地理科学进展  2018 , 37 (2): 198-213 https://doi.org/10.18306/dlkxjz.2018.02.003

专栏:地理新青年

被动微波反演土壤水分的L波段新发展及未来展望

赵天杰

中国科学院遥感与数字地球研究所 遥感科学国家重点实验室,北京 100101

Recent advances of L-band application in the passive microwave remote sensing of soil moisture and its prospects

ZHAO Tianjie

State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, CAS, Beijing 100101, China

收稿日期: 2017-09-28

修回日期:  2018-01-30

网络出版日期:  2018-02-28

版权声明:  2018 地理科学进展 《地理科学进展》杂志 版权所有

基金资助:  国家自然科学基金青年科学基金项目(41301396);国家重点研发计划政府间国际科技创新合作重点专项(2016YFE0117300);民用航天“十三五”技术预先研究项目

作者简介:

作者简介:赵天杰(1985-),男,河南周口人,博士,副研究员,从事微波遥感土壤水分及其冻融态研究,E-mail: zhaotj@radi.ac.cn

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摘要

土壤水分是陆—气交互作用的重要边界条件,在全球水循环和能量循环中扮演着关键角色,直接影响降水、径流、下渗与蒸散发等水文循环过程,并能反映洪涝和干旱的程度。随着第一颗采用被动微波干涉成像技术的SMOS(Soil Moisture and Ocean Salinity)卫星的发射成功,L波段被动微波遥感技术逐渐成为大尺度土壤水分监测的主要手段,促进了“射频干扰的检测与抑制”、“植被光学厚度反演与植被影响校正”以及“土壤粗糙度参数化方案”等关键问题的研究。本文梳理了“基于微波植被指数的L波段多角度数据反演土壤水分算法研究”项目的最新研究成果,同时评述了围绕以上关键技术问题所取得的国内外研究进展,并对土壤水分微波遥感的未来发展进行了展望。

关键词: 被动微波遥感 ; 土壤水分 ; 植被光学厚度 ; 地表粗糙度 ; L波段

Abstract

Soil moisture is an important boundary condition of land-atmosphere interactions and plays a major role in the Earth's water and energy cycles. It directly affects the hydrological processes such as precipitation, runoff, infiltration, and evapotranspiration, and can provide direct information for flood and drought monitoring. Accompanied by the continuous development of space science and technology, especially the successful launching of the first L-band satellite mission of Soil Moisture and Ocean Salinity (SMOS) using passive microwave interference imaging technology, L-band passive microwave remote sensing has become a key tool in large-scale soil moisture mapping. New issues regarding L-band application including "detection and mitigation of radio frequency interference", "vegetation optical depth retrieval and vegetation effects correction", and "soil roughness parameterization" have been studied extensively. In this article, we summarize the latest research results of the project "Vegetation effects on soil moisture estimation using multi-angle observations at L-band" funded by the National Natural Science Foundation of China, and review the research progress made regarding the above issues. The future development of soil moisture microwave remote sensing is also prospected. The review of the research progress and the prospect of the cutting-edge issues will be helpful for the demonstration and implementation of China's future satellite missions, and promote the microwave remote sensing of soil moisture and application in eco-hydrology studies at the global and regional scales.

Keywords: passive microwave remote sensing ; soil moisture ; vegetation optical depth ; soil roughness ; L-band

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赵天杰. 被动微波反演土壤水分的L波段新发展及未来展望[J]. 地理科学进展, 2018, 37(2): 198-213 https://doi.org/10.18306/dlkxjz.2018.02.003

ZHAO Tianjie. Recent advances of L-band application in the passive microwave remote sensing of soil moisture and its prospects[J]. Progress in Geography, 2018, 37(2): 198-213 https://doi.org/10.18306/dlkxjz.2018.02.003

1 引言

土壤水分是陆地水循环中最为活跃的部分,是影响水文过程、生物生态过程、生物地球化学过程的关键变量,特别是在地表水蒸散发与渗流中扮演着重要的角色。例如,土壤水分控制着地表显热通量和潜热通量的比例,以及地表水量分配,前期土壤水分含量对后期降水有一定的反馈作用。在气候时间尺度上,土壤水分和海洋表面温度一起,作为边界条件控制着进入大气的通量,在水文和陆面模式中必须精确表达。因此土壤水分成为气象学、水文学、农学、林学等研究中重点观测的参数,其有效观测有助于提高天气预报和气候预测中各种数值模型的计算精度(Entekhabi et al, 2010)。当前迅速发展的卫星遥感技术为实现大空间尺度和长时间序列的土壤水分观测提供了途径。其中,微波遥感具有全天时、全天候的特点,并对云雾、雨雪、植被及干燥地物有一定的穿透能力,利用干燥土壤和液态水分在介电特性上的巨大差异进行土壤水分估计,是一种更为直接的测量(赵天杰等, 2009)。被动微波遥感受地物形状和结构影响较小,并且具有重访周期短、时间序列长、覆盖范围宽等优点,是当前土壤水分大尺度监测的主要手段。

L波段被认为是获取表层土壤水分的最佳波段,它可以穿透稀疏和中等浓密植被,并能获取一定深度的土壤信息,但其面临的技术挑战为:如何在卫星平台上实现空间分辨率要求。在硬件技术日趋成熟之后,各种星载传感器不再仅仅局限于传统的多频段真实孔径辐射计,如AMSR-E(Advanced Microwave Scanning Radiometer for EOS)、WindSat、FY-3/MWRI (Microwave Radiation Imager)和AMSR2 (Njoku et al, 2003; Imaoka et al, 2007; Li et al, 2010; Yang et al, 2011),而是向对地表土壤水分具有更高敏感性的L波段发展,出现了第一颗采用合成孔径技术获取地表L波段微波辐射亮温的SMOS卫星(Kerr et al, 2010),同时也在发展主被动协同观测的星载传感器,包括Aquarius/SAC-D(Le Vine et al, 2007)和SMAP(Soil Moisture Active Passive)(Entekhabi et al, 2010),以期获得更高的产品精度和空间分辨率。新型L波段传感器的相继升空,使得土壤水分微波遥感成为对地观测中的热点研究领域。先进的卫星传感器和技术为土壤水分反演带来了新的发展机遇,但同时也涌现出诸多问题,值得深入研究。

以SMOS为例,其土壤水分算法(Kerr et al, 2012)主要是基于L-MEB(L-band Microwave Emission from the Biosphere)正向模型(Wigneron et al, 2007; Wigneron et al, 2011)的迭代算法,通过构建基于多角度观测的代价函数反演包括土壤水分、植被光学厚度和地表温度在内的地表参数。一份使用密集流域观测网络数据(Cosh et al, 2004)和AMSR-E土壤水分产品(Jackson et al, 2010)作为参考的验证研究论文(Jackson et al, 2012)指出,SMOS土壤水分产品基本达到预期精度需求(0.04 m3/m3),但SMOS反演植被光学厚度呈现出较强的日变异特点甚至表现出一定的随机性,不具备应有的季节变化特征。Al Bitar等(2012)使用覆盖美国大部分范围的SCAN/SNOTEL站点数据分析结果显示,SMOS土壤水分存在一定程度的低估,并且随着站点的不同反演精度各异。dall’Amico等(2012)使用多瑙河上游地区的本地测量数据同样发现了SMOS土壤水分存在估计偏低的现象。Schlenz等人在德国南部地区集成使用陆面模式和辐射传输方程对地表参数和辐射亮温进行模拟分析,同SMOS产品对比结果显示,在小角度条件下SMOS观测亮温不够稳定,土壤水分出现低估时受到射频干扰(Radio Frequency Interference, RFI)的影响,而植被光学厚度不具备季节变化规律,并且均值较高(Schlenz et al, 2012)。Chen等(2017)发现SMOS L3土壤水分产品在青藏高原半湿润的那曲地区表现较好,但在半干旱的帕里地区存在较大的不确定性。而Mialon等(2012)通过分析SMOSREX-2006数据,认为地表粗糙度在高入射角度、高土壤水分以及H极化条件下更为重要,粗糙度参数化方案会影响SMOS土壤水分产品精度。综合分析得出,SMOS土壤水分反演产品出现以上问题的原因可以归结为以下几个方面:

(1) 亮温数据的不稳定,包括亮温重建过程中的观测偏差和射频干扰。由于SMOS采用干涉测量,并通过反傅里叶变换进行亮温重建,亮温数据可能存在偏差甚至双重偏差,特别是在小角度观测情况下更为突出。此外,大面积频繁发生的RFI干扰为成像带来了诸多困扰,影响了有效反演的能力。

(2) 缺少植被影响校正方法。虽然L-MEB模型中将地表覆盖分为稀疏、中等植被和浓密植被分别进行描述,并考虑了枯枝落叶层和晨露的影响,但所使用的模型及参数基本属于经验性成果,无法将植被影响同土壤背景辐射分离。这就导致反演的植被光学厚度参数往往成为迭代算法误差累积的场所,使得其反演结果波动性强,不能反映植被的季节变化特征。

(3) 粗糙度效应的机理描述不够精确。SMOS土壤水分反演算法中使用Q/H半经验模型,在使用多角度数据进行反演时却没有考虑不同角度下粗糙度影响的差异,由于粗糙度同植被的影响作用类似,也会将一部分粗糙度的影响转移到植被光学厚度估算误差中。

因此,提高SMOS亮温数据的质量,改善对于植被、粗糙度等关键影响参数的处理方法,是提高SMOS土壤水分产品质量的有效途径。国家自然科学基金委员会“基于微波植被指数的L波段多角度数据反演土壤水分算法研究”青年基金项目即在此背景下提出。本文在回顾项目成果的基础上,一并梳理总结当前国内外研究进展及发展态势,并对土壤水分微波遥感的未来发展进行展望,以期推动土壤水分遥感的技术发展以及我国未来相关卫星计划的实施论证,促进土壤水分微波遥感在水文模拟与同化、干旱监测预警、流域水资源管理配置等研究中的应用。

2 L波段土壤水分被动微波遥感的新发展

微波遥感利用干燥土壤和液态水分在介电特性上的巨大差异反演土壤水分,大量的理论计算和试验结果表明,土壤的微波辐射强烈地依赖于土壤水分的变化。地表发射的电磁波在传播过程中,除受到土壤水分(土壤介电常数)的影响外,同时还受到土壤表面粗糙度和植被覆盖的影响。植被能通过散射作用、吸收作用以及自身的微波发射,不断削弱来自下覆土壤的信息。考虑到散射过程的辐射传输模型通常不易求得解析解,在应用于土壤水分参数的反演时,通常需要进行假设和简化。目前在进行前向模拟和参数反演时,特别在低频L波段,通常忽略植被层中的多次散射影响,同时将大气和植被看作是相对均一的介质层,并忽略大气上层、大气和植被界面的反射,而仅仅考虑植被和土壤界面的反射,即使用零阶近似模型描述地表的微波辐射过程。那么,对于植被均匀覆盖的粗糙地表,卫星传感器观测的亮温可表示为各种地物辐射能量及其间相互作用的总和(图1)。多数情况下,地表粗糙度和植被覆盖是最为关键的因素,这两种影响必须在土壤水分反演过程中进行校正和消除。在L波段,还需考虑射频干扰和电离层的影响等因素。

图1   L波段地表微波辐射传输过程及关键影响因素(赵天杰, 2012)

Fig.1   Contributing factors of the satellite observed L-band brightness temperature (from Zhao, 2012)

2.1 射频干扰的检测与抑制

射频干扰是指频率相近的人为发射电磁波被卫星传感器接收,进而对卫星观测数据造成干扰的现象。射频干扰可通过视距传播、反射传播、绕射传播以及大气折射和散射作用等途径进入卫星传感器。由于射频干扰的能量水平通常远大于自然辐射信号,因此在采用被动接收方式工作的微波辐射计上更为明显,会造成卫星观测亮温的异常增大。

其实人们早在星载微波辐射计如1978年SeaSat搭载的微波辐射计SMMR(Scanning Multichannel Microwave Radiometer)中就检测到射频干扰的存在。近年来的AMSR-E和WindSat在C、X等波段也检测到了明显的RFI信号(Li et al, 2004; Njoku et al, 2005)。C波段干扰在美国等其他地区较为严重,而X波段干扰主要在欧洲地区。L波段(1.40~1.427 GHz)是目前公认的遥感探测土壤水分和海面盐度的最佳频段,虽然该频段已被国际电信联盟组织规定只能用于卫星地球探测业务,但近年的L波段星载微波辐射计均检测到大量的射频干扰存在,已对卫星观测精度和科学数据反演造成了严重影响,使得RFI的检测与抑制问题变得尤为突出。

SMOS卫星的发射首次确认了L波段干扰的严重性,其采用被动微波干涉成像技术,亮温图像重建过程复杂,单独一个强RFI干扰源的存在可能会污染一大片区域,甚至影响整个亮温快照。SMOS的RFI检测可在可视度(L1a)、空间域和角度域(L1c)等不同层级产品开展,并通过多次观测能对RFI干扰源进行更精确的定位,但受限于载荷体制一直无法很好地对RFI进行抑制,导致SMOS土壤水分产品质量受到严重影响。CATDS(Centre Aval de Traitement des Données SMOS)团队使用大量的数据质量标识对原始观测进行筛选和过滤,然后在5°入射角间隔内进行平均,生成L3级亮温产品,在一定程度上减少了RFI干扰,但仍会出现H和V极化反转的现象,与理论预期不符。

Zhao等(2015a)面向土壤水分反演需求,从SMOS L1c角度域数据出发,发展了一种双步回归方法对亮温产品进行优化处理,建立适用于多种地表类型的目标函数对多角度数据进行回归,有效地减少了RFI等造成的数据波动和极化特征背离理论预期现象。该方法首先对SMOS亮温数据进行预处理,减少强RFI干扰造成的无效数据,其次使用双步回归的方法对多角度亮温数据进行优化。具体而言,数据预处理包括使用SMOS L1c数据中的固有数据质量标识,对于受到过多环境因素干扰的数据进行标记,采用滤波方法对原始数据进行过滤,将波动较大或者超出设定阈值范围的数据标记,仅保留较高质量的亮温数据;再使用分段的线性插值方法基于观测时间对标记后的SMOS原始观测数据进行插值。考虑电磁场的极化旋转,其中包括几何旋转和法拉第效应,将基于天线坐标系统的XY极化亮温通过法拉第效应校正转换为基于地面坐标系统HV极化亮温。该研究发现,亮温第一斯托克斯参数随角度变化呈现二次函数关系,利用这一特性可实现天顶角下的亮温值估计,此为回归第一步,可有效地减少观测偏差即天线亮温傅里叶变换反演中造成的波动噪声。第二步,基于多角度极化亮温的变化特征,建立分别适用于水平和垂直极化的混合型目标函数(公式1,2),最大程度再现亮温随角度变化的特征,特别是垂直极化条件下的布儒斯特角效应,通过回归拟合实现SMOS亮温数据的优化处理(图2)。该方法在使用模型模拟数据的验证中表现良好,经过优化处理的亮温在常用的南极冰盖、亚马逊雨林等外部定标场中与实际观测和模型模拟均比较吻合,并能减少土壤水分反演中的误差和不确定性。

图2   用于优化SMOS多角度亮温观测的双步回归方法

Fig.2   Fitting processes of the two-step regression approach for Soil Moisture and Ocean Salinity (SMOS) satellite multi-angular brightness temperature refinements

Tbvθ=avθ2+C2bvsin2(dvθ)+cos2(dvθ)(1)

Tbhθ=ahθ2+C2bhsin2(θ)+cos2(θ)(2)

