Abstract
Accurate simulations of the spatial and temporal changes in vegetation gross primary production (GPP) play an important role in ecological studies. Previous studies highlighted large uncertainties in GPP datasets based on satellite data with coarse spatial resolutions (>500 m), and implied the need to produce high-spatial-resolution datasets. However, estimating fine spatial resolution GPP is time-consuming and requires an enormous amount of computing storage space. In this study, based on the Eddy Covariance-Light Use Efficiency (EC-LUE) model, we used Google Earth Engine (GEE) to develop a web application (EC-LUE APP) to generate 30-m-spatial-resolution GPP estimates within a region of interest. We examined the accuracy of the GPP estimates produced by the APP and compared them with observed GPP at 193 global eddy covariance sites. The results showed the good performance of the EC-LUE APP in reproducing the spatial and temporal variations in the GPP. The fine-spatial-resolution GPP product (GPPL) explained 64% of the GPP variations and had fewer uncertainties (root mean square error = 2.34 g C m−2 d−1) and bias (−0.09 g C m−2 d−1) than the coarse-spatial-resolution GPP products. In particular, the GPPL significantly improved the GPP estimations for cropland and dryland ecosystems. With this APP, users can easily obtain 30-m-spatial-resolution GPP at any given location and for any given year since 1984.
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