{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T22:46:53Z","timestamp":1775861213722,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"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":["2021YFD1500101"],"award-info":[{"award-number":["2021YFD1500101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil texture is a key physical property that affects the soil\u2019s ability to retain moisture and nutrients. As a result, it is of extreme importance to conduct remote sensing monitoring of soil texture. Songnen Plain is located in the black soil belt of Northeast China. The development of satellite imagery in remote sensing technology enables the rapid monitoring of large areas. This study aimed to map the surface soil texture of cultivated land in Songnen Plain using Sentinel-2 images and Random Forest (RF) algorithm. We conducted this study by collecting 354 topsoil (0\u201320 cm) samples in Songnen Plain and evaluating the effectiveness of the bands and spectral indices of Sentinel-2 images and RF algorithm in predicting soil texture (sand, silt, and clay fractions). The results demonstrated that the 16 covariates were moderately and highly correlated with soil texture. And, Band11 of Sentinel-2 images could be used as the corresponding band of soil texture. For sand fraction, the Sentinel-2 images and RF algorithm\u2019s Coefficient of Determination (R2) and Root Mean Square Error (RMSE) were 0.77 and 10.48%, respectively, and for silt fraction, they were 0.75 and 9.38%. Sand fraction decreased from southwest to northeast in Songnen Plain, while silt and clay fractions increased. We found that the Songnen Plain was affected by water erosion and wind erosion, in the northeast and southwest, respectively, providing reference for the implementation of Conservation Tillage policies. The outcome of the study can provide reference for future soil texture mapping with a high resolution.<\/jats:p>","DOI":"10.3390\/rs15225351","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T09:46:13Z","timestamp":1699955173000},"page":"5351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Soil Texture Mapping in Songnen Plain of China Using Sentinel-2 Imagery"],"prefix":"10.3390","volume":"15","author":[{"given":"Miao","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Geographic Science and Tourism, Jilin Normal University, Siping 136000, China"},{"name":"State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Xiang","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4605-0612","authenticated-orcid":false,"given":"Sijia","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Bingxue","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Junbin","family":"Hou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Kaishan","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104289","DOI":"10.1016\/j.still.2019.06.006","article-title":"Some practical aspects of predicting texture data in digital soil mapping","volume":"194","author":"Minasny","year":"2019","journal-title":"Soil Tillage Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"541","DOI":"10.5194\/soil-8-541-2022","article-title":"On the benefits of clustering approaches in digital soil mapping: An application example concerning soil texture regionalization","volume":"8","author":"Dunkl","year":"2022","journal-title":"SOIL"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.geoderma.2009.02.004","article-title":"Changes in carbon and nitrogen in soil particle-size fractions along a grassland restoration chronosequence in northern China","volume":"150","author":"He","year":"2009","journal-title":"Geoderma"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"114061","DOI":"10.1016\/j.geoderma.2019.114061","article-title":"High-resolution and three-dimensional mapping of soil texture of China","volume":"361","author":"Liu","year":"2020","journal-title":"Geoderma"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.geomorph.2017.02.015","article-title":"Comparing the efficiency of digital and conventional soil mapping to predict soil types in a semi-arid region in Iran","volume":"285","author":"Zeraatpisheh","year":"2017","journal-title":"Geomorphology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2505","DOI":"10.5194\/hess-24-2505-2020","article-title":"Systematic comparison of five machine-learning models in classification and interpolation of soil particle size fractions using different transformed data","volume":"24","author":"Zhang","year":"2020","journal-title":"Hydrol. 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