{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:50:28Z","timestamp":1780087828668,"version":"3.54.0"},"reference-count":69,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T00:00:00Z","timestamp":1681689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project of Chongqing Science and Technology Bureau","award":["cstc2021jcyj-msxmX0384"],"award-info":[{"award-number":["cstc2021jcyj-msxmX0384"]}]},{"name":"Project of Chongqing Science and Technology Bureau","award":["SWU020015"],"award-info":[{"award-number":["SWU020015"]}]},{"name":"Project of Chongqing Science and Technology Bureau","award":["SWU2209225"],"award-info":[{"award-number":["SWU2209225"]}]},{"name":"Project of Chongqing Science and Technology Bureau","award":["41930647"],"award-info":[{"award-number":["41930647"]}]},{"name":"Project of Chongqing Science and Technology Bureau","award":["41501575"],"award-info":[{"award-number":["41501575"]}]},{"name":"Project of Chongqing Science and Technology Bureau","award":["XDA20030203"],"award-info":[{"award-number":["XDA20030203"]}]},{"name":"Project of Chongqing Science and Technology Bureau","award":["O88RA600YA"],"award-info":[{"award-number":["O88RA600YA"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["cstc2021jcyj-msxmX0384"],"award-info":[{"award-number":["cstc2021jcyj-msxmX0384"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["SWU020015"],"award-info":[{"award-number":["SWU020015"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["SWU2209225"],"award-info":[{"award-number":["SWU2209225"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["41930647"],"award-info":[{"award-number":["41930647"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["41501575"],"award-info":[{"award-number":["41501575"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["XDA20030203"],"award-info":[{"award-number":["XDA20030203"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["O88RA600YA"],"award-info":[{"award-number":["O88RA600YA"]}]},{"name":"National Natural Science Foundation of China","award":["cstc2021jcyj-msxmX0384"],"award-info":[{"award-number":["cstc2021jcyj-msxmX0384"]}]},{"name":"National Natural Science Foundation of China","award":["SWU020015"],"award-info":[{"award-number":["SWU020015"]}]},{"name":"National Natural Science Foundation of China","award":["SWU2209225"],"award-info":[{"award-number":["SWU2209225"]}]},{"name":"National Natural Science Foundation of China","award":["41930647"],"award-info":[{"award-number":["41930647"]}]},{"name":"National Natural Science Foundation of China","award":["41501575"],"award-info":[{"award-number":["41501575"]}]},{"name":"National Natural Science Foundation of China","award":["XDA20030203"],"award-info":[{"award-number":["XDA20030203"]}]},{"name":"National Natural Science Foundation of China","award":["O88RA600YA"],"award-info":[{"award-number":["O88RA600YA"]}]},{"name":"Strategic Priority Research Program (A) of the Chinese Academy of Sciences","award":["cstc2021jcyj-msxmX0384"],"award-info":[{"award-number":["cstc2021jcyj-msxmX0384"]}]},{"name":"Strategic Priority Research Program (A) of the Chinese Academy of Sciences","award":["SWU020015"],"award-info":[{"award-number":["SWU020015"]}]},{"name":"Strategic Priority Research Program (A) of the Chinese Academy of Sciences","award":["SWU2209225"],"award-info":[{"award-number":["SWU2209225"]}]},{"name":"Strategic Priority Research Program (A) of the Chinese Academy of Sciences","award":["41930647"],"award-info":[{"award-number":["41930647"]}]},{"name":"Strategic Priority Research Program (A) of the Chinese Academy of Sciences","award":["41501575"],"award-info":[{"award-number":["41501575"]}]},{"name":"Strategic Priority Research Program (A) of the Chinese Academy of Sciences","award":["XDA20030203"],"award-info":[{"award-number":["XDA20030203"]}]},{"name":"Strategic Priority Research Program (A) of the Chinese Academy of Sciences","award":["O88RA600YA"],"award-info":[{"award-number":["O88RA600YA"]}]},{"name":"Innovation Project of LREIS","award":["cstc2021jcyj-msxmX0384"],"award-info":[{"award-number":["cstc2021jcyj-msxmX0384"]}]},{"name":"Innovation Project of LREIS","award":["SWU020015"],"award-info":[{"award-number":["SWU020015"]}]},{"name":"Innovation Project of LREIS","award":["SWU2209225"],"award-info":[{"award-number":["SWU2209225"]}]},{"name":"Innovation Project of LREIS","award":["41930647"],"award-info":[{"award-number":["41930647"]}]},{"name":"Innovation Project of LREIS","award":["41501575"],"award-info":[{"award-number":["41501575"]}]},{"name":"Innovation Project of LREIS","award":["XDA20030203"],"award-info":[{"award-number":["XDA20030203"]}]},{"name":"Innovation Project of LREIS","award":["O88RA600YA"],"award-info":[{"award-number":["O88RA600YA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Climate change is closely linked to changes in soil organic carbon (SOC) content, which affects the terrestrial carbon cycle. Consequently, it