{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T15:34:18Z","timestamp":1783784058170,"version":"3.55.0"},"reference-count":142,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,26]],"date-time":"2021-07-26T00:00:00Z","timestamp":1627257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012692","name":"K. C. Wong Education Foundation","doi-asserted-by":"publisher","award":["K. C. Wong Education Foundation"],"award-info":[{"award-number":["K. C. Wong Education Foundation"]}],"id":[{"id":"10.13039\/501100012692","id-type":"DOI","asserted-by":"publisher"}]},{"name":"\u201cAcademic Backbone\u201d Project of Northeast Agricultural University","award":["\u201cAcademic Backbone\u201d Project of Northeast Agricultural University"],"award-info":[{"award-number":["\u201cAcademic Backbone\u201d Project of Northeast Agricultural University"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Many studies have attempted to predict soil organic matter (SOM), whereas mapping high-precision and high-resolution SOM maps remains a challenge due to the difficulty of selecting appropriate satellite data sources and prediction algorithms. This study aimed to investigate the influence of different remotely sensed images and machine learning algorithms on SOM prediction. We constructed two comparative experiments, i.e., full-band and common-band variable datasets of Sentinel-2A and MODIS images using Google Earth Engine (GEE). The predictive performances of random forest (RF), artificial neural network (ANN), and support vector regression (SVR) algorithms were evaluated, and the SOM map was generated for the Songnen Plain. Results showed that the model based on the full-band Sentinel-2A dataset achieved the best performance. The application of Sentinel-2A data resulted in mean relative improvements (RIs) of 7.67% and 5.87%, respectively. The RF achieved a lower root mean squared error (RMSE = 0.68%) and a higher coefficient of determination (R2 = 0.67) in all of the predicted scenarios than ANN and SVR. The resultant SOM map accurately characterized the SOM spatial distribution. Therefore, the Sentinel-2A data have obvious advantages over MODIS due to their higher spectral and spatial resolutions, and the combination of the RF algorithm and GEE is an effective approach to SOM mapping.<\/jats:p>","DOI":"10.3390\/rs13152934","type":"journal-article","created":{"date-parts":[[2021,7,26]],"date-time":"2021-07-26T22:22:46Z","timestamp":1627338166000},"page":"2934","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Mapping Regional Soil Organic Matter Based on Sentinel-2A and MODIS Imagery Using Machine Learning Algorithms and Google Earth Engine"],"prefix":"10.3390","volume":"13","author":[{"given":"Meiwei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meinan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Tsinghua University, Beijing 100089, China"},{"name":"Key Laboratory of Forest Ecology and Environment of State Forestry Administration, Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoxuan","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanliang","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Environment, Tsinghua University, Beijing 100089, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinle","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanjun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China"},{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"548","DOI":"10.2134\/jeq2005.0230","article-title":"What is Soil Organic Matter Worth?","volume":"35","author":"Sparling","year":"2006","journal-title":"J. Environ. Qual."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.geoderma.2017.10.017","article-title":"Effects of soil map scales on simulating soil organic carbon changes of upland soils in Eastern China","volume":"312","author":"Zhang","year":"2018","journal-title":"Geoderma"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.agee.2006.07.011","article-title":"Historical evolution of soil organic matter concepts and their relationships with the fertility and sustainability of cropping systems","volume":"119","author":"Manlay","year":"2007","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.geoderma.2018.08.011","article-title":"National digital soil map of organic matter in topsoil and its associated uncertainty in 1980\u2019s China","volume":"335","author":"Liang","year":"2019","journal-title":"Geoderma"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.geoderma.2016.10.010","article-title":"Comparisons of spatial and non-spatial models for predicting soil carbon content based on visible and near-infrared spectral technology","volume":"285","author":"Guo","year":"2017","journal-title":"Geoderma"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.geoderma.2007.02.012","article-title":"Temporal and spatial variability of soil organic matter and total nitrogen in an agricultural ecosystem as affected by farming