式中,Tbv, Tbh分别为垂直极化和水平极化亮温,θ为入射角,a,b,C,d为回归系数。

在SMOS观测经验基础上,Aquarius/SAC-D卫星采用L波段主被动协同观测体制,每10 ms完成一次测量,试图通过快速采样方法减轻RFI干扰的影响,即在1.44 s的时间域上比较滑动窗口内每个样本亮温与临近样本的平均亮温值,如果该样本亮温大于设定的阈值即标记为RFI,由此向前滑动完成整个样本集的检测,将未受到RFI干扰的样本平均记为有效观测亮温。Zhang等(2017)基于局部异常因子算法(Local Outlier Factor, LOF)发展了一种适用于Aquarius观测的RFI检测方法,该方法对比样本点与临近样本的局部可达密度差异计算局部异常因子,通过设定阈值进行RFI的检测,并且能够最大程度地保留未受RFI干扰的数据。但Aquarius的体制仅对于脉冲式干扰有效,事实上Aquarius发现陆表存在大量的持续性干扰信号,加之Aquarius传感器具有更大的辐射视场和更高的辐射灵敏度,其仍然无法对RFI进行有效抑制。

新近发射的SMAP卫星采用多相滤波器将整个L波段探测通道分为16个子带,并测量每个子带数据的峰度系数,通过综合频域、时域、统计和极化信息等多种检测方法对RFI进行识别和抑制,取得了较为理想的效果。随着人类活动及其涉及的电子设备的多样化,RFI的分布与强度也是不断变化的,为了保证科学探测任务的顺利实施,RFI的检测与抑制已经成为数据处理中必不可少的环节,同时各国主管部门也应实施相应措施确保卫星探测业务保护频段内不存在任何人为发射源。

2.2 植被光学厚度反演与植被影响校正

多数情况下,土壤表面有植被覆盖。植被层不仅能衰减来自底层土壤的辐射信号,同时自身也会发生电磁波。当植被覆盖增加时,微波甚至不能穿透植被层而获取底层土壤信息,给土壤水分的反演带来很大的困难。实际上,植被覆盖是影响土壤水分产品精度的最主要因素。零阶近似微波辐射传输模型(Mo et al, 1982)中的两个主要参数为单次散射反照率和植被光学厚度,其数值大小与植被含水量、植被类型及结构以及生长状况等多种因素有关。

在被动微波土壤水分反演算法中,植被光学厚度是进行植被影响校正的必要参数。它可以通过:①光学波段的辅助数据进行获取。如单通道(Single Channel Algorithm, SCA)算法中使用MODIS(Moderate Resolution Imaging Spectroradiometer)的NDVI(Normalized difference vegetation index)计算植被含水量(Jackson et al, 1991, 1999; Bindlish et al, 2011; Bindlish et al, 2015),进而与光学厚度建立经验关系。但是NDVI数据在密集植被下容易达到饱和,无法估计较高的植被含水量,并且该方法多具经验性,反演精度随地区不同而发生变化。光学植被指数同植被含水量间的物理关系并不明确,有研究报告指出,有必要考虑植被覆盖度来提高植被参数的反演精度和代表性(Sánchez et al, 2012)。②当相互独立的微波观测通道较多时,也可使用迭代算法进行统一反演。如AMSR-E和SMOS中所采用的迭代算法,但其弊端为迭代算法容易产生多解状况,从而对植被信息产生错误的判断,比如SMOS反演的植被参数缺失明显的季节特征。③综合考虑植被和粗糙度影响,植被和粗糙度对微波辐射的影响存在类似的负指数关系和去极化效应。有学者在辐射传输方程中将其合并为一个参数以减少未知变量的个数(Njoku et al, 2006; Pan et al, 2014; Zeng et al, 2015),从而达到反演土壤水分的目的。④发展基于微波观测的植被指数,以达到分离植被和土壤信号的目的,并充分发挥微波观测对于植被特征的独特表征。例如,Shi等提出被动微波植被指数理论(Microwave Vegetation Indices, MVIs)并应用于AMSR-E数据,能提高对于植被参数的定量表达(Shi et al, 2008; Shi et al, 2012)。Zhao等(2011)进一步推导了微波植被指数同植被光学厚度和含水量之间的理论关系,随后利用Aquarius的L波段散射计数据,研究了雷达植被指数同植被含水量的关系。但发现这种关系随地表覆盖类型而异,不易在全球范围内开展(Zhao et al, 2012)。

在微波植被指数理论基础上,Cui等(2015)利用SMOS卫星的多角度观测数据,提出了单极化(H极化)多角度MVIs,发展了直接由亮温数据反演植被光学厚度的新算法,避免了NDVI等辅助数据的使用。该算法首先利用AIEM(Advanced Integral Equation Model)模型(Chen et al, 2003),模拟了在不同入射角、不同土壤水分、不同地表粗糙度、在3种表面自相关函数(指数相关、高斯相关和1.5-N)条件下L波段的裸土发射率。通过对模拟数据的分析,发现相邻入射角(10°间隔)的裸土发射率呈近似线性关系(式3)。该线性关系与土壤的介电特性和地表粗糙度无关,仅与入射角有关。并且,H极化下的相关性比V极化下的相关性更强。然后,基于H极化相邻入射角间土壤发射率的线性关系,利用H极化2个角度下的地表亮度温度,消除土壤信号,得到2个角度亮度温度间的线性表达式(式4),其中斜率和截距被称为单极化(H极化)多角度的2个MVIs(式5,6)。它们仅与植被特性和温度有关,与土壤水分信息无关。利用零阶辐射传输模型的模拟数据,发现H极化多角度MVIs对植被光学厚度非常敏感,而对单次散射反照率的敏感性非常低,因此,算法根据植被类型将单次散射反照率设为定值,仅反演植被光学厚度。当土壤温度和植被单次散射反照率都已知时,相邻角度亮度温度间的线性表达式(式4)仅包含一个未知参数,即植被光学厚度。最后,利用SMOS的(30°,40°)、(35°,45°)和(40°,50°)三对亮度温度组合,结合土壤温度数据和植被单次散射反照率,通过给定初始植被光学厚度和卫星观测的小角度亮温(30°,35°,40°),可以利用公式(4)计算得到大角度亮温(40°,45°,50°),通过对比卫星观测不断迭代的方法反演出植被光学厚度。该算法在模拟土壤发射率时,考虑了不同表面自相关函数的影响;在反演植被光学厚度时,不仅考虑了植被的极化特性,而且直接由亮度温度反演植被光学厚度,不需要光学遥感等辅助数据。将该反演算法应用于经双步回归优化后的SMOS多角度亮温数据,反演出全球植被光学厚度,其空间分布与地理特征一致。利用反演的植被光学厚度进行植被效应校正,可进一步反演出全球土壤水分(图3)。

图3   基于微波植被指数理论反演的SMOS植被光学厚度(a)及土壤水分(b)(2011年7月平均值)

Fig.3   Soil Moisture and Ocean Salinity (SMOS) satellite retrieved vegetation optical depth (a) and soil moisture (b) based on microwave vegetation index (mean value for July, 2011)

ehsθ2=ahθ1,θ2+bh(θ1,θ2)ehs(θ1)(3)

Tbhθ2=Ahθ1,θ2+Bh(θ1,θ2)Tbh(θ1)(4)

Bh(θ1,θ2)=bh(θ1,θ2)Va,h(θ2)Va,h(θ1)(5)

Ahθ1,θ2=ahθ1,θ2Va,hθ2+Ve,hθ2-Bh(θ1,θ2)Ve,hθ1(6)

Ve,pθ=(1-ωp)(1-γp)(1+γp)Tv(7)

Va,pθ=γpTs-1-ωp)(1-γp)γpTv(8)

式中, ehs为水平极化下土壤发射率;ah,bh为回归系数;系数Ah,Bh为微波植被指数;Ve,p,Va,p分别为植被发射项和衰减项;ωp为植被单次散射反照率;γp为植被透过率;Tv,Ts分别为植被和土壤温度。

Cui等将NDVI数据和反演的植被光学厚度分别与热带地上生物量数据(Saatchi et al, 2011)进行对比,发现当地上生物量超过100 Mg/ha时,NDVI 出现饱和,而微波反演的植被光学厚度与地上生物量的相关性更强。这是由于NDVI仅对植被冠层顶部的一薄层叶片信息敏感,而微波具有穿透性,不仅对光合生物量敏感,而且也对非光合生物量敏感,可提供光学遥感所不能提供的植被特性信息。因此,光学遥感和微波遥感相互补充,两者的结合可更全面地监测植被。

近期,INRA(Institut National de la Recherche Agronomique)和CESBIO(Center d’Etudes Spatiales de la BIOsphère)生成一套SMOS备选产品:SMOS-IC产品(Fernandez-Moran et al, 2017),可提供全球每天的土壤水分和植被光学厚度数据。SMOS-IC产品生成算法与现有的SMOS L2和L3产品反演算法的不同之处在于:不再考虑像元内不同土地利用类型的贡献,而是将像元看作同质的整体反演土壤水分和植被光学厚度;使用的亮温数据是SMOS L3固定角度亮度温度;迭代反演过程中,植被光学厚度的初始值不再由LAI(Leaf Area Index)计算,而是将前期反演的年平均值作为初始值;基于Fernandez-Moran等人(Fernandez-Moran et al, 2016)和Parrens等人(Parrens et al, 2016)的研究结果,根据IGBP(International Geosphere-Biosphere Programme)地表分类,重新设置植被有效散射反照率和地表粗糙度参数的值。在全球尺度上,与SMOS L3的产品相比,SMOS-IC的土壤水分产品与ECMWF(European Center for Medium range Weather Forecasting)土壤水分数据的相关性更强,SMOS-IC的植被光学厚度产品与MODIS NDVI的相关性更强。Konings等(Konings et al, 2016; Konings et al, 2017)发展了多时相双通道算法(Multi-Temporal Dual Channel Algorithm, MT-DCA),对同一地区的连续两次过境观测中,假设植被光学厚度和单次散射反照率不变,在不需要光学遥感数据的情况下,反演3个地表参数:植被光学厚度、土壤水分和单次散射反照率,该算法已应用于Aquarius和SMAP反演。

植被影响校正中的另一个重要是单次散射反照率,定义为散射系数与衰减系数的比值,与植被类型有关。考虑到被动微波像元的空间尺度很大,多种植被类型的影响被平均,因此对于绝大多数的像元来说,极化对单次散射反照率的影响可以忽略(Wigneron et al, 2004)。由于零阶辐射传输模型中没有考虑多次散射,Kurum等人认为其在植被散射作用较强时不再适用,提出了形式更为复杂的植被一阶模型,但由于该模型参数不易确定,并增加了未知参数的个数,尚未用于土壤水分的反演(Kurum et al, 2011)。其进一步使用了理论物理模型讨论了植被散射作用的影响,认为植被零阶模型中的单次散射反照率实际为有效散射反照率参数,并推导了有效反照率的理论表达式,包含了多次散射作用的影响(Kurum, 2013)。一般在反演土壤水分算法中,根据地表覆盖类型的不同,有效散射反照率被赋予不同的数值。比如,SMOS L2和L3土壤水分反演算法中,低矮植被和森林的有效散射反照率默认值分别为0和0.06~0.08(Kerr et al, 2012);Fernandez-Moran等(2016)根据IGBP植被类型,森林的有效散射反照率设置为0.06,其他植被类型的有效散射反照率取值范围为0.06~0.12,并将其应用于SMOS-IC产品的反演算法;SMAP L2和L3反演算法中,根据IGBP植被类型设置相应的有效散射反照率默认值(O'Neill et al, 2016)。

利用星载辐射计观测数据,在全球尺度上开展有效散射反照率的研究相对较少。Konings等(2016)利用MT-DCA算法,基于Aquarius卫星观测数据,确定低矮植被的有效散射反照率的取值范围为0.02~0.04,森林为0.03~0.06。后又将该算法应用于空间分辨率更高的SMAP观测数据,得到全球平均有效散射反照率为0.08 (Konings et al, 2017)。Van der Schalie等(2016)基于SMOS观测数据,利用LPRM(Land Parameter Retrieval Model)算法反演土壤水分,通过与MERRA(MERRA-Land)和ERA(ERA-Interim/Land)模拟的土壤水分数据对比,得到全球有效散射反照率的最优校正值0.12,并发现有效散射反照率在全球尺度变化不明显。Parrens等(2017)认为在非常茂密的热带森林地区,植被的透过率近似为0。基于这一假设和零阶辐射传输模型,并且考虑植被的极化特性,利用5年(2011-2015)的SMOS观测数据,直接由亮度温度计算出亚马逊和刚果森林地区的有效散射反照率,发现不同极化有效散射反照率的差别很小。

以上研究表明,土壤水分反演过程中的植被影响校正正逐步脱离其他辅助数据的使用,同时微波反演获取的植被光学厚度同光学植被指数反映的信息有所差异,光学数据更多地反映叶片光谱信息的变化,而微波数据与植被整体水分含量和结构有关,特别是在植被密集地区二者的差异更为显著。因此,尽可能独立地使用微波数据获取植被光学厚度等信息,不仅是对于光学数据的补充,同时也更有利于植被影响的校正和土壤水分的反演。

2.3 土壤粗糙度参数化方案

自然的土壤表面通常情况下并不是光滑表面,而是具有一定粗糙度的粗糙地表。目前,微波遥感中经常使用两个统计几何参数来衡量地表粗糙度的变化,其中一个是均方根高度(Root Mean Square height, σ),用于形容垂直方向的粗糙程度;另一个是相关长度(Correlation Length, Lc),用于衡量水平方向的粗糙程度。土壤表面高度的自相关系数随着水平距离的增大是不断减小的,当减小至1/e 时的水平距离即为相关长度,将得到的相关长度值代入高斯、指数相关函数,便可获得理论上相应的自相关曲线。

为计算粗糙地表的微波散射与辐射,目前有很多比较成熟的方法,主要包括半经验模型和理论模型。目前半经验模型中经常使用Q/H模型(Wang et al, 1981)计算土壤表面粗糙度的影响,包括极化混合因子Q,粗糙度参数H以及角度校正常数N,将粗糙地表的反射率表示为光滑表面反射率在不同极化下的线性组合,同时考虑了粗糙度对于反射率的衰减作用。根据已经发表的研究成果,Q参数一般小于0.2,特别是在L波段,可认为等于0。Wigneron在PORTOS-93试验数据的基础上,利用Q/H模型对粗糙度参数H进行优化估计,认为其不仅是粗糙度的函数,而且随着土壤含水量的大小变化(Wigneron et al, 2001),指出土壤表面粗糙度实际上是均匀介质表面的介电粗糙度,当土壤中水分含量减少时,易出现裂痕和孔隙而显得更加碎裂化,实际上增加了介质的粗糙程度。后续研究发现,角度校正常数在不同极化下也有所差异,并据此对L-MEB模型进行了修正(Wigneron et al, 2011)。Lawrence等人使用FEM(finite-element method)数值模拟方法,研究了传统的粗糙度经验模型参数同物理参数包括均方根高度和相关长度之间的关系,通过模拟数据对经验模型进行参数化,但并未考虑不同地表相关函数的影响(Lawrence et al, 2013)。依据试验数据建立的半经验模型往往与研究中使用的数据具有较好的吻合性,但由于试验数据自身的局限性,往往不能覆盖更广阔范围的地表条件;另外,粗糙度自相关函数对于土壤发射率的影响也是试验过程中难以进行再现和研究的。

为解决半经验模型的欠准确性以及理论物理模型的复杂耗时性,针对复杂物理模型的参数化方法不失为一种更为妥当的方案。在参数化过程中,具有较高精度和较广适用范围的AIEM模型一般被作为参数化的对象。Chen等(2010)发展了L波段的参数化模型,能模拟多角度的L 波段微波辐射亮温,并且认为高斯相关地表条件下粗糙度的影响可表示为σ/Lc的函数。AIEM模拟结果表明,在相同的均方根高度和相关长度输入参数下,高斯相关地表和指数相关地表在各种角度条件下的地表发射率均有所不同。其差异的主要原因是高斯相关和指数相关刻画地表的方式有所不同,指数相关描述更小步长中的细微变化,对于土壤表面的微粗糙有着更好的描述,这是指数相关地表更加适合现实中的土壤表面情况的主要原因,这也被相关试验观测研究(Schwank et al, 2010)所证实。因此,Zhao等(2015b)针对指数相关地表,利用粗糙度校正参数Hp来表达粗糙度的存在对土壤表面反射率和发射率的总体影响,并将其表示为粗糙度斜度Zs的函数,而函数的系数与入射角度有关。

