算法的去云能力和使用范围。结果表明: SPLINE插值法的去噪效果取决于云掩模数据的质量,但有时会产生异常值; HANTS算法和Savizky-Golay算法会改变几乎所有像元的值,得到一个比较平滑的时间序列曲线,但这两种算法的输入参数没有统一标准,需多次试验才能确定最优参数。
[9] 林忠辉, 莫兴国. 2006.

NDVI时间序列谐波分析与地表物候信息获取

[J]. 农业工程学报, 22(12): 138-144.

Magsci      摘要

植被指数具有明显的季节节律,NDVI时间序列分析可以获取地表植被物候信息,但已有的AVHRR NDVI数据产品仍然存在高噪声,需要进一步校正。在考虑农业植被季节变化特征的基础上,基于先验知识对NDVI时间序列数据傅立叶校正算法进行了改进。利用1992年旬最大值合成1 km NOAA-AVHRR NDVI数据,使用该方法对河北省南部地区几种不同植被类型的NDVI数据进行校正,结果显示:改进的傅立叶谐波校正算法能更好地反映农业植被NDVI季节变化节律,且对自然植被同样适用。对校正后的NDVI 时间序列数据进行谐波分析表明:谐波的特征值(谐波余项、振幅和位相)与地表植被动态之间存在相关性,谐波余项表征NDVI 时间序列的均值,谐波振幅表征NDVI年内波动幅度大小,不同谐波的位相可以表征NDVI 季节变化的时间特征,利用这些参数可以获取地表植被物候信息,并可用于土地覆被和土地利用分类研究以及全球变化研究。

[Lin Z H, Mo X G.2006.

Phenologies from harmonics analysis of AVHRR NDVI time series

[J]. Transactions of the Chinese Society of Agricultural Engineering, 22(12): 138-144.]

Magsci      摘要

植被指数具有明显的季节节律,NDVI时间序列分析可以获取地表植被物候信息,但已有的AVHRR NDVI数据产品仍然存在高噪声,需要进一步校正。在考虑农业植被季节变化特征的基础上,基于先验知识对NDVI时间序列数据傅立叶校正算法进行了改进。利用1992年旬最大值合成1 km NOAA-AVHRR NDVI数据,使用该方法对河北省南部地区几种不同植被类型的NDVI数据进行校正,结果显示:改进的傅立叶谐波校正算法能更好地反映农业植被NDVI季节变化节律,且对自然植被同样适用。对校正后的NDVI 时间序列数据进行谐波分析表明:谐波的特征值(谐波余项、振幅和位相)与地表植被动态之间存在相关性,谐波余项表征NDVI 时间序列的均值,谐波振幅表征NDVI年内波动幅度大小,不同谐波的位相可以表征NDVI 季节变化的时间特征,利用这些参数可以获取地表植被物候信息,并可用于土地覆被和土地利用分类研究以及全球变化研究。
[10] 牟敏杰, 朱文泉, 王伶俐, . 2012.

基于通量塔净生态系统碳交换数据的植被物候遥感识别方法评价

[J]. 应用生态学报, 23(2): 319-327.

Magsci      摘要

选择北美洲72座通量塔观测的净生态系统碳交换(NEE)数据来计算植被物候,并以此作为参考数据,从可行性和准确性两方面对阈值法、移动平均法和函数拟合法三大类常用的植被物候遥感识别方法进行了综合评价.结果表明: 基于局部中值的阈值法对植被物候识别的可行性和准确性均最优;其次为Logistic函数拟合法中的一阶导数方法;移动平均法对植被物候识别的可行性和准确性与移动窗口的大小有关,对于16 d合成的归一化差值植被指数(NDVI)时间序列数据来说,移动窗口大小为15时能获得较优的结果;而全局阈值法对植被物候识别的可行性和准确性均最差;Logistic函数拟合法中的曲率变化率方法在识别植被物候时虽然与基于NEE数据得到的植被物候在数值上存在较大偏差,但二者之间具有较高的相关性,说明基于曲率变化率方法识别出的植被物候能较真实地反映植被物候在时空上的变化趋势.

[Mou M J, Zhu W Q, Wang L L, et al.2012.

Evaluation of remote sensing extraction methods for vegetation phenology based on flux tower net ecosystem carbon exchange data

[J]. Chinese Journal of Applied Ecology, 23(2): 319-327.]

Magsci      摘要

选择北美洲72座通量塔观测的净生态系统碳交换(NEE)数据来计算植被物候,并以此作为参考数据,从可行性和准确性两方面对阈值法、移动平均法和函数拟合法三大类常用的植被物候遥感识别方法进行了综合评价.结果表明: 基于局部中值的阈值法对植被物候识别的可行性和准确性均最优;其次为Logistic函数拟合法中的一阶导数方法;移动平均法对植被物候识别的可行性和准确性与移动窗口的大小有关,对于16 d合成的归一化差值植被指数(NDVI)时间序列数据来说,移动窗口大小为15时能获得较优的结果;而全局阈值法对植被物候识别的可行性和准确性均最差;Logistic函数拟合法中的曲率变化率方法在识别植被物候时虽然与基于NEE数据得到的植被物候在数值上存在较大偏差,但二者之间具有较高的相关性,说明基于曲率变化率方法识别出的植被物候能较真实地反映植被物候在时空上的变化趋势.
[11] 那晓东, 张树清, 李晓峰, . 2007.

MODIS NDVI时间序列在三江平原湿地植被信息提取中的应用

[J]. 湿地科学, 5(3): 227-236.

Magsci      [本文引用: 2]     

[Na X D, Zhang S Q, Li X F, et al.2007.

Application of MODIS NDVI time series to extracting wetland vegetation information in the Sanjiang Plain

[J]. Wetland Science, 5(3): 227-236.]

Magsci      [本文引用: 2]     

[12] 司文才, 刘峻明. 2011.

冬小麦关键物候空间分布遥感监测方法研究

[J]. 中国农业科技导报, 13(6): 82-89.

https://doi.org/10.3969/j.issn.1008-0864.2011.06.14      URL      [本文引用: 1]      摘要

物候信息对农作物生长的动态监测、田间管理具有重要意义。归一化植被指数 (normalized difference vegetation index, NDVI)能够在大范围覆盖区域内准确地反映农作物物候信息。以河北省为研究区域,选用2000至2009年的sPOT/VEGETATl0NNDVI旬 合成产品数据集,尝试结合气象站点的气温,利用Savitzky—Golay(S-G)滤波重构NDVI时序曲线,以动态阈值法监测研究区冬小麦的返青期 开始和抽穗期开始,并对监测结果进行了分析。空间分布上,河北省冬小麦物候期总体上呈现由南到北逐渐推迟的空间分布规律,从各具体年份来看,冬小麦遥感监 测结果符合实际的物候地面观测结果,并在因异常气候变化物候期发生变化的年份也有较好的反应,结果表明,通过NDVI时序曲线结合气温的方法可以准确监测 河北省冬小麦关键物候期。

[Si W C, Liu J M.2011.

Studies on remote sensing monitoring method for spatial distribution of winter wheat critical phenology

[J]. Journal of Agricultural Science and Technology, 13(6): 82-89.]

https://doi.org/10.3969/j.issn.1008-0864.2011.06.14      URL      [本文引用: 1]      摘要

物候信息对农作物生长的动态监测、田间管理具有重要意义。归一化植被指数 (normalized difference vegetation index, NDVI)能够在大范围覆盖区域内准确地反映农作物物候信息。以河北省为研究区域,选用2000至2009年的sPOT/VEGETATl0NNDVI旬 合成产品数据集,尝试结合气象站点的气温,利用Savitzky—Golay(S-G)滤波重构NDVI时序曲线,以动态阈值法监测研究区冬小麦的返青期 开始和抽穗期开始,并对监测结果进行了分析。空间分布上,河北省冬小麦物候期总体上呈现由南到北逐渐推迟的空间分布规律,从各具体年份来看,冬小麦遥感监 测结果符合实际的物候地面观测结果,并在因异常气候变化物候期发生变化的年份也有较好的反应,结果表明,通过NDVI时序曲线结合气温的方法可以准确监测 河北省冬小麦关键物候期。
[13] 宋春桥, 游松财, 柯灵红, . 2011.

藏北地区三种时序NDVI重建方法与应用分析

[J]. 地球信息科学学报, 13(1): 133-143.

https://doi.org/10.3724/SP.J.1047.2011.00133      Magsci      [本文引用: 2]      摘要

<p>遥感植被指数时间序列数据集,已广泛应用于陆地生态环境变化监测与模拟、植被覆盖动态变化分析、植被物候特征识别与信息提取等多方面的研究。但其因受遥感器采集与传输过程、大气条件、地面状况等诸多因素的影响,时序NDVI数据包含各种噪声,因此研究者们发展了一系列时间序列曲线重建方法。本文对近年来提出或改进的重建算法原理、优缺点进行阐述;然后,选择当前最为常用的3种方法,即非对称高斯函数(AG)拟合、双Logistic曲线(D-L)拟合和Savitzky-Golay(S-G)滤波法,以藏北地区不同土地覆被类型样点像元NDVI时间序列为实例,对算法的去噪效果、保真性能、生长峰值及细节处理效果等方面进行比较研究。结果表明,AG与D-L拟合两种算法具有较好的一致性,但对生长峰值模拟有所差异;3种方法对荒漠、荒漠草原、草原、灌丛、作物用地及林地等不同覆被类型各具优势,表现出区域、覆被类型和应用目的差异性。最后,基于AG拟合算法对整个藏北地区2007-2009年MODIS NDVI时间序列进行重建,处理后像元NDVI空间格局异质性减弱。</p>

[Song C Q, You S C, Ke L H, et al.2011.

Analysis on three NDVI time-series reconstruction methods and their applications in North Tibet

[J]. Journal of Geo-information Science, 13(1): 133-143.]

https://doi.org/10.3724/SP.J.1047.2011.00133      Magsci      [本文引用: 2]      摘要

<p>遥感植被指数时间序列数据集,已广泛应用于陆地生态环境变化监测与模拟、植被覆盖动态变化分析、植被物候特征识别与信息提取等多方面的研究。但其因受遥感器采集与传输过程、大气条件、地面状况等诸多因素的影响,时序NDVI数据包含各种噪声,因此研究者们发展了一系列时间序列曲线重建方法。本文对近年来提出或改进的重建算法原理、优缺点进行阐述;然后,选择当前最为常用的3种方法,即非对称高斯函数(AG)拟合、双Logistic曲线(D-L)拟合和Savitzky-Golay(S-G)滤波法,以藏北地区不同土地覆被类型样点像元NDVI时间序列为实例,对算法的去噪效果、保真性能、生长峰值及细节处理效果等方面进行比较研究。结果表明,AG与D-L拟合两种算法具有较好的一致性,但对生长峰值模拟有所差异;3种方法对荒漠、荒漠草原、草原、灌丛、作物用地及林地等不同覆被类型各具优势,表现出区域、覆被类型和应用目的差异性。最后,基于AG拟合算法对整个藏北地区2007-2009年MODIS NDVI时间序列进行重建,处理后像元NDVI空间格局异质性减弱。</p>
[14] 唐仁茂, 陈英英, 叶建元. 2010.

探空、地面及卫星资料反演水汽含量的比较

[J]. 气象科学, 30(3): 373-377.

https://doi.org/10.3969/j.issn.1009-0827.2010.03.013      URL      [本文引用: 1]      摘要

利用探空、地面等常规探测资料 及卫星遥感资料计算了我国中西部地区2007年6月—2008年5月间水汽含量的空间分布和时间演变,结果显示:由探空资料计算的整层大气水汽含量的空间 变化,总体形势是,纬度低的地区水汽含量多,纬度高的地区水汽含量少;各探空站上空水汽分布的季节演变规律比较一致,夏季水汽含量最大,冬季最小,春秋季 节基本相当。根据探空资料建立地面水汽压与大气总水汽量的经验关系,利用地面站资料确定水汽分布,与同时次探空站资料估算的水汽场相比,两者分布趋势基本 一致。利用FY-2C卫星的可见光和红外分裂窗通道资料,建立反演大气水汽含量的回归关系式,与探空资料计算的结果相比,总体上变化趋势较一致。

[Tang R M, Chen Y Y, Ye J Y.2010.

The comparison of water vapor content retrieved by radiosonde, ground station and satellite data

[J]. Scientia Meteorologica Sinica, 30(3): 373-377.]

https://doi.org/10.3969/j.issn.1009-0827.2010.03.013      URL      [本文引用: 1]      摘要

利用探空、地面等常规探测资料 及卫星遥感资料计算了我国中西部地区2007年6月—2008年5月间水汽含量的空间分布和时间演变,结果显示:由探空资料计算的整层大气水汽含量的空间 变化,总体形势是,纬度低的地区水汽含量多,纬度高的地区水汽含量少;各探空站上空水汽分布的季节演变规律比较一致,夏季水汽含量最大,冬季最小,春秋季 节基本相当。根据探空资料建立地面水汽压与大气总水汽量的经验关系,利用地面站资料确定水汽分布,与同时次探空站资料估算的水汽场相比,两者分布趋势基本 一致。利用FY-2C卫星的可见光和红外分裂窗通道资料,建立反演大气水汽含量的回归关系式,与探空资料计算的结果相比,总体上变化趋势较一致。
[15] 武永峰, 李茂松, 宋吉青. 2008.

植物物候遥感监测研究进展

[J]. 气象与环境学报, 24(3): 51-58.

Magsci      [本文引用: 2]      摘要

<FONT face=Verdana>基于遥感监测方法的植物物候研究已成为全球气候变化研究的一个热点课题,通过对植物物候生长季概念的遥感界定,归纳总结了物候监测的基本原理、遥感数据源以及4种常用的遥感监测方法,并指出各种遥感监测方法存在的问题和不足;以遥感监测技术为支撑,对地区、大洲和全球尺度上物候与气候关系的研究、物候与人类行为关系的研究进行了阐述,指出植物物候的变化常常是二者共同影响的结果。针对当前物候研究中存在的不足,探讨了今后的研究发展方向。</FONT>

[Wu Y F, Li M S, Song J Q.2008.

Advance in vegetation phenology monitoring based on remote sensing

[J]. Journal of Meteorology and Environment, 24(3): 51-58.]

Magsci      [本文引用: 2]      摘要

<FONT face=Verdana>基于遥感监测方法的植物物候研究已成为全球气候变化研究的一个热点课题,通过对植物物候生长季概念的遥感界定,归纳总结了物候监测的基本原理、遥感数据源以及4种常用的遥感监测方法,并指出各种遥感监测方法存在的问题和不足;以遥感监测技术为支撑,对地区、大洲和全球尺度上物候与气候关系的研究、物候与人类行为关系的研究进行了阐述,指出植物物候的变化常常是二者共同影响的结果。针对当前物候研究中存在的不足,探讨了今后的研究发展方向。</FONT>
[16] 颉继珍, 王红说, 黄敬峰. 2010.

基于MODIS时间序列数据的作物季相信息提取

[J]. 遥感技术与应用, 25(5): 647-652.

URL      [本文引用: 1]      摘要

基于MODIS NDVI时间序列数据对浙北平原单季稻区进行作物季相一致性分析.对NDVI时间序列数据进行离散傅立叶变换去除噪声,再利用土地利用现状图提取耕地区的 NDVI影像图,根据时间序列曲线的最大值研究作物的季相.结果表明:水稻生长期对NDVI时间序列曲线的响应和季相一致性均较小麦和油菜好;8 d合成的数据较16 d合成的数据可以更详细地反映作物季相信息.研究证实了MODIS NDVI时间序列曲线对区域作物季相分析的意义.

[Xie J Z, Wang H S, Huang J F.2010.

Crop phenology information extraction using MODIS multi-temporal data

[J]. Remote Sensing Technology and Application, 25(5): 647-652.]

URL      [本文引用: 1]      摘要

基于MODIS NDVI时间序列数据对浙北平原单季稻区进行作物季相一致性分析.对NDVI时间序列数据进行离散傅立叶变换去除噪声,再利用土地利用现状图提取耕地区的 NDVI影像图,根据时间序列曲线的最大值研究作物的季相.结果表明:水稻生长期对NDVI时间序列曲线的响应和季相一致性均较小麦和油菜好;8 d合成的数据较16 d合成的数据可以更详细地反映作物季相信息.研究证实了MODIS NDVI时间序列曲线对区域作物季相分析的意义.
[17] 徐岩岩, 张佳华, Yang L M.2012.