is essential for carbon accounting and sustainable soil management to predict SOC content accurately. Although there has been an extensive utilization of optical remote sensing data and environmental factors to predict SOC content, few studies have explored their applicability in karst areas. Therefore, it remains unclear how SOC content can be accurately simulated in these areas. In this study, 160 soil samples, 8 environmental covariates and 14 optical remote sensing variables were used to build SOC content prediction models. Three machine learning models, i.e., support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost), were applied for each of three land use classes, including the entire study area, as well as farmland and forest areas. The variables with the greatest influence were the optical remote sensing bands, derived indices, as well as precipitation and temperature for forest areas, and optical remote sensing band11 and Pop-density for farmland. The results from this study suggest that RF and XGBoost are superior to SVM in prediction accuracy. Additionally, the simulation accuracy of the RF model for the forest areas (R2 = 0.32, RMSE = 6.81, MAE = 5.63) and of the XGBoost model for farmland areas (R2 = 0.28, RMSE = 4.03, MAE = 3.27) was the greatest. The prediction model based on different land use types could obtain a higher simulation accuracy than that based on the whole study area. These findings provide new insights for the estimation of SOC content with high precision in karst areas.<\/jats:p>","DOI":"10.3390\/rs15082118","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T05:51:41Z","timestamp":1681710701000},"page":"2118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Soil Organic Carbon Prediction Using Sentinel-2 Data and Environmental Variables in a Karst Trough Valley Area of Southwest China"],"prefix":"10.3390","volume":"15","author":[{"given":"Ting","family":"Wang","sequence":"first","affiliation":[{"name":"Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China"},{"name":"Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9654-9767","authenticated-orcid":false,"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China"},{"name":"Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4218-5319","authenticated-orcid":false,"given":"Jieyun","family":"Xiao","sequence":"additional","affiliation":[{"name":"Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China"},{"name":"Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Li","sequence":"additional","affiliation":[{"name":"Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China"},{"name":"Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Yao","sequence":"additional","affiliation":[{"name":"Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China"},{"name":"Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijuan","family":"Xie","sequence":"additional","affiliation":[{"name":"Beijing Piesat Information Technology Co., Beijing 100195, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keming","family":"Wang","sequence":"additional","affiliation":[{"name":"Chongqing Municipal Public Security Bureau Special Weapon and Tactics Police Aviation Management Office, Chongqing 401147, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1002\/2017GB005678","article-title":"Large Differences in Global and Regional Total Soil Carbon Stock Estimates Based on SoilGrids, HWSD, and NCSCD: Intercomparison and Evaluation Based on Field Data from USA, England, Wales, and France","volume":"321","author":"Tifafi","year":"2018","journal-title":"Glob. Biogeochem. Cycle"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1111\/j.1365-2389.1996.tb01386.x","article-title":"Total carbon and nitrogen in the soils of the world","volume":"47","author":"Batjes","year":"1996","journal-title":"Eur. J. Soil Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2004.01.032","article-title":"Soil carbon sequestration to mitigate climate change","volume":"123","author":"Lal","year":"2004","journal-title":"Geoderma"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1890\/090153","article-title":"Measuring and monitoring soil organic carbon stocks in agricultural lands for climate mitigation","volume":"9","author":"Conant","year":"2011","journal-title":"Front. Ecol. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1126\/science.1097396","article-title":"Soil Carbon Sequestration Impacts on Global Climate Change and Food Security","volume":"304","author":"Lal","year":"2004","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.still.2017.12.002","article-title":"Matching policy and science: Rationale for the \u20184 per 1000\u2014Soils for food security and climate\u2019 initiative","volume":"188","author":"Soussana","year":"2019","journal-title":"Soil Tillage Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"37118","DOI":"10.1038\/srep37118","article-title":"Soil organic carbon accumulation during post-agricultural succession in a karst area, southwest China","volume":"6","author":"Yang","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"116467","DOI":"10.1016\/j.geoderma.2023.116467","article-title":"Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping","volume":"433","author":"Chen","year":"2023","journal-title":"Geoderma"},{"key":"ref_9","first-page":"e359","article-title":"Digital soil mapping and assessment for Australia and beyond: A propitious future","volume":"24","author":"Searle","year":"2021","journal-title":"Geoderma Reg."