文献类型：期刊文献

英文题名：Assessing the ability of MODIS EVI to estimate terrestrial ecosystem gross primary production of multiple land cover types

作者：Shi, Hao[1] Li, Longhui[1] Eamus, Derek[1,2] Huete, Alfredo[1] Cleverly, James[1,2] Tian, Xin[3] Yu, Qiang[1] Wang, Shaoqiang[4] Montagnani, Leonardo[5] Magliulo, Vincenzo[6] Rotenberg, Eyal[7] Pavelka, Marian[8] Carrara, Arnaud[9]

第一作者：Shi, Hao

通信作者：Li, LH[1]

机构：[1]Univ Technol Sydney, Sch Life Sci, Sydney, NSW 2000, Australia;[2]Univ Technol Sydney, Australian Supersite Network, Sydney, NSW 2000, Australia;[3]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[4]Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China;[5]Free Univ Bolzano, Fac Sci & Technol, Piazza Univ 1, I-39100 Bolzano, Italy;[6]CNR, Inst Agr & Forest Syst, Via Patacca 85, I-80056 Naples, Italy;[7]Weizmann Inst Sci, Dept Environm Sci & Energy Res, IL-76100 Rehovot, Israel;[8]Inst Syst Biol & Ecol AS CR, Lab Plants Ecol Physiol, Porici 3b, Brno 60300, Czech Republic;[9]Fdn Ctr Estudios Ambientales Mediterraneo CEAM, Charles Darwin 14,Parc Tecnol, Paterna 46980, Spain

年份：2017

卷号：72

起止页码：153-164

外文期刊名：ECOLOGICAL INDICATORS

收录：;EI(收录号：20164102888719);Scopus(收录号：2-s2.0-84989814980);WOS:【SCI-EXPANDED(收录号:WOS:000398426200015)】；

基金：This research was supported by an Australian Research Council Discovery Early Career Research Award (project number DE120103022). X.T. was supported by one of National Basic Research Program of China (grant number 2013CB733404) and the National Natural Science Foundation of China (grant number 41101379). This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropelP, Carboltaly, CarboMont, ChinaFlux, FLUXNET Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, University Laval, Environment Canada and U.S. Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California Berkeley, University of Virginia.

语种：英文

外文关键词：Remote sensing; MODIS; Enhanced vegetation index; Gross primary production; Land cover types; Leaf area index

摘要：Terrestrial ecosystem gross primary production (GPP) is the largest component in the global carbon cycle. The enhanced vegetation index (EVI) has been proven to be strongly correlated with annual GPP within several biomes. However, the annual GPP-EVI relationship and associated environmental regulations have not yet been comprehensively investigated across biomes at the global scale. Here we explored relationships between annual integrated EVI (iEVI) and annual GPP observed at 155 flux sites, where GPP was predicted with a log-log model: In(GPP) = a x In(iEVI) b. iEVI was computed from MODIS monthly EVI products following removal of values affected by snow or cold temperature and without calculating growing season duration. Through categorisation of flux sites into 12 land cover types, the ability of iEVI to estimate GPP was considerably improved (R-2 from 0.62 to 0.74, RMSE from 454.7 to 368.2 g C m(-2) yr(-1)). The biome-specific GPP-iEVI formulae generally showed a consistent performance in comparison to a global benchmarking dataset (R-2 = 0.79, RMSE = 387.8 g C m(-2) yr(-1)). Specifically, iEVI performed better in cropland regions with high productivity but poorer in forests. The ability of iEVI in estimating GPP was better in deciduous biomes (except deciduous broadleaf forest) than in evergreen due to the large seasonal signal in iEVI in deciduous biomes. Likewise, GPP estimated from iEVI was in a closer agreement to global benchmarks at mid and high-latitudes, where deciduous biomes are more common and cloud cover has a smaller effect on remote sensing retrievals. Across biomes, a significant and negative correlation (R-2 = 037,p <0.05) was observed between the strength (R-2) of GPP-iEVI relationships and mean annual maximum leaf area index (LAI.), and the relationship between the strength and mean annual precipitation followed a similar trend. LAIm also revealed a scaling effect on GPP-iEVI relationships. Ourresults suggest that iEVI provides a very simple but robust approach to estimate spatial patterns of global annual GPP whereas its effect is comparable to various light-use-efficiency and data driven models. The impact of vegetation structure on accuracy and sensitivity of EVI in estimating spatial GPP provides valuable clues to improve EVI-based models. (C) 2016 Elsevier Ltd. All rights reserved.