基于MODIS-EVI数据和Symlet11小波识别东北地区水稻主要物候期

[J]. 生态学报, 32(7): 2091-2098.

https://doi.org/10.5846/stxb201108131186      Magsci      摘要

作物物候信号能够反映温度和降水等变化对植被生长的影响,是进行农作物动态分析和田间管理的重要依据。基于2008年EOS-MODIS多时相卫星遥感数据,研究了我国东北地区水稻的主要物候期的识别方法。首先提取研究区24个农业气象观测站所在位置的MODIS-EVI(Enhanced Vegetation Index,增强型植被指数)指数的时间序列;同时利用小波滤波消除时间序列上的噪音,小波滤波选用函数包含Daubechies(7-20),Coiflet(3-5)和Symlet(7-15)共26种类型。然后根据水稻移栽期、抽穗期和成熟期在EVI时间序列上的表现特征来识别水稻主要物候期。最后与东北地区24个站点水稻物候观测资料对比并分析误差。结果表明,Symlet11小波滤波的效果最好,其移栽期识别结果的误差绝大部分在±16 d,抽穗期和成熟期识别结果的误差在±8 d。表明通过此方法可以较好地识别东北水稻主要物候期,并可进一步应用到整个东北地区水稻的物候空间分布和时间变化特征研究上。

[Xu Y Y, Zhang J H, Yang L M.2012.

Detecting major phenological stages of rice using MODIS-EVI data and Symlet11 wavelet in Northeast China

[J]. Acta Ecologica Sinica, 32(7): 2091-2098.]

https://doi.org/10.5846/stxb201108131186      Magsci      摘要

作物物候信号能够反映温度和降水等变化对植被生长的影响,是进行农作物动态分析和田间管理的重要依据。基于2008年EOS-MODIS多时相卫星遥感数据,研究了我国东北地区水稻的主要物候期的识别方法。首先提取研究区24个农业气象观测站所在位置的MODIS-EVI(Enhanced Vegetation Index,增强型植被指数)指数的时间序列;同时利用小波滤波消除时间序列上的噪音,小波滤波选用函数包含Daubechies(7-20),Coiflet(3-5)和Symlet(7-15)共26种类型。然后根据水稻移栽期、抽穗期和成熟期在EVI时间序列上的表现特征来识别水稻主要物候期。最后与东北地区24个站点水稻物候观测资料对比并分析误差。结果表明,Symlet11小波滤波的效果最好,其移栽期识别结果的误差绝大部分在±16 d,抽穗期和成熟期识别结果的误差在±8 d。表明通过此方法可以较好地识别东北水稻主要物候期,并可进一步应用到整个东北地区水稻的物候空间分布和时间变化特征研究上。
[18] 徐永明, 覃志豪, 陈爱军. 2010.

基于查找表的MODIS逐像元大气校正方法研究

[J]. 武汉大学学报: 信息科学版, 35(8): 959-962.

URL      [本文引用: 1]      摘要

?以6s大气辐射传输模型为基础,计算了气溶胶光学厚度、太阳天顶角、传感器天顶角以及地表海拔变化对于校正得到的地表反射率的影响,讨论了6s模型对于这些参数的敏感性,提出了一种基于查找表的大气校正方法,利用6s模型离线计算建立了不同气溶胶的光学厚度、太阳天顶角、传感器天顶角以及地表海拔条件下大气校正系数的查找表,基于该查找表对modis影像进行逐像元大气校正。通过对本文方法、6s在线校正方法和利用统一输入参数校正方法的比较表明,本文方法的计算结果与6s在线校正方法很接近,说明本文方法可以有效地改善由于大气条件、传感器位置等空间分布差异对modis图像大气校正的影响。

[Xu Y M, Qin Z H, Chen A J.2010.

A pixel-by-pixel atmospheric correction algorithm for MODIS data based on look-up table

[J]. Geomatics and Information Science of Wuhan University, 35(8): 959-962.]

URL      [本文引用: 1]      摘要

?以6s大气辐射传输模型为基础,计算了气溶胶光学厚度、太阳天顶角、传感器天顶角以及地表海拔变化对于校正得到的地表反射率的影响,讨论了6s模型对于这些参数的敏感性,提出了一种基于查找表的大气校正方法,利用6s模型离线计算建立了不同气溶胶的光学厚度、太阳天顶角、传感器天顶角以及地表海拔条件下大气校正系数的查找表,基于该查找表对modis影像进行逐像元大气校正。通过对本文方法、6s在线校正方法和利用统一输入参数校正方法的比较表明,本文方法的计算结果与6s在线校正方法很接近,说明本文方法可以有效地改善由于大气条件、传感器位置等空间分布差异对modis图像大气校正的影响。
[19] 许青云, 杨贵军, 龙慧灵, . 2014.

基于MODIS NDVI多年时序数据的农作物种植识别

[J]. 农业工程学报, 30(11): 134-144.

Magsci      [本文引用: 1]      摘要

为了获取陕西省农作物种植模式和类型分布信息,实现对于多年农作物长势分析及精确的估产和耕地生产力的估算,该文以2003-2012年的MOD09Q1时间序列遥感数据集为数据源,以陕西省主要农作物冬小麦、夏玉米、春玉米、水稻和油菜为研究对象,利用Savitzky-Golay滤波方法重建NDVI长时间序列数据集,充分利用农作物的物候信息,构建农作物年际间动态阈值方法,实现了农作物种植模式和类型的识别。通过对混合像元进行分解,更精确地提取农作物种植面积信息。利用空间和定量2种方式对农作物类型识别结果进行分析验证,空间对比分析得到分类的总体精度和Kappa系数为88.18%和59.64%,定量对比分析得到分类的总体一致性为87.56%。研究结果表明,结合物候信息与时间序列数据利用该文的分类方法可以有效的识别大尺度农作物信息。

[Xu Q Y, Yang G J, Long H L, et al.2014.

Crop information identification based on MODIS NDVI time-series data

[J]. Transactions of the Chinese Society of Agricultural Engineering, 30(11): 134-144.]

Magsci      [本文引用: 1]      摘要

为了获取陕西省农作物种植模式和类型分布信息,实现对于多年农作物长势分析及精确的估产和耕地生产力的估算,该文以2003-2012年的MOD09Q1时间序列遥感数据集为数据源,以陕西省主要农作物冬小麦、夏玉米、春玉米、水稻和油菜为研究对象,利用Savitzky-Golay滤波方法重建NDVI长时间序列数据集,充分利用农作物的物候信息,构建农作物年际间动态阈值方法,实现了农作物种植模式和类型的识别。通过对混合像元进行分解,更精确地提取农作物种植面积信息。利用空间和定量2种方式对农作物类型识别结果进行分析验证,空间对比分析得到分类的总体精度和Kappa系数为88.18%和59.64%,定量对比分析得到分类的总体一致性为87.56%。研究结果表明,结合物候信息与时间序列数据利用该文的分类方法可以有效的识别大尺度农作物信息。
[20] 闫慧敏, 曹明奎, 刘纪远, . 2005.

基于多时相遥感信息的中国农业种植制度空间格局研究

[J]. 农业工程学报, 21(4): 85-90.

Magsci      [本文引用: 1]      摘要

多熟种植是中国重要的种植制度,对保持和增加粮食产量和促进农村经济发展有重要意义。复种指数受自然条件和农村社会状况的影响处于不断变化之中,及时获取其变化信息对估计粮食产量变化及其原因和农业发展科学决策有非常重要的意义。中国地域辽阔,作物种植制度复杂多样,传统的统计方法不能及时满足政府获取种植制度变化的要求。卫星遥感是探测大尺度土地覆被格局及变化最有效手段,因此可以作为获取区域和全国尺度作物复种指数的一个重要途径。该研究探讨了应用多时相遥感数据定量表达全国种植制度信息提取的方法及可行性,采用峰值特征点检测法结合作物生长季相特征及农田管理特点(播种和收获)提取了中国农田的多熟种植信息,并与统计数据的复种指数进行比较验证,为进一步进行农业种植制度变化研究奠定了基础。

[Yan H M, Cao M K, Liu J Y, et al.2005.

Characterizing spatial patterns of multiple cropping system in China from multi- temporal remote sensing images

[J]. Transactions of the Chinese Society of Agricultural Engineering, 21(4): 85-90.]

Magsci      [本文引用: 1]      摘要

多熟种植是中国重要的种植制度,对保持和增加粮食产量和促进农村经济发展有重要意义。复种指数受自然条件和农村社会状况的影响处于不断变化之中,及时获取其变化信息对估计粮食产量变化及其原因和农业发展科学决策有非常重要的意义。中国地域辽阔,作物种植制度复杂多样,传统的统计方法不能及时满足政府获取种植制度变化的要求。卫星遥感是探测大尺度土地覆被格局及变化最有效手段,因此可以作为获取区域和全国尺度作物复种指数的一个重要途径。该研究探讨了应用多时相遥感数据定量表达全国种植制度信息提取的方法及可行性,采用峰值特征点检测法结合作物生长季相特征及农田管理特点(播种和收获)提取了中国农田的多熟种植信息,并与统计数据的复种指数进行比较验证,为进一步进行农业种植制度变化研究奠定了基础。
[21] 姚晨, 黄微, 李先华. 2009.

地形复杂区域的典型植被指数评估

[J]. 遥感技术与应用, 24(4): 496-501.

Magsci      [本文引用: 1]      摘要

<p>植被多分布在复杂地形,不规则的地形会造成遥感影像中同种或相似地物呈现不同的反射率/辐射值,也会使得不同种地物呈现相似的反射率/辐射值,这种反射率的变化会给植被的生物、物理量评估带来误差,所以对地形复杂区域的典型植被指数的评估是十分必要的。选用Landsat卫星影像对复杂地形条件下典型植被指数进行了评估。经过详细的分析,植被指数对由地形引起的红光波段反射率变化敏感,导致它们对地形的变化敏感,所以植被指数受地形的影响总体上是不容忽视的。<br /></p>

[Yao C, Huang W, Li X H.2009.

Evaluation of topographical influence on vegetation indices of rugged terrain

[J]. Remote Sensing Technology and Application, 24(4): 496-501.]

Magsci      [本文引用: 1]      摘要

<p>植被多分布在复杂地形,不规则的地形会造成遥感影像中同种或相似地物呈现不同的反射率/辐射值,也会使得不同种地物呈现相似的反射率/辐射值,这种反射率的变化会给植被的生物、物理量评估带来误差,所以对地形复杂区域的典型植被指数的评估是十分必要的。选用Landsat卫星影像对复杂地形条件下典型植被指数进行了评估。经过详细的分析,植被指数对由地形引起的红光波段反射率变化敏感,导致它们对地形的变化敏感,所以植被指数受地形的影响总体上是不容忽视的。<br /></p>
[22] 姚延娟, 刘强, 柳钦火, . 2007.

异质性地表的叶面积指数反演的不确定性分析

[J]. 遥感学报, 11(6): 763-770.

https://doi.org/10.11834/jrs.200706104      Magsci      [本文引用: 1]      摘要

叶面积指数(Leaf Area Index)可用来反映作物的生长状况,常作为主要指标应用于农作物估产。本文研究遥感中常见的混合像元问题对LAI反演所带来的不确定性问题。研究的混合像元由两种情况构成,一种是由不同长势的作物所构成的混合像元,另一种情况是由不同端元形成的混合像元。结果表明,不同长势形成的混合像元对LAI的准确反演影响不大;不同组分形成的混合像元对LAI反演影响很大。从验证的角度讲,地面实测点的LAI数据不能代表一定分辨率区域的LAI的值,对于像元LAI的验证要注意正确获得像元的LAI。

[Yao Y J, Liu Q, Liu Q H, et al.2007.

LAI inversion uncertainties in heterogeneous surface

[J]. Journal of Remote Sensing, 11(6): 763-770.]

https://doi.org/10.11834/jrs.200706104      Magsci      [本文引用: 1]      摘要

叶面积指数(Leaf Area Index)可用来反映作物的生长状况,常作为主要指标应用于农作物估产。本文研究遥感中常见的混合像元问题对LAI反演所带来的不确定性问题。研究的混合像元由两种情况构成,一种是由不同长势的作物所构成的混合像元,另一种情况是由不同端元形成的混合像元。结果表明,不同长势形成的混合像元对LAI的准确反演影响不大;不同组分形成的混合像元对LAI反演影响很大。从验证的角度讲,地面实测点的LAI数据不能代表一定分辨率区域的LAI的值,对于像元LAI的验证要注意正确获得像元的LAI。
[23] 于信芳, 庄大方. 2006.

基于MODIS NDVI数据的东北森林物候期监测

[J]. 资源科学, 28(4): 111-117.

Magsci      [本文引用: 1]      摘要

物候是指示气候与自然环境变化的重要指标。遥感技术的发展为物候监测和研究提供了新的手段。本文研究对象是中国东北森林,森林分布范围由Landsat TM影像解译得到的2000年土地利用数据确定。遥感数据源是2003年500m空间分辨率的MODIS NDVI 8天合成时间序列数据。通过分析东北主要森林树种的NDVI时间序列特征,表明不同树种的同一遥感参数时间序列基本形状近似,在关键物候期和变化振幅上存在差异,这为根据遥感参数时间序列曲线监测森林物候期奠定了理论基础。将MODIS NDVI 8天合成时间序列数据应用时间序列谐波分析法(HANTS)重构成每天的NDVI时间序列数据影像。基于每天的NDVI时间序列数据,研究采用动态阈值法获取了东北森林物候期及其空间分布格局。研究表明东北大部分地区树木在第100天~150天开始生长,到第260天~290天逐渐停止生长,生长季长度集中在140天~180天。通过与部分物候观测数据的比较验证,表明基于MODIS NDVI数据获取的树木生长始末日期与调查资料具有可比性,获取的森林物候期具有一定的可靠性。

[Yu X F, Zhuang D F.2006.

Monitoring forest phenophases of Northeast China based on MODIS NDVI data

[J]. Resources Science, 28(4): 111-117.]

Magsci      [本文引用: 1]      摘要

物候是指示气候与自然环境变化的重要指标。遥感技术的发展为物候监测和研究提供了新的手段。本文研究对象是中国东北森林,森林分布范围由Landsat TM影像解译得到的2000年土地利用数据确定。遥感数据源是2003年500m空间分辨率的MODIS NDVI 8天合成时间序列数据。通过分析东北主要森林树种的NDVI时间序列特征,表明不同树种的同一遥感参数时间序列基本形状近似,在关键物候期和变化振幅上存在差异,这为根据遥感参数时间序列曲线监测森林物候期奠定了理论基础。将MODIS NDVI 8天合成时间序列数据应用时间序列谐波分析法(HANTS)重构成每天的NDVI时间序列数据影像。基于每天的NDVI时间序列数据,研究采用动态阈值法获取了东北森林物候期及其空间分布格局。研究表明东北大部分地区树木在第100天~150天开始生长,到第260天~290天逐渐停止生长,生长季长度集中在140天~180天。通过与部分物候观测数据的比较验证,表明基于MODIS NDVI数据获取的树木生长始末日期与调查资料具有可比性,获取的森林物候期具有一定的可靠性。
[24] 张峰, 吴炳方, 刘成林, . 2004.

利用时序植被指数监测作物物候的方法研究

[J]. 农业工程学报, 20(1): 155-159.

Magsci      [本文引用: 2]      摘要

该文是对全国主要产粮县旱地和水田作物的物候期进行遥感监测。在数据预处理中采用最小二乘法和谐函数分解重构方法相结合,去除时序植被指数影像的云噪声影响。基于土地利用数据,通过耕地植被指数加权平均的方法提取区旱地和水田作物生长过程。结合野外观察数据,对一年一熟作物用作物生长过程的最大上升斜率、最大值和最大下降斜率作为作物出苗(返青)期、抽穗期和收获期的遥感识别标志。对一年两熟、多熟县作物物候期依据轮作规律进行了监测。同时进行物候年际间对比和农业灾害监测分析。遥感监测出苗(返青)期和收获期与野外采样照片实测信息有90%的相同率,抽穗期遥感监测与实测信息相同率95%。

[Zhang F, Wu B F, Liu C L, et al.2004.

Methods of monitoring crop phonological stages using time series of vegetation indicator

[J]. Transactions of the Chinese Society of Agricultural Engineering, 20(1): 155-159.]