},{"key":"ref_10","first-page":"1016","article-title":"Mapping high resolution National Soil Information Grids of China","volume":"10","author":"Liu","year":"2021","journal-title":"Sci. Bull."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.geoderma.2015.07.017","article-title":"Digital soil mapping: A brief history and some lessons","volume":"264","author":"Minasny","year":"2016","journal-title":"Geoderma"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecolind.2020.106288","article-title":"Mapping soil organic carbon content using multi-source remote sensing variables in the Heihe River Basin in China","volume":"114","author":"Zhou","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108545","DOI":"10.1016\/j.ecolind.2022.108545","article-title":"Comparison of feature selection methods for mapping soil organic matter in subtropical restored forests","volume":"135","author":"Chen","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Taghizadeh-Mehrjardi, R., Schmidt, K., Amirian-Chakan, A., Rentschler, T., Zeraatpisheh, M., Sarmadian, F., Valavi, R., Davatgar, N., Behrens, T., and Scholten, T. (2020). Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space. Remote Sens., 12.","DOI":"10.3390\/rs12071095"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Emadi, M., Taghizadeh-Mehrjardi, R., Cherati, A., Danesh, M., Mosavi, A., and Scholten, T. (2020). Predicting and mapping of soil organic carbon using machine learning algorithms in Northern Iran. Remote Sens., 12.","DOI":"10.3390\/rs12142234"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.geoderma.2019.01.007","article-title":"Modelling and mapping soil organic carbon stocks in Brazil","volume":"340","author":"Gomes","year":"2019","journal-title":"Geoderma"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"906","DOI":"10.2136\/sssaj2009.0158","article-title":"Predicting the Spatial Variation of the Soil Organic Carbon Pool at a Regional Scale","volume":"74","author":"Mishra","year":"2010","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.ecolind.2017.02.010","article-title":"Assessing soil organic carbon stocks under current and potential forest cover using digital soil mapping and spatial generalisation","volume":"77","author":"Ottoy","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.geoderma.2012.04.002","article-title":"A plant ecology approach to digital soil mapping, improving the prediction of soil organic carbon content in alpine grasslands","volume":"187","author":"Ballabio","year":"2012","journal-title":"Geoderma"},{"key":"ref_20","first-page":"1","article-title":"Estimating temporal changes in soil carbon stocks at ecoregional scale in Madagascar using remote-sensing","volume":"54","author":"Grinand","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1002\/ldr.2833","article-title":"Large-scale soil organic carbon mapping based on multivariate modelling: The case of grasslands on the Loess Plateau","volume":"29","author":"Wang","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"115538","DOI":"10.1016\/j.geoderma.2021.115538","article-title":"Storage or loss of soil active carbon in cropland soils: The effect of agricultural practices and hydrology","volume":"407","author":"Garnier","year":"2022","journal-title":"Geoderma"},{"key":"ref_23","first-page":"1","article-title":"Factors controlling the spatial distribution of soil organic carbon in Daxing\u2019anling Mountain","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.geoderma.2017.04.019","article-title":"Soil class and attribute dynamics and their relationship with natural vegetation based on satellite remote sensing","volume":"302","author":"Sayo","year":"2017","journal-title":"Geoderma"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"114713","DOI":"10.1016\/j.geoderma.2020.114713","article-title":"Revealing the scale- and location-specific controlling factors of soil organic carbon in Tibet","volume":"382","author":"Zhou","year":"2021","journal-title":"Geoderma"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1016\/j.scitotenv.2016.11.078","article-title":"Assimilation of optical and radar remote sensing data in 3D mapping of soil properties over large areas","volume":"579","author":"Poggio","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1007\/s10661-019-7580-3","article-title":"Using time-series Sentinel-1 data for soil prediction on invaded coastal wetlands","volume":"191","author":"Yang","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hengl, T., Mendes De Jesus, J., Heuvelink, G.B.M., Ruiperez Gonzalez, M., Kilibarda, M., Blagoti\u0107, A., Shangguan, W., Wright, M.N., Geng, X., and Bauer-Marschallinger, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169748"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.earscirev.2014.01.005","article-title":"Rocky desertification in Southwest China: Impacts, causes, and restoration","volume":"132","author":"Jiang","year":"2014","journal-title":"Earth-Sci. Rev."