集成遥感数据的陆地净初级生产力估算模型研究综述

doi:10.18306/dlkxjz.2017.08.002

摘要/Abstract

摘要：

净初级生产力(NPP)是衡量碳循环、指导土地利用、评估生态安全、指示环境变化、反映粮食安全等的重要参量,其估算受模型构建机理和生态系统关键地表参数输入的影响。近年来,随着遥感数据的不断丰富和遥感处理技术的快速发展,集成遥感数据的NPP估算模型相较于仅采用气候、土壤等传统观测数据的非遥感模型,在分析时空异质性等方面的优势日益凸显。本文基于Web of Science和CNKI两大数据库,采用文献统计分析方法,系统回顾NPP研究概况及国内外集成遥感数据的NPP估算模型的近期进展;并将集成遥感数据进行NPP估算的模型分为统计模型、光能利用率模型、过程模型及耦合模型四类;重点阐述了各类遥感估算模型的机理、差异性、适宜性及局限性;最后,在分析NPP遥感估算面临困境和科学挑战的基础上,从机理与影响因素、数据基础、参数反演、时空尺度拓展、软硬件支撑等方面对未来研究进行了展望。

关键词: 净初级生产力(NPP), 遥感数据, 同化方式, 驱动方式, 估算模型, 综述

Abstract:

As a general term that describes the accumulation of organic matters within specific temporal and spatial scopes on crop land, woodland, grassland, or other types of lands, land net primary productivity (NPP) is considered an important parameter to measure carbon cycle, guide land use, assess ecological security, reflect environmental changes, and indicate the level of food security. The estimation precision of NPP is significantly influenced by the type of models and input of key surface parameters of ecosystems. In recent years, with the continuous growth of remote sensing data and the rapid development of remote sensing data processing technologies, NPP estimation models based on remote sensing data, as compared to NPP estimation using traditional observation data such as climate and soil data with coarse spatiotemporal resolutions, have become very prominent in analyzing temporal and spatial heterogeneity. Based on the Web of Science and CNKI databases and statistical analysis methods, this study systematically reviewed research on NPP and its estimation models integrating remote sensing data in China and internationally. The commonly used models can be divided into four categories: statistical models, light use efficiency models, process models, and coupling models. We examined the mechanisms, differences, suitability, and limitation of the various kinds of models, Based on an analysis of the difficulties and scientific challenges that face integrating remote sensing data into NPP estimation models, research prospects are put forward with regard to model mechanism, influencing factors, data provision, parameter derivation, expansion of spatiotemporal scales, and hardware and software supports.

Key words: net primary productivity (NPP), remote sensing data, assimilation modes, drive modes, estimation model, overview

洪长桥, 金晓斌, 陈昌春, 王慎敏, 杨绪红, 项晓敏. 集成遥感数据的陆地净初级生产力估算模型研究综述[J]. 地理科学进展, 2017, 36(8): 924-939.

Changqiao HONG, Xiaobin JIN, Changchun CHEN, Shenmin WANG, Xuhong YANG, Xiaomin XIANG. Overview on estimation models of land net primary productivity integrating remote sensing data[J]. PROGRESS IN GEOGRAPHY, 2017, 36(8): 924-939.

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表1

集成遥感数据的光能利用率模型应用案例"