Zs=σ2Lc(9)

Hp=Apexp(BpZs2+CpZs)(10)

Ap,Bp,Cp=aθ2+bθ+c(11)

式中,Ap,Bp,Cpa,b,c均为回归系数。

图4显示了粗糙度校正参数随粗糙度斜度的变化关系,以及各种入射角度条件下的最佳回归方程。可以看出,随着粗糙度斜度的增加,即粗糙度的增加,粗糙度校正参数一般减小,但是粗糙度校正参数随着入射角度的增加而增大。在大多数情况下,粗糙度校正参数小于1,意味着粗糙度的存在能减少土壤表面的反射率,从而增加土壤的发射率,这种现象在H 极化更为明显。但是,粗糙度的作用在V 极化的大入射角度情况下,其方向和幅度均会产生变化。比如在55°时,粗糙度参数可能大于1,说明此时的粗糙度作用可以增加土壤反射率而降低土壤发射率。将参数化计算结果同原始的AIEM模型输出进行比较,发现参数化模型在H和V极化下很好地重现了AIEM模型的输出,二者的模拟结果存在着良好的线性关系。对于H极化,参数化模型精度大致随着入射角度的增加而降低,最大误差存在于55°左右,约为0.019。而对于V极化来讲,参数化模型精度随入射角度的增加先增大后减小,并在35°左右达到最佳精度,约为0.003。作者认为,不应将粗糙度的影响与土壤水分混淆,试验数据中体现的关系可能是由于降雨过程中雨滴击打对于粗糙度的平缓作用,而这种变化在稀疏的粗糙度测量中难以体现。

图4   粗糙度校正参数同粗糙度斜度之间的关系(受权引用自Zhao et al, 2015b)

Fig.4   Roughness parameter versus slope parameter at different angles of incidence (AOI) (from Zhao et al, 2015b)

为全面比较不同粗糙度参数化方案的性能,Peng等(2017)基于Surface Monitoring of the Soil Reservoir Experiment(SMOSREX 2004-2006)裸土观测数据,对比了文献中列举的15种粗糙度参数化方案(表1),各个模型与试验测量数据之间的ubRMSE(Unbiased Root Mean Square Error)和Bias如图5所示。整体上来看,光滑情况(SMOSREX 2004)下的土壤粗糙度参数化方案模拟效果整体优于粗糙情况(SMOSREX 2006),V极化模拟效果整体优于H极化。特别在2006年相对粗糙的情形下,随着入射角度的增加,V极化的ubRMSE随之降低,而H极化逐渐增加,这与地表土壤在不同角度下的辐射特性有关。综合所有角度,S06模型(Schwank et al, 2006)的ubRMSE最低为23.14K,而SMOS模型(Kerr et al, 2012)的ubRMSE最大为26.21K,L13模型(Lawrence et al, 2013)的bias最小仅为2.51K,而S06模型的bias最大达到47.88K。基于理论和试验数据发展的参数化方案在模拟性能上没有明显差距,粗糙情形下H极化的模拟效果还有待改善。

表1   适用于L波段的土壤粗糙度参数化方案及其默认参数设置(受权引用自Peng et al, 2017)

Tab.1   Soil roughness parameterization schemes in L-band (from Peng et al, 2017)

类别名称参数化方案参数设置文献
基于试验数据C79rs,p=Γs,hHp
Hp=exp-hcosNp(θi)
h=2)2,Np=2Choudhury等(1979)
W83h=2)2,Np=0Wang等(1983)
W01h=1.3972m0.5879,Np=0, m=σLcWigneron等(2001)
W01smh=0.5761ws-0.3475m0.4230,Np=0, m=σLcWigneron等(2001)
E07h=2)2,Np=1 p=h-1 p=vEscorihuela等(2007)
E07smNp=2)2-4.4ws-wFC,ifwswFC2)2, ifwswFC, Np=1 p=h-1 p=vEscorihuela等(2007)
W11h=0.9437σ0.8865σ+2.29136, Np=0Wigneron等(2011)
SMOSh=0.10, if wswt0.10-0.05(ws-wt)wFC-wt, if wtwswFC0.05, if wswFC,
Np=1 p=h-1 p=v
Kerr等(2012)
W99rs,h=Γs,hHh
rs,v=rs,hcosNθi
Hh=exp -h0.1cosθi
h=, N=0.655, forθi60Wegmuller等(1999)
S06rs,h=Γs,hHp
Hp=exp (-hG(εr,θi,p))
h=σλ22,
G(εr,θi,p)=a-2(εr,θi,p),
a(εr,θi,p)=k1θi,p+k2θi,pεr
Schwank等(2006)
L13rs,p=1-QΓs,p+QΓs,qHp
Hp=exp -hcosNp(Qi)
h=2.2651-exp-Zs2.023,Zs1.2531.046,Zs>1.253,Zs=σ2Lc
Q=0.253h,Nv=0.999h-0.54,Nh=Nv+N
N=2.029-0.7457Zs
Lawrence等(2013)
G14Tbp=Ts,eff+aeff,pTa-Ts,eff+rs,p(TbDN-Ts,eff)
rs,p=1-Q)Γs,pGp+QΓs,qGq
aeff,p=1-QAs,p+QAs,Q
Gp=exp-δR2(c0+c1Γs,p)cosc2(θi)103
Q=0.51.0-sin (2βR)2βR)
As,p=1.0-exp-δR1.5(c3+c4Γs,p)cosc5(θi)102
Goodberlet等(2014)
基于理论模型S02rs,p=Γs,pexp-22csoθi2+ApΓs,pBpAporBp=exp aθi,p,ρ+bθi,p,ρlog+cθi,p,ρ+dθi,p,ρW
a,b,c,dθi,p,ρ=ep,ρ+gp,ρθi+h(p,ρ)θi2
Shi等(2002)
C10rs,p=ApmBpΓs,pAp,Bp,Cp=apθi3+bpθi2+cpθi+dp ,
m=σLc
Chen等(2010)
Z15rs,p=Γs,pHp
Hp=Apexp (BpZs2+CpZs)
Ap,Bp,Cp=apθi2+bpθi+cp,
Zs=σ2Lc
Zhao, Shi et al. (2015b)

新窗口打开

图5   基于SMOSREX试验数据的土壤粗糙度参数化方案对比(受权引用自Peng et al, 2017)
注:不同通道下的最佳及最差性能分别用○和×表示,横坐标中的蓝色及红色标识分别代表所有入射角及极化条件下表现最佳及最差的模型

Fig.5   Heatmap for unbiased root mean square error (ubRMSE) and bias statistical results for 15 literature-based models. For each incident angle at horizontal or vertical polarizations, the models with the best and the worst performances are marked by black circles and crosses, respectively. The blue and red x-labels represent the best and the worst performances averaged over all incident angles and polarizations (from Peng et al, 2017)

土壤粗糙度模型的发展已经历经30余年,但是粗糙度参数化方案的研究仍然包含较多的不确定性,主要原因在于不同的模型通常基于不同的试验数据发展而来,意味着土壤类型、水分及粗糙条件均有所差异。此外,模型发展基于的假设条件也有所不同,比如研究中对于土壤有效温度以及土壤介电常数的计算方法。以上分析结果显示:在较为粗糙的情形下,H极化的模拟性能差异尤为突出。各种模型具有各自的优势和劣势,尚未有一个模型能在所有情形下均表现出最佳性能。随着多频段、多极化、主被动等综合传感器的发展,研究能够适用于各种地表条件(宽泛的土壤粗糙度和水分条件,不同的土壤类型及相关函数)以及多频率、多角度、多极化的粗糙度参数化方案显得尤为必要,当然这也是一个巨大的挑战。

3 土壤水分微波遥感的未来展望

土壤水分微波遥感信息能用于陆地水循环模拟与数据同化研究,特别是为资料匮乏区提供空间分布数据的支持。土壤水分遥感信息可直接作为初始条件或用于模型参数率定和验证,也可以通过数据同化实现模型观测的耦合用于提高模型对水文过程的模拟能力(Houser et al, 1998),有助于加深人们对于地气间水分和能量交换过程的认识,改善天气变化和气候演变模式的预测精度,并且能直接服务于洪涝干旱等自然灾害的监测、农业灌溉及流域水资源的管理等。然而现有的土壤水分产品还存在着诸多不确定性、时空分辨率和精度不足、难以获取深层土壤信息等问题,成为限制遥感驱动水文模拟发展的主要因素之一,同时也为载荷研制与遥感技术的发展提出了新的科学要求。

针对土壤水分被动微波遥感观测在水平方向上空间采样不足的问题,需要发展主被动一体化载荷技术,加强主被动协同遥感算法以及尺度转换方法的研究,以提高土壤水分产品的空间分辨率;在垂直方向上,需要发展更长波长的P波段遥感技术以获取更深层的土壤信息,并研究发展土壤分层辐射传输模型。此外,陆面模型以及数据同化需要更高时间频次的数据输入,需要发展静止轨道卫星以增强时间分辨率,或加强多源传感器的配合,形成土壤水分观测的虚拟星座。遥感观测自身的误差以及不确定性,则需在涉及大气及电离层、植被和土壤等的遥感机理模型中进一步改进完善,特别是植被参数的反演及其覆盖影响的校正,并加强土壤水分遥感产品的真实性检验方法研究。未来土壤水分微波遥感的发展需面向水文、土壤等地学学科发展需求,综合卫星系统及载荷技术、遥感机理模型与反演方法等共同推动。

3.1 加强多频率、多极化和主被动协同信息综合利用

众所周知,不同的波段对陆表环境组分(如大气、植被、土壤)有不同的响应,每一个波段都有各自的探测优势和劣势。比如,高频波段对于植被甚至大气参数较为敏感,而低频波段具有更强的穿透性对土壤参数更为敏感;土壤水分的增加能够提高极化差异,而粗糙度和植被的影响却有去极化效应;被动观测主要是地表介电属性和温度的函数,空间分辨率较低,而主动观测对温度不敏感,包含较多地表形态、结构等信息,具有较高的空间分辨率。卫星观测会受到大气—植被—土壤连续偶合体的综合影响,因此利用不同波段的主被动极化信息可更好地解缠卫星信号以获取精确的土壤水分。在中国科学院空间科学战略先导专项支持下,施建成等人提出的全球水循环观测卫星计划(Water Cycle Observation Mission, WCOM)(Shi et al, 2014)的辐射观测覆盖L~W波段,散射观测含有X、Ku波段,并且大部分频段采用全极化观测模式,为土壤水分等水循环关键要素的反演提供了新的机遇,当前必须加强多频率和主被动协同反演理论研究。

3.2 加强土壤水分尺度转换方法研究以促进产品真实性检验

土壤水分遥感产品的真实性检验有赖于尺度转换方法的发展,目前常用的方法包括地面实测数据(Chan et al, 2016)、卫星观测以及模型模拟(Li et al, 2015)结果。使用地面实测数据验证被动微波土壤水分产品一直以来都是具有挑战性的,主要是因为被动微波遥感同地面观测之间的空间尺度及代表性差异。不同的地表覆盖类型、土壤特性以及地形变化都会对微波遥感产生重要影响,因此被动微波土壤水分产品的误差可来自诸多方面,包括卫星观测误差、辐射传输建模结构的不确定性以及辅助数据的影响等等,在其所代表的地面视场范围内往往需要多点观测,以反映整个区域的土壤水分状况。已有研究表明,使用随机分布的测点进行空间平均确实不太可取,必须考虑土壤水分的空间变化,而如何从有限的单点测量到大尺度范围内的平均状况则依赖于升尺度方法。常用的升尺度方法包括时间稳定性(Cosh et al, 2006)、地理统计方法、陆面模型(Crow et al, 2005)以及基于遥感观测的方法(Qin et al, 2013)等,测点位置的优化选择以及不同升尺度方法的结合有利于更为有效和鲁棒性的结果。此外,一些新的测量技术(全球导航卫星系统反射技术、宇宙射线快中子法)的发展也为土壤水分的真实性检验特别是区域土壤水分的测量提供了新的思路。

3.3 发展P波段遥感反演理论与载荷技术以获取更深层土壤水分

根区土壤水分的获取对于农业以及粮食生产尤为重要,同时土壤水分剖面信息的获取能与当前的陆面/水文模式相契合,更好地支撑数据同化研究、天气与气候预报、水资源管理等应用需求。P波段波长能达到40 cm,约为L波段的两倍,因而具有更强的穿透性,能更好地规避植被覆盖的影响,更重要的是有望获取森林地区的土壤水分。值得注意的是,不同的土壤水分剖面可能会有类似/相同的微波辐射散射特性,这给反演某一深度的土壤水分带来了极大的困难,需要水文模型的帮助作为先验知识。目前仅有个别研究进行了P波段下的土壤水分的遥感建模与机载飞行观测研究,比如美国在2012年开展的AirMOSS(Airborne Microwave Observatory of Sub-canopy and Subsurface)试验,反演得到的土壤水分剖面与实测结果的RMSE在0.06~0.099 m3/m3之间,并且随着深度的增加反演误差增大。欧空局计划发射的P波段合成孔径雷达BIOMASS的主要目的是监测全球的森林生物量,以此为基础的有关P波段土壤水分的研究工作同样值得期待。

3.4 发展地球静止轨道卫星以增强土壤水分产品时间分辨率

当前土壤水分观测卫星计划大多基于极轨轨道,一般在中高纬度地区能达到2~3天的覆盖重访能力。然而土壤水分的变化是相对迅速的,特别是在降雨事件前后,目前的遥感观测往往无法再现这种动态变化。同时,土壤水分收支平衡关系的建立需要很多驱动因素,包括降雨,径流以及土壤导水率等,其中导水率决定了土壤水分的渗透与再分配过程。土壤导水率具有很强的空间异质性,目前尚未有相关能力进行其空间分布的观测。土壤导水率能从降水之后土壤水分随时间的变化得出,因而对遥感获取的土壤水分时间分辨率提出了更高的要求。地球静止卫星能实现同一区域的连续观测(1小时内数次),对于解决某一区域的土壤水分测量及水资源管理问题极为有效,当然如何在载荷技术上实现空间分辨率的需求以及平台搭载尤为重要。

3.5 增强土壤水分卫星遥感产品在陆面水文模拟与数据同化中的应用

地表过程模型经过30年来的发展日趋完善,但对水文循环的模拟仍然存在严重不足,特别是土壤水分和蒸散发的模拟误差很大,这一方面归因于模型驱动数据的误差,另一方面是由于参数的不确定性。依赖全球调查数据库和查找表指定的模型参数存在较大误差,而卫星遥感为全球和区域的模型参数率定及优化提供了新途径。土壤水分卫星遥感产品(状态变量),可作为陆面水文模型的初始条件或直接插入变量,从而优化水文过程模型的状态估计;或者在模型参数率定中将土壤水分作为约束条件之一,避免径流模拟中的“异参同效”现象;或者在模型动力框架内同化微波观测或土壤水分产品,利用水文模型约束遥感反演,利用遥感反演调整模型运行轨迹、校正模型参数,达到减少误差积累和提高模型预测能力的目的。尤其是未来WCOM卫星能实现多种水循环要素的同步测量,可更精确地估计地表水文状况,结合陆面水文模型发展新的参数率定和多目标优化方法以及“状态—参数”同步估计方法,建立多变量、多尺度水循环数据同化系统,可能是未来结合模式发展和微波遥感提高对水循环理解的新动向。

中国在陆地水循环与水资源科学研究、农业、水利、气象等行业对土壤水分的测量数据需求强烈,未来国家空间科学先导专项“全球水循环观测卫星”及民用空间基础设施“陆地水资源卫星的实施将有力地推动土壤水分微波遥感及相关领域的发展。

致谢:感谢中国科学院遥感与数字地球研究所崔倩、彭彬、李東洋、李云青、兰慧敏等人在本研究中的参与和贡献,衷心感谢施建成研究员的指导和建议。

The authors have declared that no competing interests exist.