practices","volume":"139","author":"Huang","year":"2007","journal-title":"Geoderma"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.geoderma.2012.07.020","article-title":"Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy","volume":"199","author":"Nocita","year":"2013","journal-title":"Geoderma"},{"key":"ref_8","first-page":"2126","article-title":"Hyperspectral prediction of soil organic matter content in the Reclamation cropland of Coal Mining Areas in the Loess Platesu","volume":"49","author":"Feng","year":"2016","journal-title":"Sci. Agric. Sin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e01221","DOI":"10.1002\/ecs2.1221","article-title":"Ecosystem warming increases sap flow rates of northern red oak trees","volume":"7","author":"Juice","year":"2016","journal-title":"Ecosphere"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.geoderma.2012.06.022","article-title":"Improving regional soil carbon inventories: Combining the IPCC carbon inventory method with regression kriging","volume":"189\u2013190","author":"Mishra","year":"2012","journal-title":"Geoderma"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2007.08.025","article-title":"A multiple regression approach to assess the spatial distribution of Soil Organic Carbon (SOC) at the regional scale (Flanders, Belgium)","volume":"143","author":"Meersmans","year":"2008","journal-title":"Geoderma"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1097\/00010694-193401000-00003","article-title":"An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method","volume":"37","author":"Walkley","year":"1934","journal-title":"Soil Sci."},{"key":"ref_14","unstructured":"Van Raij, B., Andrade, J.C., de Cantarella, H., and Quaggio, J.A. (2001). An\u00e1lise Qu\u00edmica para Avalia\u00e7\u00e3o da Fertilidade de Solos Tropicais."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lagacherie, P. (2008). Digital Soil Mapping: A State of the Art. Digit. Soil Mapp. Ltd. Data, 3\u201314.","DOI":"10.1007\/978-1-4020-8592-5_1"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1111\/j.1365-2389.1971.tb01630.x","article-title":"Quality control in soil survey: II. The costs of soil survey","volume":"22","author":"Bie","year":"1971","journal-title":"J. Soil Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"73","DOI":"10.4141\/cjss2011-095","article-title":"Model prediction of soil drainage classes over a large area using a limited number of field samples: A case study in the province of Nova Scotia, Canada","volume":"93","author":"Zhao","year":"2013","journal-title":"Can. J. Soil Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.2113\/gsecongeo.58.8.1246","article-title":"Principles of geostatistics","volume":"58","author":"Matheron","year":"1963","journal-title":"Econ. Geol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.geoderma.2011.07.012","article-title":"Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information","volume":"171\u2013172","author":"Zhang","year":"2012","journal-title":"Geoderma"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.catena.2018.11.010","article-title":"Assessment of spatial hybrid methods for predicting soil organic matter using DEM derivatives and soil parameters","volume":"174","author":"Tziachris","year":"2019","journal-title":"Catena"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"470","DOI":"10.2136\/sssaj2001.652470x","article-title":"Comparison of Methods for Interpolating Soil Properties Using Limited Data","volume":"65","author":"Schloeder","year":"2001","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.2136\/sssaj2008.0045","article-title":"Spatial Prediction of Soil Organic Matter Content Using Cokriging with Remotely Sensed Data","volume":"73","author":"Wu","year":"2009","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.ecolind.2014.04.003","article-title":"Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau","volume":"45","author":"Dai","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0016-7061(92)90002-O","article-title":"Combining soil maps with interpolations from point observations to predict quantitative soil properties","volume":"55","author":"Heuvelink","year":"1992","journal-title":"Geoderma"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"75","DOI":"10.4141\/CJSS08057","article-title":"Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes","volume":"90","author":"Zhao","year":"2010","journal-title":"Can. J. Soil Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.geoderma.2019.07.010","article-title":"A remote sensing adapted approach for soil organic carbon prediction based on the spectrally clustered LUCAS soil database","volume":"353","author":"Ward","year":"2019","journal-title":"Geoderma"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Webster, R., and Oliver, M.A. (2007). Geostatistics for Environmental Scientists, John Wiley & Sons.","DOI":"10.1002\/9780470517277"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s10705-013-9566-9","article-title":"Prediction of soil organic matter using artificial neural network and topographic indicators in hilly areas","volume":"95","author":"Guo","year":"2013","journal-title":"Nutr. Cycl. Agroecosyst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.1747-0765.2007.00142.x","article-title":"Spatial prediction of soil organic matter in northern Kazakhstan based on topographic and vegetation information","volume":"53","author":"Takata","year":"2007","journal-title":"Soil Sci. Plant Nutr."