A global gross primary productivity of sunlit and shaded canopies dataset from 2002 to 2020 via embedding random forest into two-leaf light use efficiency modelZenodo
Zhilong Li,
Ziti Jiao,
Ge Gao,
Jing Guo,
Chenxia Wang,
Sizhe Chen,
Zheyou Tan
Affiliations
Zhilong Li
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Ziti Jiao
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University, Beijing 100875, China; Corresponding author at: State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China.
Ge Gao
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jing Guo
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Chenxia Wang
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Sizhe Chen
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Zheyou Tan
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China; Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Gross primary productivity (GPP) is crucial for understanding the carbon cycle and maintaining ecosystem balance under climate change. We attempt to generate a long-term global dataset for GPP of sunlit (GPPsu) and shaded leaves (GPPsh) by a hybrid model combining the random forest (RF) submodule with the two-leaf light use efficiency (TL-LUE) model. First, the TL-LUE model was optimized by considering the seasonal differences in the clumping index on a global scale (TL-CLUE). Then, we used the RF technique to integrate various environmental stress factors, including meteorological factors, hydrological variables, soil properties, and elevation, which originate from the NASA MERRA-2 dataset, ISRIC soil Grids, and USGS data center. Furthermore, the RF submodule was embedded into the TL-CLUE model to construct the hybrid model (TL-CRF), which was trained and evaluated based on global eddy covariance (EC) site data from the AmeriFlux and FLUXNET2015 datasets. We produced a global GPP, GPPsu, and GPPsh dataset with a spatial resolution of 0.05 × 0.05° over 2002–2020 by the TL-CRF model driven by the LP DACC leaf area index and land cover, NASA MERRA-2 incoming shortwave solar radiation, and the above environmental variables. This GPP product provides a data basis for improving our understanding of the dynamics of global vegetation productivity and its interactions with the changes in environmental conditions.