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[J]. IEEE Geoscience and Remote Sensing Letters, 12(5): 923-927.

https://doi.org/10.1109/LGRS.2014.2364151      URL      [本文引用: 1]      摘要

Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology. Initial assessments were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011-June 2013 (rmse = 0.031 m3/m3, Bias = -0.007 m3/m3, and R = 0.855). This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive-active soil moisture algorithms. The products are routinely available through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.
[5] Bindlish R, Jackson T, Zhao T J.2011.

A MODIS-based vegetation index climatology

[C]//Proceedings of SPIE 8156, remote sensing and modeling of ecosystems for sustainability VIII. San Diego, CA: SPIE, 8156: 815603.

[本文引用: 1]     

[6] Chan S K, Bindlish R, O'Neill P E, et al.2016.

Assessment of the SMAP passive soil moisture product

[J]. IEEE Transactions on Geoscience and Remote Sensing, 54(8): 4994-5007.

https://doi.org/10.1109/TGRS.2016.2561938      URL      [本文引用: 1]      摘要

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m3/m3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.
[7] Chen K S, Wu T-D, Tsang L, et al.2003.

Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulations

[J]. IEEE Transactions on Geoscience and Remote Sensing, 41(1): 90-101.

https://doi.org/10.1109/TGRS.2002.807587      URL      [本文引用: 1]     

[8] Chen L, Shi J C, Wigneron J-P, et al.2010.

A parameterized surface emission model at L-band for soil moisture retrieval

[J]. IEEE Geoscience and Remote Sensing Letters, 7(1): 127-130.

https://doi.org/10.1109/LGRS.2009.2028443      URL      [本文引用: 1]      摘要

The effects of soil surface roughness play a significant role in the microwave emission from the surface. Therefore, a good parameterization of the effects is a prerequisite for retrieving surface soil moisture information. With recent physical model developments, the advanced integral equation model (AIEM) has been proven to provide accurate representation over a wide range of surface-roughness conditions. We evaluated the capability of the AIEM model in simulating multiangular surface emission signals in comparison with a field experiment data set. A simplified multiangular surface emission model was developed based on simulated database using the AIEM model. Based on the parameterized model, an inversion procedure was developed using dual-polarization microwave brightness temperatures to retrieve soil moisture. Two data sets were used to test the inversion algorithm, and the accuracies in root-mean-square error were about 4% for incidence angles from 20?? to 50??. This new simple model is suitable for soil moisture retrieval from future L-band satellite data.
[9] Chen Y Y, Yang K, Qin J, et al.2017.

Evaluation of SMAP, SMOS, and AMSR2 soil moisture retrievals against observations from two networks on the Tibetan Plateau

[J]. Journal of Geophysical Research: Atmospheres, 122(11): 5780-5792.

https://doi.org/10.1002/2016JD026388      URL      [本文引用: 1]      摘要

Two soil moisture and temperature monitoring networks were established in the Tibetan Plateau (TP) during recent years. One is located in a semihumid area (Naqu) of central TP and consists of 56 soil moisture and temperature measurement (SMTM) stations, the other is located in a semiarid area (Pali) of southern TP and consists of 21 SMTM stations. In this study, the station data are used to evaluate soil moisture retrievals from three microwave satellites, i.e., the Soil Moisture Active Passive (SMAP) of NASA, the Soil Moisture and Ocean Salinity (SMOS) of European Space Agency, and the Advanced Microwave Scanning Radiometer 2 (AMSR2) of Japan Aerospace Exploration Agency. It is found that the SMAP retrievals tend to underestimate soil moisture in the two TP networks, mainly due to the negative biases in the effective soil temperature that is derived from a climate model. However, the SMAP product well captures the amplitude and temporal variation of the soil moisture. The SMOS product performs well in Naqu network with acceptable error metrics but fails to capture the temporal variation of soil moisture in Pali network. The AMSR2 products evidently exaggerate the temporal variation of soil moisture in Naqu network but dampen it in Pali network, suggesting its retrieval algorithm needs further improvements for the TP.
[10] Choudhury B J, Schmugge T J, Chang A, et al.1979.

Effect of surface roughness on the microwave emission from soils

[J]. Journal of Geophysical Research: Oceans, 84(C9): 5699-5706.

https://doi.org/10.1029/JC084iC09p05699      URL      摘要

The effect of surface roughness on the brightness temperature of a moist terrain has been studied through the modification of Fresnel reflection coefficient and using the radiative transfer equation. The modification involves introduction of a single parameter to characterize the roughness. It is shown that this parameter depends on both the surface height variance and the horizontal scale of the roughness. Model calculations are in good quantitative agreement with the observed dependence of the brightness temperature on the moisture content in the surface layer. Data from truck mounted and airborne radiometers are presented for comparison. The results indicate that the roughness effects are great for wet soils where the difference between smooth and rough surfaces can be as great as 50K.
[11] Cosh M H, Jackson T J, Bindlish R, et al.2004.

Watershed scale temporal and spatial stability of soil moisture and its role in validating satellite estimates

[J]. Remote Sensing of Environment, 92(4): 427-435.

https://doi.org/10.1016/j.rse.2004.02.016      URL      [本文引用: 1]      摘要

Watershed scale soil moisture estimates are necessary to validate current remote sensing products, such as those from the Advanced Microwave Scanning Radiometer (AMSR). Unfortunately, remote sensing technology does not currently resolve the land surface at a scale that is easily observed with ground measurements. One approach to validation is to use existing soil moisture measurement networks and scale these point observations up to the resolution of remote sensing footprints. As part of the Soil Moisture Experiment 2002 (SMEX02), one such soil moisture gaging system in the Walnut Creek Watershed, Iowa, provided robust estimates of the soil moisture average for a watershed throughout the summer of 2002. Twelve in situ soil moisture probes were installed across the watershed. These probes recorded soil moisture at a depth of 5 cm from June 29, 2002 to August 19, 2002. The sampling sites were analyzed for temporal and spatial stability by several measures including mean relative difference, Spearman rank, and correlation coefficient analysis. Representative point measurements were used to estimate the watershed scale ( 25 km) soil moisture average and shown to be accurate indicators with low variance and bias of the watershed scale soil moisture distribution. This work establishes the validity of this approach to provide watershed scale soil moisture estimates in this study region for the purposes of satellite validation with estimation errors as small as 3%. Also, the potential sources of error in this type of analysis are explored. This study is a first step in the implementation of large-scale soil moisture validation using existing networks such as the Soil Climate Analysis Network (SCAN) and several Agricultural Research Service watersheds as a basis for calibrating satellite soil moisture products, for networks design, and designing field experiments.
[12] Cosh M H, Jackson T J, Starks P, et al.2006.

Temporal stability of surface soil moisture in the Little Washita River watershed and its applications in satellite soil moisture product validation

[J]. Journal of Hydrology, 323(1-4): 168-177.

https://doi.org/10.1016/j.jhydrol.2005.08.020      URL      [本文引用: 1]      摘要

The concept of temporal stability can be used to identify persistent soil moisture patterns and estimate the large scale average from select representative sensor locations. Accurate and efficient estimation of large-scale surface soil moisture is a primary component of soil moisture satellite validation programs. However, monitoring the soil surface at large grid scales is difficult. As part of the aqua satellite advanced microwave scanning radiometer (AMSR) Validation Program, a soil moisture sensor network was installed in the little Washita river watershed in Oklahoma, USA in 2002. Along with data from the soil moisture experiment 2003 (SMEX03), this network will provide a valuable dataset for satellite soil moisture product validation. Analysis shows that most of the network sensors are temporally stable at multiple scales and four sites are identified as representative with negligible bias and small standard deviation to the watershed mean. As part of this analysis, the protocols established for large-scale soil moisture sampling campaigns such as in the soil moisture experiments (SMEX) are validated. This analysis showed that basing grid scale estimates on six sampling points is reasonable and accurate. Temporal stability is shown to be a valuable tool for soil moisture network analysis and can provide an efficient means to large-scale satellite validation.
[13] Crow W T, Koster R D, Reichle R H, et al.2005.

Relevance of time-varying and time-invariant retrieval error sources on the utility of spaceborne soil moisture products

[J]. Geophysical Research Letters, 32(24): L24405.

https://doi.org/10.1029/2005GL024889      URL      [本文引用: 1]      摘要

Errors in remotely-sensed soil moisture retrievals originate from a combination of time-invariant and time-varying sources. For land modeling applications such as forecast initialization, some of the impact of time-invariant sources can be removed given known differences between observed and modeled soil moisture climatologies. Nevertheless, the distinction is seldom made when evaluating remotely-sensed soil moisture products. Here we describe an Observing System Simulation Experiment (OSSE) for radiometer-only soil moisture products derived from the NASA Hydrosphere States (Hydros) mission where the impact of time-invariant errors is explicitly removed via the linear rescaling of retrievals. OSSE results for the 575,000 kmRed-Arkansas River Basin indicate that climatological rescaling may significantly reduce the perceived magnitude of Hydros soil moisture retrieval errors and expands the geographic areas over which retrievals demonstrate value for land surface modeling applications.
[14] Cui Q, Shi J C, Du J Y, et al.2015.

An approach for monitoring global vegetation based on multiangular observations from SMOS

[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2): 604-616.

https://doi.org/10.1109/JSTARS.2015.2388698      URL      [本文引用: 1]      摘要

Vegetation monitoring is important for the study of the global carbon cycle and ecosystem. The soil moisture and ocean salinity (SMOS) mission that launched in 2009 is the first operational L-band passive microwave spaceborne sensor using synthetic aperture techniques; the sensor provides global L-band multiangular observations. In this study, based on the commonly used zero-order radiative transfer model ( - model), we developed an approach for retrieving vegetation optical depth (VOD) using only SMOS H-polarized multiangular measurements. This was done by minimizing the soil signal and separating the vegetation signal from the multiangular brightness temperature. The uniqueness of this approach is that the angular feature of soil emission is used and that the VOD is retrieved directly from the H-polarized multiangular brightness temperature without any field correction or auxiliary soil or vegetation data. This approach is first validated by theoretical modeling and experimental data. The results demonstrate that VOD can be reliably estimated using this approach. The retrieved VOD is then compared with aboveground biomass, which shows strong correlation. Global mean VOD for the years 2010-2011 generally shows a clear global pattern and corresponds well to the land cover types. The VOD of nine representative regions that are homogeneously covered with different vegetation types from 2010 to 2011 is compared with normalized difference vegetation index (NDVI). The results indicate that the VOD can generally reveal vegetation seasonal changes and can provide unique information for vegetation monitoring.
[15] Dall'Amico J T, Schlenz F, Loew A, et al.2012.

First results of SMOS soil moisture validation in the upper danube catchment

[J]. IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1507-1516.

https://doi.org/10.1109/TGRS.2011.2171496      URL      [本文引用: 1]      摘要

With the Soil Moisture and Ocean Salinity (SMOS) satellite launched in 2009, global measurements of L-band microwave emissions and processed “soil moisture” products at a fine time resolution are available. They may, after validation, lead to quantitative maps of global soil moisture dynamics. This paper presents a first validation of the SMOS “soil moisture” product delivered by the European Space Agency in the upper Danube catchment (southern Germany). Processing of the SMOS “soil moisture” product and the methodology to compare it with in situ and model data are described. The in situ data were taken from May to mid-July 2010 in a small and homogeneous area within the catchment, while the modeled time series spans from April to October 2010 for the whole catchment. The comparisons exhibit a dry bias of the SMOS data of about 0.2 m3·m-3 with respect to in situ measurements. Throughout the catchment, the SMOS data product shows a dry bias between 0.11 and 0.3 m3·m-3 when compared to modeled soil moisture. Correlation coefficients between both data were found to be mostly below 0.3. Radio-frequency interference (RFI) over Europe appears to be the main problem in obtaining valuable information from the SMOS soil moisture product over this region. RFI is not adequately captured by current methods for filtering and flagging. Nevertheless, some improvements of these results might be achievable through refinements of the soil moisture modeling as well as through improvements to the processors used to generate the SMOS soil moisture product.
[16] Entekhabi D, Njoku E G, O'Neill P E, et al.2010.

The soil moisture active passive (SMAP) mission

[J]. Proceedings of the IEEE, 98(5): 704-716.

https://doi.org/10.1109/JPROC.2010.2043918      URL      [本文引用: 2]     

[17] Escorihuela M J, Kerr Y H, De Rosnay P, et al.2007.

A simple model of the bare soil microwave emission at L-band

[J]. IEEE Transactions on Geoscience and Remote Sensing, 45(7): 1978-1987.

https://doi.org/10.1109/TGRS.2007.894935      URL     

[18] Fernandez-Moran R, Al-Yaari A, Mialon A, et al.2017.

SMOS-IC: An alternative SMOS soil moisture and vegetation optical depth product

[J]. Remote Sensing, 9(5): 457.

https://doi.org/10.3390/rs9050457      URL      [本文引用: 1]      摘要

The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (tau) based on multi-angular brightness temperature (TB) measurements at L-band. The operational SMOS Level 2 and Level 3 soil moisture algorithms account for different surface effects, such as vegetation opacity and soil roughness at 4 km resolution, in order to produce global retrievals of SM and t. In this study, we present an alternative SMOS product that was developed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d'Etudes Spatiales de la BIOsphere). One of the main goals of this SMOS-INRA-CESBIO (SMOS-IC) product is to be as independent as possible from auxiliary data. The SMOS-IC product provides daily SM and t at the global scale and differs from the operational SMOS Level 3 (SMOSL3) product in the treatment of retrievals over heterogeneous pixels. Specifically, SMOS-IC is much simpler and does not account for corrections associated with the antenna pattern and the complex SMOS viewing angle geometry. It considers pixels as homogeneous to avoid uncertainties and errors linked to inconsistent auxiliary datasets which are used to characterize the pixel heterogeneity in the SMOS L3 algorithm. SMOS-IC also differs from the current SMOSL3 product (Version 300, V300) in the values of the effective vegetation scattering albedo (omega) and soil roughness parameters. An inter-comparison is presented in this study based on the use of ECMWF (European Center for Medium range Weather Forecasting) SM outputs and NDVI (Normalized Difference Vegetation Index) from MODIS (Moderate-Resolution Imaging Spectroradiometer). A six-year (2010-2015) inter-comparison of the SMOS products SMOS-IC and SMOSL3 SM (V300) with ECMWF SM yielded higher correlations and lower ubRMSD (unbiased root mean square difference) for SMOS-IC over most of the pixels. In terms of tau, SMOS-IC t was found to be better correlated to MODIS NDVI in most regions of the globe, with the exception of the Amazonian basin and the northern mid-latitudes.
[19] Fernandez-Moran R, Wigneron J-P, De Lannoy G, et al.2016.

Calibrating the effective scattering albedo in the SMOS algorithm: Some first results

[C]//Proceedings of 2016 IEEE international geoscience and remote sensing symposium. Beijing, China: IEEE, 826-829.

[本文引用: 2]     

[20] Goodberlet M A, Mead J B.2014.

A model of surface roughness for use in passive remote sensing of bare soil moisture

[J]. IEEE Transactions on Geoscience and Remote Sensing, 52(9): 5498-5505.

https://doi.org/10.1109/TGRS.2013.2289979      URL      摘要

Spaceborne radiometers operating near 1.4 GHz are the primary instrument for recent efforts to remotely sense nearsurface soil moisture around the globe. Generally, these instruments must contend with the effects of vegetation growing in the soil. However, an important first step is to model the measurements made by a radiometer that is viewing bare (vegetation-free) soil. The proposed model uses a matching layer and a random depolarizer to describe bare soil surface roughness and some aspects of antenna beamwidth. The model suggests that the effects of nearsurface soil moisture and roughness upon the radiometer measurement are more distinct than is currently thought. Furthermore, it appears that both moisture and roughness can be retrieved from a single set of radiometer measurements made at orthogonal linear polarizations. This retrieval precision is predicted to be poor at soil observation angles near nadir but improves for larger angles. At observation angles near 50 , the vertically polarized radiometer measurements are predicted to be nearly insensitive to roughness. A convenient parameterization of the model is provided and permits quick implementation.
[21] Houser P R, Shuttleworth W J, Famiglietti J S, et al.1998.

