Exploring the Individualized Effect of Climatic Drivers on MODIS Net Primary Productivity through an Explainable Machine Learning Framework

Li, Luyi1; Zeng, Zhenzhong2; Zhang, Guo3,4; Duan, Kai1; Liu, Bingjun1; Cai, Xitian1,5

2022-09-01

10.3390/rs14174401

REMOTE SENSING   影响因子和分区

2072-4292

Along with the development of remote sensing technology, the spatial-temporal variability of vegetation productivity has been well observed. However, the drivers controlling the variation in vegetation under various climate gradients remain poorly understood. Identifying and quantifying the independent effects of driving factors on a natural process is challenging. In this study, we adopted a potent machine learning (ML) model and an ML interpretation technique with high fidelity to disentangle the effects of climatic variables on the long-term averaged net primary productivity (NPP) across the Amazon rainforests. Specifically, the eXtreme Gradient Boosting (XGBoost) model was employed to model the Moderate-resolution Imaging Spectroradiometer (MODIS) NPP data, and the Shapley addictive explanation (SHAP) method was introduced to account for nonlinear relationships between variables identified by the model. Results showed that the dominant driver of NPP across the Amazon forests varied in different regions, with temperature dominating the most considerable portion of the ecoregion with a high importance score. In addition, light augmentation, increased CO2 concentration, and decreased precipitation positively contributed to Amazonia NPP. The wind speed for most vegetated areas was under the optimum, which benefits NPP, while sustained high wind speed would bring substantial NPP loss. We also found a non-monotonic response of Amazonia NPP to VPD and attributed this relationship to the moisture load in Amazon forests. Our application of the explainable machine learning framework to identify the underlying physical mechanism behind NPP could be a reference for identifying relationships between components in natural processes.

net primary productivity MODIS vegetation response climatic drivers machine learning Amazon

SCI ; EI

英语

其他

National Key Research and Development Program of China[2021YFC3200205] ; Natural Science Foundation of Guangdong Province, China[2022A1515010676] ; National Natural Science Foundation of China[51909285]

Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology

Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology

WOS:000851992000001

MDPI

20223812750918

Ecosystems ; Forestry ; Learning systems ; Machine components ; Photosynthesis ; Phytoplankton ; Radiometers ; Remote sensing ; Vegetation ; Wind

Atmospheric Properties:443.1 ; Ecology and Ecosystems:454.3 ; Biology:461.9 ; Marine Science and Oceanography:471 ; Machine Components:601.2 ; Artificial Intelligence:723.4 ; Light/Optics:741.1 ; Chemical Reactions:802.2 ; Agricultural Equipment and Methods; Vegetation and Pest Control:821 ; Radiation Measuring Instruments:944.7

Web of Science

1.Sun Yat Sen Univ, Ctr Water Resources & Environm, Sch Civil Engn, Guangzhou 510275, Peoples R China
2.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Peoples R China
3.China Meteorol Adm, CMA Earth Syst Modeling & Predict Ctr, Beijing 100081, Peoples R China
4.Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China
5.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China

Li, Luyi,Zeng, Zhenzhong,Zhang, Guo,et al. Exploring the Individualized Effect of Climatic Drivers on MODIS Net Primary Productivity through an Explainable Machine Learning Framework[J]. REMOTE SENSING,2022,14(17).

Li, Luyi,Zeng, Zhenzhong,Zhang, Guo,Duan, Kai,Liu, Bingjun,&Cai, Xitian.(2022).Exploring the Individualized Effect of Climatic Drivers on MODIS Net Primary Productivity through an Explainable Machine Learning Framework.REMOTE SENSING,14(17).

Li, Luyi,et al."Exploring the Individualized Effect of Climatic Drivers on MODIS Net Primary Productivity through an Explainable Machine Learning Framework".REMOTE SENSING 14.17(2022).