{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T00:39:35Z","timestamp":1783730375276,"version":"3.55.0"},"reference-count":90,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,4]],"date-time":"2022-09-04T00:00:00Z","timestamp":1662249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFC3200205"],"award-info":[{"award-number":["2021YFC3200205"]}]},{"name":"National Key Research and Development Program of China","award":["2022A1515010676"],"award-info":[{"award-number":["2022A1515010676"]}]},{"name":"National Key Research and Development Program of China","award":["51909285"],"award-info":[{"award-number":["51909285"]}]},{"name":"Natural Science Foundation of Guangdong Province, China","award":["2021YFC3200205"],"award-info":[{"award-number":["2021YFC3200205"]}]},{"name":"Natural Science Foundation of Guangdong Province, China","award":["2022A1515010676"],"award-info":[{"award-number":["2022A1515010676"]}]},{"name":"Natural Science Foundation of Guangdong Province, China","award":["51909285"],"award-info":[{"award-number":["51909285"]}]},{"name":"National Natural Science Foundation of China","award":["2021YFC3200205"],"award-info":[{"award-number":["2021YFC3200205"]}]},{"name":"National Natural Science Foundation of China","award":["2022A1515010676"],"award-info":[{"award-number":["2022A1515010676"]}]},{"name":"National Natural Science Foundation of China","award":["51909285"],"award-info":[{"award-number":["51909285"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Along with the development of remote sensing technology, the spatial\u2013temporal 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.<\/jats:p>","DOI":"10.3390\/rs14174401","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4401","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Exploring the Individualized Effect of Climatic Drivers on MODIS Net Primary Productivity through an Explainable Machine Learning Framework"],"prefix":"10.3390","volume":"14","author":[{"given":"Luyi","family":"Li","sequence":"first","affiliation":[{"name":"Center for Water Resources and Environment, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenzhong","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4545-3375","authenticated-orcid":false,"given":"Guo","family":"Zhang","sequence":"additional","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing 100081, China"},{"name":"State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Duan","sequence":"additional","affiliation":[{"name":"Center for Water Resources and Environment, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9393-0674","authenticated-orcid":false,"given":"Bingjun","family":"Liu","sequence":"additional","affiliation":[{"name":"Center for Water Resources and Environment, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xitian","family":"Cai","sequence":"additional","affiliation":[{"name":"Center for Water Resources and Environment, School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1126\/science.aam8328","article-title":"Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models","volume":"359","author":"Bonan","year":"2018","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lovenduski, N.S., and Bonan, G.B. 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