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
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 wi...
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Elsevier
2025-02-01
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| Series: | Data in Brief |
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| author | Zhilong Li Ziti Jiao Ge Gao Jing Guo Chenxia Wang Sizhe Chen Zheyou Tan |
| author_facet | Zhilong Li Ziti Jiao Ge Gao Jing Guo Chenxia Wang Sizhe Chen Zheyou Tan |
| author_sort | Zhilong Li |
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| description | 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. |
| format | Article |
| id | doaj-art-f937056f5e704fdfbbb1f43f09192216 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
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| spelling | doaj-art-f937056f5e704fdfbbb1f43f091922162025-01-31T05:11:50ZengElsevierData in Brief2352-34092025-02-0158111298A global gross primary productivity of sunlit and shaded canopies dataset from 2002 to 2020 via embedding random forest into two-leaf light use efficiency modelZenodoZhilong Li0Ziti Jiao1Ge Gao2Jing Guo3Chenxia Wang4Sizhe Chen5Zheyou Tan6State 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, ChinaState 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.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, ChinaState 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, ChinaState 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, ChinaState 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, ChinaState 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, ChinaGross 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.http://www.sciencedirect.com/science/article/pii/S2352340925000307GPPEnvironmental stress factorsHybrid modelTemporal-spatial patterns |
| spellingShingle | Zhilong Li Ziti Jiao Ge Gao Jing Guo Chenxia Wang Sizhe Chen Zheyou Tan 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 Data in Brief GPP Environmental stress factors Hybrid model Temporal-spatial patterns |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | GPP Environmental stress factors Hybrid model Temporal-spatial patterns |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925000307 |
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