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"109350","DOI":"10.1016\/j.agrformet.2023.109350","article-title":"Asymmetric response of primary productivity to precipitation anomalies in Southwest China","volume":"331","author":"Dong","year":"2023","journal-title":"Agric. For. Meteorol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.5194\/se-5-1329-2014","article-title":"Characterization and interaction of driving factors in karst rocky desertification: A case study from Changshun, China","volume":"5","author":"Zhang","year":"2014","journal-title":"Solid Earth"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.agee.2006.11.008","article-title":"Potential and sustainability for carbon sequestration with improved soil management in agricultural soils of China","volume":"121","author":"Yan","year":"2007","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_33","first-page":"97","article-title":"Carbon storage and its spatial pattern of terrestrial ecosystem in China","volume":"1","author":"Yu","year":"2010","journal-title":"J. Resour. Ecol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1504\/IJGW.2019.096764","article-title":"Patterns and influencing factors of spatio-temporal variability of soil organic carbon in karst catchment","volume":"17","author":"Zhang","year":"2019","journal-title":"Int. J. Glob. Warm."},{"key":"ref_35","unstructured":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud, S. (2022, March 01). ESA WorldCover 10 m 2020 v100. Available online: https:\/\/viewer.esa-worldcover.org\/worldcover\/."},{"key":"ref_36","first-page":"12","article-title":"ASF radiometrically terrain corrected ALOS PALSAR products","volume":"1","author":"Laurencelle","year":"2015","journal-title":"ASF-Alaska Satell. Facil."},{"key":"ref_37","unstructured":"Socioeconomic, D.A.A.C. (2022, March 05). Gridded Population of the World (GPW), v4. Available online: https:\/\/sedac.ciesin.columbia.edu\/data\/set\/gpw-v4-population-density-rev11."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"149","DOI":"10.5194\/gi-6-149-2017","article-title":"Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques","volume":"6","author":"Elhag","year":"2017","journal-title":"Geosci. Instrum. Methods Data Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.geoderma.2016.09.024","article-title":"Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability","volume":"285","author":"Maynard","year":"2017","journal-title":"Geoderma"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.agrformet.2015.12.062","article-title":"Remote estimation of soil organic matter content in the Sanjiang Plain, Northest China: The optimal band algorithm versus the GRA-ANN model","volume":"218","author":"Jin","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.agrformet.2017.05.018","article-title":"Comparison of different satellite bands and vegetation indices for estimation of soil organic matter based on simulated spectral configuration","volume":"244","author":"Jin","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.catena.2015.05.010","article-title":"Prediction of soil organic matter variability associated with different land use types in mountainous landscape in southwestern Yunnan province, China","volume":"133","author":"Liu","year":"2015","journal-title":"Catena"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/0273-1177(89)90481-X","article-title":"Remote sensing of arid soil surface color with Landsat thematic mapper","volume":"9","author":"Escadafal","year":"1989","journal-title":"Adv. Space Res."},{"key":"ref_45","unstructured":"Pouget, M., Madeira, J., Le Floc H, E., and Kamal, S. (1991). Caract\u00e9risation et Suivi des Milieux Terrestres en R\u00e9gions Arides et Tropicales, ORSTOM."},{"key":"ref_46","unstructured":"Hengl, T. (2009). A Practical Guide to Geostatistical Mapping, Office for Official Publications of the European Communities."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_48","first-page":"309","article-title":"Monitoring vegetation systems in the great plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"93","DOI":"10.2307\/3628024","article-title":"Transformed vegetation index for measuring spatial variation in drought impacted biomass on Konza Prairie, Kansas","volume":"95","author":"Nellis","year":"1992","journal-title":"Trans. Kans. Acad. