{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T02:58:20Z","timestamp":1774493900985,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,25]],"date-time":"2022-12-25T00:00:00Z","timestamp":1671926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences (Pan-Third Pole Environment Study for a Green Silk Road)","award":["XDA20040202"],"award-info":[{"award-number":["XDA20040202"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences (Pan-Third Pole Environment Study for a Green Silk Road)","award":["2022YFC3201701"],"award-info":[{"award-number":["2022YFC3201701"]}]},{"name":"National Key Scientific Research Project","award":["XDA20040202"],"award-info":[{"award-number":["XDA20040202"]}]},{"name":"National Key Scientific Research Project","award":["2022YFC3201701"],"award-info":[{"award-number":["2022YFC3201701"]}]},{"name":"Western Light Interdisciplinary Team-Key Laboratory Cooperative Research Project, Chinese Academy Sciences","award":["XDA20040202"],"award-info":[{"award-number":["XDA20040202"]}]},{"name":"Western Light Interdisciplinary Team-Key Laboratory Cooperative Research Project, Chinese Academy Sciences","award":["2022YFC3201701"],"award-info":[{"award-number":["2022YFC3201701"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil organic carbon (SOC) is a critical indicator for the global carbon cycle and the overall carbon pool balance. Obtaining soil maps of surface SOC is fundamental to evaluating soil quality, regulating climate change, and global carbon cycle modeling. However, efficient approaches for obtaining accurate SOC information remain challenging, especially in remote or inaccessible regions of the Qinghai\u2013Tibet Plateau (QTP), which is influenced by complex terrains, climate change, and human activities. This study employed field measurements, SoilGrids250m (SOC_250m, a spatial resolution of 250 m \u00d7 250 m), and Sentinel-2 images with different machine learning methods to map SOC content in the QTP. Four machine learning methods including partial least squares regression (PLSR), support vector machines (SVM), random forest (RF), and artificial neural network (ANN) were used to construct spatial prediction models based on 396 field-collected sampling points and various covariates from remote sensing images. Our results revealed that the RF model outperformed the PLSR, SVM, and ANN models, with a higher determination coefficient (R2 of 0.82 is from the training datasets) and the ratio of performance to deviation (RPD = 2.54). The selected covariates according to the variable importance in projection (VIP) were: SOC_250m, B2, B11, Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), B5, and Soil-Adjusted Total Vegetation Index (SATVI). The predicted SOC map showed an overall decrease in SOC content ranging from 69.30 g\u00b7kg\u22121 in the southeast to 1.47 g\u00b7kg\u22121 in the northwest. Our prediction showed spatial heterogeneity of SOC content, indicating that Sentinel-2 images were acceptable for characterizing the variability of SOC. The findings provide a scientific basis for carbon neutrality in the QTP and a reference for the digital mapping of SOC in the alpine region.<\/jats:p>","DOI":"10.3390\/rs15010114","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T02:50:01Z","timestamp":1672109401000},"page":"114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Improved Surface Soil Organic Carbon Mapping of SoilGrids250m Using Sentinel-2 Spectral Images in the Qinghai\u2013Tibetan Plateau"],"prefix":"10.3390","volume":"15","author":[{"given":"Jiayi","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Soil Erosion and Dryland Framing on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Xianyang 712100, China"},{"name":"Institute of Soil and Water Conservation, Chinese Academy of Science and Ministry of Water Resources, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjian","family":"Fan","sequence":"additional","affiliation":[{"name":"Institute of Soil and Water Conservation, Chinese Academy of Science and Ministry of Water Resources, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zefan","family":"Lan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Soil Erosion and Dryland Framing on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Xianyang 712100, China"},{"name":"Institute of Soil and Water Conservation, Chinese Academy of Science and Ministry of Water Resources, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingmin","family":"Mu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Soil Erosion and Dryland Framing on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Xianyang 712100, China"},{"name":"Institute of Soil and Water Conservation, Chinese Academy of Science and Ministry of Water Resources, Xianyang 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5163-0884","authenticated-orcid":false,"given":"Yiping","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Earth and Environmental Science, School of Human Settlements and Civil Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongbao","family":"Xin","sequence":"additional","affiliation":[{"name":"Institute of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Puqiong","family":"Miping","sequence":"additional","affiliation":[{"name":"Hydrology and Water Resources Branch Bureau of Shigatsa, Hydrology and Water Resources Geological Bureau of Tibet Autonomous Region, Shigatsa 857000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4233-9403","authenticated-orcid":false,"given":"Guangju","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Soil Erosion and Dryland Framing on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Xianyang 712100, China"},{"name":"Institute of Soil and Water Conservation, Chinese Academy of Science and Ministry of Water Resources, Xianyang 712100, China"},{"name":"Sate Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112914","DOI":"10.1016\/j.rse.2022.112914","article-title":"Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing","volume":"271","author":"Wang","year":"2022","journal-title":"Remote Sens. 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