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

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 deriv...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2015-01, Vol.7 (1), p.135-152
Main Authors: Liu, Zhengjia, Shao, Quanqin, Liu, Jiyuan
Format: Article
Language:English
Subjects:
Agricultural land
Bias
China
ChinaFLUX
Evapotranspiration
evapotranspiration (ET)
Fluctuations
Flux
Forests
Grasslands
gross primary production (GPP)
Growing season
Land cover
land cover types
Leaf area index
Mixed forests
MODIS
Primary production
Remote sensing
Seasons
uncertainty
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs70100135