Magsci      [本文引用: 2]      摘要

该文是对全国主要产粮县旱地和水田作物的物候期进行遥感监测。在数据预处理中采用最小二乘法和谐函数分解重构方法相结合,去除时序植被指数影像的云噪声影响。基于土地利用数据,通过耕地植被指数加权平均的方法提取区旱地和水田作物生长过程。结合野外观察数据,对一年一熟作物用作物生长过程的最大上升斜率、最大值和最大下降斜率作为作物出苗(返青)期、抽穗期和收获期的遥感识别标志。对一年两熟、多熟县作物物候期依据轮作规律进行了监测。同时进行物候年际间对比和农业灾害监测分析。遥感监测出苗(返青)期和收获期与野外采样照片实测信息有90%的相同率,抽穗期遥感监测与实测信息相同率95%。
[25] 张云松, 冯钟葵, 石丹. 2007.

卫星观测方位对遥感成像的影响

[J]. 遥感学报, 11(4): 433-438.

https://doi.org/10.11834/jrs.20070460      Magsci      [本文引用: 1]      摘要

光学卫星成像主要是通过传感器接收地表对太阳入射光线的反射、地表辐射和大气散射等能量,形成影像数据。卫星侧视成像时,太阳与卫星之间的位置关系随日期、地理位置和地方时变化,当条件适当时,两者处于对称关系,从地面上看类似镜面反射状态。此时,由于地表的非朗伯性,就使卫星接收的反射能量偏高,造成接收得到的图像亮度高于正常水平,甚至过饱和而不可用。本文以SPOT卫星为例,从卫星的观测方位和太阳的照射方位入手,讨论卫星观测方位对遥感成像质量的影响。经分析,每年5―9月期间,在北纬32&#176;―45&#176;地区,当SPOT卫星以较大的侧视角向左侧成像时,卫星观测方向和太阳入射方向在成像区投影共线并且太阳的高度角与卫星的高度角近似相等的可能性很大,从而影响卫星成像的质量。

[Zhang Y S, Feng Z H, Shi D.2007.

The influence of satellite observation direction on remote sensing image

[J]. Journal of Remote Sensing, 11(4): 433-438.]

https://doi.org/10.11834/jrs.20070460      Magsci      [本文引用: 1]      摘要

光学卫星成像主要是通过传感器接收地表对太阳入射光线的反射、地表辐射和大气散射等能量,形成影像数据。卫星侧视成像时,太阳与卫星之间的位置关系随日期、地理位置和地方时变化,当条件适当时,两者处于对称关系,从地面上看类似镜面反射状态。此时,由于地表的非朗伯性,就使卫星接收的反射能量偏高,造成接收得到的图像亮度高于正常水平,甚至过饱和而不可用。本文以SPOT卫星为例,从卫星的观测方位和太阳的照射方位入手,讨论卫星观测方位对遥感成像质量的影响。经分析,每年5―9月期间,在北纬32&#176;―45&#176;地区,当SPOT卫星以较大的侧视角向左侧成像时,卫星观测方向和太阳入射方向在成像区投影共线并且太阳的高度角与卫星的高度角近似相等的可能性很大,从而影响卫星成像的质量。
[26] 赵英时, . 2003. 遥感应用分析原理与方法[M]. 北京: 科学出版社.

[本文引用: 2]     

[Zhao Y S, et al.2003. Yaogan yingyong fenxi yuanli yu fangfa[M]. Beijing, China: Science Press.]

[本文引用: 2]     

[27] 朱博, 王新鸿, 唐伶俐, . 2010.

光学遥感图像信噪比评估方法研究进展

[J]. 遥感技术与应用, 25(2): 303-309.

Magsci      [本文引用: 2]      摘要

<p>遥感图像数据的信噪比是评价遥感传感器获取数据质量的一项重要指标,图像数据的信噪比能够在很大程度上反映遥感仪器的信噪比性能。介绍了通过遥感图像分析评估传感器信噪比的常用方法,以及这些方法的优缺点。并从原理上对各种方法进行了方法间的性能对比分析,包括方法的自动化程度、运算速度、鲁棒性、适用面、准确程度和对图像计算区域的要求等。此外,提出有必要对各种算法进行在实际应用中的比较分析,从而能够针对不同遥感器和不同类型的遥感图像选择最好的评估方法,达到合理、准确地应用这些方法的目的。</p>

[Zhu B, Wang X H, Tang L L, et al.2010.

Review on methods for SNR estimation of optical remote sensing imagery

[J]. Remote Sensing Technology and Application, 25(2): 303-309.]

Magsci      [本文引用: 2]      摘要

<p>遥感图像数据的信噪比是评价遥感传感器获取数据质量的一项重要指标,图像数据的信噪比能够在很大程度上反映遥感仪器的信噪比性能。介绍了通过遥感图像分析评估传感器信噪比的常用方法,以及这些方法的优缺点。并从原理上对各种方法进行了方法间的性能对比分析,包括方法的自动化程度、运算速度、鲁棒性、适用面、准确程度和对图像计算区域的要求等。此外,提出有必要对各种算法进行在实际应用中的比较分析,从而能够针对不同遥感器和不同类型的遥感图像选择最好的评估方法,达到合理、准确地应用这些方法的目的。</p>
[28] Ahrends H E, Etzold S, Kutsch W L, et al.2009.

Tree phenology and carbon dioxide fluxes: Use of digital photography for process-based interpretation at the ecosystem scale

[J]. Climate Research, 39(3): 261-274.

https://doi.org/10.3354/cr00811      URL      摘要

Vegetation phenology is an important indicator of climate change and climate variability and it is strongly connected to biospheric-atmospheric gas exchange. We aimed to evaluate the applicability of phenological information derived from digital imagery for the interpretation of CO2 exchange measurements. For the years 2005-2007 we analyzed seasonal phenological development of 2 temperate mixed...
[29] Atkinson P M, Jeganathan C, Dash J, et al.2012.

Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology

[J]. Remote Sensing of Environment, 123: 400-417.

https://doi.org/10.1016/j.rse.2012.04.001      URL      [本文引用: 1]      摘要

Several models have been fitted in the past to smooth time-series vegetation index data from different satellite sensors to estimate vegetation phenological parameters. However, differences between the models and fine tuning of model parameters lead to potential differences, uncertainty and bias between the results amongst users. The current research assessed four techniques: Fourier analysis, asymmetric Gaussian model, double logistic model and the Whittaker filter for smoothing multi-temporal satellite sensor observations with the ultimate purpose of deriving an appropriate annual vegetation growth cycle and estimating phenological parameters reliably. The research used Level 3 Medium Resolution Imaging Spectrometer (MERIS, spatial resolution ~ 4.6 km) Terrestrial Chlorophyll Index (MTCI) data over the years 2004 to 2006 composited at eight day intervals covering the Indian sub-continent. First, the four models were fitted to representative sample time-series of the major vegetation types in India, and the quality of the fit was analysed. Second, the effect of noise on model fitting was analysed by adding Gaussian noise to a standard profile. Finally, the four models were fitted to the whole study area to characterise variation in the quality of model fitting as a function of single and double vegetation seasons. These smoothed data were used to estimate the onset of greenness (OG), a major phenological parameter. The models were evaluated using the root mean square error (RMSE), Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC). The first test (fitting to representative sample time series) revealed the consistently superior performance of the Whittaker and Fourier approaches in most cases. The second test (fitting after the addition of Gaussian noise) revealed the superior performance of the double logistic and Fourier approaches. Finally, when the approaches were applied to the whole study, thus, including vegetation with different phenological profiles and multiple growing seasons (third test), it was found that it was necessary to tune each of the models according to the number of annual growing seasons to produce reliable fits. The double logistic and asymmetric Gaussian models did not perform well for areas with more than one growing season per year. The mean absolute deviation in OG derived from these models was a maximum (3 to 4 weeks) within the dry deciduous vegetation type and minimum (1 week) in evergreen vegetation. All techniques yielded consistent results over the south-western and north-eastern regions of India characterised by tropical climate.
[30] Arvor D, Jonathan M, Meirelles M S P, et al.2011.

Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil

[J]. International Journal of Remote Sensing, 32(22): 7847-7871.

https://doi.org/10.1080/01431161.2010.531783      URL      [本文引用: 1]      摘要

Agriculture in Brazilian Amazonia is going through a period of intensification. Crop mapping is important in understanding the way this intensification is occurring and the impact it is having. Two successive classifications based on MODIS (MODerate Resolution Imaging Spectroradiometer)-TERRA/EVI (Enhanced Vegetation Index) time series are applied (1) to map agricultural areas and (2) to identify five crop classes. These classes represent agricultural practices involving three commercial crops (soybean, maize and cotton) planted in single or double cropping systems. Both classifications are based on five steps: (1) analysis of the MODIS/EVI time series, (2) application of a smoothing algorithm, (3) application of a feature selection/extraction process to reduce the data set dimensionality, (4) application of a classifier and (5) application of a post-classification treatment. The first classification detected 95% of the agricultural areas (5 617 250 ha during the 2006–2007 harvest) and correlation coefficients with agricultural statistics exceeded 0.98 for the three crop classes at municipality level. The second classification (overall accuracy65=6574% and kappa index65=650.675) allowed us to obtain the spatial variability mapping of agricultural practices
[31] Badeck F W, Bondeau A, Böttcher K, et al.2004.

Responses of spring phenology to climate change

[J]. New Phytologist, 162(2): 295-309.

[本文引用: 2]     

[32] Balzter H, Gerard F, George C, et al.2007.

Coupling of vegetation growing season anomalies and fire activity with hemispheric and regional-scale climate patterns in central and east Siberia

[J]. Journal of Climate, 20(15): 3713-3729.

https://doi.org/10.1175/JCLI4226      URL      [本文引用: 1]      摘要

Not Available
[33] Beck P S A, Atzberger C, Høgda K A, et al.2006.

Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI

[J]. Remote Sensing of Environment, 100(3): 321-334.

https://doi.org/10.1016/j.rse.2005.10.021      URL      [本文引用: 1]      摘要

Current models of vegetation dynamics using the normalized vegetation index (NDVI) time series perform poorly for high-latitude environments. This is due partly to specific attributes of these environments, such as short growing season, long periods of darkness in winter, persistence of snow cover, and dominance of evergreen species, but also to the design of the models. We present a new method for monitoring vegetation activity at high latitudes, using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI. It estimates the NDVI of the vegetation during winter and applies a double logistic function, which is uniquely defined by six parameters that describe the yearly NDVI time series. Using NDVI data from 2000 to 2004, we illustrate the performance of this method for an area in northern Scandinavia (35聽脳聽162 km, 68掳 N 23掳 E) and compare it to existing methods based on Fourier series and asymmetric Gaussian functions. The double logistic functions describe the NDVI data better than both the Fourier series and the asymmetric Gaussian functions, as quantified by the root mean square errors. Compared with the method based on Fourier series, the new method does not overestimate the duration of the growing season. In addition, it handles outliers effectively and estimates parameters that are related to phenological events, such as the timing of spring and autumn. This makes the method most suitable for both estimating biophysical parameters and monitoring vegetation phenology.
[34] Chappell A, Seaquist J W, Eklundh L.2001.

Improving the estimation of noise from NOAA AVHRR NDVI for Africa using geostatistics

[J]. International Journal of Remote Sensing, 22(6): 1067-1080.

https://doi.org/10.1080/014311601300074603      URL      [本文引用: 1]      摘要

Abstract. The accuracy of NOAA AVHRR NDVI data can be poor because of interference from several sources, including cloud cover. A parameter of the variogram model can be used to estimate the contribution of noise from the total variation in an image. However, remotely sensed information over large areas incorporates non-stationary (regional) trend and directional effects, resulting in violation of the assumptions for noise estimation. These assumptions were investigated at five sites across Africa for a range of ecological environments over several seasons. An unsupervised spectral classification of multi-temporal NDVI data partially resolved the problem of non-stationarity. Quadratic polynomials removed the remaining regional trend and directional effects. Isotropic variograms were used to estimate the noise contributing variation to the image. Standardized estimates of noise ranged from a minimum of 18.5% in west Zambia to 68.2% in northern Congo. Cloud cover and atmospheric particulates (e.g. dust) explained some of the regional and seasonal variations in noise levels. Image artifacts were also thought to contribute noise to image variation. The magnitude of the noise levels and its temporal variation appears to seriously constrain the use of AVHRR NDVI data.
[35] Chen J, Jönsson P, Tamura M, et al.2004.

A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter

[J]. Remote Sensing of Environment, 91(3-4): 332-344.

https://doi.org/10.1016/j.rse.2004.03.014      URL      [本文引用: 3]      摘要

Although the Normalized Difference Vegetation Index (NDVI) time-series data, derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, has been successfully used in research regarding global environmental change, residual noise in the NDVI time-series data, even after applying strict pre-processing, impedes further analysis and risks generating erroneous results. Based on the assumptions that NDVI time-series follow annual cycles of growth and decline of vegetation, and that clouds or poor atmospheric conditions usually depress NDVI values, we have developed in the present study a simple but robust method based on the Savitzky鈥揋olay filter to smooth out noise in NDVI time-ser
[36] Chuine I.2000.

A unified model for budburst of trees

[J]. Journal of Theoretical Biology, 207(3): 337-347.

https://doi.org/10.1006/jtbi.2000.2178      URL      PMID: 11082304      [本文引用: 1]      摘要

Accurate plant phenology (seasonal plant activity driven by environmental factors) models are vital tools for ecosystem simulation models and for predicting the response of ecosystems to climate change. Since the early 1970s, efforts have concentrated on predicting phenology of the temperate and boreal forests because they represent one-third of the carbon captured in plant ecosystems and they are the principal ecosystems with seasonal patterns of growth on Earth (one-fifth of the plant ecosystems area). Numerous phenological models have been developed to predict the growth timing of temperate or boreal trees. They are in general empirical, nonlinear and non-nested. For these reasons they are particularly difficult to fit, to test and to compare with each other. The methodological difficulties as well as the diversity of models used have greatly slowed down their improvement. The aim of this study was to show that the most widely used models simulating vegetative or reproductive phenology of trees are particular cases of a more general model. This unified model has three main advantages. First, it allows for a direct estimation of (i) the response of bud growth to either chilling or forcing temperatures and (ii) the periods when these temperatures affect the bud growth. Second, it can be simplified according to standard statistical tests for any particular species. Third, it provides a standardized framework for phenological models, which is essential for comparative studies as well as for robust model identification.
[37] Coops N C, Hilker T, Bater C W, et al.2012.

Linking ground-based to satellite-derived phenological metrics in support of habitat assessment

[J]. Remote Sensing Letters, 3(3): 191-200.

https://doi.org/10.1080/01431161.2010.550330      URL      摘要

=650.70) of the growing season across all sites. We conclude that some predictable bias exists however unlike visual field measures of the collected data represent both a spectral and a visual archive for later use.
[38] Delbart N, Kergoat L, Le Toan T, et al.2005.

Determination of phenological dates in boreal regions using normalized difference water index

[J]. Remote Sensing of Environment, 97(1): 26-38.

https://doi.org/10.1016/j.rse.2005.03.011      URL      [本文引用: 1]      摘要

Monitoring and understanding plant phenology are important in the context of studies of terrestrial productivity and global change. Vegetation phenology, such as dates of onsets of greening up and leaf senescence, has been determined by remote sensing using mainly the normalized difference vegetation index (NDVI). In boreal regions, the results suffer from significant uncertainties because of the effect of snow on NDVI. In this paper, SPOT VEGETATION S10 data over Siberia have been analysed to define a more appropriate method. The analysis of time series of NDVI, normalized difference snow index (NDSI), and normalized difference water index (NDWI), together with an analysis of in situ phenological records in Siberia, shows that the vegetation phenology can be detected using NDWI, with small effect of snow. In spring, the date of onset of greening up is taken as the date at which NDWI starts increasing, since NDWI decreases with snowmelt and increases with greening up. In the fall, the date of onset of leaf coloring is taken as the date at which NDWI starts decreasing, since NDWI decreases with senescence and increases with snow accumulation. The results are compared to the results obtained using NDVI-based methods, taking in situ phenological records as the reference. NDWI gives better estimations of the start of greening up than NDVI (reduced RMSE, bias and dispersions, and higher correlation), whereas it does not improve the determination of the start of leaf coloring. A map of greening up dates in central Siberia obtained from NDWI is shown for year 2002 and the reliability of the method is discussed.
[39] Delbart N, Le Toan T, Kergoat L, et al.2006.

Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982-2004)

[J]. Remote Sensing of Environment, 101(1): 52-62.

https://doi.org/10.1016/j.rse.2005.11.012      URL      [本文引用: 1]      摘要

ABSTRACT Measurements of spring phenological dates in boreal regions using NDVI can be affected by snowmelt. This impacts the analysis of interannual variations in phenology and the estimates of annual carbon fluxes. For these two objectives, snowmelt effect must be removed from the phenological detection. We propose a methodology for determining the date of onset of greening in the 1982–2004 period using SPOT-VEGETATION (VGT) and NOAA Advanced Very High Resolution Radiometer (AVHRR) data. From 1998 onwards, the date of onset of greening is taken as the date at which the Normalized Difference Water Index (NDWI), calculated from SPOT-VGT near and short-wave infrared bands, starts increasing. This index decreases with snowmelt but increases with vegetation greening. For the 1982–2001 period, the date of onset of greening is the date at which AVHRR-NDVI equals a pixel specific threshold (PST), determined using the results of the NDWI method in the years common to the two datasets. The methods are validated using in situ measurements of the dates of leaf appearance. RMSE of 6.7 and 7.8 days, respectively, is found using NDWI-VGT and PST-NOAA methodologies, and the difference between the two methodologies in the common years is small. Very importantly, the dates are not biased. The interannual variations of the 23-year spring phenology dataset on the study area in northern Eurasia are analysed. In average over the study area, an advance of 8 days and a delay of 3.6 days are, respectively, found over the periods 1982–1991 and 1993–2004. These results confirm and complete previous studies about the greening trend, remove the uncertainty due to snow, and may improve carbon budget calculations.
[40] Dragoni D, Schmid H P, Wayson C A, et al.2011.

Evidence of increased net ecosystem productivity associated with a longer vegetated season in a deciduous forest in south-central Indiana, USA

[J]. Global Change Biology, 17(2): 886-897.

https://doi.org/10.1111/j.1365-2486.2010.02281.x      URL      [本文引用: 1]      摘要

Observations of net ecosystem exchange (NEE) of and its biophysical drivers have been collected at the AmeriFlux site in the Morgan-Monroe State Forest (MMSF) in Indiana, USA since 1998. Thus, this is one of the few deciduous forest sites in the world, where a decadal analysis on net ecosystem productivity () trends is possible. Despite the large interannual variability in , the observations show a significant increase in forest productivity over the past 10 years (by an annual increment of about 10 g C m63642; yr63641;). There is evidence that this trend can be explained by longer vegetative seasons, caused by extension of the vegetative activity in the fall. Both phenological and flux observations indicate that the vegetative season extended later in the fall with an increase in length of about 3 days yr63641; for the past 10 years. However, these changes are responsible for only 50% of the total annual gain in forest productivity in the past decade. A negative trend in air and soil temperature during the winter months may explain an equivalent increase in through a decrease in ecosystem respiration.
[41] Duchemin B, Goubier J, Courrier G.1999.

Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA/AVHRR data

[J]. Remote Sensing of Environment, 67(1): 68-82.

https://doi.org/10.1016/S0034-4257(98)00067-4      URL      摘要

In this study we attempted to monitor two main key stages in the phenological cycle of deciduous forests-budburst and senescence-using the normalized difference vegetation index (NDVI) derived from NOAA/AVHRR. These stages induce rapid (time scale of a month), large (>0.3) and nearly linear NDVI variations. The method we developed consists of a fit of NDVI predicted by line segment to AVHRR-NDVI time series. It made it possible to derive the budburst and senescence timing, and then the phenological cycle duration. A relationship found in the literature between leaf area index (LAI) and NDVI showed that LAI was about 1 for the satellite-derived budburst and about 1.5 for the satellite-derived senescence. We tested the method on three nearly monospecific (Quercus petraea and Fagus sylvatica L.) forests located in France using a 6-year NOAA/AVHRR archive during the 1989-1994 period. The satellite-derived phenology revealed differences in relation to the composition and the climatic features of the study areas: 1) the phenological cycle duration of oak was longer (34 days) for southern than for northern forests, 2) in the North of France, beech trees were budding earlier (5.6 days) than oak trees, 3) the interannual variability of budburst was significantly lower for beech than for oak trees. The comparison with ground phenological observations found in the literature showed the spatio-temporal coherence of the satellite-derived phenology. A good correlation was also found between the satellite-derived budburst and the budburst timing predicted from air temperatures using the thermal time model. These tests provided a first validation of the method we developed to monitor the phenological cycle of deciduous forests with NOAA/AVHHR data. The limitations of the method and the perspectives for modeling temperate deciduous ecosystems are finally discussed.
[42] Fan D Q, Zhu W Q, Zheng Z T, et al.2015.

Change in the green-up dates for Quercus mongolica in Northeast China and its climate-driven mechanism from 1962 to 2012

[J]. PLoS ONE, 10(6): e130516.

https://doi.org/10.1371/journal.pone.0130516      URL      [本文引用: 1]      摘要

The currently available studies on the green-up date were mainly based on ground observations and/or satellite data, and few model simulations integrated with wide coverage satellite data have been reported at large scale over a long time period (i.e., > 30 years). In this study, we combined phenology mechanism model, long-term climate data and synoptic scale remote sensing data to investigate the change in the green-up dates for Quercus mongolica over 33 weather stations in Northeast China and its climate-driven mechanism during 1962-2012. The results indicated that the unified phenology model can be well parameterized with the satellite derived green-up dates. The optimal daily mean temperature for chilling effect was between -27掳C and 1掳C for Q . mongolica in Northeast China, while the optimal daily mean temperature for forcing effect was above -3掳C. The green-up dates for Q . mongolica across Northeast China showed a delayed latitudinal gradient of 2.699 days degree -1 , with the earliest date on the Julian day 93 (i.e., 3 th April) in the south and the latest date on the Julian day 129 (i.e., 9 th May) in the north. The green-up date for Q . mongolica in Northeast China has advanced 6.6 days (1.3 days decade -1 ) from 1962 to 2012. With the prevailing warming in autumn, winter and spring in Northeast China during the past 51 years, the chilling effect for Q . mongolica has been weakened, while the forcing effect has been enhanced. The advancing trend in the green-up dates for Q . mongolica implied that the enhanced forcing effect to accelerate green-up was stronger than the weakened chilling effect to hold back green-up while the changes of both effects were caused by the warming climate.
[43] Fischer A.1994.

A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters

[J]. Remote Sensing of Environment, 48(2): 220-230.

https://doi.org/10.1016/0034-4257(94)90143-0      URL      摘要

The GRP receptor mediated growth response in Swiss 3T3 cells has been used to identify BN/GRP antagonists. Analysis of bombesin antagonism by substance P analogues and by truncated GRP analogues revealed that deletion of the C-terminal methionine residue was important for antagonism. Des-Met analogues showing potent antagonist activity in the in vitro 3T3 system (IC50 approximately 2nM) were synthesized. Further structural modification of these peptides led to the identification of (CH3)2CHCO-His-Trp-Ala-Val-D-Ala-His-Leu-NHCH3 (ICI 216140) which reduced bombesin-stimulated rat pancreatic amylase secretion to basal levels when administered subcutaneously at 2.0 mg per kg.
[44] Fisher J I, Mustard J F, Vadeboncoeur M A.2006.

Green leaf phenology at Landsat resolution: Scaling from the field to the satellite

[J]. Remote Sensing of Environment, 100(2): 265-279.

https://doi.org/10.1016/j.rse.2005.10.022      URL      [本文引用: 2]      摘要

Despite the large number of in situ, plot-level phenological measurements and satellite-derived phenological studies, there has been little success to date in merging these records temporally or spatially. In this research, we bridge this scale gap through higher resolution satellite records (Landsat) and quantify the accuracy of satellite-derived metrics with direct field measurements. We compiled fifty-seven Landsat scenes from southern New England (P12 R51) from 1984 to 2002. Green vegetation areal abundance for each scene was derived from spectral mixture analysis and a single set of endmembers. The leaf area signal was fit with a logistic-growth simulating sigmoid curve to derive phenological markers (half-maximum leaf-onset and offset). Spring leaf-onset dates in homogenous stands of deciduous forests displayed significant and persistent local variability. The local variability was validated with multiple springtime ground observations ( r 2 02=020.91). The highest degree of verified small-scale variation occurred where contiguous forests displayed leaf-onset gradients of 10–14 days over short distances (<02500 m). These dramatic gradients occur in of low-relief (<0240 m) upland regions. The patterns suggest that microclimates resulting from springtime cold-air drainage may be influential in governing the start of leaf growth; every 4.16 m loss in elevation delayed spring leaf onset by 1 day. These microclimates may be of crucial importance in interpreting in situ records and interpolating phenology from satellite data. Regional patterns from the Landsat analyses suggest topographic, coastal, and land-use controls on phenology. Our results indicate that deciduous forests in the Providence, RI metropolitan area leaf out 5–7 days earlier than comparable rural areas. The platform-independent curve-fit methodology may be extended across platforms and field data. The methodologically consistent approach, in tandem with Landsat data, allows an effective scaling from plot to satellite phenological observations.
[45] Gao B C, Li R R.2000.

Quantitative improvement in the estimates of NDVI values from remotely sensed data by correcting thin cirrus scattering effects

[J]. Remote Sensing of Environment, 74(3): 494-502.

https://doi.org/10.1016/S0034-4257(00)00141-3      URL      [本文引用: 3]      摘要

The Normalized Difference Vegetation Index (NDVI) has been used extensively for remote sensing of vegetation for many years. This index uses radiances or reflectances from a red channel at 0.66 μm and near-infrared channel at 0.86 μm. Thin cirrus clouds frequently contaminate remotely sensed data acquired from aircraft and satellite platforms. They introduce additional scattered radiances to the 0.66-μm and 0.86-μm channels. To obtain unbiased estimates of NDVI values from remotely sensed data, the cirrus scattering effects must be removed. We recently have developed an empirical technique for removing thin cirrus scattering effects using the sensitive 1.375-μm cirrus-detecting channel. In this article, we demonstrate that the estimates of NDVI values can be improved quantitatively after the removal of thin cirrus effects. Pairs of spectral imaging data, with and nearly without thin cirrus contamination, acquired with the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) are used in this study. Because a special channel centered at 1.375 μm has been implemented on the Moderate Resolution Imaging Spectrometer (MODIS) for detecting thin cirrus clouds from space, it is possible to remove thin cirrus scattering effects from MODIS channels below 1 μm and to yield improved global estimates of NDVI values from MODIS data.
[46] Graham E A, Riordan E C, Yuen E M, et al.2010.

Public internet-connected cameras used as a cross-continental ground-based plant phenology monitoring system

[J]. Global Change Biology, 16(11): 3014-3023.

https://doi.org/10.1111/j.1365-2486.2010.02164.x      URL      摘要

Plant phenology is highly sensitive to changes in environmental conditions and can vary widely across landscapes. Current observation methods are either manual for small-scale, high precision measurements or by satellite remote sensing for large-scale, low spatial resolution measurement. The development of inexpensive approaches is necessary to advance large scale, high precision phenology monitoring. The use of publicly available, Internet-connected cameras, often associated with airports, national parks, and roadway conditions, for detecting and monitoring plant phenology at a continental scale can augment existing ground and satellite-based methodologies. We collected twice-daily images from over 1100 georeferenced public cameras across North America from February 2008 to 2009. Using a test subset of these cameras, we compared modeled spring `green-up' with that from co-occurring remote sensing products. Although varying image exposure and color correction introduced noise to camera measurements, we were able to correlate spring green-up across North America with visual validation from images and detect a latitudinal trend. Public cameras had an equivalent or higher ability to detect spring compared with satellite-based data for corresponding locations, with fewer numbers of poor quality days, shorter continuous bad data days, and significantly lower errors of spring estimates. Manual image segmentation into deciduous, evergreen, and understory vegetation allowed detection of spring and fall onset for multiple vegetation types. Additional advantages of a public camera-based monitoring system include frequent image capture (subdaily) and the potential to detect quantitative responses to environmental changes in organisms, species, and communities. Public cameras represent a relatively untapped and freely available resource for supporting large-scale ecological and environmental monitoring.
[47] Hartfield K A, Marsh S E, Kirk C D, et al.2013.

Contemporary and historical classification of crop types in Arizona

[J]. International Journal of Remote Sensing, 34(17): 6024-6036.

https://doi.org/10.1080/01431161.2013.793861      URL      [本文引用: 1]      摘要

ABSTRACT This research compares three different classification algorithms for mapping crops in Pinal County, Arizona, using both present and historical image data. The study area lacked past crop maps, and farmers were dealing with the risk of evolution of resistance to insecticides in the whitefly, a global pest of cotton, fruits, and vegetables. The ability to create historical crop maps without concurrent training data is an invaluable tool for historical integrated pest management research. Comparison of maximum likelihood, object-oriented, and regression tree classifiers was done with Landsat Thematic Mapper imagery and high quality crop maps. Classification outputs for the three years in this research all achieved overall accuracies above the traditional benchmark of 85%. Comparison of the classification results shows that the classification and regression tree technique clearly outperformed the other classifiers. Using training data from one year and applying that data to another year for classification purposes demonstrated that overall accuracies from 71% to 83% are achievable, although accuracies were consistently greater than 85% for some crops.
[48] Hilker T, Gitelson A, Coops N C, et al.2011.

Tracking plant physiological properties from multi-angular tower-based remote sensing

[J]. Oecologia, 165(4): 865-876.

https://doi.org/10.1007/s00442-010-1901-0      Magsci      [本文引用: 1]      摘要

Imaging spectroscopy is a powerful technique for monitoring the biochemical constituents of vegetation and is critical for understanding the fluxes of carbon and water between the land surface and the atmosphere. However, spectral observations are subject to the sun&#8211;observer geometry and canopy structure which impose confounding effects on spectral estimates of leaf pigments. For instance, the sun&#8211;observer geometry influences the spectral brightness measured by the sensor. Likewise, when considering pigment distribution at the stand level scale, the pigment content observed from single view angles may not necessarily be representative of stand-level conditions as some constituents vary as a function of the degree of leaf illumination and are therefore not isotropic. As an alternative to mono-angle observations, multi-angular remote sensing can describe the anisotropy of surface reflectance and yield accurate information on canopy structure. These observations can also be used to describe the bi-directional reflectance distribution which then allows the modeling of reflectance independently of the observation geometry. In this paper, we demonstrate a method for estimating pigment contents of chlorophyll and carotenoids continuously over a year from tower-based, multi-angular spectro-radiometer observations. Estimates of chlorophyll and carotenoid content were derived at two flux-tower sites in western Canada. Pigment contents derived from inversion of a CR model (PROSAIL) compared well to those estimated using a semi-analytical approach (<i>r</i> <sup>2</sup>&nbsp;=&nbsp;0.90 and <i>r</i> <sup>2</sup>&nbsp;=&nbsp;0.69, <i>P</i>&nbsp;&lt;&nbsp;0.05 for both sites, respectively). Analysis of the seasonal dynamics indicated that net ecosystem productivity was strongly related to total canopy chlorophyll content at the deciduous site (<i>r</i> <sup>2</sup>&nbsp;=&nbsp;0.70, <i>P</i>&nbsp;&lt;&nbsp;0.001), but not at the coniferous site. Similarly, spectral estimates of photosynthetic light-use efficiency showed strong seasonal patterns in the deciduous stand, but not in conifers. We conclude that multi-angular, spectral observations can play a key role in explaining seasonal dynamics of fluxes of carbon and water and provide a valuable addition to flux-tower-based networks.
[49] Hill M J, Román M O, Schaaf C B, et al.2011.

Characterizing vegetation cover in global savannas with an annual foliage clumping index derived from the MODIS BRDF product

[J]. Remote Sensing of Environment, 115(8): 2008-2024.

https://doi.org/10.1016/j.rse.2011.04.003      URL      [本文引用: 3]      摘要

The global savanna biome is characterized by enormous diversity in the physiognomy and spatial structure of the vegetation. The foliage clumping index can be calculated from bidirectional reflectance distribution function (BRDF) data. It measures the response of the darkspot reflectance to increased shadow associated with clumped vegetation and is related to leaf area index. Clumping index theoretically declines with increasing woody cover until the tree canopy begins to become uniform. In this study, clumping index is calculated for Moderate Resolution Imaging Spectroradiometer BRDF data for the Australian tropical savanna, the tropical savannas of South America, and the tropical savannas of east, west and southern Africa and compared with site-based measurements of tree canopy cover, and with area-based classifications of land cover. There were differences in sensitivity of clumping index between red and near-infrared reflectance channels, and between savanna systems with markedly different woody vegetation physiognomy. Clumping index was broadly related to foliage cover from historical site data in Australia and in West Africa and Kenya, but not in Southern Africa nor with detailed site-based demographic data in the cerrado of Brazil. However, clumping index decreased with proportion of woody cover in land cover datasets for east Africa, Australia and the Colombian Llanos. There was overlap in the range of clumping index values for forest, cerrado and campo land covers in Brazil. Clumping index was generally negatively correlated with percentage tree cover from the MODIS Vegetation Continuous Fields product, but regional differences in the relationship were evident. There were large differences in the frequency distributions of clumping index from savanna, woody savanna and grassland land cover classes between global ecoregions. The clumping index shows differing sensitivity to savanna woody cover for red and NIR reflectance, and requires regional calibration for application as a universal indicator.
[50] Holben B N.1986.