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1999). The Nature of Statistical Learning Theory, Springer Science & Business Media.","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","article-title":"A working guide to boosted regression trees","volume":"77","author":"Elith","year":"2008","journal-title":"J. Anim. Ecol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2013.04.005","article-title":"Soil carbon stocks vary predictably with altitude in tropical forests: Implications for soil carbon storage","volume":"204","author":"Dieleman","year":"2013","journal-title":"Geoderma"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3176","DOI":"10.1111\/j.1365-2486.2010.02235.x","article-title":"Net primary productivity allocation and cycling of carbon along a tropical forest elevational transect in the Peruvian Andes","volume":"16","author":"Girardin","year":"2010","journal-title":"Glob. Change Biol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.scitotenv.2018.02.204","article-title":"High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia","volume":"630","author":"Wang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhou, T., Geng, Y., Chen, J., Sun, C., and Lausch, A. (2019). Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables. Remote Sens., 11.","DOI":"10.3390\/rs11242934"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.catena.2018.03.023","article-title":"Mapping total soil nitrogen from a site in northeastern China","volume":"166","author":"Wang","year":"2018","journal-title":"Catena"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ceddia, M.B., Gomes, A.S., Vasques, G.M., and Pinheiro, E.F.M. (2017). Soil Carbon Stock and Particle Size Fractions in the Central Amazon Predicted from Remotely Sensed Relief, Multispectral and Radar Data. Remote Sens., 9.","DOI":"10.3390\/rs9020124"},{"key":"ref_59","first-page":"1595","article-title":"Multi-temporal, multi-frequency radar measurements of agricultural crops during the Agriscatt-88 campaign in The Netherlands","volume":"14","author":"Bouman","year":"1993","journal-title":"Titleremote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1109\/TGRS.2008.2009642","article-title":"Potential of estimating soil moisture under vegetation cover by means of PolSAR","volume":"47","author":"Hajnsek","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4832","DOI":"10.1109\/TGRS.2011.2172949","article-title":"A generalized radar backscattering model based on wave theory for multilayer multispecies vegetation","volume":"49","author":"Burgin","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.2136\/sssaj2004.0322","article-title":"Soil Carbon Storage Estimation in a Forested Watershed Using Quantitative Soil-Landscape Modeling","volume":"69","author":"Thompson","year":"2005","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Tomislav, H., Jorge, M.D.J., Heuvelink, G.B.M., Maria, R.G., Milan, K., Aleksandar, B., Wei, S., Wright, M.N., Xiaoyuan, G., and Bernhard, B.M. (2017). Soil Grids 250m: Global gridded soil information based on machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169748"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.geoderma.2017.05.048","article-title":"Mapping stocks of soil organic carbon and soil total nitrogen in Liaoning Province of China","volume":"305","author":"Wang","year":"2017","journal-title":"Geoderma"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.geoderma.2013.06.013","article-title":"Soil organic carbon stocks in relation to elevation gradients in volcanic ash soils of Taiwan","volume":"209","author":"Tsui","year":"2013","journal-title":"Geoderma"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1002\/ppp.1881","article-title":"Effect of Terrain Characteristics on Soil Organic Carbon and Total Nitrogen Stocks in Soils of Herschel Island, Western Canadian Arctic","volume":"28","author":"Obu","year":"2017","journal-title":"Permafr. Periglac. Process."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/nature14338","article-title":"Climate change and the permafrost carbon feedback","volume":"520","author":"Schuur","year":"2015","journal-title":"Nature"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Hengl, T., de Jesus, J.M., MacMillan, R.A., Batjes, N.H., Heuvelink, G.B., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J.G., and Walsh, M.G. (2014). SoilGrids1km\u2014Global soil information based on automated mapping. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0105992"},{"key":"ref_69","unstructured":"FAO (2012). Harmonized World Soil Database, IIASA. version 1.2."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2118\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:17:25Z","timestamp":1760123845000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,17]]},"references-count":69,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15082118"],"URL":"https:\/\/doi.org\/10.3390\/rs15082118","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,17]]}}}