Assessment of Satellite-Derived FAPAR Products With Different Spatial Resolutions for Gross Primary Productivity Estimation

Accurate estimation of gross primary productivity (GPP) is crucial for understanding terrestrial carbon cycles and assessing ecosystem health. Light use efficiency (LUE) models, which are widely used for the generation of regional or global GPP products, often rely on the fraction of absorbed photosynthetically active radiation (FAPAR). However, most of the existing FAPAR products with moderate to coarse spatial resolutions introduce uncertainties in GPP estimations across heterogeneous landscapes. In this work, the MODerate resolution Imaging Spectroradiometer (MODIS) FAPAR product at the 500-m resolution, along with a new HIgh-spatial-resolution Global LAnd Surface Satellite (Hi-GLASS) FAPAR dataset at the 30-m resolution, was used to drive an LUE model for GPP estimations at 188 eddy covariance (EC) sites. Then, they were compared and evaluated based on the EC GPP measurements. Results showed that Hi-GLASS FAPAR provided the GPP estimates with more detailed spatial information compared with MODIS FAPAR. Moreover, Hi-GLASS FAPAR significantly improved GPP estimations, with an overall <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> increase from 0.54 (MODIS) to 0.63 (Hi-GLASS) and a root-mean-square error (RMSE) decrease from 3.04 to 2.70 gC⋅m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup>⋅day<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup>. In addition, 75% of the selected sites exhibited enhanced <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values with Hi-GLASS FAPAR, demonstrating its application potential in GPP estimations across different vegetation types. Specifically, crop sites exhibited the most notable improvements, with an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> increase of 0.16 and an RMSE decrease of 0.70 gC⋅m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup>⋅day<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup>. These findings highlight the advantages of high-resolution FAPAR data in capturing spatial heterogeneity and improving the accuracy of GPP estimations and underscore its potential for refined ecosystem monitoring.