Characteristics of maximum-value composite images from temporal AVHRR data

[J]. International Journal of Remote Sensing, 7(11): 1417-1434.

https://doi.org/10.1080/01431168608948945      URL      [本文引用: 1]      摘要

Not Available
[51] Hudson I L, Keatley M R.2010.

Phenological research: Methods for environmental and climate change analysis

[M]. Heidelberg, Germany: Springer.

[本文引用: 3]     

[52] Huete A R, Didan K, Miura T, et al.2002.

Overview of the radiometric and biophysical performance of the MODIS vegetation indices

[J]. Remote Sensing of Environment, 83(1-2): 195-213.

https://doi.org/10.1016/S0034-4257(02)00096-2      URL      [本文引用: 2]      摘要

We evaluated the initial 12 months of vegetation index product availability from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Earth Observing System-Terra platform. Two MODIS vegetation indices (VI), the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are produced at 1-km and 500-m resolutions and 16-day compositing periods. This paper presents an initial analysis of the MODIS NDVI and EVI performance from both radiometric and biophysical perspectives. We utilize a combination of site-intensive and regionally extensive approaches to demonstrate the performance and validity of the two indices. Our results showed a good correspondence between airborne-measured, top-of-canopy reflectances and VI values with those from the MODIS sensor at four intensively measured test sites representing semi-arid grass/shrub, savanna, and tropical forest biomes. Simultaneously derived field biophysical measures also demonstrated the scientific utility of the MODIS VI. Multitemporal profiles of the MODIS VIs over numerous biome types in North and South America well represented their seasonal phenologies. Comparisons of the MODIS-NDVI with the NOAA-14, 1-km AVHRR-NDVI temporal profiles showed that the MODIS-based index performed with higher fidelity. The dynamic range of the MODIS VIs are presented and their sensitivities in discriminating vegetation differences are evaluated in sparse and dense vegetation areas. We found the NDVI to asymptotically saturate in high biomass regions such as in the Amazon while the EVI remained sensitive to canopy variations.
[53] Hufkens K, Friedl M, Sonnentag O, et al.2012.

Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology

[J]. Remote Sensing of Environment, 117: 307-321.

https://doi.org/10.1016/j.rse.2011.10.006      URL      [本文引用: 1]      摘要

Green leaf phenology is known to be sensitive to climate variation. Phenology is also important because it exerts significant control on terrestrial carbon cycling and sequestration. High-quality measurements of green leaf phenology are therefore increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. In this paper, we compare “near-surface” and satellite remote sensing-based observations of vegetation phenology at four deciduous forest sites. Specifically, we addressed three questions related to how observations of plant phenology measured by red–green–blue (RGB) cameras mounted on towers above forest canopies are related to measurements of phenology acquired by moderate resolution sensors on satellites. First, how are estimated phenophase transition dates — or the observable stages in the life cycle of plants — influenced by the choice of vegetation index (VI) measured by remote sensing? Second, are VIs and phenological metrics derived from near-surface and satellite remote sensing comparable, and what is the nature and magnitude of covariation between near-surface and satellite-remote sensing-based estimates of phenology at seasonal and interannual time scales? Third, does near-surface remote sensing data provide a basis for validating satellite-derived land surface phenology products and what are the requirements for achieving this goal? Our study provides substantial support for future efforts linking satellite and near-surface remote sensing. We show significant agreement between phenological time series and metrics derived from these two data sources. However, issues of scale and representation strongly influence the relationship between near surface and satellite remote sensing measures of phenology. In particular, intra- and interannual correlation between time series from each source are dependent on how representative the camera FOV is of the regional landscape. Further, our results show that the specific VI used to monitor phenology exerts substantial influence on satellite VI derived phenological metrics, and by extension, how they compare to VI time series and metrics obtained from near-surface remote sensing. These results improve understanding of how near-surface and satellite remote sensing complement each other. However, more work is required to develop formal protocols for evaluating, calibrating and validating satellite remote sensing phenology products using near surface remote sensing at a regional to continental scale.
[54] Jiang N, Zhu W Q, Zheng Z T, et al.2013.

A comparative analysis between GIMSS NDVIg and NDVI3g for monitoring vegetation activity change in the Northern Hemisphere during 1982-2008

[J]. Remote Sensing, 5(8): 4031-4044.

https://doi.org/10.3390/rs5084031      Magsci      [本文引用: 1]      摘要

The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group was released recently. The comparisons between the new and old versions should be conducted for linking existing studies with future applications of NDVI3g in monitoring vegetation activity change. Based on simple and piecewise linear regression methods, this study made a comparative analysis between NDVIg and NDVI3g for monitoring vegetation activity change and its responses to climate change in the middle and high latitudes of the Northern Hemisphere during 1982-2008. Our results indicated that there were large differences between NDVIg and NDVI3g in the spatial patterns for both the overall changing trends and the timing of Turning Points (TP) in NDVI time series, which spread over almost the entire study region. The average NDVI trend from NDVI3g was almost twice as great as that from NDVIg and the detected average timing of TP from NDVI3g was about one year later. Although the general spatial patterns were consistent between two data sets for detecting the responses of growing-season NDVI to temperature and precipitation changes, there were large differences in the response magnitude, with a higher response magnitude to temperature in NDVI3g and an opposite response to precipitation change for the two data sets. These results demonstrated that the NDVIg data set may underestimate the vegetation activity change trend and its response to climate change in the middle and high latitudes of the Northern Hemisphere during the past three decades.
[55] Jiang Z Y, Huete A R, Didan K, et al.2008.

Development of a two-band enhanced vegetation index without a blue band

[J]. Remote Sensing of Environment, 112(10): 3833-3845.

https://doi.org/10.1016/j.rse.2008.06.006      URL      [本文引用: 1]      摘要

The enhanced vegetation index (EVI) was developed as a standard satellite vegetation product for the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS). EVI provides improved sensitivity in high biomass regions while minimizing soil and atmosphere influences, however, is limited to sensor systems designed with a blue band, in addition to the red and near-infrared bands, making it difficult to generate long-term EVI time series as the normalized difference vegetation index (NDVI) counterpart. The purpose of this study is to develop and evaluate a 2-band EVI (EVI2), without a blue band, which has the best similarity with the 3-band EVI, particularly when atmospheric effects are insignificant and data quality is good. A linearity-adjustment factor 尾 is proposed and coupled with the soil-adjustment factor L used in the soil-adjusted vegetation index (SAVI) to develop EVI2. A global land cover dataset of Terra MODIS data extracted over land community validation and FLUXNET test sites is used to develop the optimal parameter (L, 尾 and G) values in EVI2 equation and achieve the best similarity between EVI and EVI2. The similarity between the two indices is evaluated and demonstrated with temporal profiles of vegetation dynamics at local and global scales. Our results demonstrate that the differences between EVI and EVI2 are insignificant (within 卤 0.02) over a very large sample of snow/ice-free land cover types, phenologies, and scales when atmospheric influences are insignificant, enabling EVI2 as an acceptable and accurate substitute of EVI. EVI2 can be used for sensors without a blue band, such as the Advanced Very High Resolution Radiometer (AVHRR), and may reveal different vegetation dynamics in comparison with the current AVHRR NDVI dataset. However, cross-sensor continuity relationships for EVI2 remain to be studied. 漏 2008 Elsevier Inc.
[56] Jones M O, Kimball J S, Jones L A, et al.2012.

Satellite passive microwave detection of North America start of season

[J]. Remote Sensing of Environment, 123: 324-333.

https://doi.org/10.1016/j.rse.2012.03.025      URL      摘要

The start of season (SOS) phenological metric indicates the seasonal onset of vegetation activity, including canopy growth, photosynthesis and associated increases in land鈥揳tmosphere water, energy and carbon (CO) exchanges influencing weather and climate variability. Satellite optical-infrared (IR) remote sensing is responsive to vegetation greenness and SOS, but measurement accuracy and global monitoring are constrained by atmosphere cloud/aerosol contamination and seasonal decreases in solar illumination for many areas. The vegetation optical depth (VOD) parameter from satellite passive microwave remote sensing provides an alternative means for global phenology monitoring that is sensitive to vegetation canopy biomass and water content, and insensitive to atmosphere and solar illumination constraints. A global VOD record from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) was used to estimate North America SOS patterns and annual variability at the ecoregion scale. The SOS metrics were derived for a four year (2004鈥2007) record using TIMESAT and AMSR-E 10.7 GHz frequency VOD retrievals composited to 4-day median time series. The VOD SOS corresponded favorably with MODIS-for-NACP NDVI (0.73 < R < 0.81; p < 0.01) and LAI (0.66 < R < 0.89; p < 0.01) greenup dates, and stand level SOS estimates derived from flux tower gross primary production (r= 0.61, p < 0.01) and ecosystem respiration (r= 0.44, p < 0.01) estimates. The VOD SOS was temporally offset from MODIS greenup and tower SOS metrics by up to 4鈥7 weeks (RMSE) and the offset patterns coincided with the primary climate constraints (temperature and water) to vegetation growth. The VOD SOS generally preceded greenup in cold temperature constrained ecoregions and followed greenup in warmer, water limited ecoregions, with delays increasing for areas with greater woody vegetation cover. The AMSR-E VOD record captures canopy biomass changes independent of NDVI greenness or LAI measures, providing new and complementary phenological information for regional carbon, water and energy cycle studies.
[57] Jönsson P, Eklundh L.2002.

Seasonality extraction by function fitting to time-series of satellite sensor data

[J]. IEEE Transactions on Geoscience and Remote Sensing, 40(8): 1824-1832.

https://doi.org/10.1109/TGRS.2002.802519      URL      [本文引用: 1]      摘要

Not Available
[58] Jönsson P, Eklundh L.2004.

TIMESAT: A program for analyzing time-series of satellite sensor data

[J]. Computers & Geosciences, 30(8): 833-845.

https://doi.org/10.1016/j.cageo.2004.05.006      URL      [本文引用: 1]      摘要

Three different least-squares methods for processing time-series of satellite sensor data are presented. The first method uses local polynomial functions and can be classified as an adaptive Savitzky-Golay filter. The other two methods are more clear cut least-squares methods, where data are fit to a basis of harmonic functions and asymmetric Gaussian functions, respectively. The methods incorporate qualitative information on cloud contamination from ancillary datasets. The resulting smooth curves are used for extracting seasonal parameters related to the growing seasons. The methods are implemented in a computer program, TIMESAT, and applied to NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index data over Africa, giving spatially coherent images of seasonal parameters such as beginnings and ends of growing seasons, seasonally integrated NDVI and seasonal amplitudes. Based on general principles, the TIMESAT program can be used also for other types of satellite-derived time-series data.
[59] Kurc S A, Benton L M.2010.

Digital image-derived greenness links deep soil moisture to carbon uptake in a creosotebush-dominated shrubland

[J]. Journal of Arid Environments, 74(5): 585-594.

https://doi.org/10.1016/j.jaridenv.2009.10.003      Magsci      [本文引用: 1]      摘要

<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">Changes in the timing, frequency, and magnitude of precipitation events are projected for semiarid ecosystems worldwide. The ecological consequences associated with these precipitation changes will be better understood if the hydrological triggers of vegetation response can be better identified. Previous research has suggested that soil moisture, likely from large monsoon rainstorms, plays a critical role in triggering the phenological response of semiarid shrublands. Here we propose that the recent emergence of time-lapse repeat digital photography (pheno-cams) can play a role in further explaining the hydrological triggers of phenological response in semiarid shrublands. This study is focused on a creosotebush-dominated ecosystem of the Santa Rita Experimental Range, southeastern Arizona. In addition to typical eddy covariance instrumentation, this site offers continuous measurements of soil moisture in 6 one-meter profiles. Additionally, three pheno-cams have been installed in the footprint of the eddy covariance tower at the site. We demonstrate (1) that the green-up of evergreen creosotebush can be tracked using an average greenness index calculated from multiple pheno-cams within a tower footprint; (2) that the green-up of creosotebush is driven by deep soil moisture (e.g.&nbsp;&gt;&nbsp;30&nbsp;cm); and (3) that carbon uptake can be predicted using image-derived green-up of creosotebush.</p>
[60] Lloyd D.1990.

A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery

[J]. International Journal of Remote Sensing, 11(12): 2269-2279.

https://doi.org/10.1080/01431169008955174      URL      [本文引用: 2]      摘要

Not Available
[61] Ma M G, Veroustraete F.2006.

Reconstructing pathfinder AVHRR Land NDVI time-series data for the Northwest of China

[J]. Advances in Space Research, 37(4): 835-840.

https://doi.org/10.1016/j.asr.2005.08.037      Magsci      [本文引用: 1]      摘要

<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">Vegetation typically elicits dynamics at the seasonal and annual level. Time-series of normalized difference vegetation index (NDVI) datasets, such as the pathfinder AVHRR land (PAL) NDVI dataset, have proven to be appropriate for the detection of long-term vegetation cover changes. It has been applied in modelling experiments for terrestrial ecosystems at the global, continental, and regional scales. But some PAL NDVI time series remain significant residual effects and noise levels. A simple method, the mean-value iteration filter (MVI), has been developed to reduce the noise and to enable the reconstruction of high quality NDVI time-series. A comparison between the newly developed method and other existing methods (the modified BISE algorithm and a fast Fourier transform algorithm) indicates that the newly developed method is an effective tool for reconstructing high-quality time series of PAL NDVI time series.</p>
[62] Matsushita B, Yang W, Chen J, et al.2007.

Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: A case study in high-density Cypress forest

[J]. Sensors, 7(11): 2636-2651.

https://doi.org/10.3390/s7112636      URL      [本文引用: 2]      摘要

Vegetation indices play an important role in monitoring variations in vegetation.The Enhanced Vegetation Index (EVI) proposed by the MODIS Land Discipline Groupand the Normalized Difference Vegetation Index (NDVI) are both global-based vegetationindices aimed at providing consistent spatial and temporal information regarding globalvegetation. However, many environmental factors such as atmospheric conditions and soilbackground may produce errors in these indices. The topographic effect is another veryimportant factor, especially when the indices are used in areas of rough terrain. In thispaper, we theoretically analyzed differences in the topographic effect on the EVI and theNDVI based on a non-Lambertian model and two airborne-based images acquired from amountainous area covered by high-density Japanese cypress plantation were used as a casestudy. The results indicate that the soil adjustment factor “L” in the EVI makes it moresensitive to topographic conditions than is the NDVI. Based on these results, we stronglyrecommend that the topographic effect should be removed in the reflectance data beforethe EVI was calculated—as well as from other vegetation indices that similarly include a term without a band ratio format (e.g., the PVI and SAVI)—when these indices are used in the area of rough terrain, where the topographic effect on the vegetation indices having only a band ratio format (e.g., the NDVI) can usually be ignored.
[63] Melaas E K, Friedl M A, Zhu Z.2013.

Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data

[J]. Remote Sensing of Environment, 132: 176-185.

https://doi.org/10.1016/j.rse.2013.01.011      URL      Magsci      [本文引用: 1]      摘要

Observations of vegetation phenology provide valuable information regarding ecosystem responses to climate variability and change. Phenology is also a first-order control on terrestrial carbon and energy budgets, and remotely sensed observations of phenology are often used to parameterize seasonal vegetation dynamics in ecosystem models. Current land surface phenology products are only available at moderate spatial resolution and possess considerable uncertainty. Higher resolution products that resolve finer spatial detail are therefore needed. A need also exists for data sets and methods that link ground-based observations of phenology to moderate resolution land surface phenology products. Data from the Landsat TM and ETM + sensors have the potential to meet these needs, but have been largely unexplored by the phenology research community. In this paper we present a method for characterizing both long-term average and interannual dynamics in the phenology of temperate deciduous broadleaf forest using multi-decadal time series of Landsat TM/ETM + images. Results show that spring and autumn phenological transition dates estimated from Landsat data agree closely with in-situ measurements of phenology collected at the Harvard Forest in central Massachusetts, and that Landsat-derived estimates for the start and end of the growing season in Southern New England varied by as much as 4 weeks over the 30-year record of Landsat images. Application of this method over larger scales has the potential to provide valuable information related to landscape-scale patterns and long term dynamics in phenology, and for bridging the gap between in-situ phenological measurements collected at local scales and land surface phenology metrics derived from moderate spatial resolution of instruments such as MODIS and AVHRR. (C) 2013 Elsevier Inc. All rights reserved.
[64] Menzel A, Sparks T H, Estrella N, et al.2006.

