{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T13:55:39Z","timestamp":1775483739920,"version":"3.50.1"},"reference-count":81,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:00:00Z","timestamp":1604361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018M642349"],"award-info":[{"award-number":["2018M642349"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801075"],"award-info":[{"award-number":["41801075"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences","award":["WSGS2017009"],"award-info":[{"award-number":["WSGS2017009"]}]},{"name":"Natural Science Foundation Project of Universities and Institutes in Jiangsu Province","award":["18KJB170002"],"award-info":[{"award-number":["18KJB170002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties.<\/jats:p>","DOI":"10.3390\/rs12213609","type":"journal-article","created":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T09:09:32Z","timestamp":1604394572000},"page":"3609","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods"],"prefix":"10.3390","volume":"12","author":[{"given":"Xinchuan","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"School of Urban and Environmental Sciences, Huaiyin Normal University, Huai\u2019an 223300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juhua","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2720-6247","authenticated-orcid":false,"given":"Xiuliang","family":"Jin","sequence":"additional","affiliation":[{"name":"Institute of Crop Sciences, Chinese Academy of Agricultural Sciences\/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiaoning","family":"He","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Sciences, Huaiyin Normal University, Huai\u2019an 223300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Sciences, Huaiyin Normal University, Huai\u2019an 223300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"83","DOI":"10.5194\/soil-4-83-2018","article-title":"A systemic approach for modeling soil functions","volume":"4","author":"Vogel","year":"2018","journal-title":"Soil"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s11104-007-9235-3","article-title":"Influence of soil thickness on stand characteristics in a Sierra Nevada mixed-conifer forest","volume":"294","author":"Meyer","year":"2007","journal-title":"Plant Soil."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.jaridenv.2009.11.002","article-title":"The impact of soil depth on land surface energy and water fluxes in the North American Monsoon region","volume":"74","author":"Gochis","year":"2010","journal-title":"J. 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