Integration of soil moisture remote sensing and hydrologic modeling using data assimilation

[J]. Water Resources Research, 34(12): 3405-3420.

https://doi.org/10.1029/1998WR900001      URL      [本文引用: 1]      摘要

The feasibility of synthesizing distributed fields of soil moisture by the novel application of four-dimensional data assimilation (4DDA) applied in a hydrological model is explored. Six 160-km2 push broom microwave radiometer (PBMR) images gathered over the Walnut Gulch experimental watershed in southeast Arizona were assimilated into the Topmodel-based Land-Atmosphere Transfer Scheme (TOPLATS) using several alternative assimilation procedures. Modification of traditional assimilation methods was required to use these high-density PBMR observations. The images were found to contain horizontal correlations that imply length scales of several tens of kilometers, thus allowing information to be advected beyond the area of the image. Information on surface soil moisture also was assimilated into the subsurface using knowledge of the surface- subsurface correlation. Newtonian nudging assimilation procedures are preferable to other techniques because they nearly preserve the observed patterns within the sampled region but also yield plausible patterns in unmeasured regions and allow information to be advected in time.
[22] Imaoka K, Kachi M, Shibata A, et al.2007.

Five years of AMSR-E monitoring and successive GCOM-W1/AMSR2 instrument

[C]//Proceedings of SPIE 6744, sensors, systems, and next-generation satellites XI. Florence, Italy: SPIE, 6744: 67440J .

[本文引用: 1]     

[23] Jackson T J, Bindlish R, Cosh M H, et al.2012.

Validation of soil moisture and ocean salinity (SMOS) soil moisture over watershed networks in the U.S

[J]. IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1530-1543.

https://doi.org/10.1109/TGRS.2011.2168533      URL      [本文引用: 1]      摘要

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m3/m3 for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve.
[24] Jackson T J, Cosh M H, Bindlish R, et al.2010.

Validation of advanced microwave scanning radiometer soil moisture products

[J]. IEEE Transactions on Geoscience and Remote Sensing, 48(12): 4256-4272.

https://doi.org/10.1109/TGRS.2010.2051035      URL      [本文引用: 1]      摘要

Validation is an important and particularly challenging task for remote sensing of soil moisture. A key issue in the validation of soil moisture products is the disparity in spatial scales between satellite and in situ observations. Conventional measurements of soil moisture are made at a point, whereas satellite sensors provide an integrated area/volume value for a much larger spatial extent. In this paper, four soil moisture networks were developed and used as part of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) validation program. Each network is located in a different climatic region of the U.S., and provides estimates of the average soil moisture over highly instrumented experimental watersheds and surrounding areas that approximate the size of the AMSR-E footprint. Soil moisture measurements have been made at these validation sites on a continuous basis since 2002, which provided a seven-year period of record for this analysis. The National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) standard soil moisture products were compared to the network observations, along with two alternative soil moisture products developed using the single-channel algorithm (SCA) and the land parameter retrieval model (LPRM). The metric used for validation is the root-mean-square error (rmse) of the soil moisture estimate as compared to the in situ data. The mission requirement for accuracy defined by the space agencies is 0.06 m3/m3. The statistical results indicate that each algorithm performs differently at each site. Neither the NASA nor the JAXA standard products provide reliable estimates for all the conditions represented by the four watershed sites. The JAXA algorithm performs better than the NASA algorithm under light-vegetation conditions, but the NASA algorithm is more reliable for moderate vegetation. However, both algorithms have a moderate to large bias in all cases. The SCA had the lowest overall rmse with a small bias. The LPRM had a very large overestimation bias and retrieval errors. When site-specific corrections were applied, all algorithms had approximately the same error level and correlation. These results clearly show that there is much room for improvement in the algorithms currently in use by JAXA and NASA. They also illustrate the potential pitfalls in using the products without a careful evaluation.
[25] Jackson T J, Le Vine D M, Hsu A Y, et al.1999.

Soil moisture mapping at regional scales using microwave radiometry: The Southern Great Plains hydrology experiment

[J]. IEEE Transactions on Geoscience and Remote Sensing, 37(5): 2136-2151.

https://doi.org/10.1109/36.789610      URL      [本文引用: 1]      摘要

for a one month period. Results show that the soil moisture retrieval algorithm performed the same as in previous investigations, demonstrating consistency of both the retrieval and the instrument. Error levels were on the order of 3% for area Integrated averages of sites used for validation. This result showed that for the coarser resolution used that the theory and techniques employed in the algorithm apply at this scale. Spatial patterns observed in the Little Washita Watershed in previous investigations were also observed. These results showed that soil texture dominated the spatial pattern at this scale. However, the regional soil moisture patterns were a reflection of the spatially variable rainfall and soil texture patterns were not as obvious
[26] Jackson T J, Schmugge T J.1991.

Vegetation effects on the microwave emission of soils

[J]. Remote Sensing of Environment, 36(3): 203-212.

https://doi.org/10.1016/0034-4257(91)90057-D      URL      [本文引用: 1]     

[27] Kerr Y H, Waldteufel P, Richaume P, et al.2012.

The SMOS Soil Moisture Retrieval Algorithm

[J]. IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1384-1403.

https://doi.org/10.1109/TGRS.2012.2184548      URL      [本文引用: 3]      摘要

The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS algorithm theoretical basis documents to be used to produce the operational algorithm. The principle of the SM retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5 , flags and quality indices, and other parameters of interest. This paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises.
[28] Kerr Y H, Waldteufel P, Wigneron J-P, et al.2010.

The SMOS mission: New tool for monitoring key elements of the global water cycle

[J]. Proceedings of the IEEE, 98(5): 666-687.

https://doi.org/10.1109/JPROC.2010.2043032      URL      [本文引用: 1]      摘要

It is now well understood that data on soil moisture and sea surface salinity (SSS) are required to improve meteorological and climate predictions. These two quantities are not yet available globally or with adequate temporal or spatial sampling. It is recognized that a spaceborne L-band radiometer with a suitable antenna is the most promising way of fulfilling this gap. With these scientific objectives and technical solution at the heart of a proposed mission concept the European Space Agency (ESA) selected the Soil Moisture and Ocean Salinity (SMOS) mission as its second Earth Explorer Opportunity Mission. The development of the SMOS mission was led by ESA in collaboration with the Centre National d'Etudes Spatiales (CNES) in France and the Centro para el Desarrollo Tecnologico Industrial (CDTI) in Spain. SMOS carries a single payload, an L-Band 2-D interferometric radiometer operating in the 1400-1427-MHz protected band . The instrument receives the radiation emitted from Earth's surface, which can then be related to the moisture content in the first few centimeters of soil over land, and to salinity in the surface waters of the oceans. SMOS will achieve an unprecedented maximum spatial resolution of 50 km at L-band over land (43 km on average over the field of view), providing multiangular dual polarized (or fully polarized) brightness temperatures over the globe. SMOS has a revisit time of less than 3 days so as to retrieve soil moisture and ocean salinity data, meeting the mission's science objectives. The caveat in relation to its sampling requirements is that SMOS will have a somewhat reduced sensitivity when compared to conventional radiometers. The SMOS satellite was launched successfully on November 2, 2009.
[29] Konings A G, Piles M, Das N, et al.2017.

L-band vegetation optical depth and effective scattering albedo estimation from SMAP

[J]. Remote Sensing of Environment, 198(1): 460-470.

https://doi.org/10.1016/j.rse.2017.06.037      URL      [本文引用: 2]      摘要

Globally, albedo values tend to be high over regions with heterogeneous land cover types. The estimated effective scattering albedo values are generally higher than those used in previous soil moisture estimation algorithms and linked to biome classifications. MT-DCA retrievals of soil moisture show only small random differences with soil moisture retrievals from the Baseline SMAP algorithm, which uses a prior estimate of VOD based on land cover and optical data. However, significant biases exist between the two datasets. The soil moisture biases follow the pattern of differences between the MT-DCA retrieved and Baseline-assigned VOD values.
[30] Konings A G, Piles M, Rötzer K, et al.2016.

Vegetation optical depth and scattering albedo retrieval using time series of dual-polarized L-band radiometer observations

[J]. Remote Sensing of Environment, 172: 178-189.

https://doi.org/10.1016/j.rse.2015.11.009      URL      [本文引用: 2]      摘要

Passive microwave measurements have the potential to estimate vegetation optical depth (VOD), an indicator of aboveground vegetation water content. They are also sensitive to the vegetation scattering albedo and soil moisture. In this work, we propose a novel algorithm to retrieve VOD and soil moisture from time series of dual-polarized L-band radiometric observations along with time-invariant scattering albedo. The method takes advantage of the relatively slow temporal dynamics of early morning vegetation water content and combines a number of consecutive observations to estimate a single VOD. It is termed the multi-temporal dual channel algorithm (MT-DCA). The soil dielectric constant (directly related to soil moisture) of each observation is also retrieved simultaneously. Additionally, the method retrieves a constant albedo, thereby providing for the first time information on global single-scattering albedo variations. The algorithm is tested using three years of L-band passive observations from the NASA Aquarius sensor. The global VOD distribution follows expected gradients of climate and canopy biomass conditions. Its seasonal dynamics follow expected behavior based on precipitation and land cover. The retrieved VOD is closely related to coincident cross-polarized backscatter coefficients. The VOD and dielectric retrievals from MT-DCA are compared to those obtained from implementing the commonly used Land Parameter Retrieval Model (LPRM) algorithm and shown to have less high-frequency noise. There is almost as much variation in MT-DCA retrieved albedo between pixels of a given land cover class than between land cover classes, suggesting the common approach of assigning albedo based on land cover class may not capture its spatial variability. Globally, albedo appears to be primarily sensitive to woody biomass. The proposed algorithm allows for a more accurate accounting of the effects of vegetation on radiometric soil moisture retrievals, and generates new observations of L-band VOD and effective single-scattering albedo. These new datasets are complementary to existing remotely sensed vegetation measurements such as fluorescence and optical-infrared indices.
[31] Kurum M.2013.

Quantifying scattering albedo in microwave emission of vegetated terrain

[J]. Remote Sensing of Environment, 129: 66-74.

https://doi.org/10.1016/j.rse.2012.10.021      URL      [本文引用: 1]      摘要

This study provides a theoretical/physical framework to quantify the vegetation scattering effects on radiometric microwave measurements of soil moisture. The model development and analysis is presented to assess the limitations of the existing τ 026102 ω (tau-omega) model with respect to vegetated landscapes and thus to extend the usefulness of the τ 026102 ω model to a wider range of vegetation conditions. An explicit expression is driven for an effective albedo of vegetated terrain from the zero- and multiple-order radiative transfer solutions. The formulation establishes a direct physical link between the effective vegetation parameterization and the theoretical description of absorption and scattering within the canopy. Evaluation of the derived albedo for corn canopies (stem-dominated vegetation) with data taken during the Huntsville 1998 field experiment (Hsv98) are shown and discussed. The simulation results are in good agreement with the data and show that the effective albedo values are significantly smaller than the single-scattering albedo values and increase monotonically as soil moisture increases. The model is also used to simulate effective albedo from a soybean canopy (leaf dominated vegetation) at L-band. Both results illustrate that the fitted albedo values, which are found in the literature, represent effective albedo values rather than the single-scattering albedo values.
[32] Kurum M, Lang R H, O'Neill P E, et al.2011.

A first-order radiative transfer model for microwave radiometry of forest canopies at L-band

[J]. IEEE Transactions on Geoscience and Remote Sensing, 49(9): 3167-3179.

https://doi.org/10.1109/TGRS.2010.2091139      URL      [本文引用: 1]      摘要

In this study, a first-order radiative transfer (RT) model is developed to more accurately account for vegetation canopy scattering by modifying the basic τ-ω model (the zero-order RT solution). In order to optimally utilize microwave radiometric data in soil moisture (SM) retrievals over vegetated landscapes, a quantitative understanding of the relationship between scattering mechanisms within vegetation canopies and the microwave brightness temperature is desirable. The first-order RT model is used to investigate this relationship and to perform a physical analysis of the scattered and emitted radiation from vegetated terrain. This model is based on an iterative solution (successive orders of scattering) of the RT equations up to the first order. This formulation adds a new scattering term to the τ-ω model. The additional term represents emission by particles (vegetation components) in the vegetation layer and emission by the ground that is scattered once by particles in the layer. The model is tested against 1.4-GHz brightness temperature measurements acquired over deciduous trees by a truck-mounted microwave instrument system called ComRAD in 2007. The model predictions are in good agreement with the data, and they give quantitative understanding for the influence of first-order scattering within the canopy on the brightness temperature. The model results show that the scattering term is significant for trees and modifications are necessary to the τ-ω model when applied to dense vegetation. Numerical simulations also indicate that the scattering term has a negligible dependence on SM and is mainly a function of the incidence angle and polarization of the microwave observation.
[33] Lawrence H, Wigneron J-P, Demontoux F, et al.2013.

Evaluating the semiempirical H-Q model used to calculate the L-band emissivity of a rough bare soil

[J]. IEEE Transactions on Geoscience and Remote Sensing, 51(7): 4075-4084.

https://doi.org/10.1109/TGRS.2012.2226995      URL      [本文引用: 2]      摘要

In this paper, a numerical modeling approach was used to evaluate the semiempirical H-Q model used in the Soil Moisture and Ocean Salinity (SMOS) retrieval algorithm to account for roughness effects over bare soil. The H-Q model uses four parameters, HR, QR, NRH , and NRV, which are usually calibrated at the ground scale for different surface types. The aim of this paper is to investigate whether these empirical parameters could be linked to the physical roughness parameters of standard deviation of surface heights and autocorrelation length Lc. First, a numerical modeling approach was used to calculate rough soil emissivities for different roughness and soil moisture conditions. Second, H -Q model parameters were retrieved by minimizing a cost function between these emissivities and those calculated by the H -Q model. It was found that the retrieved HR could be related directly to Zs = 蟽2/Lc and that QR, NRV, and NRH were dependent on HR. HR was found to have a negligible dependence on soil moisture. Based on these results, a new model was proposed where the four H-Q model parameters were calibrated to Zs. This model was tested on the PORTOS 1993 data set and found to yield a root-mean-square difference between the retrieved and measured soil moisture values of ~ 0.03 m3/m3, which was within the desired 0.04-m3/m3 error margin for the SMOS mission.
[34] Le Vine D M, Lagerloef G S E, Colomb F R, et al.2007.

Aquarius: An instrument to monitor sea surface salinity from space

[J]. IEEE Transactions on Geoscience and Remote Sensing, 45(7): 2040-2050.

https://doi.org/10.1109/TGRS.2007.898092      URL      [本文引用: 1]      摘要

Aquarius is a combined passive/active L-band microwave instrument that is being developed to map the salinity field at the surface of the ocean from space. The data will support studies of the coupling between ocean circulation, global water cycle, and climate. Aquarius is part of the Aquarius/Satelite de Aplicaciones Cientiflcas-D mission, which is a partnership between the U.S. (National Aeronautics and Space Administration) and Argentina (Comision Nacional de Actividades Espaciales). The primary science objective of this mission is to monitor the seasonal and interannual variation of the large-scale features of the surface salinity field in the open ocean with a spatial resolution of 150 km and a retrieval accuracy of 0.2 psu globally on a monthly basis.
[35] Li D Y, Zhao T J, Shi J C, et al.2015.

