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

Jiyuan Liu,Zhengjia Liu,Quanqin Shao

doi:10.3390/rs70100135

Abstract

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.

Highlights

The evaluation of terrestrial ecosystem carbon and water dynamic is a key issue in global climate change research [1,2]
For evergreen broadleaf forests (Figure 2b,d), MODIS-Gross primary production (GPP) has the worst quality with R2 in a range of 0.03–0.44, bias from
For croplands (Figure 2e), it has higher R2 (0.80) and smaller bias (−4.8 gC/m2/8-day), measured GPP is still underestimated by MODIS-GPP in growing season and overestimated in non-growing season (Figure 2e), leading to a greater RMSE (30.0 gC/m2/8-day)

Summary

Introduction

The evaluation of terrestrial ecosystem carbon and water dynamic is a key issue in global climate change research [1,2]. Gross primary production (GPP) and evapotranspiration (ET) can provide useful information for water and soil resource management, global carbon-water cycle analysis and environmental change monitoring. To understand the mechanisms of climate change and improve the prediction of possible future climate change, we require accurate quantifications of carbon and water processes in terrestrial vegetation [3]. Substantial differences of GPP and ET are observed among estimated results from different models, which prevent us from fully understanding global carbon-water cycle. The uncertainties of estimated GPP and ET still need to be addressed at global and regional scales

Objectives

Results

Discussion

Conclusion