{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T04:59:21Z","timestamp":1783141161006,"version":"3.54.6"},"reference-count":77,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,15]],"date-time":"2022-10-15T00:00:00Z","timestamp":1665792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32101301"],"award-info":[{"award-number":["32101301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41977041"],"award-info":[{"award-number":["41977041"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020A1515110172"],"award-info":[{"award-number":["2020A1515110172"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZJIEES-2021-01"],"award-info":[{"award-number":["ZJIEES-2021-01"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZJIEES-2022-02"],"award-info":[{"award-number":["ZJIEES-2022-02"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202151"],"award-info":[{"award-number":["202151"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["32101301"],"award-info":[{"award-number":["32101301"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["41977041"],"award-info":[{"award-number":["41977041"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2020A1515110172"],"award-info":[{"award-number":["2020A1515110172"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["ZJIEES-2021-01"],"award-info":[{"award-number":["ZJIEES-2021-01"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["ZJIEES-2022-02"],"award-info":[{"award-number":["ZJIEES-2022-02"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["202151"],"award-info":[{"award-number":["202151"]}]},{"name":"Foundation of President of the Zhongke-Ji\u2019an Institute for Eco-Environmental Sciences","award":["32101301"],"award-info":[{"award-number":["32101301"]}]},{"name":"Foundation of President of the Zhongke-Ji\u2019an Institute for Eco-Environmental Sciences","award":["41977041"],"award-info":[{"award-number":["41977041"]}]},{"name":"Foundation of President of the Zhongke-Ji\u2019an Institute for Eco-Environmental Sciences","award":["2020A1515110172"],"award-info":[{"award-number":["2020A1515110172"]}]},{"name":"Foundation of President of the Zhongke-Ji\u2019an Institute for Eco-Environmental Sciences","award":["ZJIEES-2021-01"],"award-info":[{"award-number":["ZJIEES-2021-01"]}]},{"name":"Foundation of President of the Zhongke-Ji\u2019an Institute for Eco-Environmental Sciences","award":["ZJIEES-2022-02"],"award-info":[{"award-number":["ZJIEES-2022-02"]}]},{"name":"Foundation of President of the Zhongke-Ji\u2019an Institute for Eco-Environmental Sciences","award":["202151"],"award-info":[{"award-number":["202151"]}]},{"name":"Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone","award":["32101301"],"award-info":[{"award-number":["32101301"]}]},{"name":"Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone","award":["41977041"],"award-info":[{"award-number":["41977041"]}]},{"name":"Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone","award":["2020A1515110172"],"award-info":[{"award-number":["2020A1515110172"]}]},{"name":"Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone","award":["ZJIEES-2021-01"],"award-info":[{"award-number":["ZJIEES-2021-01"]}]},{"name":"Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone","award":["ZJIEES-2022-02"],"award-info":[{"award-number":["ZJIEES-2022-02"]}]},{"name":"Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone","award":["202151"],"award-info":[{"award-number":["202151"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rapid and accurate mapping of soil organic carbon (SOC) is of great significance to understanding the spatial patterns of soil fertility and conducting soil carbon cycle research. Previous studies have dedicated considerable efforts to the spatial prediction of SOC content, but few have systematically quantified the effects of environmental covariates selection, the spatial scales and the model types on SOC prediction accuracy. Here, we spatially predicted SOC content through digital soil mapping (DSM) based on 186 topsoil (0\u201320 cm) samples in a typical hilly red soil region of southern China. Specifically, we first determined an optimal covariate set from different combinations of multiple environmental variables, including multi-sensor remote sensing images (Sentinel-1 and Sentinel-2), climate variables and DEM derivatives. Furthermore, we evaluated the impacts of spatial resolution (10 m, 30 m, 90 m, 250 m and 1000 m) of covariates and the model types (three linear and three non-linear machine learning techniques) on the SOC prediction. The results of the performance analysis showed that a combination of Sentinel-1\/2-derived variables, climate and topographic predictors generated the best predictive performance. Among all variables, remote sensing covariates, especially Sentinel-2-derived predictors, were identified as the most important explanatory variables controlling the variability of SOC content. Moreover, the prediction accuracy declined significantly with the increased spatial scales and achieved the highest using the XGBoost model at 10 m resolution. Notably, non-linear machine learners yielded superior predictive capability in contrast with linear models in predicting SOC. Overall, our findings revealed that the optimal combination of predictor variables, spatial resolution and modeling techniques could considerably improve the prediction accuracy of the SOC content. Particularly, freely accessible Sentinel series satellites showed great potential in high-resolution digital mapping of soil properties.