First evaluation of Aquarius soil moisture products using in situ observations and GLDAS model simulations

[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(12): 5511-5525.

https://doi.org/10.1109/JSTARS.2015.2452955      URL      [本文引用: 1]      摘要

L-band satellite remote sensing is one of the most promising techniques for global monitoring of soil moisture (SM). In addition to soil moisture and ocean salinity (SMOS) SM products, another global SM product has been developed using Aquarius, which is the first operational active/passive L-band satellite sensor. The spatial resolution of Aquarius SM products is about 100 km, which presents more challenges to the groundbased validation. This study explores approaches to validate and evaluate the Aquarius SM products in terms of their spatial and temporal distributions, through synergistic use of in situ measurements and model products from the global land data assimilation system (GLDAS). A dense soil moisture/temperature monitoring network over the central Tibetan plateau (CTP-SMTMN) and sparse stations from the soil climate analysis network (SCAN) over United States are used for the reliability assessment of Aquarius SM products. Results show that the Aquarius SM captures the spatial-temporal variability of CTP-SMTMN reference dataset with an overall RMSD of 0.078 m3 m-3 and correlation coefficient of 0.767. The comparison results with reference to SCAN datasets suggest that the RMSD can reach to the target value of 0.04 m3 m-3 over specific stations, but the impacts from different orbits, seasons, and land cover types are also found to be significant. The comparison between Aquarius retrievals and GLDAS/common land model (CLM) simulations presents a general well statistical agreement with correlation coefficients above 0.5 for most terrestrial areas. These results are considered to support the use of Aquarius SM products in future applications.
[36] Li L, Gaiser P W, Gao B C, et al.2010.

WindSat global soil moisture retrieval and validation

[J]. IEEE Transactions on Geoscience and Remote Sensing, 48(5): 2224-2241.

https://doi.org/10.1109/TGRS.2009.2037749      URL      [本文引用: 1]      摘要

A physically based six-channel land algorithm is developed to simultaneously retrieve global soil moisture (SM), vegetation water content (VWC), and land surface temperature. The algorithm is based on maximum-likelihood estimation and uses dual-polarization WindSat passive microwave data at 10, 18.7, and 37 GHz. The global retrievals are validated at multispatial and multitemporal scales against SM climatologies,network data, precipitation patterns, and Advanced Very High Resolution Radiometer (AVHRR) vegetation data.SM observations from the U.S., France, and Mongolia for diverse land/vegetation cover were used to validate the results. The performance of the estimated volumetric SM was within the requirements for most science and operational applications (standard error of 0.04 m/m, bias of 0.004 m/m, and correlation coefficient of 0.89). The retrieved SM and VWC distributions are very consistent with global climatology and mesoscale precipitation patterns. The comparisons between the WindSat vegetation retrievals and the AVHRR Green Vegetation Fraction data also reveal the consistency of these two independent data sets in terms of spatial and temporal variations.
[37] Li L, Njoku E G, Im E, et al.2004.

A preliminary survey of radio-frequency interference over the U.S. in Aqua AMSR-E data

[J]. IEEE Transactions on Geoscience and Remote Sensing, 42(2): 380-390.

https://doi.org/10.1109/TGRS.2003.817195      URL      [本文引用: 1]      摘要

ABSTRACT A spectral difference method is used to quantify the magnitude and extent of radio-frequency interference (RFI) observed over the United States in the Aqua AMSR-E radiometer channels. A survey using data from the AMSR-E instrument launched in May 2002 shows the interference to be widespread in the C-band (6.9 GHz) channels. The RFI is located mostly, but not always, near large highly populated urban areas. The locations of interference are persistent in time, but the magnitudes show temporal and directional variability. Strong and moderate RFI can be identified relatively easily using an RFI index derived from the spectral difference between the 6.9- and 10.7-GHz channels. Weak RFI is difficult to distinguish, however, from natural geophysical variability. These findings have implications for future microwave sensing at C-band, particularly over land areas. An innovative concept for radiometer system design is also discussed as a possible mitigation approach.
[38] Mialon A, Wigneron J-P, De Rosnay P, et al.2012.

Evaluating the L-MEB model from long-term microwave measurements over a rough field, SMOSREX 2006

[J]. IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1458-1467.

https://doi.org/10.1109/TGRS.2011.2178421      URL      [本文引用: 1]      摘要

The present paper analyzes the effects of roughness on the surface emission at L-band based on observations acquired during a long-term experiment. At the Surface Monitoring of the Soil Reservoir Experiment site near Toulouse, France, a bare soil was plowed and monitored over more than a year by means of an L-band radiometer, profile soil moisture and temperature sensors, and a local weather station, accompanied by 12 roughness campaigns. The aims of this paper are the following: 1) to present this unique database and 2) to use this data set to investigate the semiempirical parameters for the roughness in L-band Microwave Emission of the Biosphere, which is the forward model used in the Soil Moisture and Ocean Salinity soil moisture retrieval algorithm. In particular, we studied the link between these semiempirical parameters and the soil roughness characteristics expressed in terms of standard deviation of surface height ( ) and the correlation length (LC). The data set verifies that roughness effects decrease the sensitivity of surface emission to soil moisture, an effect which is most pronounced at high incidence angles and soil moisture and at horizontal polarization. Contradictory to previous studies, the semiempirical parameter Qr was not found to be equal to 0 for rough conditions. A linear relationship between the semiempirical parameters N and was established, while NH and NV appeared to be lower for a rough (NH ~ 0.59 and NV ~ -0.3) than for a quasi-smooth surface. This paper reveals the complexity of roughness effects and demonstrates the great value of a sound long-term data set of rough L-band surface emissions to improve our understanding on the matter.
[39] Mo T, Choudhury B J, Schmugge T J, et al.1982.

A model for microwave emission from vegetation-covered fields

[J]. Journal of Geophysical Research: Oceans, 87(C13): 11229-11237.

https://doi.org/10.1029/JC087iC13p11229      URL      [本文引用: 1]      摘要

A radiative transfer model for simulating the measured brightness temperatures over vegetation-covered fields is studied. The model treats the vegetation as a uniform layer, or canopy, with a constant temperature over a moist soil which emits polarized microwave radiation. The equation of radiative transfer is solved analytically subject to boundary conditions at the soil surface and canopy top. Scattering by the vegetation is primarily in the forward direction and given by a single scattering albedo 0309*. The effect of soil surface roughness is introduced by modifying the smooth surface reflectivity with a roughness height parameter h and polarization mixing factor Q. The analytic formula for the microwave emission has four parameters, h, Q, 03040* (effective canopy optical thickness), and 0309*. The model provides a good representation of the observed angular variations for both the horizontally and vertically polarized brightness temperatures at 1.4 GHz and 5 GHz frequencies over fields covered with grass, soybean, and corn. The effective canopy optical thickness is found to be directly proportional to the amount of water present in the vegetation canopy, while the effective canopy single scattering albedo depends on the type of vegetation.
[40] Njoku E G, Ashcroft P, Chan T K, et al.2005.

Global survey and statistics of radio-frequency interference in AMSR-E land observations

[J]. IEEE Transactions on Geoscience and Remote Sensing, 43(5): 938-947.

https://doi.org/10.1109/TGRS.2004.837507      URL      [本文引用: 1]      摘要

Radio-frequency interference (RFI) is an increasingly serious problem for passive and active microwave sensing of the Earth. To satisfy their measurement objectives, many spaceborne passive sensors must operate in unprotected bands, and future sensors may also need to operate in unprotected bands. Data from these sensors are likely to be increasingly contaminated by RFI as the spectrum becomes more crowded. In a previous paper we reported on a preliminary investigation of RFI observed over the United States in the 6.9-GHz channels of the Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System Aqua satellite. Here, we extend the analysis to an investigation of RFI in the 6.9- and 10.7-GHz AMSR-E channels over the global land domain and for a one-year observation period. The spatial and temporal characteristics of the RFI are examined by the use of spectral indices. The observed RFI at 6.9 GHz is most densely concentrated in the United States, Japan, and the Middle East, and is sparser in Europe, while at 10.7 GHz the RFI is concentrated mostly in England, Italy, and Japan. Classification of RFI using means and standard deviations of the spectral indices is effective in identifying strong RFI. In many cases, however, it is difficult, using these indices, to distinguish weak RFI from natural geophysical variability. Geophysical retrievals using RFI-filtered data may therefore contain residual errors due to weak RFI. More robust radiometer designs and continued efforts to protect spectrum allocations will be needed in future to ensure the viability of spaceborne passive microwave sensing.
[41] Njoku E G, Chan S K.2006.

Vegetation and surface roughness effects on AMSR-E land observations

[J]. Remote Sensing of Environment, 100(2): 190-199.

https://doi.org/10.1016/j.rse.2005.10.017      URL      [本文引用: 1]      摘要

Characteristics of the land surface including soil moisture, vegetation cover, and soil roughness among others influence the microwave emissivity and brightness temperature of the surface as observed from space. Knowledge of the variability of microwave signatures of vegetation and soil roughness is necessary to separate these influences from those of soil moisture for remote sensing applications to global hydrology and climate. We describe here a characterization of vegetation and soil roughness at the frequencies and spatial resolution of the EOS Aqua Advanced Microwave Scanning Radiometer (AMSR-E). A single parameter has been used to approximate the combined effects of vegetation and roughness. AMSR-E data have been analyzed to determine the frequency dependence of this parameter and to generate a global vegetation/roughness map and an estimate of seasonal variability. A physical model is used for the analysis with approximations appropriate to the AMSR-E footprint scale and coefficients calibrated empirically against the AMSR-E data. The spatial variabilities of roughness and vegetation cannot be estimated independently using this approach, but their temporal dynamics allow separation of predominantly static roughness effects from time-varying vegetation effects using multitemporal analysis. Global signals of time-varying vegetation water content derived from this analysis of AMSR-E data are consistent with time-varying biomass estimates obtained by optical/infrared remote sensing techniques.
[42] Njoku E G, Jackson T J, Lakshmi V, et al.2003.

Soil moisture retrieval from AMSR-E

[J]. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 215-229.

https://doi.org/10.1109/TGRS.2002.808243      URL      [本文引用: 1]      摘要

The Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System (EOS) Aqua satellite was launched on May 4, 2002. The AMSR-E instrument provides a potentially improved soil moisture sensing capability over previous spaceborne radiometers such as the Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager due to its combination of low frequency and higher spatial resolution (approximately 60 km at 6.9 GHz). The AMSR-E soil moisture retrieval approach and its implementation are described in this paper. A postlaunch validation program is in progress that will provide evaluations of the retrieved soil moisture and enable improved hydrologic applications of the data. Key aspects of the validation program include assessments of the effects on retrieved soil moisture of variability in vegetation water content, surface temperature, and spatial heterogeneity. Examples of AMSR-E brightness temperature observations over land are shown from the first few months of instrument operation, indicating general features of global vegetation and soil moisture variability. The AMSR-E sensor calibration and extent of radio frequency interference are currently being assessed, to be followed by quantitative assessments of the soil moisture retrievals.
[43] O'Neill P, Chan S, Njoku E, et al.2016.

Algorithm theoretical basis document level 2 & 3 soil moisture (passive) data products

[R]. JPL D-66480. Pasadena, CA: Jet Propulsion Laboratory.

[本文引用: 1]     

[44] Pan M, Sahoo A K, Wood E F.2014.

Improving soil moisture retrievals from a physically-based radiative transfer model

[J]. Remote Sensing of Environment, 140: 130-140.

https://doi.org/10.1016/j.rse.2013.08.020      URL      [本文引用: 1]      摘要

Near surface soil moisture is being estimated from space-borne passive microwave observations through inverting a physically-based radiative transfer model (RTM), the land surface microwave emission model (LSMEM) at Princeton University for the past several years. The existing retrieval scheme utilizes only the horizontal ( H ) polarization measurement from a single channel (10.65 GHz). This physically-based approach requires a relatively large number of parameters, and it generally suffers from large biases/errors due to the difficulty in determining the correct parameters. This study characterizes these errors in order to improve the retrieval performance. Through model sensitivity analysis, this study finds that a dual polarization approach (using both horizontal and vertical polarizations) is needed to infer the correct vegetation opacity and correct polarization mixing measured by the space-borne sensor. Revisions are then made to the LSMEM formulations and soil moisture retrieval algorithm by 1) combining two vegetation parameters and one roughness parameter into one effective vegetation optical depth (VOD) value; and 2) providing an additional model equation that estimates the effective VOD from both polarizations and an initial guess of soil moisture value. The new retrieval algorithm is implemented to produce a daily 0.25 gridded soil moisture dataset based on observations from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E). Validations are performed globally against land surface model simulations and at local/point scale against in-situ data within the continental United States. The new retrievals are shown to have good and robust performance over most parts of the world in terms of reproducing the spatial and temporal dynamics of soil moisture.
[45] Parrens M, Al Bitar A, Mialon A, et al.2017.

Estimation of the L-band effective scattering albedo of tropical forests using SMOS observations

[J]. IEEE Geoscience and Remote Sensing Letters, 14(8): 1223-1227.

https://doi.org/10.1109/LGRS.2017.2703637      URL      [本文引用: 1]      摘要

This letter aims to estimate the effective scattering albedo (ωp) over the tropical forests using L-band (1.4 GHz) microwave remote sensing. It is carried out using Soil Moisture and Ocean Salinity (SMOS) mission data over five years (2011-2015). We find similar values of ω p computed over the Congo and Amazon forests. The ωp values depend slightly on the polarization. The values of ωp at H-polarization and at 52° ± 5° (40° ± 5°) of incidence angle are within the range 0.064-0.069 ± 0.015 (0.061 - 0.067 ± 0.012). At V-polarization, the values of ωp are slightly lower (0.060-0.061 ± 0.013 at 52° ± 5° of incidence angle and 0.052 - 0.055 ± 0.013 at 40° ± 5° of incidence angle). These findings should contribute to a better calibration of the value of ωp over the tropical forests in both the SMOS and SM active and passive retrieval algorithms, leading to increase the SM retrieval accuracy over heterogeneous pixels.
[46] Parrens M, Wigneron J-P, Richaume P, et al.2016.

Global-scale surface roughness effects at L-band as estimated from SMOS observations

[J]. Remote Sensing of Environment, 181: 122-136.

https://doi.org/10.1016/j.rse.2016.04.006      URL      [本文引用: 1]      摘要

The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite dedicated to providing global surface soil moisture products. SMOS operates at L-band (1.402GHz) and, at this frequency, the signal not only depends on soil moisture and vegetation optical depth but is also significantly affected by surface effects and, in particular, by the soil roughness. However, when dense vegetation is present, the L-band signal is poorly sensitive to the surface effects. First, by using multiple regressions between soil moisture (SM) and brightness temperature (TB) at different incidence angles and polarizations, the SMOS sensitivity to the surface effects was evaluated. A global-scale map of SMOS sensitivity to the surface effects was computed and showed that, for 87% of the land surface, the SMOS observations were sensitive to these effects, while very low sensitivity to the surface effects was estimated over 13% of the land surfaces. For instance, over broadleaf evergreen forest (mainly the Amazon and Congo forests), SMOS was sensitive to the surface effects over only half of the pixels considered. In a second step, in L-MEB (L-band Microwave Emission of the Biosphere), the forward emission model of the SMOS algorithm, the vegetation and roughness effects were combined in a single parameter, referred to as TR in this study. By inverting L-MEB, SM and TR were retrieved at global scale from the SMOS Level 3 (L3) TB observations during 2011. Assuming a linear relationship between TR and the Leaf Area Index (LAI) obtained from MODIS data, the effects of roughness ( H r ) and vegetation were decoupled and a global map of soil roughness effects was estimated. It was found that the spatial pattern of the H r values could be related to the main vegetation types. Higher values of roughness ( H r 02=020.32–0.39) were obtained for forests (broadleaf evergreen, deciduous and mixed coniferous) while lower values ( H r 02=020.14–0.16) were obtained for deserts, shrubs and bare soils. Intermediate values ( H r 02=020.20–0. 23) were obtained over grasslands, tundra and cultivated land. Over vegetation biomes composed of forests and wooded grasslands, the H r values were mainly correlated to the vegetation density ( r 02~020.55). For deserts, shrubs and bare soils, the H r values were mainly correlated to the topography slopes ( r 02~020.53). The global maps presented in this study could lead to improved retrievals of soil moisture and vegetation optical depth for present and future microwave remote sensing missions such as SMOS and Soil Moisture Active Passive (SMAP).
[47] Peng B, Zhao T J, Shi J C, et al.2017.

