{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T21:01:38Z","timestamp":1769115698370,"version":"3.49.0"},"reference-count":55,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T00:00:00Z","timestamp":1698969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41930754"],"award-info":[{"award-number":["41930754"]}]},{"name":"National Natural Science Foundation of China","award":["42071072"],"award-info":[{"award-number":["42071072"]}]},{"name":"National Natural Science Foundation of China","award":["ISSASIP2202"],"award-info":[{"award-number":["ISSASIP2202"]}]},{"name":"Institute of Soil Science, Chinese Academy of Sciences","award":["41930754"],"award-info":[{"award-number":["41930754"]}]},{"name":"Institute of Soil Science, Chinese Academy of Sciences","award":["42071072"],"award-info":[{"award-number":["42071072"]}]},{"name":"Institute of Soil Science, Chinese Academy of Sciences","award":["ISSASIP2202"],"award-info":[{"award-number":["ISSASIP2202"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Readily available environmental covariates in current digital soil mapping usually do not indicate the spatial differences between deep soil attributes. This, to a large extent, leads to a decrease in the accuracy of 3D soil mapping with depth, which seriously affects the quality of soil information generated. This study tested the hypothesis that spatialized laboratory soil spectral information can be used as environmental covariates to improve the accuracy of 3D soil attribute mapping and proposed a new type of environmental covariable. In the first step, with soil-forming environmental covariates and independent soil profiles, laboratory vis-NIR spectral data of soil samples resampled into six bands in Anhui province, China, were spatially interpolated to generate spatial distributions of soil spectral measurements at multiple depths. In the second step, we constructed three sets of covariates using the laboratory soil spectral distribution maps at multiple depths: conventional soil-forming variables (C), conventional soil-forming variables plus satellite remote sensing wavebands (C+SRS) and conventional soil-forming variables plus spatialized laboratory soil spectral information (C+LSS). In the third step, we used the three sets of environmental covariates to develop random forest models for predicting soil attributes (pH; CEC, cation exchange capacity; Silt; SOC, soil organic carbon; TP, total phosphorus) at multiple depths. We compared the 3D soil mapping accuracies between these three sets of covariates based on another dataset of 132 soil profiles (collected in the 1980s). The results show that the use of spatialized laboratory soil spectral information as additional environmental covariates has a 50% improvement in prediction accuracy compared with that of only conventional covariates, and a 30% improvement in prediction accuracy compared with that of the satellite remote sensing wavebands as additional covariates. This indicates that spatialized laboratory soil spectral information can improve the accuracy of 3D digital soil mapping.<\/jats:p>","DOI":"10.3390\/rs15215228","type":"journal-article","created":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T10:59:54Z","timestamp":1699009194000},"page":"5228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improving 3D Digital Soil Mapping Based on Spatialized Lab Soil Spectral Information"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7847-4703","authenticated-orcid":false,"given":"Zheng","family":"Sun","sequence":"first","affiliation":[{"name":"State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"College of Forestry, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Decai","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Forestry, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Huayong","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China"}]},{"given":"Ganlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Watershed Geographic Science, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0016-7061(03)00223-4","article-title":"On digital soil mapping","volume":"117","author":"McBratney","year":"2003","journal-title":"Geoderma"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.geoderma.2011.05.007","article-title":"Soil texture mapping over low relief areas using land surface feedback dynamic patterns extracted from MODIS","volume":"171\u2013172","author":"Liu","year":"2012","journal-title":"Geoderma"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"114556","DOI":"10.1016\/j.geoderma.2020.114556","article-title":"A soil colour map of China","volume":"379","author":"Liu","year":"2020","journal-title":"Geoderma"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"115567","DOI":"10.1016\/j.geoderma.2021.115567","article-title":"Digital mapping of GlobalSoilMap soil properties at a broad scale: A review","volume":"409","author":"Chen","year":"2022","journal-title":"Geoderma"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.geoderma.2006.03.042","article-title":"Mechanistic soil-landscape modelling as an approach to developing pedogenetic classifications","volume":"113","author":"Minasny","year":"2006","journal-title":"Geoderma"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1071\/SR05136","article-title":"Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley","volume":"44","author":"Minasny","year":"2006","journal-title":"Aust. 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