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.still.2019.01.011","article-title":"Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR)","volume":"190","author":"Abrougui","year":"2019","journal-title":"Soil Tillage Res."},{"key":"ref_32","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_33","first-page":"425","article-title":"Using GIS spatial distribution to predict soil organic carbon in subtropical China","volume":"14","author":"Cheng","year":"2004","journal-title":"Pedosphere"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.scitotenv.2018.06.193","article-title":"Mapping soil organic matter in the Baranja region (Croatia): Geological and anthropic forcing parameters","volume":"643","author":"Bogunovic","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_35","first-page":"042803","article-title":"Modeling soil organic matter and texture from satellite data in areas affected by wildfires and cropland abandonment in Arag\u00f3n, Northern Spain","volume":"12","author":"Vlassova","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1016\/j.ecolmodel.2009.12.013","article-title":"Modelling dynamics of soil organic matter under different historical land-use management techniques in European Russia","volume":"221","author":"Bobrovsky","year":"2010","journal-title":"Ecol. Model."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"965","DOI":"10.2136\/sssaj2009.0427","article-title":"An Artificial Neural Network Approach for Predicting Soil Carbon Budget in Agroecosystems","volume":"75","author":"Alvarez","year":"2011","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.biosystemseng.2011.06.002","article-title":"Residual soil nitrate prediction from imagery and non-imagery information using neural network technique","volume":"110","author":"Gautam","year":"2011","journal-title":"Biosyst. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.geoderma.2016.04.026","article-title":"Bayesian spatial modelling of soil properties and their uncertainty: The example of soil organic matter in Scotland using R-INLA","volume":"277","author":"Poggio","year":"2016","journal-title":"Geoderma"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"105172","DOI":"10.1016\/j.compag.2019.105172","article-title":"Extended model prediction of high-resolution soil organic matter over a large area using limited number of field samples","volume":"169","author":"Zhao","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.scitotenv.2018.04.251","article-title":"A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information","volume":"636","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.2136\/sssaj2012.0321","article-title":"Soil Series Mapping by Knowledge Discovery from an Ohio County Soil Map","volume":"77","author":"Subburayalu","year":"2013","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s11104-010-0425-z","article-title":"Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem","volume":"340","author":"Wiesmeier","year":"2011","journal-title":"Plant Soil"},{"key":"ref_44","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_45","first-page":"1074","article-title":"Soil organic matter prediction based on remote sensing data and random forest model in Shaanxi Province","volume":"32","author":"Qi","year":"2017","journal-title":"J. Nat. Resour."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.geoderma.2019.04.044","article-title":"Estimation of soil organic matter content by modeling with artificial neural networks","volume":"350","author":"Fernandes","year":"2019","journal-title":"Geoderma"},{"key":"ref_47","unstructured":"R Core Team (2013). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"114227","DOI":"10.1016\/j.geoderma.2020.114227","article-title":"Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy","volume":"365","author":"Deiss","year":"2020","journal-title":"Geoderma"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.geoderma.2009.04.022","article-title":"Spatial prediction of soil properties in temperate mountain regions using support vector regression","volume":"151","author":"Ballabio","year":"2009","journal-title":"Geoderma"},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"3128","DOI":"10.1039\/c1em10520e","article-title":"Mapping the organic carbon stocks of surface soils using local spatial interpolator","volume":"13","author":"Kumar","year":"2011","journal-title":"J. Environ. Monit."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.still.2014.07.011","article-title":"Prediction of soil organic matter in peak-cluster depression region using kriging and terrain indices","volume":"144","author":"Hui","year":"2014","journal-title":"Soil Tillage Res."