<\/jats:p>","DOI":"10.3390\/rs14205151","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T03:43:58Z","timestamp":1665978238000},"page":"5151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Exploring the Impacts of Data Source, Model Types and Spatial Scales on the Soil Organic Carbon Prediction: A Case Study in the Red Soil Hilly Region of Southern China"],"prefix":"10.3390","volume":"14","author":[{"given":"Qiuyuan","family":"Tan","sequence":"first","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1775-3490","authenticated-orcid":false,"given":"Jing","family":"Geng","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China"},{"name":"Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huajun","family":"Fang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"The Zhongke-Ji\u2019an Institute for Eco-Environmental Sciences, Ji\u2019an 343000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuna","family":"Li","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1038\/nature10386","article-title":"Persistence of soil organic matter as an ecosystem property","volume":"478","author":"Schmidt","year":"2011","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3238","DOI":"10.1111\/gcb.12305","article-title":"Soil organic carbon dust emission: An omitted global source of atmospheric CO2","volume":"19","author":"Chappell","year":"2013","journal-title":"Glob. Change Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"114481","DOI":"10.1016\/j.jenvman.2022.114481","article-title":"The coupling interaction of soil organic carbon stock and water storage after vegetation restoration on the Loess Plateau, China","volume":"306","author":"Chen","year":"2022","journal-title":"J. Environ. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1111\/grs.12267","article-title":"Potential for soil organic carbon sequestration in grasslands in East African countries: A review","volume":"66","author":"Tessema","year":"2020","journal-title":"Grassl. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.agsy.2005.08.011","article-title":"Soil organic carbon dynamics, functions and management in West African agro-ecosystems","volume":"94","author":"Bationo","year":"2007","journal-title":"Agric. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.scitotenv.2019.03.151","article-title":"Mapping dynamics of soil organic matter in croplands with MODIS data and machine learning algorithms","volume":"669","author":"Chen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Forkuor, G., Hounkpatin, O.K.L., Welp, G., and Thiel, M. (2017). High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0170478"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106002","DOI":"10.1016\/j.ecolind.2019.106002","article-title":"Estimating the spatial distribution of soil total nitrogen and available potassium in coastal wetland soils in the Yellow River Delta by incorporating multi-source data","volume":"111","author":"Xu","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1080\/01431161.2014.990645","article-title":"A simple Landsat\u2013MODIS fusion approach for monitoring seasonal evapotranspiration at 30 m spatial resolution","volume":"36","author":"Bhattarai","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhou, T., Zhao, M., Sun, C., and Pan, J. (2017). Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region. ISPRS Int. J. Geo Inf., 7.","DOI":"10.3390\/ijgi7010003"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Guo, L., Chen, Y., Shi, T., Luo, M., Ju, Q., Zhang, H., and Wang, S. (2019). Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China. Remote Sens., 11.","DOI":"10.3390\/rs11141683"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.ecolind.2016.06.022","article-title":"Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives","volume":"70","author":"Lausch","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_13","first-page":"102182","article-title":"Predicting soil organic carbon content in Spain by combining Landsat TM and ALOS PALSAR images","volume":"92","author":"Wang","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf. ITC J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, X., Wu, W., and Liu, H. (2021). Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1\/-2 Data in a Small Watershed. Remote Sens., 13.","DOI":"10.3390\/rs13071229"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.catena.2017.02.006","article-title":"Spatial soil nutrients prediction using three supervised learning methods for assessment of land potentials in complex terrain","volume":"154","author":"Jeong","year":"2017","journal-title":"Catena"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1007\/s11769-017-0869-7","article-title":"Mapping soil organic carbon stocks of northeastern China using expert knowledge and GIS-based methods","volume":"27","author":"Song","year":"2017","journal-title":"Chin. Geogr. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.geoderma.2013.07.031","article-title":"Hyper-scale digital soil mapping and soil formation analysis","volume":"213","author":"Behrens","year":"2014","journal-title":"Geoderma"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2871","DOI":"10.1016\/S2095-3119(17)61762-3","article-title":"Recent progress and future prospect of digital soil mapping: A review","volume":"16","author":"Zhang","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.geoderma.2008.05.008","article-title":"Soil organic carbon concentrations and stocks on Barro Colorado Island\u2014Digital soil mapping using Random Forests analysis","volume":"146","author":"Grimm","year":"2008","journal-title":"Geoderma"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.geoderma.2014.01.005","article-title":"Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale","volume":"223","author":"Martin","year":"2014","journal-title":"Geoderma"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.geoderma.2017.05.017","article-title":"Digital soil mapping based on wavelet decomposed components of environmental covariates","volume":"303","author":"Sun","year":"2017","journal-title":"Geoderma"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105695","DOI":"10.1016\/j.catena.2021.105695","article-title":"Scale- and location-specific multivariate controls of topsoil organic carbon density depend on landform heterogeneity","volume":"207","author":"Zhu","year":"2021","journal-title":"Catena"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"115600","DOI":"10.1016\/j.geoderma.2021.115600","article-title":"Revealing the scale-and location-specific relationship between soil organic carbon and environmental factors in China\u2019s north-south transition zone","volume":"409","author":"Tian","year":"2022","journal-title":"Geoderma"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.scitotenv.2018.06.210","article-title":"Identifying localized and scale-specific multivariate controls of soil organic matter variations using multiple wavelet coherence","volume":"643","author":"Zhao","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.geoderma.2014.09.018","article-title":"Impact of multi-scale predictor selection for modeling soil properties","volume":"239\u2013240","author":"Miller","year":"2015","journal-title":"Geoderma"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"142661","DOI":"10.1016\/j.scitotenv.2020.142661","article-title":"Prediction of soil organic carbon and the C: N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images","volume":"755","author":"Zhou","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, X., Ding, J., Liu, J., Ge, X., and Zhang, J. (2021). Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang. Remote Sens., 13.","DOI":"10.3390\/rs13040769"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e00513","DOI":"10.1016\/j.geodrs.2022.e00513","article-title":"Effects of different sources and spatial resolutions of environmental covariates on predicting soil organic carbon using machine learning in a semi-arid region of Iran","volume":"29","author":"Garosi","year":"2022","journal-title":"Geoderma Reg."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"114008","DOI":"10.1016\/j.geoderma.2019.114008","article-title":"Spatial prediction of soil organic carbon stocks in Ghana using legacy data","volume":"360","author":"Owusu","year":"2020","journal-title":"Geoderma"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1478","DOI":"10.1016\/j.scitotenv.2017.12.254","article-title":"No significant changes in topsoil carbon in the grasslands of northern China between the 1980s and 2000s","volume":"624","author":"Liu","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"104477","DOI":"10.1016\/j.still.2019.104477","article-title":"Mapping field-scale soil organic carbon with unmanned aircraft system-acquired time series multispectral images","volume":"196","author":"Guo","year":"2020","journal-title":"Soil Tillage Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.scitotenv.2017.03.021","article-title":"Soil organic carbon distribution in Mediterranean areas under a climate change scenario via multiple linear regression analysis","volume":"592","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"106288","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_36","doi-asserted-by":"crossref","unstructured":"John, K., Isong, I.A., Kebonye, N.M., Ayito, E.O., Agyeman, P.C., and Afu, S.M. (2020). Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil. Land, 9.","DOI":"10.3390\/land9120487"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.ecolind.2014.12.028","article-title":"A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape","volume":"52","author":"Were","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.geoderma.2019.05.031","article-title":"Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review","volume":"352","author":"Lamichhane","year":"2019","journal-title":"Geoderma"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"114981","DOI":"10.1016\/j.geoderma.2021.114981","article-title":"Environmental covariates improve the spectral predictions of organic carbon in subtropical soils in southern Brazil","volume":"393","author":"Dalmolin","year":"2021","journal-title":"Geoderma"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, Z., Zhang, M., and Wilson, M.J. (2004). Distribution and Classification of Red Soils in China. The Red Soils of China, Springer.","DOI":"10.1007\/978-1-4020-2138-1_3"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"106074","DOI":"10.1016\/j.catena.2022.106074","article-title":"Response of spatiotemporal variability in soil pH and associated influencing factors to land use change in a red soil hilly region in southern China","volume":"212","author":"Han","year":"2022","journal-title":"Catena"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1007\/s11442-018-1490-0","article-title":"Spatiotemporal patterns and characteristics of land-use change in China during 2010\u20132015","volume":"28","author":"Ning","year":"2018","journal-title":"J. Geogr. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xue, J., Chen, S., Wang, N., Shi, Z., Huang, Y., and Zhuo, Z. (2022). Digital Mapping of Soil Organic Carbon with Machine Learning in Dryland of Northeast and North Plain China. Remote Sens., 14.","DOI":"10.3390\/rs14102504"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105442","DOI":"10.1016\/j.catena.2021.105442","article-title":"Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images","volume":"205","author":"He","year":"2021","journal-title":"Catena"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4302","DOI":"10.1002\/joc.5086","article-title":"WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas","volume":"37","author":"Fick","year":"2017","journal-title":"Int. J. Climatol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107975","DOI":"10.1016\/j.ecolind.2021.107975","article-title":"Incorporation of high accuracy surface modeling into machine learning to improve soil organic matter mapping","volume":"129","author":"Wang","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.compag.2018.07.016","article-title":"Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield","volume":"153","author":"Khanal","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"104465","DOI":"10.1016\/j.still.2019.104465","article-title":"Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data","volume":"196","author":"Yang","year":"2020","journal-title":"Soil Tillage Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1016\/j.scitotenv.2019.06.205","article-title":"Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility","volume":"688","author":"Arabameri","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1016\/j.ecolind.2015.08.036","article-title":"Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem","volume":"60","author":"Yang","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"145292","DOI":"10.1016\/j.scitotenv.2021.145292","article-title":"Patterns and driving factors of biomass carbon and soil organic carbon stock in the Indian Himalayan region","volume":"770","author":"Ahirwal","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.geoderma.2018.12.037","article-title":"Digital mapping of soil carbon fractions with machine learning","volume":"339","author":"Keskin","year":"2019","journal-title":"Geoderma"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1086\/214027","article-title":"Measuring Geographic Concentration by Means of the Standard Deviational Ellipse","volume":"32","author":"Lefever","year":"1926","journal-title":"Am. J. Sociol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"104150","DOI":"10.1016\/j.infrared.2022.104150","article-title":"Quantitative spatial analysis of thermal infrared radiation temperature fields by the standard deviational ellipse method for the uniaxial loading of sandstone","volume":"123","author":"Huang","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_56","unstructured":"Jenks, G.F. (1977). Optimal Data Classification for Choropleth Maps, Department of Geographiy, University of Kansas Occasional Paper."