{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T14:31:36Z","timestamp":1774621896972,"version":"3.50.1"},"reference-count":123,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T00:00:00Z","timestamp":1728086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hulunbuir Grassland Ecological Restoration Comprehensive Survey Project","award":["DD20230474"],"award-info":[{"award-number":["DD20230474"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precisely estimating the grassland biomass carbon storage is vital for evaluating grassland carbon sequestration potential and the monitoring and management of grassland resources. With the increasing intensity of climate change (CC) and human activities (HA), it is necessary to explore spatiotemporal variations in biomass carbon storage and its response to CC and HA. In this study, we focused on the Hulunbuir Grassland, utilizing sample plots data, MODIS data, environmental factors (terrain, soil, and climate), location factor, and texture characteristics to assess the performance of four machine learning algorithms: random forest, support vector machine, gradient boosting decision tree, and extreme gradient boosting in estimating grassland aboveground biomass (AGB). Based on the optimal model combined with root-shoot ratio data, grassland distribution data, and carbon content coefficients, the spatiotemporal characteristics and driving factors of biomass carbon storage from 2001\u20132022 were analyzed. The results showed that (1) the random forest achieved the highest prediction accuracy for grassland AGB, making it appropriate for AGB estimation in the Hulunbuir Grassland. (2) The spectral indices were the key variables of the grassland AGB, especially the enhanced vegetation index and difference vegetation index. (3) The 22-year average total biomass (TB) of the study area was 1037.10 gC\/m2, of which the 22-year average AGB was 48.73 gC\/m2 and 22-year average belowground biomass was 988.37 gC\/m2, showing a spatial distribution feature of gradual increase from west to east. (4) From 2001\u20132022, TB carbon storage showed an insignificant growth trend (p &gt; 0.05). The 22-year average carbon storage of TB was 72.34 \u00b1 18.07 gC. (5) Climate factors were the main driving factors for the spatial pattern of grassland TB carbon density, while the combined effects of CC and HA were the main contributors to the interannual increase in grassland TB carbon density.<\/jats:p>","DOI":"10.3390\/rs16193709","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T07:30:18Z","timestamp":1728286218000},"page":"3709","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Estimation, Spatiotemporal Dynamics, and Driving Factors of Grassland Biomass Carbon Storage Based on Machine Learning Methods: A Case Study of the Hulunbuir Grassland"],"prefix":"10.3390","volume":"16","author":[{"given":"Qiuying","family":"Zhi","sequence":"first","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences, Beijing 100012, China"},{"name":"College of Ecology, Lanzhou University, Lanzhou 730000, China"},{"name":"China Geological Survey Comprehensive Survey Command Center for Natural Resources, Beijing 100055, China"}]},{"given":"Xiaosheng","family":"Hu","sequence":"additional","affiliation":[{"name":"China Geological Survey Comprehensive Survey Command Center for Natural Resources, Beijing 100055, China"}]},{"given":"Ping","family":"Wang","sequence":"additional","affiliation":[{"name":"China Geological Survey Comprehensive Survey Command Center for Natural Resources, Beijing 100055, China"}]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[{"name":"China Geological Survey Comprehensive Survey Command Center for Natural Resources, Beijing 100055, China"}]},{"given":"Yi","family":"Ding","sequence":"additional","affiliation":[{"name":"China Geological Survey Comprehensive Survey Command Center for Natural Resources, Beijing 100055, China"}]},{"given":"Yuxuan","family":"Wu","sequence":"additional","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences, Beijing 100012, China"}]},{"given":"Tiantian","family":"Peng","sequence":"additional","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences, Beijing 100012, China"}]},{"given":"Wenjie","family":"Li","sequence":"additional","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences, Beijing 100012, China"}]},{"given":"Xiao","family":"Guan","sequence":"additional","affiliation":[{"name":"Chinese Research Academy of Environmental Sciences, Beijing 100012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0089-0571","authenticated-orcid":false,"given":"Xiaoming","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Ecology, Lanzhou University, Lanzhou 730000, China"}]},{"given":"Junsheng","family":"Li","sequence":"additional","affiliation":[{"name":"China Geological Survey Comprehensive Survey Command Center for Natural Resources, Beijing 100055, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1109\/JSTARS.2014.2321432","article-title":"Pan-European Grassland Mapping Using Seasonal Statistics From Multisensor Image Time Series","volume":"7","author":"Zillmann","year":"2014","journal-title":"IEEE J. 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