Reappraisal of the roughness effect parameterization schemes for L-band radiometry over bare soil

[J]. Remote Sensing of Environment, 199: 63-77.

https://doi.org/10.1016/j.rse.2017.07.006      URL      [本文引用: 4]      摘要

Roughness effect parameterization is critical to accurately simulate brightness temperature (Tb) signals observed by a radiometer over bare soil surface. However, current roughness parameterization schemes usually suffer from severe error, which dominates the error budget in current Tb modeling over bare soil surface. In this study, uncertainty of soil roughness parameterization schemes is comprehensively assessed using data set collected during 2004 to 2006 at the Surface Monitoring Of the Soil Reservoir Experiment (SMOSREX) bare soil experimental site. To reduce uncertainty from sampling depth mismatch, the soil moisture profile with a 102cm thickness from a calibrated Hydus-1D (H1D) model is utilized to determine the optimal soil moisture inputs to soil emission model. Uncertainties of 15 literature-based roughness effect parameterization schemes developed for L-band Tb modeling are inter-compared. The “ Q / H ” model is further calibrated against multi-angle and dual-polarization Tb observations at the SMOSREX bare soil site under different roughness conditions. Our results show that: (1) soil moisture sampling depth varies with soil moisture content and roughness condition. When soil is drier and rougher, the soil moisture sampling depth gets deeper. (2) The 15 roughness schemes generally perform better at vertical polarization than at horizontal polarization and better when soil surface is relative smooth than when soil surface gets rougher. The 15 roughness correction schemes have their own advantages and disadvantages with diverse error and bias characteristics. None of them has a superior performance at all conditions in terms of roughness, polarizations and incident angles. (3) A non-zero Q configuration is preferred in parameter retrieval experiments and the observed linear relationship between Δ N and root-mean-square height ( σ ) or σ 2 / L C can only be reproduced when Q is non-zero in parameter retrieval. (4) The effective roughness parameters ( Q , N p and h ) generally increase when soil get rougher. The calibrated Q , N h and N v show exponential dependence on the effective parameter h . The calibrated h still shows dependence on surface soil moisture after accounting the impact from soil sampling depth and also shows strong power-law dependence on Tb at incident angle of 40°. The non-zero- Q fitting models have comparable performance in Tb modeling with zero- Q models but may be more physically realistic.
[48] Qin J, Yang K, Lu N, et al.2013.

Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia

[J]. Remote Sensing of Environment, 138: 1-9.

https://doi.org/10.1016/j.rse.2013.07.003      URL      [本文引用: 1]      摘要

61A soil moisture network is established in the central Tibetan Plateau.61An algorithm is designed to upscale in-situ moisture measurements.61The evaluation of the upscaling algorithm is performed.61The results show the upscaling method works well.61The area-averaged moisture over a large area can be obtained by the algorithm.
[49] Saatchi S S, Harris M L, Brown S, et al.2011.

Benchmark map of forest carbon stocks in tropical regions across three continents

[J]. Proceedings of the National Academy of Sciences of the United States of America, 108(24): 9899-9904.

https://doi.org/10.1073/pnas.1019576108      URL      [本文引用: 1]     

[50] Sánchez N, Martínez-Fernández J, González-Piqueras J, et al.2012.

Water balance at plot scale for soil moisture estimation using vegetation parameters

[J]. Agricultural and Forest Meteorology, 166-167: 1-9.

https://doi.org/10.1016/j.agrformet.2012.07.005      URL      [本文引用: 1]      摘要

The experiment presented and discussed in this paper attempts to understand the relationships between vegetation cover measurements and soil moisture estimations, to improve soil moisture calculations based on the FAO56 water balance, also known as the K c - ET 0 (crop coefficient-reference evapotranspiration) approach. The experiment provided a detailed dataset of in situ measurements, such as soil moisture, vegetation parameters, and spectral and meteorological measurements. The experiment was performed in two 4002m 2 plots of barley and grass. The study primarily addressed the relationships among vegetation canopy parameters. The results showed that the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Vegetation Water Content (VWC), and Fraction of Vegetation Cover (FVC) were strongly correlated. Based on these relationships, different approaches for calculating the basal crop coefficient ( K cb ) were tested, and their influences on the water balance calculation were evaluated. The water content of the soil layer ( θ ) was calculated in the water balance as a residual of the daily calculation. Actual Evapotranspiration (AET) was also estimated. The results were validated by comparing the field soil moisture values against the soil moisture calculated through FAO56. The results of this comparison were satisfactory for barley (determination coefficient, R 2 02=020.73 and root mean square error, RMSE02=020.08402cm 3 02cm 613 ) and grass ( R 2 02=020.77, RMSE02=020.05802cm 3 02cm 613 ). After testing other formulations of the K cb using the LAI and FVC, the results showed that an indirect estimation of the K cb based on a growing parameter, such as the LAI, NDVI or FVC, was adequate for estimating the soil moisture through FAO56. The different approaches used to estimate the K cb had little impact on the estimate of AET (the differences were smaller than 302mm in the total annual AET for both crops). However, the root depth and the upper limit of potential water content (field capacity) were crucial in the soil moisture and AET estimations. If the field capacity of soils, determined in the laboratory, was forced to match the upper limit of the observed soil moisture in the field, a much better estimate was obtained ( R 2 02=020.92 and RMSE02=020.03502cm 3 02cm 613 for barley and R 2 02=020.91 and 0.02302cm 3 02cm 613 for grass); otherwise, the AET increased by 50%.
[51] Schlenz F, Dall'Amico J T, Mauser W, et al.2012.

Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

[J]. Hydrology and Earth System Sciences Discussions, 16(10): 3517-3533.

https://doi.org/10.5194/hessd-9-5389-2012      URL      [本文引用: 1]      摘要

Soil Moisture and Ocean Salinity (SMOS) L1c brightness temperature and L2 optical depth data are analysed with a coupled land surface (PROMET) and radiative transfer model (L-MEB) that are used as tool for the analysis and validation of passive microwave satellite observations. The coupled models are validated with ground and airborne measurements under contrasting soil moisture, vegetation and temperature conditions during the SMOS Validation Campaign in May and June 2010 in the SMOS test site Upper Danube Catchment in Southern Germany with good results. The brightness temperature root-mean-squared errors are between 6 K and 9 K and can partly be attributed to a known bias in the airborne L-band measurements. The L-MEB parameterization is considered appropriate under local conditions even though it might possibly further be optimised. SMOS L1c brightness temperature data are processed and analysed in the Upper Danube Catchment using the coupled models in 2011 and during the SMOS Validation Campaign 2010 together with airborne L-band brightness temperature data. Only low to fair correlations are found for this comparison (lt;igt;Rlt;/igt; lt; 0.5). SMOS L1c brightness temperature data do not show the expected seasonal behaviour and are positively biased. It is concluded that RFI is responsible for most of the observed problems in the SMOS data products in the Upper Danube Catchment. This is consistent with the observed dry bias in the SMOS L2 soil moisture products which can also be related to RFI. It is confirmed that the brightness temperature data from the lower SMOS look angles are less reliable. This information could be used to improve the brightness temperature data filtering before the soil moisture retrieval. SMOS L2 optical depth values have been compared to modelled data and are not considered a reliable source of information about vegetation due to missing seasonal behaviour and a very high mean value. A fairly strong correlation between SMOS L2 soil moisture and optical depth was found (lt;igt;Rlt;/igt; = 0.65) even though the two variables are considered independent in the study area. The value of coupled models as a tool for the analysis of passive microwave remote sensing data is demonstrated by extending this SMOS data analysis from a few days during a field campaign to a long term comparison.
[52] Schwank M, Matzler C.2006.

Air-to-soil transition model

[M]//Mätzler C, Rosenkranz P W, Battaglia A, et al. Thermal microwave radiation: Applications for remote sensing. Stevenage, England: Institute of Engineering and Technology.

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[53] Schwank M, Volksch I, Wigneron J-P, et al.2010.

Comparison of two bare-soil reflectivity models and validation with L-band radiometer measurements

[J]. IEEE Transactions on Geoscience and Remote Sensing, 48(1): 325-337.

https://doi.org/10.1109/TGRS.2009.2026894      URL      [本文引用: 1]      摘要

The emission of bare soils at microwave L-band (1-2 GHz) frequencies is known to be correlated with surface soil moisture. Roughness plays an important role in determining soil emissivity although it is not clear which roughness length scales are most relevant. Small-scale (i.e., smaller than the resolution limit) inhomogeneities across the soil surface and with soil depth caused by both spatially varying soil properties and topographic features may affect soil emissivity. In this paper, roughness effects were investigated by comparing measured brightness temperatures of well-characterized bare soil surfaces with the results from two reflectivity models. The selected models are the air-to-soil transition model and Shi's parameterization of the integral equation model (IEM). The experimental data taken from the Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) consist of surface profiles, soil permittivities and temperatures, and brightness temperatures at 1.4 GHz with horizontal and vertical polarizations. The types of correlation functions of the rough surfaces were investigated as required to evaluate Shi's parameterization of the IEM. The correlation functions were found to be clearly more exponential than Gaussian. Over the experimental period, the diurnal mean root mean square (rms) height decreased, while the correlation length and the type of correlation function did not change. Comparing the reflectivity models with respect to their sensitivities to the surface rms height and correlation length revealed distinct differences. Modeled reflectivities were tested against reflectivities derived from measured brightness, which showed that the two models perform differently depending on the polarization and the observation angle.
[54] Shi J C, Chen K S, Li Q, et al.2002.

A parameterized surface reflectivity model and estimation of bare-surface soil moisture with L-band radiometer

[J]. IEEE Transactions on Geoscience and Remote Sensing, 40(12): 2674-2686.

https://doi.org/10.1109/TGRS.2002.807003      URL      摘要

Soil moisture is an important parameter for hydrological and climatic investigations. Future satellite missions with L-band passive microwave radiometers will significantly increase the capability of monitoring earth's soil moisture globally. Understanding the effects of surface roughness on microwave emission and developing quantitative bare-surface soil moisture retrieval algorithms is one of the essential components in many applications of geophysical properties in the complex earth terrain by microwave remote sensing. In this study, we explore the use of the integral equation model (IEM) for modeling microwave emission. This model was validated using a three-dimensional Monte Carlo model. The results indicate that the IEM model can be used to simulate the surface emission quite well for a wide range of surface roughness conditions with high confidence. Several important characteristics of the effects of surface roughness on radiometer emission signals at L-band 1.4 GHz that have not been adequately addressed in the current semiempirical surface effective reflectivity models are demonstrated by using IEM-simulated data. Using an IEM-simulated database for a wide range of surface soil moisture and roughness properties, we developed a parameterized surface effective reflectivity model with three typically used correlation functions and an inversion model that puts different weights on the polarization measurements to minimize surface roughness effects and to estimate the surface dielectric properties directly from dual-polarization measurements. The inversion technique was validated with four years (1979-1982) of ground microwave radiometer experiment data over several bare-surface test sites at Beltsville, MD. The accuracies in random-mean-square error are within or about 3% for incidence aneles from 20 to 50 .
[55] Shi J C, Dong X L, Zhao T J, et al.2014.

WCOM: The science scenario and objectives of a global water cycle observation mission

[C]//Proceedings of 2014 IEEE international geoscience and remote sensing symposium. Quebec City, QC, Canada: IEEE, 3646-3649.

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[56] Shi J C, Jackson T, Tao J, et al.2008.

Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E

[J]. Remote Sensing of Environment, 112(12): 4285-4300.

https://doi.org/10.1016/j.rse.2008.07.015      URL      [本文引用: 1]      摘要

Vegetation indices are valuable in many fields of geosciences. Conventional, visible-near infrared, indices are often limited by the effects of atmosphere, background soil conditions, and saturation at high levels of vegetation. In this study, we will establish the theoretical basis for our new passive microwave vegetation indices (MVIs) based on data from the Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite. Through the analysis of numerical simulations by surface emission model, the Advanced Integral Equation Model (AIEM), we found that bare soil surface emissivities at different frequencies can be characterized by a linear function with parameters that are dependent on the pair of frequencies used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multi-frequency radiometer measurements. Using a radiative transfer model ( model), a linear relationship between the brightness temperatures observed at two adjacent radiometer frequencies can be derived. The intercept and slope of this linear function depend only on vegetation properties and can be used as vegetation indices. These can be derived from the dual-frequency and dual-polarization satellite measurements under assumption that there is no significant impact of the polarization dependence on the vegetation signals. To demonstrate the potential of the new microwave vegetation indices, we compared them with the Normalized Difference of Vegetation Index (NDVI) derived using MODIS the optical sensor at continental and global scales. The major purpose of this paper is to describe the concept and techniques involved in these newly developed MVIs and explore the general relationships between these MVIs and NDVI. In this first investigation, the information content of NDVI and MVIs, both are qualitative indices, was compared by examining its response in global pattern and to seasonal vegetation phenology. The results indicate that the MVIs can provide significant new information since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy when the microwave sensor can ee through it. In combination with conventional optical sensor derived vegetation indices, they provide a possible complementary dataset for monitoring global short vegetation and seasonal phenology from space.
[57] Shi J C, Yang D, Du J Y, et al.2012.

Progresses on microwave remote sensing of land surface parameters

[J]. Science China Earth Sciences, 55(7): 1052-1078.

https://doi.org/10.1007/s11430-012-4444-x      URL      [本文引用: 1]     

[58] Van Der Schalie R, Kerr Y H, Wigneron J P, et al.2016.

Global SMOS soil moisture retrievals from the land parameter retrieval model

[J]. International Journal of Applied Earth Observation and Geoinformation, 45(Pt B): 125-134.

https://doi.org/10.1016/j.jag.2015.08.005      URL      [本文引用: 1]      摘要

A recent study by Van der Schalie et al. (2015) showed good results for applying the Land Parameter Retrieval Model (LPRM) on SMOS observations over southeast Australia and optimizing and evaluating the retrieved soil moisture ( θ in m 3 02m 613 ) against ground measurements from the OzNet sites. In this study, the LPRM parameterization is globally updated for SMOS against modelled θ from MERRA-Land (MERRA) and ERA-Interim/Land (ERA) over the period of July 2010–December 2010, mainly focusing on two parameters: the single scattering albedo ( ω ) and the roughness ( h ). The Pearson's coefficient of correlation ( r ) increased rapidly when increasing the ω up to 0.12 and reached a steady state from thereon, no significant spatial pattern was found in the estimation of the single scattering albedo, which could be an artifact of the used parameter estimation procedure, and a single value of 0.12 was therefore used globally. The h was defined as a function of θ and varied slightly for the different angle bins, with maximum values of 1.1–1.3 as the angle changes from 42.5° to 57.5°.This resulted in an average r of 0.51 and 0.47, with a bias (m 3 02m 613 ) of 610.02 and 610.01 and an unbiased root mean square error ( ubrmse in m 3 02m 613 ) of 0.054 and 0.056 against MERRA (ascending and descending). For ERA this resulted in an r of 0.61 and 0.53, with a bias of 610.03 and an ubrmse 0.055 and 0.059. The resulting parameterization was then used to run LPRM on SMOS observations over the period of July 2010–December 2013 and evaluated against SMOS Level 3 (L3) θ and available in situ measurements from the International Soil Moisture Network (ISMN). The comparison with L3 shows that the LPRM θ retrievals are very similar, with for the ascending set very high r of over 0.9 in large parts of the globe, with an overall average of 0.85 and the descending set performing less with an average of 0.74, mainly due to the negative r over the Sahara. The mean bias is 0.03, with an ubrmse of 0.038 and 0.044. In this study there are three major areas where the LPRM retrievals do not perform well: very dry sandy areas, densely forested areas and over high latitudes, which are all known limitations of LPRM. The comparison against in situ measurement from the ISMN give very similar results, with average r for LPRM of 0.65 and 0.61 (0.64 and 0.59 for L3) for the ascending and descending sets, while having a comparable bias and ubrmse over the different networks. This shows that LPRM used on SMOS observations produce θ retrievals with a similar quality as the SMOS L3 product.
[59] Wang J R, Choudhury B J.1981.