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"114358","DOI":"10.1016\/j.geoderma.2020.114358","article-title":"Hyperspectral imaging for high-resolution mapping of soil carbon fractions in intact paddy soil profiles with multivariate techniques and variable selection","volume":"370","author":"Xu","year":"2020","journal-title":"Geoderma"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chen, L., Ren, C., Li, L., Wang, Y., Zhang, B., Wang, Z., and Li, L. (2019). A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content. ISPRS Int. J. Geo Inf., 8.","DOI":"10.3390\/ijgi8040174"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.biosystemseng.2016.04.018","article-title":"Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy","volume":"152","author":"Morellos","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, J., Liu, G., Yang, Y., Liu, Z., and Deng, H. (2019). Hyperspectral Estimation Model of Forest Soil Organic Matter in Northwest Yunnan Province, China. Forests, 10.","DOI":"10.3390\/f10030217"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Guo, P., Li, T., Gao, H., Chen, X., Cui, Y., and Huang, Y. (2021). Evaluating Calibration and Spectral Variable Selection Methods for Predicting Three Soil Nutrients Using Vis-NIR Spectroscopy. Remote Sens., 13.","DOI":"10.3390\/rs13194000"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"155","DOI":"10.2136\/sssaj2014.09.0392","article-title":"Digital Mapping of Topsoil Carbon Content and Changes in the Driftless Area of Wisconsin, USA","volume":"79","author":"Adhikari","year":"2015","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.geoderma.2019.01.023","article-title":"Selection of terrain attributes and its scale dependency on soil organic carbon prediction","volume":"340","author":"Guo","year":"2019","journal-title":"Geoderma"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.jenvman.2017.06.017","article-title":"Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings","volume":"200","author":"Xu","year":"2017","journal-title":"J. Environ. Manag."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/BF02447512","article-title":"The modifiable areal unit problem and implications for landscape ecology","volume":"11","author":"Jelinski","year":"1996","journal-title":"Landsc. Ecol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"11188","DOI":"10.1038\/s41598-018-29306-9","article-title":"Visible\u2013Near-Infrared Spectroscopy can predict Mass Transport of Dissolved Chemicals through Intact Soil","volume":"8","author":"Katuwal","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Castaldi, F., Chabrillat, S., Don, A., and van Wesemael, B. (2019). Soil Organic Carbon Mapping Using LUCAS Topsoil Database and Sentinel-2 Data: An Approach to Reduce Soil Moisture and Crop Residue Effects. Remote Sens., 11.","DOI":"10.3390\/rs11182121"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.isprsjprs.2018.11.026","article-title":"Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands","volume":"147","author":"Castaldi","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.catena.2018.10.045","article-title":"Soil prediction for coastal wetlands following Spartina alterniflora invasion using Sentinel-1 imagery and structural equation modeling","volume":"173","author":"Yang","year":"2019","journal-title":"Catena"},{"key":"ref_71","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_72","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s12665-018-7367-9","article-title":"Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model","volume":"77","author":"Kalambukattu","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"e00260","DOI":"10.1016\/j.geodrs.2020.e00260","article-title":"Spatial prediction of soil organic carbon using machine learning techniques in western Iran","volume":"21","author":"Mahmoudzadeh","year":"2020","journal-title":"Geoderma Reg."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/S1002-0160(17)60445-4","article-title":"Mapping soil organic carbon using local terrain attributes: A comparison of different polynomial models","volume":"27","author":"Xiaodong","year":"2017","journal-title":"Pedosphere"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"115407","DOI":"10.1016\/j.geoderma.2021.115407","article-title":"The effectiveness of digital soil mapping with temporal variables in modeling soil organic carbon changes","volume":"405","author":"Yang","year":"2022","journal-title":"Geoderma"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Ning, L., Cheng, C., Lu, X., Shen, S., Zhang, L., Mu, S., and Song, Y. (2022). Improving the Prediction of Soil Organic Matter in Arable Land Using Human Activity Factors. Water, 14.","DOI":"10.3390\/w14101668"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1109\/JSTARS.2019.2919936","article-title":"Downscaling of Urban Land Surface Temperature Based on Multi-Factor Geographically Weighted Regression","volume":"12","author":"Wu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5151\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:54:39Z","timestamp":1760144079000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5151"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,15]]},"references-count":77,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205151"],"URL":"https:\/\/doi.org\/10.3390\/rs14205151","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,15]]}}}