{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T18:55:32Z","timestamp":1772650532268,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T00:00:00Z","timestamp":1615939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31870459"],"award-info":[{"award-number":["31870459"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2016YFA0602703"],"award-info":[{"award-number":["2016YFA0602703"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project of Sun Yat-sen University","award":["19lgjc02"],"award-info":[{"award-number":["19lgjc02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To forecast the terrestrial carbon cycle and monitor food security, vegetation growth must be accurately predicted; however, current process-based ecosystem and crop-growth models are limited in their effectiveness. This study developed a machine learning model using the extreme gradient boosting method to predict vegetation growth throughout the growing season in China from 2001 to 2018. The model used satellite-derived vegetation data for the first month of each growing season, CO2 concentration, and several meteorological factors as data sources for the explanatory variables. Results showed that the model could reproduce the spatiotemporal distribution of vegetation growth as represented by the satellite-derived normalized difference vegetation index (NDVI). The predictive error for the growing season NDVI was less than 5% for more than 98% of vegetated areas in China; the model represented seasonal variations in NDVI well. The coefficient of determination (R2) between the monthly observed and predicted NDVI was 0.83, and more than 69% of vegetated areas had an R2 &gt; 0.8. The effectiveness of the model was examined for a severe drought year (2009), and results showed that the model could reproduce the spatiotemporal distribution of NDVI even under extreme conditions. This model provides an alternative method for predicting vegetation growth and has great potential for monitoring vegetation dynamics and crop growth.<\/jats:p>","DOI":"10.3390\/rs13061147","type":"journal-article","created":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T11:48:22Z","timestamp":1615981702000},"page":"1147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["A Machine Learning Method for Predicting Vegetation Indices in China"],"prefix":"10.3390","volume":"13","author":[{"given":"Xiangqian","family":"Li","sequence":"first","affiliation":[{"name":"School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China"},{"name":"Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai 519082, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China"}]},{"given":"Wenping","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China"},{"name":"Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai 519082, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China"}]},{"given":"Wenjie","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China"},{"name":"Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai 519082, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1126\/science.1192666","article-title":"Drought-induced reduction in global terrestrial net primary production from 2000 through 2009","volume":"329","author":"Zhao","year":"2010","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"915","DOI":"10.5194\/esd-9-915-2018","article-title":"Recent changes in relative humidity: Regional connections with land and ocean processes","volume":"9","author":"Nieto","year":"2018","journal-title":"Earth Syst. 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