},{"key":"ref_53","first-page":"1021","article-title":"Quantitative analysis of reflectance spectrum of Black soil as affected by soil moisture for prediction of soil moisture in black soil","volume":"51","author":"Liu","year":"2014","journal-title":"Acta Pedol. Sin."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"113896","DOI":"10.1016\/j.geoderma.2019.113896","article-title":"Prediction of soil organic matter using multi-temporal satellite images in the Songnen Plain, China","volume":"356","author":"Dou","year":"2019","journal-title":"Geoderma"},{"key":"ref_55","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_56","first-page":"143","article-title":"Remote sensing inversion model of soil organic matter in farmland by introducing temporal information","volume":"34","author":"Zhang","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_57","first-page":"127","article-title":"Soil organic matter content inversion model with remote sensing image in field scale of blacksoil area","volume":"34","author":"Liu","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_58","first-page":"102111","article-title":"Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data","volume":"89","author":"Meng","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"104703","DOI":"10.1016\/j.catena.2020.104703","article-title":"Vis-SWIR spectral prediction model for soil organic matter with different grouping strategies","volume":"195","author":"Bao","year":"2020","journal-title":"Catena"},{"key":"ref_60","first-page":"647","article-title":"Study on Quantitatively Remote Sensing Typical Soils in Songnen Plain, Northeast China","volume":"12","author":"Liu","year":"2008","journal-title":"J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2005.03.007","article-title":"Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties","volume":"131","author":"Rossel","year":"2006","journal-title":"Geoderma"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"746","DOI":"10.2136\/sssaj2000.642746x","article-title":"Field-Scale Mapping of Surface Soil Organic Carbon Using Remotely Sensed Imagery","volume":"64","author":"Chen","year":"2000","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_63","first-page":"640","article-title":"Mid-Infrared and Near-Infrared Diffuse Reflectance Spectroscopy for Soil Carbon Measurement","volume":"66","author":"Mccarty","year":"2002","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.ecolind.2009.05.001","article-title":"Visible near-infrared reflectance spectroscopy as a predictive indicator of soil properties","volume":"11","author":"Summers","year":"2011","journal-title":"Ecol. Indic."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.jaridenv.2009.08.011","article-title":"Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey","volume":"74","author":"Bilgili","year":"2010","journal-title":"J. Arid. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Xiao, W., Chen, W., He, T., Ruan, L., and Guo, J. (2020). Multi-Temporal Mapping of Soil Total Nitrogen Using Google Earth Engine across the Shandong Province of China. Sustainability, 12.","DOI":"10.3390\/su122410274"},{"key":"ref_67","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_68","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S2095-3119(16)61349-7","article-title":"The renewability and quality of shallow groundwater in Sanjiang and Songnen Plain, Northeast China","volume":"16","author":"Zhang","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/S1002-0160(13)60040-5","article-title":"Seasonal Variability in Soil Inorganic Nitrogen Across Borders Between Woodland and Farmland in the Songnen Plain of Northeast China","volume":"23","author":"Lin","year":"2013","journal-title":"Pedosphere"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1007\/s11769-010-0414-4","article-title":"Field capacity in black soil region, Northeast China","volume":"20","author":"Duan","year":"2010","journal-title":"Chin. Geogr. Sci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/j.catena.2018.07.045","article-title":"The influence of the conversion of grassland to cropland on changes in soil organic carbon and total nitrogen stocks in the Songnen Plain of Northeast China","volume":"171","author":"Song","year":"2018","journal-title":"Catena"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.geoderma.2009.12.017","article-title":"Cross-reference for relating Genetic Soil Classification of China with WRB at different scales","volume":"155","author":"Shi","year":"2010","journal-title":"Geoderma"},{"key":"ref_73","unstructured":"IUSS Working Group WRB (2006). World Reference Base for Soil Resources, FAO. World Soil Resources Reports No. 103."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1081\/DRT-200025642","article-title":"Accurate determination of moisture content of organic soils using the oven drying method","volume":"22","year":"2004","journal-title":"Dry. Technol."