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

Remote Sensing

Zhengjia Liu¹  Quanqin Shao¹  Jiyuan Liu¹  Janet Nichol² 

[1] Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; E-Mails:Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

关键词: gross primary production (GPP);    evapotranspiration (ET);    MODIS;    uncertainty;    land cover types;    ChinaFLUX;   

DOI  :  10.3390/rs70100135

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.