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eLUE-GPP (MODIS): A global gross primary productivity product based on ecosystem light-use-efficiency model and MODIS EVI

Ma, Xuanlong1; Cao, Chunyan1; Yang, Wei2; Yan, Kai3; Liu, Feng4; Wang, Yuanyuan5; Huete, Alfredo6

Published Jun 25, 2024; Updated Jan 16, 2025 on Dryad. https://doi.org/10.5061/dryad.v9s4mw74h

Data files

Jun 25, 2024 version files 8.30 GB
Aug 16, 2024 version files 7.07 GB
Jan 16, 2025 version files 7.46 GB

Abstract

Gross Primary Productivity (GPP) represents the cumulative amount of carbon dioxide (CO2) assimilated by green plants through photosynthesis at specific time intervals and spatial scales. It is the main component of the carbon exchange between the terrestrial biosphere and the atmosphere, and has a major influence on global climate and terrestrial ecosystem functioning. Over the last two decades, the continuous and reliable collection of global land surface variables by EOS-MODIS, and the parallel development of the eddy-covariance flux tower network (FLUXNET) have enabled the integration of MODIS observations with tower measurements for the calibration and validation of remote sensing models to obtain global GPP estimates. Despite the significant progress and success to date, current remote sensing GPP models based on the light use efficiency (LUE) concept share several limitations, including the difficulty in accurately predicting LUE variability and the associated use of land cover maps and look-up tables for biome specific maximum LUE, further down-regulated by coarse resolution interpolated meteorological data, which introduce significant uncertainties in the predicted GPP. To address the above limitations, here we applied a simple yet ecologically sound remote sensing GPP model based on the ecosystem light use efficiency (eLUE) concept, using the more than two decades of global MODIS Enhanced Vegetation Index (EVI) product and the publicly available FLUXDATA2015 dataset, to generate a global 5 km, 16-d GPP product (eLUE-GPP) from February 2000 to December 2024. Cross-validation with 120 flux tower sites (952.66 site/year) showed favorable accuracy of eLUE-GPP (hereafter GPPeLUE) (R2 = 0.74, RMSE = 2.05 g C m-2 d-1). The uncertainty associated with GPPeLUE is comparatively lower than that of the other global GPP datasets (MOD17,  VPM, among others). We have also calculated the uncertainty analytically for each GPP estimate based on the law of error propagation, which allows quantification of the error budget in applications such as Earth system model benchmarking and atmospheric inversion. Our estimate of global total annual GPP, averaged over the period 2001-2024, was 135.53±11.03 Pg C yr-1. Furthermore, we found a significant increasing trend in global total annual GPP at a rate of 0.26±0.06 Pg C yr-1 (p < 0.001) from 2001 to 2024, particularly in eastern Asia, northern India, Europe, eastern North America, and central South America. We expect that the eLUE-GPP product will enable a more accurate diagnostic analysis of the global carbon budget and thus contribute to climate change research.