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内蒙古自治区呼和浩特市赛罕区大学西街235号 邮编: 010021
作者机构:Southwest Univ Minist Nat Resources Sch Geog Sci Res Base Karst Ecoenvironm Nanchuan Chongqing Chongqing 400715 Peoples R China Univ New Hampshire Inst Study Earth Oceans Earth Syst Res Ctr Durham NH 03824 USA Southwest Univ Chongqing Engn Res Ctr Remote Sensing Big Data Ap Sch Geog Sci Chongqing 400715 Peoples R China
出 版 物:《REMOTE SENSING》 (遥感)
年 卷 期:2019年第11卷第15期
页 面:1823-1823页
核心收录:
学科分类:0830[工学-环境科学与工程(可授工学、理学、农学学位)] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术]
基 金:National Natural Science Foundation of China [41830648, 41771453] National Key Technology R&D Program of China [2016YFC0500106] National Aeronautics and Space Administration (NASA) through the Carbon Cycle Science Program [NNX14AJ18G] Climate Indicators and Data Products for Future National Climate Assessments [NNX16AG61G]
主 题:carbon cycle gross primary production eddy covariance vegetation productivity MODIS vegetation activity photosynthesis remote sensing light use efficiency environmental stresses
摘 要:Satellite-derived vegetation indices (VIs) have been widely used to approximate or estimate gross primary productivity (GPP). However, it remains unclear how the VI-GPP relationship varies with indices, biomes, timescales, and the bidirectional reflectance distribution function (BRDF) effect. We examined the relationship between VIs and GPP for 121 FLUXNET sites across the globe and assessed how the VI-GPP relationship varied among a variety of biomes at both monthly and annual timescales. We used three widely-used VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and 2-band EVI (EVI2) as well as a new VI - NIRV and used surface reflectance both with and without BRDF correction from the moderate resolution imaging spectroradiometer (MODIS) to calculate these indices. The resulting traditional (NDVI, EVI, EVI2, and NIRV) and BRDF-corrected (NDVIBRDF, EVIBRDF, EVI2(BRDF), and NIRV, BRDF) VIs were used to examine the VI-GPP relationship. At the monthly scale, all VIs were moderate or strong predictors of GPP, and the BRDF correction improved their performance. EVI2(BRDF) and NIRV, BRDF had similar performance in capturing the variations in tower GPP as did the MODIS GPP product. The VIs explained lower variance in tower GPP at the annual scale than at the monthly scale. The BRDF-correction of surface reflectance did not improve the VI-GPP relationship at the annual scale. The VIs had similar capability in capturing the interannual variability in tower GPP as MODIS GPP. VIs were influenced by temperature and water stresses and were more sensitive to temperature stress than to water stress. VIs in combination with environmental factors could improve the prediction of GPP than VIs alone. Our findings can help us better understand how the VI-GPP relationship varies among indices, biomes, and timescales and how the BRDF effect influences the VI-GPP relationship.