Remote sensing of soil moisture content, over bare field at 1.4 GHz frequency

[J]. Journal of Geophysical Research: Oceans, 86(C6): 5277-5282.

https://doi.org/10.1029/JC086iC06p05277      URL      [本文引用: 1]      摘要

An algorithm for estimating moisture content of a bare soil from the observed brightness temperature at 1.4 GHz is discussed and applied to a limited data base. The method is based on a radiative transfer model calculation, which has been successfully used in the past to account for many observational results, with some modifications to take into account the effect of surface roughness. Besides the measured brightness temperatures, the three additional inputs required by the method are the effective soil thermodynamic temperature, the precise relation between moisture content and the smooth field brightness temperatures and a pair of parameters related to surface roughness. The procedures of estimating surface roughness parameters and of obtaining moisture content from observed brightness temperature are discussed. The algorithm is applied to observations from truck mounted and airborne radiometers. The estimated moisture contents compare favorably with the observations in the top 2 cm layer.
[60] Wang J R, O'Neill P E, Jackson T J, et al.1983.

Multifrequency measurements of the effects of soil moisture, soil texture, and surface roughness

[J]. IEEE Transactions on Geoscience and Remote Sensing, GE-21(1): 44-51.

https://doi.org/10.1109/TGRS.1983.350529      URL      摘要

An experiment on remote sensing of soil moisture content was conducted over bare fields with microwave radiometers at the frequencies of 1.4, 5, and 10.7 GHz, during July-September of 1981. Three bare fields with different surface roughnesses and soil textures were prepared for the experiment. Ground-truth acquisition of soil temperatures and moisture contents for 5 layers down to the depths of 15 cm was made concurrently with radiometric measurements. The experimental results show that the effect of surface roughness is to increase the soil's brightness temperature and to reduce the slope of regression between brightness temperature and moisture content. The slopes of regression for soils with different textures are found to be comparable and the effect of soil texture is reflected in the difference of regression line intercepts at brightness-temperature axis. The result is consistent with laboratory measurement of soil's dielectric permittivity. Measurements on wet smooth bare fields give lower brightness temperatures at 5 than at 1.4 GHz. This phenomenon is not expected from current radiative transfer theory, using laboratory measurements of the relationship between dielectric permittivity and moisture content for different soil-water mixtures at frequencies of <5 GHz.
[61] Wegmuller U, Matzler C.1999.

Rough bare soil reflectivity model

[J]. IEEE Transactions on Geoscience and Remote Sensing, 37(3): 1391-1395.

https://doi.org/10.1109/36.763303      URL      摘要

Abstract A semiempirical model for the reflectivity of rough bare soil is presented. One of the main objectives of this new model development was to derive a simple model with few model parameters and a wide applicability. A large number of ground-based measurements in the 1-100-GHz range at H and V-polarization and incidence angles between 20° and 70° were used for the model development
[62] Wigneron J-P, Chanzy A, Kerr Y H, et al.2011.

Evaluating an improved parameterization of the soil emission in L-MEB

[J]. IEEE Transactions on Geoscience and Remote Sensing, 49(4): 1177-1189.

https://doi.org/10.1109/TGRS.2013.2253972      URL      [本文引用: 2]      摘要

In the forward model [L-band microwave emission of the biosphere (L-MEB)] used in the Soil Moisture and Ocean Salinity level-2 retrieval algorithm, modeling of the roughness effects is based on a simple semiempirical approach using three main “roughness” model parameters: HR, QR, and NR. In many studies, the two parameters QR and NR are set to zero. However, recent results in the literature showed that this is too approximate to accurately simulate the microwave emission of the rough soil surfaces at L-band. To investigate this, a reanalysis of the PORTOS-93 data set was carried out in this paper, considering a large range of roughness conditions. First, the results confirmed that QR could be set to zero. Second, a refinement of the L-MEB soil model, considering values of NR for both polarizations (namely, NRV and NRH), improved the model accuracy. Furthermore, simple calibrations relating the retrieved values of the roughness model parameters HR and (NRH 61 NRV) to the standard deviation of the surface height were developed. This new calibration of L-MEB provided a good accuracy (better than 5 K) over a large range of soil roughness and moisture conditions of the PORTOS-93 data set. Conversely, the calibrations of the roughness effects based on the Choudhury approach, which is still widely used, provided unrealistic values of surface emissivities for medium or large roughness conditions.
[63] Wigneron J-P, Kerr Y, Waldteufel P, et al.2007.

L-band microwave emission of the biosphere (L-MEB) model: Description and calibration against experimental data sets over crop fields

[J]. Remote Sensing of Environment, 107(4): 639-655.

https://doi.org/10.1016/j.rse.2006.10.014      URL      [本文引用: 1]      摘要

In the near future, the SMOS (Soil Moisture and Ocean Salinity) mission will provide global maps of surface soil moisture (SM). The SMOS baseline payload is an L-band (1.4 GHz) two dimensional interferometric microwave radiometer which will provide multi-angular and dual-polarization observations. In the framework of the ground segment activities for the SMOS mission an operational SMOS Level 2 Soil Moisture algorithm was developed. The principle of the algorithm is to exploit multi-angular data in order to retrieve simultaneously several surface parameters including soil moisture and vegetation characteristics. The algorithm uses an iterative approach, minimizing a cost function computed from the differences between measured and modelled brightness temperature ( T B) data, for all available incidence angles. In the algorithm, the selected forward model is the so-called L-MEB (L-band Microwave Emission of the Biosphere) model which was the result of an extensive review of the current knowledge of the microwave emission of various land covers. This model is a key element in the SMOS L2 algorithm and could be used in future assimilation studies. There is thus a strong need for a reference study, describing the model and its implementation. In order to address these needs a detailed description of soil and vegetation modelling in L-MEB is given in this study. In a second step, the use of L-MEB in soil moisture retrievals is evaluated for several experimental data sets over agricultural crops. Calibrations of the soil and vegetation L-MEB parameters are investigated for corn, soybean and wheat. Over the different experiments, very consistent results are obtained for each vegetation type in terms of calibration and soil moisture retrievals.
[64] Wigneron J-P, Laguerre L, Kerr Y H.2001.

A simple parameterization of the L-band microwave emission from rough agricultural soils

[J]. IEEE Transactions on Geoscience and Remote Sensing, 39(8): 1697-1707.

https://doi.org/10.1109/36.942548      URL      [本文引用: 1]      摘要

A simple model for simulating the L-band microwave emission from bare soils is developed. The model is calibrated on a large set of measurements obtained during a three-month period over seven plots covering a wide range of surface roughness (representing the total range which can be expected on agricultural fields), soil moisture, and temperature conditions. The approach is based on the parameterization of an effective roughness parameter as a function of surface characteristics: surface roughness (standard deviation of height and correlation length) and the surface soil moisture. The parameterizations that are developed are independent of incidence angle and polarization and are valid over a large range in surface roughness conditions, representative of most of typical agricultural bare fields, from very smooth (rolled field after sowing) to very rough surfaces (deeply plowed soil). This approach will enable the use of microwave radiometric observations for soil moisture retrieval over agricultural areas
[65] Wigneron J-P, Parde M, Waldteufel P, et al.2004.

Characterizing the dependence of vegetation model parameters on crop structure, incidence angle, and polarization at L-band

[J]. IEEE Transactions on Geoscience and Remote Sensing, 42(2): 416-425.

https://doi.org/10.1109/TGRS.2003.817976      URL      [本文引用: 1]      摘要

To retrieve soil moisture over vegetation-covered areas from microwave radiometry, it is necessary to account for vegetation effects. At L-band, many retrieval approaches are based on a simple model that relies on two vegetation parameters: the optical depth (τ) and the single-scattering albedo (ω). When the retrievals are based on multiconfiguration measurements, it is necessary to take into account the dependence of τ and ω on the system configuration, in terms of incidence angle and polarization. In this paper, this dependence was investigated for several crop types (corn, soybean, wheat, grass, and alfalfa) based on L-band experimental datasets. The results should be useful for developing more accurate forward modeling and retrieval methods over mixed pixels including a variety of vegetation types.
[66] Yang H, Weng F Z, Lv L Q, et al.2011.

The FengYun-3 microwave radiation imager on-orbit verification

[J]. IEEE Transactions on Geoscience and Remote Sensing, 49(11): 4552-4560.

https://doi.org/10.1109/TGRS.2011.2148200      URL      [本文引用: 1]      摘要

The Microwave Radiation Imager (MWRI) on board the FengYun-3A/B satellites observes the Earth atmosphere at 10.65, 18.7, 23.8, 36.5, and 89.0 GHz with each having dual polarization. Its calibration system is uniquely designed with a main reflector viewing both cold and hot calibration targets. Two quasi-optical reflectors are used to reflect the radiation from the hot load and cold space to the main reflector. In the MWRI calibration process, a radiation loss in the beam transmission path must be taken into account. The loss factor in the hot load transmission path is derived using the antenna pattern data measured on ground and satellite data observing over the Amazon forest where the scene temperature is steady and close to the hot load. The instrument nonlinearity factors at different channels are also evaluated over a wide range of brightness temperatures and compared with the results from the ground vacuum test. After a cross-calibration with Windsat data, atmospheric products are derived from MWRI brightness temperatures with the accuracy similar to those from the legacy sensors (e.g., the Special Sensor Microwave/Imager).
[67] Zeng J Y, Li Z, Chen Q, et al.2015.

Method for soil moisture and surface temperature estimation in the Tibetan Plateau using spaceborne radiometer observations

[J]. IEEE Geoscience and Remote Sensing Letters, 12(1): 97-101.

https://doi.org/10.1109/LGRS.2014.2326890      URL      [本文引用: 1]      摘要

A method for soil moisture and surface temperature estimation in the Tibetan Plateau (TP) using spaceborne radiometer observations was presented. Based on the physical basis that the 36.5-GHz (Ka-band) vertical brightness temperature is highly sensitive to the topsoil temperature, a new surface temperature model was developed using all ground measurements available from three networks named CAMP/Tibet, Maqu, and Naqu, established in the TP, which can significantly improve the accuracy of surface temperature derived from the land parameter retrieval model (LPRM). Then, the new surface temperature model, which was calibrated with in situ data, was integrated into the soil moisture retrieval algorithm proposed in this letter using Advanced Microwave Scanning Radiometer (AMSR-E) observations. The algorithm combines the vegetation optical depth and roughness into an integrated factor to avoid making unreliable assumptions and using auxiliary data to get these two parameters. Finally, the algorithm was validated by ground measurements from the dense Naqu network and was compared with NASA AMSR-E and Soil Moisture and Ocean Salinity (SMOS) official algorithms. The results show that the proposed algorithm can provide much more accurate soil moisture retrievals than the other two satellite algorithms in the Naqu network region. The algorithm can be applied to the areas with spare vegetation but may not be very suitable for densely vegetated surfaces.
[68] Zhang Z J, Lan H M, Zhao T J.2017.

Detection and mitigation of radiometers radio-frequency interference by using the local outlier factor

[J]. Remote Sensing Letters, 8(4): 311-319.

https://doi.org/10.1080/2150704X.2016.1266408      URL      [本文引用: 1]      摘要

Large amounts of radio-frequency interference (RFI) are present in Earth observations at the L-band frequencies of European Space Agency090005s Soil Moisture and Ocean Salinity, the National Aeronautics and Space Administration090005s Aquarius and Soil Moisture Active and Passive missions. Multiple approaches have been proposed to detect and eliminate the RFI signals in the past few decades, including time, statistical, polarimetric and frequency domain methods. This letter focuses on a new potential RFI detection and mitigation algorithm that is based on the local outlier factor (LOF). Experimental results show that a satisfactory performance can be obtained by an LOF algorithm even in detecting moderate RFI.
[69] Zhao T J, Jackson T J, Bindlish R, et al.2012.

Potential use of aquarius scatterometer observations to estimate vegetation water content

[C]. Barc Poster Day. 2012 CDROM.

[本文引用: 1]     

[70] Zhao T J, Shi J C, Bindlish R, et al.2015

a. Refinement of SMOS multiangular brightness temperature toward soil moisture retrieval and its analysis over reference targets

[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2): 589-603.

https://doi.org/10.1109/JSTARS.2014.2336664      URL      [本文引用: 1]      摘要

Soil moisture ocean salinity (SMOS) mission has been providing L-band multiangular brightness temperature observations at a global scale since its launch in November 2009 and has performed well in the retrieval of soil moisture. The multiple incidence angle observations also allow for the retrieval of additional parameters beyond soil moisture, but these are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio-frequency interference (RFI) issues and aliasing at lower look angles increase the uncertainty of observations and thereby affect the soil moisture retrieval that utilizes observations at specific angles. In this study, we proposed a two-step regression approach that uses a mixed objective function based on SMOS L1c data products to refine characteristics of multiangular observations. The approach was found to be robust by validation using simulations from a radiative transfer model, and valuable in improving soil moisture estimates from SMOS. In addition, refined brightness temperatures were analyzed over three external targets: Antarctic ice sheet, Amazon rainforest, and Sahara desert, by comparing with WindSat observations. These results provide insights for selecting and utilizing external targets as part of the upcoming soil moisture active passive (SMAP) mission.
[71] Zhao T J, Shi J C, Bindlish R, et al.2015

b. Parametric exponentially correlated surface emission model for L-band passive microwave soil moisture retrieval

[J]. Physics and Chemistry of the Earth, Parts A/B/C, 83-84: 65-74.

https://doi.org/10.1109/URSIGASS.2014.6929685      URL      [本文引用: 2]      摘要

Soil Moisture and Ocean Salinity (SMOS) satellite observations offer a unique resource for near-surface soil moisture retrievals. It could provide multi-angular microwave emission signals at L-band and thus provide an entree to infer soil moisture and roughness for bare soil. In this study, a simple model for simulating the L-band microwave emission from exponentially correlated surface was developed and implemented into soil moisture retrieval algorithm. The approach was based on the parameterization of an effective roughness parameter in relation with surface roughness variables (root mean square height and correlation length) and incidence angle. The parameterization was developed on a large set of simulations by a physical model of the advanced integral equation model (AIEM) over a wide range of geophysical properties. This methodology was then implemented using SMOSREX observations to estimate both soil roughness and surface soil moisture content. Results indicated the model developed in this paper can be very useful in understanding the effects of surface roughness on microwave emission and soil moisture retrieval algorithms from L-band passive microwave observations.
[72] Zhao T J, Zhang L X, Shi J C, et al.2011.

A physically based statistical methodology for surface soil moisture retrieval in the Tibet Plateau using microwave vegetation indices

[J]. Journal of Geophysical Research: Atmospheres, 116(D8): D08116.

https://doi.org/10.1029/2010JD015229      URL      [本文引用: 1]      摘要

[1] Surface soil moisture is the key state variable in various hydrological processes. A physically based statistical methodology for surface soil moisture measurement in the Tibet Plateau was developed in this study. The approach was established based on theoretical relationships from the derivation of physical models. The methodology was calibrated using statistical analysis of a large data set obtained during a long-term experiment in Tibet. The procedure was conducted using multichannel brightness temperature observations from the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E). The most interesting results of this study were that the newly developed microwave vegetation indices (MVIs) are a function of vegetation water content or vegetation transmissivity. The B parameter of MVIs decreased with increased vegetation water content but increased with increased vegetation transmissivity. This enabled the use of MVIs for the correction of vegetation effects in soil moisture inversion. The methodology was tested against several experimental data sets collected from Tibet and was shown to be an effective method of soil moisture retrieval for areas with sparse vegetation coverage. The results also provided a complementary data set of soil moisture for hydrology and climatology studies in the Tibet Plateau.

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