Altered geographic and temporal variability in phenology in response to climate change

[J]. Global Ecology and Biogeography, 15(5): 498-504.

[本文引用: 1]     

[65] Migliavacca M, Galvagno M, Cremonese E, et al.2011.

Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake

[J]. Agricultural and Forest Meteorology, 151(10): 1325-1337.

https://doi.org/10.1016/j.agrformet.2011.05.012      Magsci      [本文引用: 2]      摘要

The continuous and automated monitoring of canopy phenology is of increasing scientific interest for the multiple implications of vegetation dynamics on ecosystem carbon and energy fluxes. For this purpose we evaluated the applicability of digital camera imagery for monitoring and modeling phenology and physiology of a subalpine grassland over the 2009 and 2010 growing seasons.<br/>We tested the relationships between color indices (i.e. the algebraic combinations of RGB brightness levels) tracking canopy greenness extracted from repeated digital images against field measurements of green and total biomass, leaf area index (LAI), greenness visual estimation, vegetation indices computed from continuous spectroradiometric measurements and CO2 fluxes observed with the eddy covariance technique. A strong relationship was found between canopy greenness and (i) structural parameters (i.e.. LAI) and (ii) canopy photosynthesis (i.e. Gross Primary Production; GPP). Color indices were also well correlated with vegetation indices typically used for monitoring landscape phenology from satellite, suggesting that digital repeat photography provides high-quality ground data for evaluation of satellite phenology products.<br/>We demonstrate that by using canopy greenness we can refine phenological models (Growing Season Index, GSI) by describing canopy development and considering the role of ecological factors (e.g., snow, temperature and photoperiod) controlling grassland phenology. Moreover, we show that canopy greenness combined with radiation use efficiency (RUE) obtained from spectral indices related to photochemistry (i.e., scaled Photochemical Reflectance Index) or meteorology (i.e.. MOD17 RUE) can be used to predict daily GPP.<br/>Building on previous work that has demonstrated that seasonal variation in the structure and function of plant canopies can be quantified using digital camera imagery, we have highlighted the potential use of these data for the development and parameterization of phenological and RUE models, and thus point toward an extension of the proposed methodologies to the dataset collected within PhenoCam Network. (C) 2011 Elsevier B.V. All rights reserved.
[66] Moody A, Johnson D M.2001.

Land-surface phenologies from AVHRR using the discrete Fourier transform

[J]. Remote Sensing of Environment, 75(3): 305-323.

https://doi.org/10.1016/S0034-4257(00)00175-9      URL      摘要

The first and second harmonics of the discrete Fourier transform (DFT) concisely summarize the amplitude and phase of annual and biannual signals embedded in time-series of Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data. We applied and evaluated the DFT using monthly composited NDVI data over a 7-year period for a 150 脳 150-km study area in southern California. The study area contains strong gradients in environmental conditions and basic vegetation formations. Analysis of the DFT harmonics for six point-locations provided a basis for linking the DFT results to basic vegetation types according their characteristic phenologies. The mean NDVI, or 0th-order harmonic, indicated overall productivity, allowing the differentiation of unproductive, moderately productive, and highly productive sites. The amplitude of the first harmonic indicated the variability of productivity over the year as expressed in a single annual pulse of net primary production. This summarized the relative dominance of evergreen vs. deciduous or annual habit. The phase of the first harmonic summarized the timing of green-up relative to the timing of winter and spring rains. This differentiated rapidly responding annual grasslands, slowly responding evergreen life-forms, and irrigated agriculture. The second harmonic indicated the strength and timing of any biannual signal. This provided information on secondary vegetation types, such as subcanopy grasses beneath evergreen woodlands or mixtures of annual grasslands and irrigated agriculture. The point-based analysis provided the foundation for a regional analysis of the entire study area. The mean NDVI and first- and second-order amplitude and phase, in conjunction with 148 field-sampled polygons, were used to produce an unsupervised classification of basic vegetation formations for the study area. These results were evaluated by comparison with other land cover products, and through assessment using field-sampled test regions.
[67] Morin X, Lechowicz M J, Augspurger C, et al.2009.

Leaf phenology in 22 North American tree species during the 21st century

[J]. Global Change Biology, 15(4): 961-975.

https://doi.org/10.1111/j.1365-2486.2008.01735.x      URL      [本文引用: 1]      摘要

Recent shifts in phenology are the best documented biological response to current anthropogenic climate change, yet remain poorly understood from a functional point of view. Prevailing analyses are phenomenological and approximate, only correlating temperature records to imprecise records of phenological events. To advance our understanding of phenological responses to climate change, we developed, calibrated, and validated process-based models of leaf unfolding for 22 North American tree species. Using daily meteorological data predicted by two scenarios (A2: +3.2 pC and B2: +1 pC) from the HadCM3 GCM, we predicted and compared range-wide shifts of leaf unfolding in the 20th and 21st centuries for each species. Model predictions suggest that climate change will affect leaf phenology in almost all species studied, with an average advancement during the 21st century of 5.0 days in the A2 scenario and 9.2 days in the B2 scenario. Our model also suggests that lack of sufficient chilling temperatures to break bud dormancy will decrease the rate of advancement in leaf unfolding date during the 21st century for many species. Some temperate species may even have years with abnormal budburst due to insufficient chilling. Species fell into two groups based on their sensitivity to climate change: (1) species that consistently had a greater advance in their leaf unfolding date with increasing latitude and (2) species in which the advance in leaf unfolding differed from the center to the northern vs. southern margins of their range. At the interspecific level, we predicted that early-leafing species tended to show a greater advance in leaf unfolding date than late-leafing species; and that species with larger ranges tend to show stronger phenological changes. These predicted changes in phenology have significant implications for the frost susceptibility of species, their interspecific relationships, and their distributional shifts.
[68] Moulin S, Kergoat L, Viovy N, et al.1997.

Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements

[J]. Journal of Climate, 10(6): 1154-1170.

URL     

[69] Myneni R B, Keeling C D, Tucker C J, et al.1997.

Increased plant growth in the northern high latitudes from 1981 to 1991

[J]. Nature, 386: 698-702.

https://doi.org/10.1038/386698a0      URL      [本文引用: 1]      摘要

ABSTRACT Variations in the amplitude and timing of the seasonal cycle of atmospheric CO2 have shown an association with surface air temperature consistent with the hypothesis that warmer temperatures have promoted increases in plant growth during summer1 and/or plant respiration during winter2 in the northern high latitudes. Here we present evidence from satellite data that the photosynthetic activity of terrestrial vegetation increased from 1981 to 1991 in a manner that is suggestive of an increase in plant growth associated with a lengthening of the active growing season. The regions exhibiting the greatest increase lie between 45掳N and 70掳N, where marked warming has occurred in the spring time3 due to an early disappearance of snow4. The satellite data are concordant with an increase in the amplitude of the seasonal cycle of atmospheric carbon dioxide exceeding 20% since the early 1970s, and an advance of up to seven days in the timing of the drawdown of CO2 in spring and early summer1. Thus, both the satellite data and the CO2 record indicate that the global carbon cycle has responded to interannual fluctuations in surface air temperature which, although small at the global scale, are regionally highly significant.
[70] Peñuelas J, Filella I, Comas P.2002.

Changed plant and animal life cycles from 1952 to 2000 in the Mediterranean region

[J]. Global Change Biology, 8(6): 531-544.

https://doi.org/10.1046/j.1365-2486.2002.00489.x      URL      [本文引用: 1]      摘要

Abstract The available data on climate over the past century indicate that the earth is warming. Important biological effects, including changes of plant and animal life cycle events, have already been reported. However, evidence of such effects is still scarce and has been mostly limited to northern latitudes. Here we provide the first long-term (1952鈥2000) evidence of altered life cycles for some of the most abundant Mediterranean plants and birds, and one butterfly species. Average annual temperatures in the study area (Cardedeu, NE Spain) have increased by 1.4掳C over the observation period while precipitation remained unchanged. A conservative linear treatment of the data shows that leaves unfold on average 16days earlier, leaves fall on average 13days later, and plants flower on average 6days earlier than in 1952. Fruiting occurs on average 9days earlier than in 1974. Butterflies appear 11days earlier, but spring migratory birds arrive 15days later than in 1952. The stronger changes both in temperature and in phenophases timing occurred in the last 25years. There are no significant relationships among changes in phenophases and the average date for each phenophase and species. There are not either significant differences among species with different Raunkiaer life-forms or different origin (native, exotic or agricultural). However, there is a wide range of phenological alterations among the different species, which may alter their competitive ability, and thus, their ecology and conservation, and the structure and functioning of ecosystems. Moreover, the lengthening of plant growing season in this and other northern hemisphere regions may contribute to a global increase in biospheric activity.
[71] Peñuelas J, Rutishauser T, Filella I.2009.

Phenology feedbacks on climate change

[J]. Science, 324: 887-888.

[本文引用: 1]     

[72] Piao S L, Ciais P, Friedlingstein P, et al.2008.

Net carbon dioxide losses of northern ecosystems in response to autumn warming

[J]. Nature, 451: 49-52.

https://doi.org/10.1038/nature06444      URL      PMID: 18172494      [本文引用: 1]      摘要

The carbon balance of terrestrial ecosystems is particularly sensitive to climatic changes in autumn and spring, with spring and autumn temperatures over northern latitudes having risen by about 1.1 degrees C and 0.8 degrees C, respectively, over the past two decades. A simultaneous greening trend has also been observed, characterized by a longer growing season and greater photosynthetic activity. These observations have led to speculation that spring and autumn warming could enhance carbon sequestration and extend the period of net carbon uptake in the future. Here we analyse interannual variations in atmospheric carbon dioxide concentration data and ecosystem carbon dioxide fluxes. We find that atmospheric records from the past 20 years show a trend towards an earlier autumn-to-winter carbon dioxide build-up, suggesting a shorter net carbon uptake period. This trend cannot be explained by changes in atmospheric transport alone and, together with the ecosystem flux data, suggest increasing carbon losses in autumn. We use a process-based terrestrial biosphere model and satellite vegetation greenness index observations to investigate further the observed seasonal response of northern ecosystems to autumnal warming. We find that both photosynthesis and respiration increase during autumn warming, but the increase in respiration is greater. In contrast, warming increases photosynthesis more than respiration in spring. Our simulations and observations indicate that northern terrestrial ecosystems may currently lose carbon dioxide in response to autumn warming, with a sensitivity of about 0.2 PgC degrees C(-1), offsetting 90% of the increased carbon dioxide uptake during spring. If future autumn warming occurs at a faster rate than in spring, the ability of northern ecosystems to sequester carbon may be diminished earlier than previously suggested.
[73] Piao S L, Fang J Y, Zhou L M, et al.2006.

Variations in satellite-derived phenology in China's temperate vegetation

[J]. Global Change Biology, 12(4): 672-685.

https://doi.org/10.1111/j.1365-2486.2006.01123.x      URL      [本文引用: 1]      摘要

Abstract The relationship between vegetation phenology and climate is a crucial topic in global change research because it indicates dynamic responses of terrestrial ecosystems to climate changes. In this study, we investigate the possible impact of recent climate changes on growing season duration in the temperate vegetation of China, using the advanced very high resolution radiometer (AVHRR)/normalized difference vegetation index (NDVI) biweekly time-series data collected from January 1982 to December 1999 and concurrent mean temperature and precipitation data. The results show that over the study period, the growing season duration has lengthened by 1.1665days65yr 611 in temperate region of China. The green-up of vegetation has advanced in spring by 0.7965days65yr 611 and the dormancy delayed in autumn by 0.3765days65yr 611 . The dates of onset for phenological events are most significantly related with the mean temperature during the preceding 2–3 months. A warming in the early spring (March to early May) by 1°C could cause an earlier onset of green-up of 7.5 days, whereas the same increase of mean temperature during autumn (mid-August through early October) could lead to a delay of 3.8 days in vegetation dormancy. Variations in precipitation also influenced the duration of growing season, but such influence differed among vegetation types and phenological phases.
[74] Rahman H.2001.

Influence of atmospheric correction on the estimation of biophysical parameters of crop canopy using satellite remote sensing

[J]. International Journal of Remote Sensing, 22(7): 1245-1268.

https://doi.org/10.1080/01431160151144332      URL      [本文引用: 1]      摘要

Not Available
[75] Rathcke B, Lacey E P.1985.

Phenological patterns of terrestrial plants

[J]. Annual Review of Ecology and Systematics, 16(1): 179-214.

URL      [本文引用: 1]     

[76] Reed B C, Brown J F.2005.

Trend analysis of time-series phenology derived from satellite data

[C]//Proceedings of the 3rd International Workshop on the analysis of multi-temporal remote sensing images. Biloxi, MS: IEEE: 166-168.

[本文引用: 1]     

[77] Reed B C, Brown J F, VanderZee D, et al.1994.

Measuring phenological variability from satellite imagery

[J]. Journal of Vegetation Science, 5(5): 703-714.

https://doi.org/10.2307/3235884      URL      [本文引用: 1]      摘要

Abstract. Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variability of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and predicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large-area land cover mapping and monitoring. The utility of remotely sensed data as input to vegetation mapping is demonstrated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particularly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.
[78] Richardson A D, Braswell B H, Hollinger D Y, et al.2009.

Near-surface remote sensing of spatial and temporal variation in canopy phenology

[J]. Ecological Applications, 19(6): 1417-1428.

https://doi.org/10.1890/08-2022.1      URL      PMID: 19769091      [本文引用: 2]      摘要

There is a need to document how plant phenology is responding to global change factors, particularly warming trends. "Near-surface" remote sensing, using radiometric instruments or imaging sensors, has great potential to improve phenological monitoring because automated observations can be made at high temporal frequency. Here we build on previous work and show how inexpensive, networked digital cameras ("webcams") can be used to document spatial and temporal variation in the spring and autumn phenology of forest canopies. We use two years of imagery from a deciduous, northern hardwood site, and one year of imagery from a coniferous, boreal transition site. A quantitative signal is obtained by splitting images into separate red, green, and blue color channels and calculating the relative brightness of each channel for "regions of interest" within each image. We put the observed phenological signal in context by relating it to seasonal patterns of gross primary productivity, inferred from eddy covariance measurements of surface-atmosphere CO2 exchange. We show that spring increases, and autumn decreases, in canopy greenness can be detected in both deciduous and coniferous stands. In deciduous stands, an autumn red peak is also observed. The timing and rate of spring development and autumn senescence varies across the canopy, with greater variability in autumn than spring. Interannual variation in phenology can be detected both visually and quantitatively; delayed spring onset in 2007 compared to 2006 is related to a prolonged cold spell from day 85 to day 110. This work lays the foundation for regional- to continental-scale camera-based monitoring of phenology at network observatory sites, e.g., National Ecological Observatory Network (NEON) or AmeriFlux.
[79] Richardson A D, Hollinger D Y, Dail D B, et al.2009.

Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests

[J]. Tree Physiology, 29(3): 321-331.

https://doi.org/10.1093/treephys/tpn040      URL      PMID: 19203967      [本文引用: 1]      摘要

Spring phenology is thought to exert a major influence on the (C) balance of temperate and boreal ecosystems. We investigated this hypothesis using four spring onset phenological indicators in conjunction with surface-atmosphere CO(2) exchange data from the conifer-dominated Howland Forest and deciduous-dominated Harvard Forest AmeriFlux sites. All phenological measures, including CO(2) source-sink transition dates, could be well predicted on the basis of a simple two-parameter spring warming model, indicating good potential for improving the representation of phenological transitions and their dynamic responsiveness to climate variability in land surface models. The date at which canopy-scale photosynthetic capacity reached a threshold value of 12 micromol m(-2) s(-1) was better correlated with spring and annual flux integrals than were either deciduous or coniferous bud burst dates. For all phenological indicators, earlier spring onset consistently, but not always significantly, resulted in higher gross primary productivity () and ecosystem respiration (RE) for both seasonal (spring months, April-June) and annual flux integrals. The increase in RE was less than that in ; depending on the phenological indicator used, a one-day advance in spring onset increased springtime net ecosystem productivity () by 2-4 g C m(-2) day(-1). In general, we could not detect significant differences between the two forest types in response to earlier spring, although the response to earlier spring was generally more pronounced for Harvard Forest than for Howland Forest, suggesting that future climate warming may favor deciduous species over coniferous species, at least in this region. The effect of earlier spring tended to be about twice as large when annual rather than springtime flux integrals were considered. This result is suggestive of both immediate and lagged effects of earlier spring onset on ecosystem C cycling, perhaps as a result of accelerated N cycling rates and cascading effects on N , foliar N concentrations and photosynthetic capacity.
[80] Richardson A D, Jenkins J P, Braswell B H, et al.2007.

