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Remote Sens. 2015, 7(1), 135-152; doi:10.3390/rs70100135

The Performances of MODIS-GPP and -ET Products in China and Their Sensitivity to Input Data (FPAR/LAI)

Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
Author to whom correspondence should be addressed.
Academic Editors: Janet Nichol and Prasad S. Thenkabail
Received: 8 October 2014 / Accepted: 15 December 2014 / Published: 24 December 2014
View Full-Text   |   Download PDF [2318 KB, uploaded 24 December 2014]   |  


The aims are to validate and assess the performances of MODIS gross primary production (MODIS-GPP) and evapotranspiration (MODIS-ET) products in China’s different land cover types and their sensitivity to remote sensing input data. In this study, MODIS-GPP and -ET are evaluated using flux derived/measured data from eight sites of ChinaFLUX. Results show that MODIS-GPP generally underestimates GPP (R2 is 0.58, bias is −6.7 gC/m2/8-day and RMSE is 19.4 gC/m2/8-day) at all sites and MODIS-ET overestimates ET (R2 is 0.36, bias is 6 mm/8-day and RMSE is 11 mm/8-day) when comparing with derived GPP and measured ET, respectively. For evergreen forests, MODIS-GPP gives a poor performance with R2 varying from 0.03 to 0.44; in contrast, MODIS-ET provides more reliable results. In croplands, MODIS-GPP can explain 80% of GPP variance, but it overestimates flux derived GPP in non-growing season and underestimates flux derived GPP in growing season; similar overestimations also presented in MODIS-ET. For grasslands and mixed forests, MODIS-GPP and -ET perform good estimating accuracy. By designing four experimental groups and taking GPP simulation as an example, we suggest that the maximum light use efficiency of croplands should be optimized, and the differences of meteorological data have little impact on GPP estimation, whereas remote sensing leaf area index/fraction of photo-synthetically active radiation (LAI/FPAR) can greatly affect GPP/ET estimations for all land cover types. Thus, accurate remote sensing parameters are important for achieving reliable estimations. View Full-Text
Keywords: gross primary production (GPP); evapotranspiration (ET); MODIS; uncertainty; land cover types; ChinaFLUX gross primary production (GPP); evapotranspiration (ET); MODIS; uncertainty; land cover types; ChinaFLUX

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Liu, Z.; Shao, Q.; Liu, J. The Performances of MODIS-GPP and -ET Products in China and Their Sensitivity to Input Data (FPAR/LAI). Remote Sens. 2015, 7, 135-152.

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