模型	表达形式	范围/土地覆盖类型	?*来源(取值)	参考文献
CASA	APAR×?*(N)×f(T)×f(W)	湖南省/5大类19小类	文献(0.39~0.99)	陈晓玲等, 2016
SEBAL	APAR×?*(N)×f(T)×f(W)	河北省2县/农作物	—	苏伟等, 2014
N_NPP	APAR×?*(N)×min[f(T), f(W)]	青海省/6类草地	文献、实测反演	卫亚星等, 2012
AgI-LUE	APAR×?(N)	美国2州/玉米、大豆	统计数据反演	Bandaru et al, 2013
PEM	APAR×?*(N)×f(T_a, T_s)	美国2地/城郊草坪	涡度通量观测	Wu et al, 2012
Modis-derived NPP	APAR×?*(N)×f(T)×f(W)	中国2站点/林地	文献(55.2α(C3);2.76(C4))	Chen et al, 2008
GPP/NPP	APAR×?*(G)×f(T)×f(W)	黑河流域/5类植被	文献	Gao S et al, 2012
C-Fix	APAR×?(G)×f(T)×f(CO₂*)	中国/陆地植被	1.1	陈斌等, 2007
GLO-PEM	APAR×?(G)×f(T)×f(W)×f(VPD*)	中国西南地区/陆地植被	55.2α(C3);2.76(C4)	赵志平等, 2015
VPM	APAR×?*(G)×f(T)×f(W)×f(P)	中国/耕地	涡度通量观测	冀咏赞等, 2015
Beams	APAR×?*(G)×(P_actual/P_max)	全球/陆地植被	0~1	Sasai et al, 2005
TL-LUE	(APAR_msu×?_msu(G)+APAR_msh×?_msh(G))×f(T)× f(VPD)	中国/5类植被	实测反演	He et al, 2013
TL-LUEn	[(APAR_msu×?_msu(G)×β)/(APAR_msu×?_msu(G)+β)×LAI_msu+(APAR_msh×?_msh(G)×β)/(APAR_msh×?_msh(G)+β)×LAI_msh]×f(T)×f(VPD)	美国18站点、加拿大 4站点/4类植被	—	Wang F M et al, 2014
3PG	APAR×?(G)×f(T)×f(VPD)×f(CO₂*)×f(F)× f(θ_s)×f(N)	俄勒冈州西部/冷杉	—	McAdam, 2015
EC-LUE	APAR×?*(G)×min[f(T), f(W)]	全球36站点/C3/C4作物	2.14	Yuan et al, 2016
TEC	APAR×?*(G)×f(T)×f(W)	美国18站点/3类植被	文献(1.8(C3);2.76(C4)	Yan et al, 2015
GEO-LUE	APAR×?(G)×f(T)×f(W)×f(VPD*)	中国/陆地植被	—	Gao Z Q et al, 2012
TURC	APAR×?(G)	全球/陆地植被	1.21	Ruimy et al, 1996
CFLUX	APAR×?(G)×f(T)×min[f(W)×f(VPD*)]×f(SA)	北美/8类植被	实测反演(0.7~4.9)	King et al, 2011

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模型	动态模拟				模型主要结构或表达	范围/土地覆盖类型	参考文献
模型	碳				氮	水	能量
TEM	√	√			WBM水分平衡模型、物候模型、植物摄取N模型	全球/10类功能型植被	Liao et al, 2015
BEPS	√				气孔导度模型(Jarvis经验模型)、叶片瞬时光合作用模型(Farquhar模型)、两叶模型	南京/9类土地覆盖	Zhou et al, 2015
Biome-BGC	√	√	√	√	包括初始化文件、气象数据文件及包含44个生理生态参数的文件^*	加拿大/半干旱草地	He et al, 2015
InTEC	√	√			叶片瞬时光合作用模型(Farquhar模型)、CENTURY土壤碳和氮循环模型、净氮矿化模型、age-NPP关系模型、土壤三维水文模型	东北地区/森林	李明泽等, 2015
EPPML	√		√		气孔导度模型与气体传输模型相结合、叶片瞬时光合作用模型(Farquhar模型)、两叶模型	长白山自然保护区/9类植被	张娜等, 2003
SiB2	√		√	√	空气动力学转移、水力学扩散、重力引流、一维能量平衡、转移模型(冠层结构为单叶,包含融雪模块)	位山引黄灌区/小麦、玉米	雷慧闽等, 2012
MOD-Sim-CYCLE	√				叶片光合作用速率采用Michaelis型函数、气孔导度模型使用Ball修正公式、冠层尺度过程是基于叶片光合作用速率、光合作用量子通量密度,在日长、叶面积指数上的积分	全球/7大类31小类	Hazarika et al, 2005

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