Use of digital webcam images to track spring green-up in a deciduous broadleaf forest

[J]. Oecologia, 152(2): 323-334.

https://doi.org/10.1007/s00442-006-0657-z      Magsci      摘要

<a name="Abs1"></a>Understanding relationships between canopy structure and the seasonal dynamics of photosynthetic uptake of CO<sub>2</sub> by forest canopies requires improved knowledge of canopy phenology at eddy covariance flux tower sites. We investigated whether digital webcam images could be used to monitor the trajectory of spring green-up in a deciduous northern hardwood forest. A standard, commercially available webcam was mounted at the top of the eddy covariance tower at the Bartlett AmeriFlux site. Images were collected each day around midday. Red, green, and blue color channel brightness data for a 640&nbsp;×&nbsp;100-pixel region-of-interest were extracted from each image. We evaluated the green-up signal extracted from webcam images against changes in the fraction of incident photosynthetically active radiation that is absorbed by the canopy (<i>f</i> <sub>APAR</sub>), a broadband normalized difference vegetation index (NDVI), and the light-saturated rate of canopy photosynthesis (<i>A</i> <sub>max</sub>), inferred from eddy flux measurements. The relative brightness of the green channel (green %) was relatively stable through the winter months. A steady rising trend in green % began around day 120 and continued through day 160, at which point a stable plateau was reached. The relative brightness of the blue channel (blue %) also responded to spring green-up, although there was more day-to-day variation in the signal because blue % was more sensitive to changes in the quality (spectral distribution) of incident radiation. Seasonal changes in blue % were most similar to those in <i>f</i> <sub>APAR</sub> and broadband NDVI, whereas changes in green % proceeded more slowly, and were drawn out over a longer period of time. Changes in <i>A</i> <sub>max</sub> lagged green-up by at least a week. We conclude that webcams offer an inexpensive means by which phenological changes in the canopy state can be quantified. A network of cameras could offer a novel opportunity to implement a regional or national phenology monitoring program.
[81] Roerink G J, Menenti M, Verhoef W.2000.

Reconstructing cloudfree NDVI composites using Fourier analysis of time series

[J]. International Journal of Remote Sensing, 21(9): 1911-1917.

URL      [本文引用: 1]      摘要

This letter describes the Harmonic ANalysis of Time Series (HANTS) algorithm. It performs two tasks: (i) screening and removal of cloud affected observations; and (ii) temporal interpolation of the remaining observations to reconstruct gapless images at a prescribed time. HANTS was applied to 36 AVHRR 10-days-maximum-NDVI composites covering most of Europe. The results show that cloud affected data are recognized successfully and replaced. Up to half the data points were rejected with no consequence for the successful reconstruction of seasonal NDVI profiles.
[82] Sakamoto T, Yokozawa M, Toritani H, et al.2005.

A crop phenology detection method using time-series MODIS data

[J]. Remote Sensing of Environment, 96(3-4): 366-374.

https://doi.org/10.1016/j.rse.2005.03.008      URL      摘要

Information of crop phenology is essential for evaluating crop productivity and crop management. Therefore we developed a new method for remotely determining phenological stages of paddy rice. The method consists of three procedures: (i) prescription of multi-temporal MODIS/Terra data; (ii) filtering time-series Enhanced Vegetation Index (EVI) data by time-frequency analysis; and (iii) specifying the phenological stages by detecting the maximum point, minimal point and inflection point from the smoothed EVI time profile. Applying this method to MODIS data, we determined the planting date, heading date, harvesting date, and growing period in 2002. And we validated the performance of the method against statistical data in 30 paddy fields. As for the filtering, we adopted wavelet and Fourier transforms. Three types of mother wavelet (Daubechies, Symlet and Coiflet) were used in Wavelet transform. As the results of validation, the wavelet transform performed better than the Fourier transform. Specifically, the case using Coiflet (order&#xA0;=&#xA0;4) gave remarkably good results in determining phenological stages and growing periods. The root mean square errors of the estimated phenological dates against the statistical data were: 12.1 days for planting date, 9.0 days for heading date, 10.6 days for harvesting date, and 11.0 days for growing period. The method using wavelet transform with Coiflet (order&#xA0;=&#xA0;4) allows the determination of regional characteristics of rice phenology. We proposed this new method using the wavelet transform; Wavelet based Filter for determining Crop Phenology (WFCP).
[83] Schwartz M D, Ahas R, Aasa A.2006.

Onset of spring starting earlier across the Northern Hemisphere

[J]. Global Change Biology, 12(2): 343-351.

https://doi.org/10.1111/j.1365-2486.2005.01097.x      URL      [本文引用: 2]      摘要

Abstract Recent warming of Northern Hemisphere (NH) land is well documented and typically greater in winter/spring than other seasons. Physical environment responses to warming have been reported, but not details of large-area temperate growing season impacts, or consequences for ecosystems and agriculture. To date, hemispheric-scale measurements of biospheric changes have been confined to remote sensing. However, these studies did not provide detailed data needed for many investigations. Here, we show that a suite of modeled and derived measures (produced from daily maximum&ndash;minimum temperatures) linking plant development (phenology) with its basic climatic drivers provide a reliable and spatially extensive method for monitoring general impacts of global warming on the start of the growing season. Results are consistent with prior smaller area studies, confirming a nearly universal quicker onset of early spring warmth (spring indices (SI) first leaf date, 611.2daysdecade 611 ), late spring warmth (SI first bloom date, 611.0daysdecade 611 ; last spring day below 5°C, 611.4daysdecade 611 ), and last spring freeze date (611.5daysdecade 611 ) across most temperate NH land regions over the 1955&ndash;2002 period. However, dynamics differ among major continental areas with North American first leaf and last freeze date changes displaying a complex spatial relationship. Europe presents a spatial pattern of change, with western continental areas showing last freeze dates getting earlier faster, some central areas having last freeze and first leaf dates progressing at about the same pace, while in portions of Northern and Eastern Europe first leaf dates are getting earlier faster than last freeze dates. Across East Asia last freeze dates are getting earlier faster than first leaf dates.
[84] Schwartz M D, Reed B C, White M A.2002.

Assessing satellite-derived start-of-season measures in the conterminous USA

[J]. International Journal of Climatology, 22(14): 1793-1805.

https://doi.org/10.1002/joc.819      URL      [本文引用: 2]      摘要

Abstract Top of page Abstract REFERENCES National Oceanic and Atmospheric Administration (NOAA)-series satellites, carrying advanced very high-resolution radiometer (AVHRR) sensors, have allowed moderate resolution (1 km) measurements of the normalized difference vegetation index (NDVI) to be collected from the Earth's land surfaces for over 20 years. Across the conterminous USA, a readily accessible and decade-long data set is now available to study many aspects of vegetation activity in this region. One feature, the onset of deciduous plant growth at the start of the spring season (SOS) is of special interest, as it appears to be crucial for accurate computation of several important biospheric processes, and a sensitive measure of the impacts of global change. In this study, satellite-derived SOS dates produced by the delayed moving average (DMA) and seasonal midpoint NDVI (SMN) methods, and modelled surface phenology (spring indices, SI) were compared at widespread deciduous forest and mixed woodland sites during 1990–93 and 1995–99, and these three measures were also matched to native species bud-break data collected at the Harvard Forest (Massachusetts) over the same time period. The results show that both SOS methods are doing a modestly accurate job of tracking the general pattern of surface phenology, but highlight the temporal limitations of biweekly satellite data. Specifically, at deciduous forest sites: (1) SMN SOS dates are close in time to SI first bloom dates (average bias of +0.74 days), whereas DMA SOS dates are considerably earlier (average bias of 6141.24 days) and also systematically earlier in late spring than in early spring; (2) SMN SOS tracks overall yearly trends in deciduous forests somewhat better than DMA SOS, but with larger average error (MAEs 8.64 days and 7.37 days respectively); and (3) error in both SOS techniques varies considerably by year. Copyright 08 2002 Royal Meteorological Society.
[85] Sonnentag O, Hufkens K, Teshera-Sterne C, et al.2012.

Digital repeat photography for phenological research in forest ecosystems

[J]. Agricultural and Forest Meteorology, 152: 159-177.

https://doi.org/10.1016/j.agrformet.2011.09.009      URL      [本文引用: 3]      摘要

Comparison of eleven different digital cameras at Harvard Forest (autumn 2010) indicates that camera and image file format choice might be of secondary importance for phenological research: with the exception of inexpensive indoor webcams, autumn patterns of changes in g cc and ExG from images in common JPEG image file format were in good agreement, especially toward the end of senescence. Due to its greater effectiveness in suppressing changes in scene illumination, especially in combination with per90 , we advocate the use of g cc for phenological research. Our results indicate that g cc from different digital cameras can be used for comparing the timing of key phenological events (e.g., complete leaf coloring) across sites. However, differences in how specific cameras “see” the forest canopy may obscure subtle phenological changes that could be detectable if a common protocol was implemented across sites.
[86] Soudani K, Le Maire G, Dufrêne E, et al.2008.

Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from moderate resolution imaging spectroradiometer (MODIS) data

[J]. Remote Sensing of Environment, 112(5): 2643-2655.

https://doi.org/10.1016/j.rse.2007.12.004      URL      PMID: 18329025      [本文引用: 1]      摘要

Vegetation phenology is the chronology of periodic phases of development. It constitutes an efficient bio-indicator of impacts of climate changes and a key parameter for understanding and modelling vegetation-climate interactions and their implications on carbon cycling. Numerous studies were devoted to the remote sensing of vegetation phenology. Most of these were carried out using data acquired by AVHRR instrument onboard NOAA meteorological satellites. Since 1999, multispectral images were acquired over the whole earth surface every one to two days by MODIS instrument onboard Terra and Aqua platforms. In comparison with AVHRR, MODIS constitutes a significant technical improvement in terms of spatial resolution, spectral resolution, geolocation accuracy, atmospheric corrections scheme and cloud screening and sensor calibration. In this study, 250m daily MODIS data were used to derive precise vegetation phenological dates over deciduous forest stands. Phenological markers derived from MODIS time-series and provided by MODIS Global Land Cover Dynamics product (MOD12Q2) were compared to field measurements carried out over the main deciduous forest stands across France and over five years. We show that the inflexion point of the asymmetric double-sigmoid function fitted to NDVI temporal profile is a good marker of the onset of green-up in deciduous stands. At plot level, the prediction uncertainty is 8.5days and the bias is 3.5days. MODIS Global Land Cover Dynamics MOD12Q2 provides estimates of onset of green-up dates which deviate substantially from in situ observations and do not perform better than the null model. RMSE values are 20.5days (bias -17days) using the onset of greenness increase and 36.5days (bias 34.5days) using the onset of greenness maximum. An improvement of prediction quality is obtained if we consider the average of MOD12Q2 onset of greenness increase and maximum as marker of onset of green-up date. RMSE decreases to 16.5days and bias to 7.5days.
[87] Sparks T H, Menzel A.2002.

Observed changes in seasons: An overview

[J]. International Journal of Climatology, 22(14): 1715-1725.

https://doi.org/10.1002/joc.821      URL      [本文引用: 1]      摘要

Abstract Within the last decade the study of phenology has taken on a new legitimacy in the area of climate change research. A growing literature reveals that a change in the timing of natural events is occurring in a wide range of locations and affecting a wide range of species. Changes in spring have been those most commonly reported, with the emphasis on an advance in spring linked to an increase in temperature. Detection of change in autumn is hampered by a smaller pool of available data, events that are harder to define (such as leaf coloration), and various influencing environmental factors triggering autumnal phases. Despite this, the general pattern may be towards a delay in autumn. Plant, animal and abiotic responses, especially in spring, are quite similar. Thus, it would appear that winter is being squeezed at both ends, and this effect, of increasing the growing season, should become more pronounced in the face of predicted global warming. Copyright 漏 2002 Royal Meteorological Society.
[88] Van Niel T G, McVicar T R.2004.

Determining temporal windows for crop discrimination with remote sensing: A case study in south-eastern Australia

[J]. Computers and Electronics in Agriculture, 45(1-3): 91-108.

https://doi.org/10.1016/j.compag.2004.06.003      URL      [本文引用: 1]      摘要

The classification of crops from remote sensing has become an important part of agricultural management, and as a result, has instigated a great deal of research aimed at increasing classification accuracy through various methods and techniques. However, comparatively little research has been performed on determining the best time(s) of image acquisition for crop discrimination even though this could impact classification accuracy as much as choice of clustering algorithm or selection of training data, for example. This case study was conducted to: (1) determine temporal windows for highest overall and individual crop discrimination; and (2) compare simple methods for combining best single-date results to increase overall accuracy. Seventeen single-date classifications of four major summer crops (rice, maize, sorghum, and soybeans) were assessed for a single growing season at the Coleambally Irrigation Area, Australia using Landsat Enhanced Thematic Mapper data. Per-pixel classifications were generated using a maximum likelihood classifier and were then combined with field boundaries to produce per-field classifications, based on the majority crop type within each field. Multi-date classifications were performed by: (1) combining various numbers of bands per date into a single image stack prior to classification (2-date, and 3-date鈥攖ermed standard multi-date classification); as well as (2) extracting maximum accuracy single-crop classes from different dates and combining them, post-classification (termed iterative multi-date classification). Results showed that the general time-frame for highest overall single-date classification accuracy was late February to mid March. For the individual crops, late November to early December resulted in the highest accuracy for discriminating rice, maize was best discriminated from mid-February to mid-March, the maximum sorghum separability occurred from early April till at least early May, and the soybean temporal window extended from early January to mid-March, with late-February to early March being best. The iterative approach resulted in higher accuracy than the standard multi-date image stack of the same dates. Highest multi-date accuracy resulted from the 3-date per-field iterative classification (overall classification accuracy of 95.8%), an improvement of more than 6% over best per-field single-date results (10 March, 89.4%). Determination of temporal windows for crop discrimination and use of an iterative technique to combine multiple-date images both greatly improved overall crop classification, thus increasing the benefit of remote sensing in operational management.
[89] Viovy N, Arino O, Belward A S.1992.

The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series

[J]. International Journal of Remote Sensing, 13(8): 1585-1590.

https://doi.org/10.1080/01431169208904212      URL      [本文引用: 1]      摘要

CiteSeerX - Scientific documents that cite the following paper: The best index slope extraction(BISE): A method for reducing noise in NDVI time-series
[90] Walther G R, Post E, Convey P, et al.2002.

Ecological responses to recent climate change

[J]. Nature, 416: 389-395.

https://doi.org/10.1038/416389a      URL      PMID: 11919621      [本文引用: 2]      摘要

There is now ample evidence of the ecological impacts of recent climate change, from polar terrestrial to tropical marine environments. The responses of both flora and fauna span an array of ecosystems and organizational hierarchies, from the species to the community levels. Despite continued uncertainty as to community and ecosystem trajectories under global change, our review exposes a coherent pattern of ecological change across systems. Although we are only at an early stage in the projected trends of global warming, ecological responses to recent climate change are already clearly visible.
[91] Wang M, Tao F L, Shi W T.2014.

Corn yield forecasting in Northeast China using remotely sensed spectral indices and crop phenology metrics

[J]. Journal of Integrative Agriculture, 13(7): 1538-1545.

https://doi.org/10.1016/S2095-3119(14)60817-0      URL     

[92] Wang Y T, Hou X Y, Wang M J, et al.2013.

Topographic controls on vegetation index in a hilly landscape: A case study in the Jiaodong Peninsula, Eastern China

[J]. Environmental Earth Sciences, 70(2): 625-634.

https://doi.org/10.1007/s12665-012-2146-5      Magsci      [本文引用: 1]      摘要

This study examined topographic influence on spatial and temporal variability in the normalized difference vegetation index (NDVI) derived from the Satellite Pour l'Observation de la Terre-Vegetation at the regional and landscape scales in the Jiaodong Peninsula. The generalized additive models were used to quantify the spatial variation of NDVI attributable to local terrain and topographically related variables including altitude, exposure to incoming solar radiation, topographic wetness index, distance to the nearest stream and distance from the coast. NDVI distribution shows significant dependence on topography. The variables explained 38.3 % of variance in NDVI at the peninsula, and 30-45.3 % of variance in NDVI at the woodland, cropland, and grassland landscapes. At the Jiaodong Peninsula scale, NDVI is influenced primarily by distance from the coast. However, topographic wetness index has the most explanatory power for NDVI at the woodland, cropland, and grassland landscapes. Through a statistical nonparametric correlation analysis (Spearman's r), the study indicates that spatial distribution of NDVI changes during the period 1998-2009 and future change trend of persistence determined by Hurst exponent is closely associated with topography and topography-based attribution. These results highlight the importance of topographic changes at landscape and regional scales as an important control factor on NDVI patterns.
[93] Wardlow B D, Egbert S L, Kastens J H.2007.

Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains

[J]. Remote Sensing of Environment, 108(3): 290-310.

https://doi.org/10.1016/j.rse.2006.11.021      URL      [本文引用: 1]      摘要

The global environmental change research community requires improved and up-to-date land use/land cover (LULC) datasets at regional to global scales to support a variety of science and policy applications. Considerable strides have been made to improve large-area LULC datasets, but little emphasis has been placed on thematically detailed crop mapping, despite the considerable influence of management activities in the cropland sector on various environmental processes and the economy. Time-series MODIS 250m Vegetation Index (VI) datasets hold considerable promise for large-area crop mapping in an agriculturally intensive region such as the U.S. Central Great Plains, given their global coverage, intermediate spatial resolution, high temporal resolution (16-day composite period), and cost-free status. However, the specific spectral鈥搕emporal information contained in these data has yet to be thoroughly explored and their applicability for large-area crop-related LULC classification is relatively unknown. The objective of this research was to investigate the general applicability of the time-series MODIS 250m Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) datasets for crop-related LULC classification in this region. A combination of graphical and statistical analyses were performed on a 12-month time-series of MODIS EVI and NDVI data from more than 2000 cropped field sites across the U.S. state of Kansas. Both MODIS VI datasets were found to have sufficient spatial, spectral, and temporal resolutions to detect unique multi-temporal signatures for each of the region's major crop types (alfalfa, corn, sorghum, soybeans, and winter wheat) and management practices (double crop, fallow, and irrigation). Each crop's multi-temporal VI signature was consistent with its general phenological characteristics and most crop classes were spectrally separable at some point during the growing season. Regional intra-class VI signature variations were found for some crops across Kansas that reflected the state's climate and planting time differences. The multi-temporal EVI and NDVI data tracked similar seasonal responses for all crops and were highly correlated across the growing season. However, differences between EVI and NDVI responses were most pronounced during the senescence phase of the growing season.
[94] Webb R H, Boyer D E, Turne R M.2010. Repeat photography: Methods and applications in the natural sciences[M]. Washington DC: Island Press.

[本文引用: 1]     

[95] White M A, De Beurs K M, Didan K, et al.2009.

Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006

[J]. Global Change Biology, 15(10): 2335-2359.

[本文引用: 2]     

[96] White M A, Nemani R R.2006.

Real-time monitoring and short-term forecasting of land surface phenology

[J]. Remote Sensing of Environment, 104(1): 43-49.

https://doi.org/10.1016/j.rse.2006.04.014      URL      摘要

Land surface phenology is an important process for real-time monitoring and short-term forecasting in diverse land management, health, and hydrologic modeling applications. Yet current efforts to characterize phenological processes are limited by remote sensing challenges and lack of uncertainty estimates. Here, for a global distribution of phenologically and climatically similar phenoregions, we used the Advanced Very High Resolution Radiometer to develop a conceptually and computationally simple technique for real-time and forecast applications. Our overall approach was to analyze the phenological behavior of groups of pixels without recourse to smoothing or fitting. We used a 3-step initial process: (1) define a phenoregion specific normalized difference vegetation index threshold; (2) for all days from 1982鈥2003, calculate the percent of pixels above the threshold (PAT); (3) calculate daily 1982鈥2003 empirical distributions of PAT. For real-time monitoring, the current PAT may then be compared to the historical range of variability and visualized in relation to user-defined levels. Using similar concepts, we projected daily PAT up to one month in the future and compared predicted and actual dates at which a hypothetical PAT was reached. We found that the maximum lead-time of phenological forecasts could be analytically defined for user-specified uncertainty levels. The approach is adaptable to different remote sensing technologies and provides a foundation for ascribing a sequence of ground conditions (e.g. snowmelt, vegetative growth, pollen production, insect phenology) to remotely sensed land surface phenology observations.
[97] White M A, Thornton P E, Running S W.1997.

A continental phenology model for monitoring vegetation responses to interannual climatic variability

[J]. Global Biogeochemical Cycles, 11(2): 217-234.

https://doi.org/10.1029/97GB00330      URL      [本文引用: 1]      摘要

Regional phenology is important in ecosystem simulation models and coupled biosphere/atmosphere models. In the continental United States, the timing of the onset of greenness in the spring (leaf expansion, grass green-up) and offset of greenness in the fall (leaf abscission, cessation of height growth, grass brown-off) are strongly influenced by meteorological and climatological conditions. We developed predictive phenology models based on traditional phenology research using commonly available meteorological and climatological data. Predictions were compared with satellite phenology observations at numerous 20 km 脳 20 km contiguous landcover sites. Onset mean absolute error was 7.2 days in the deciduous broadleaf forest (DBF) biome and 6.1 days in the grassland biome. Offset mean absolute error was 5.3 days in the DBF biome and 6.3 days in the grassland biome. Maximum expected errors at a 95% probability level ranged from 10 to 14 days. Onset was strongly associated with temperature summations in both grassland and DBF biomes; DBF offset was best predicted with a photoperiod function, while grassland offset required a combination of precipitation and temperature controls. A long-term regional test of the DBF onset model captured field-measured interannual variability trends in lilac phenology. Continental application of the phenology models for 1990鈥1992 revealed extensive interannual variability in onset and offset. Median continental growing season length ranged from a low of 129 days in 1991 to a high of 146 days in 1992. Potential uses of the models include regulation of the timing and length of the growing season in large-scale biogeochemical models and monitoring vegetation response to interannual climatic variability.
[98] Xin J F, Yu Z R, Van Leeuwen L, et al.2002.

Mapping crop key phenological stages in the North China Plain using NOAA time series images

[J]. International Journal of Applied Earth Observation and Geoinformation, 4(2): 109-117.

https://doi.org/10.1016/S0303-2434(02)00007-7      URL      摘要

Six key phenological stages were defined based on NOAA/AVHRR NDVI time series data collected in the Huang-Huai-Hai (HHH) Plain of China from 1990 through 2000. In a winter wheat-summer maize rotation, the recovering, heading and maturity stages of winter wheat and the emergence, tasseling and maturity stages of summer maize were recorded using 6km resolution 10-day composite NDVI. The satellite-derived data proved to be consistent with the ‘green wave’ moving through the HHH Plain in spring. The recovering stage of winter wheat recorded by satellite was closely correlated to the temperatures measured in February whereas summer maize yields (at zone level) were correlated well with the satellite-derived length of the crop cycle. Comparison with synchronous phenological observations on the ground confirmed the coherence of satellite-derived phenology data. It is expected that satellite data with greater spatial and temporal resolutions and improved smoothing methods will increase the precision of the estimated data still further.
[99] Xu H, Twine T E, Yang X.2014.

Evaluating remotely sensed phenological metrics in a dynamic ecosystem model

[J]. Remote Sensing, 6(6): 4660-4686.

https://doi.org/10.3390/rs6064660      URL      摘要

Vegetation phenology plays an important role in regulating processes of terrestrial ecosystems. Dynamic ecosystem models (DEMs) require representation of phenology to simulate the exchange of matter and energy between the land and atmosphere. Location-specific parameterization with phenological observations can potentially improve the performance of phenological models embedded in DEMs. As ground-based phenological observations are limited, phenology derived from remote sensing can be used as an alternative to parameterize phenological models. It is important to evaluate to what extent remotely sensed phenological metrics are capturing the phenology observed on the ground. We evaluated six methods based on two vegetation indices (VIs) (i.e., Normalized Difference Vegetation Index and Enhanced Vegetation Index) for retrieving the phenology of temperate forest in the Agro-IBIS model. First, we compared the remotely sensed phenological metrics with observations at Harvard Forest and found that most of the methods have large biases regardless of the VI used. Only two methods for the leaf onset and one method for the leaf offset showed a moderate performance. When remotely sensed phenological metrics were used to parameterize phenological models, the bias is maintained, and errors propagate to predictions of gross primary productivity and net ecosystem production. Our results show that Agro-IBIS has different sensitivities to leaf onset and offset in terms of carbon assimilation, suggesting it might be better to examine the respective impact of leaf onset and offset rather than the overall impact of the growing season length.
[100] Xin Q C, Broich M, Zhu P, et al.2015.

Modeling grassland spring onset across the western United States using climate variables and MODIS-derived phenology metrics

[J]. Remote Sensing of Environment, 161: 63-77.

https://doi.org/10.1016/j.rse.2015.02.003      URL      [本文引用: 1]      摘要

Vegetation phenology strongly controls photosynthetic activity and ecosystem function and is essential for monitoring the response of vegetation to climate change and variability. Terrestrial ecosystem models require robust phenology models to understand and simulate the relationship between ecosystems and a changing climate. While current phenology models are able to capture inter-annual variation in the timing of vegetation spring onset, their spatiotemporal performances are not well understood. Using green-up dates derived from MODIS, we test 9 phenological models that predict the timing of grassland spring onset via commonly available climatological variables. Model evaluation using satellite observations suggests that Modified Growing-Degree Day (MGDD) models and Accumulated Growing Season Index (AGSI) models achieve reasonable accuracy (RMSE聽<聽20聽days) after model calibration. Inclusion of a photoperiod trigger and varied critical forcing thresholds in the temperature-based phenology model improves model applicability at a regional scale. In addition, we observe that AGSI models outperform MGDD models by capturing inter-annual phenology variation in large semi-arid areas, likely due to the explicit consideration of water availability. Further validation based on flux tower sites shows good agreement between the modeled timing of spring onset and references derived from satellite observations and in-situ measurements. Our results confirm recent studies and indicate that there is a need to calibrate current phenology models to predict grassland spring onsets accurately across space and time. We demonstrate the feasibility of combining satellite observations and climatic datasets to develop and refine phenology models for characterizing the spatiotemporal patterns of grassland green-up variations.
[101] Yu F F, Price K P, Ellis J, et al.2003.

Response of seasonal vegetation development to climatic variations in eastern central Asia

[J]. Remote Sensing of Environment, 87(1): 42-54.

https://doi.org/10.1016/S0034-4257(03)00144-5      URL      摘要

Meteorological records show that central Asia has experienced one of the strongest warming signals in the world over the last 30 years. The objective of this study was to examine the seasonal vegetation response to the recent climatic variation on the Mongolian steppes, the third largest grassland in the world. The onset date of green-up for central Asia was estimated using time-series analysis of advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) biweekly composite data collected between January 1982 and December 1991. Monthly precipitation and mean temperature data (1982–1990) were acquired from 19 meteorological stations throughout the grasslands of the eastern Mongolian steppes in China. Our results showed that while the taiga forest north of the Mongolian steppes (>50°N) experienced an earlier onset of green-up during the study period, a later onset was observed at the eastern and northern edges of the Gobi Desert (40°N–50°N). Responses of different vegetation types to climatic variability appeared to vary with vegetation characteristics and spring soil moisture availability of specific sites. Plant stress caused by drought was the most significant contributor to later vegetation green-up as observed from satellite imagery over the desert steppe. Areas with greater seasonal soil moisture greened up earlier in the growing season. Our results suggested that water budget limitations determine the pattern of vegetation responses to atmospheric warming.
[102] Yu H Y, Luedeling E, Xu J C.2010.

Winter and spring warming result in delayed spring phenology on the Tibetan Plateau

[J]. Proceedings of the National Academy of Sciences of the United States of America, 107(51): 22151-22156.

https://doi.org/10.1073/pnas.1012490107      URL      PMID: 21115833      [本文引用: 1]      摘要

Climate change has caused advances in spring phases of many plant species. Theoretically, however, strong warming in winter could slow the fulfillment of chilling requirements, which may delay spring phenology. This phenomenon should be particularly pronounced in regions that are experiencing rapid temperature increases and are characterized by highly temperature-responsive vegetation. To test this hypothesis, we used the Normalized Difference Vegetation Index ratio method to determine the beginning, end, and length of the growing season of meadow and steppe vegetation of the Tibetan Plateau in Western China between 1982 and 2006. We then correlated observed phenological dates with monthly temperatures for the entire period on record. For both vegetation types, spring phenology initially advanced, but started retreating in the mid-1990s in spite of continued warming. Together with an advancing end of the growing season for steppe vegetation, this led to a shortening of the growing period. Partial least-squares regression indicated that temperatures in both winter and spring had strong effects on spring phenology. Although warm springs led to an advance of the growing season, warm conditions in winter caused a delay of the spring phases. This delay appeared to be related to later fulfillment of chilling requirements. Because most plants from temperate and cold climates experience a period of dormancy in winter, it seems likely that similar effects occur in other environments. Continued warming may strengthen this effect and attenuate or even reverse the advancing trend in spring phenology that has dominated climate-change responses of plants thus far.
[103] Zhang G L, Zhang Y J, Dong J W, et al.2013.

Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011

[J]. Proceedings of the National Academy of Sciences of the United States of America, 110(11): 4309-4314.

https://doi.org/10.1073/pnas.1210423110      URL      PMID: 23440201      [本文引用: 3]      摘要

ABSTRACT
[104] Zhang Q Y, Cheng Y B, Lyapustin A I, et al.2014.

Estimation of crop Gross Primary Production (GPP): I. impact of MODIS observation footprint and impact of vegetation BRDF characteristics

[J]. Agricultural and Forest Meteorology, 191: 51-63.

[本文引用: 1]     

[105] Zhang X Y, Friedl M A, Schaaf C B, et al.2003.

Monitoring vegetation phenology using MODIS

[J]. Remote Sensing of Environment, 84(3): 471-475.

https://doi.org/10.1016/S0034-4257(02)00135-9      URL      [本文引用: 1]      摘要

Accurate measurements of regional to global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate鈥揵iosphere interactions. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success.
[106] Zhu W Q, Pan Y Z, He H, et al.2012.

A changing-weight filter method for reconstructing a high-quality NDVI time series to preserve the integrity of vegetation phenology

[J]. IEEE Transactions on Geoscience and Remote Sensing, 50(4): 1085-1094.

https://doi.org/10.1109/TGRS.2011.2166965      URL      [本文引用: 2]      摘要

Not Available
[107] Zhu W Q, Tian H Q, Xu X F, et al.2012.

Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982-2006

[J]. Global Ecology and Biogeography, 21(2): 260-271.

https://doi.org/10.1111/j.1466-8238.2011.00675.x      URL      [本文引用: 1]      摘要

ABSTRACT Aim&ensp; We intend to characterize and understand the spatial and temporal patterns of vegetation phenology shifts in North America during the period 1982&ndash;2006. Location&ensp; North America. Methods&ensp; A piecewise logistic model is used to extract phenological metrics from a time-series data set of the normalized difference vegetation index (NDVI). An extensive comparison between satellite-derived phenological metrics and ground-based phenology observations for 14,179 records of 73 plant species at 802 sites across North America is made to evaluate the information about phenology shifts obtained in this study. Results&ensp; The spatial pattern of vegetation phenology shows a strong dependence on latitude but a substantial variation along the longitudinal gradient. A delayed dormancy onset date (0.551 days year 鈭1 , P = 0.013) and an extended growing season length (0.683 days year 鈭1 , P = 0.011) are found over the mid and high latitudes in North America during 1982&ndash;2006, while no significant trends in greenup onset are observed. The delayed dormancy onset date and extended growing season length are mainly found in the shrubland biome. An extensive validation indicates a strong robustness of the satellite-derived phenology information. Main conclusions&ensp; It is the delayed dormancy onset date, rather than an advanced greenup onset date, that has contributed to the prolonged length of the growing season over the mid and high latitudes in North America during recent decades. Shrublands contribute the most to the delayed dormancy onset date and the extended growing season length. This shift of vegetation phenology implies that vegetation activity in North America has been altered by climatic change, which may further affect ecosystem structure and function in the continent.

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