},{"key":"ref_75","unstructured":"Nelson, D.W., and Sommers, L.E. (1996). Total carbon, organic carbon, and organic matter. Methods of Soil Analysis: Part 3 Chemical Methods, 5.3, Soil Science Society of America."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.ecolind.2012.10.027","article-title":"Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data","volume":"26","author":"Huang","year":"2013","journal-title":"Ecol. Indic."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.geoderma.2016.10.033","article-title":"Assessing soil organic matter of reclaimed soil from a large surface coal mine using a field spectroradiometer in laboratory","volume":"288","author":"Bao","year":"2017","journal-title":"Geoderma"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/0034-4257(89)90123-5","article-title":"Remote sensing of soils in the Eastern Palouse region with landsat thematic mapper","volume":"28","author":"Frazier","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_80","first-page":"566","article-title":"A Study on Predicting Model of Organic Matter Contend Incorporating Soil Moisture Variation","volume":"37","author":"Liu","year":"2017","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.catena.2019.03.027","article-title":"Can the spatial prediction of soil organic matter be improved by incorporating multiple regression confidence intervals as soft data into BME method?","volume":"178","author":"Zhang","year":"2019","journal-title":"Catena"},{"key":"ref_83","first-page":"1541","article-title":"Distinguishing vegetation from soil background information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1016\/j.jenvman.2010.01.001","article-title":"Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: The case study of Denmark","volume":"91","author":"Kheir","year":"2010","journal-title":"J. Environ. Manag."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.geoderma.2010.06.017","article-title":"Can the spatial prediction of soil organic matter contents at various sampling scales be improved by using regression kriging with auxiliary information?","volume":"159","author":"Li","year":"2010","journal-title":"Geoderma"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.geoderma.2018.08.011","article-title":"Baseline map of soil organic matter in China and its associated uncertainty","volume":"335","author":"Liang","year":"2019","journal-title":"Geoderma"},{"key":"ref_88","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_89","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\u2013219","author":"Jin","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1097\/00010694-196508000-00009","article-title":"Reflection of Radiant Energy from Soil","volume":"100","author":"Bowers","year":"1965","journal-title":"Soil Sci."},{"key":"ref_91","first-page":"151","article-title":"Relationships between water indexes and soil moisture\/crop physiological indexes using ground-based remote sensing and field experiments","volume":"26","author":"Zhang","year":"2010","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_93","first-page":"199","article-title":"Multitemporal settlement and population mapping from Landsat using Google Earth Engine","volume":"35","author":"Patel","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"9541","DOI":"10.1080\/01431161.2019.1633702","article-title":"Mapping bamboo with regional phenological characteristics derived from dense Landsat time series using Google Earth Engine","volume":"40","author":"Zhang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Zhang, M., Huang, H., Li, Z., Hackman, K.O., Liu, C., Andriamiarisoa, R.L., Raherivelo, T.N.A.N., Li, Y., and Gong, P. (2020). Automatic High-Resolution Land Cover Production in Madagascar Using Sentinel-2 Time Series, Tile-Based Image Classification and Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12213663"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1037\/a0016973","article-title":"An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests","volume":"14","author":"Strobl","year":"2009","journal-title":"Psychol. Methods"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1214\/10-AOAS427","article-title":"Remembering Leo Breiman","volume":"4","author":"Cutler","year":"2010","journal-title":"Ann. Appl. Stat."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R.J., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_100","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.geoderma.2017.09.013","article-title":"Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis\u2013NIR spectroscopy","volume":"310","author":"Xu","year":"2018","journal-title":"Geoderma"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"100052","DOI":"10.1016\/j.ifacsc.2019.100052","article-title":"SVM-tree and SVM-forest algorithms for imbalanced fault classification in industrial processes","volume":"8","author":"Chen","year":"2019","journal-title":"IFAC J. Syst. Control."},{"key":"ref_103","first-page":"153","article-title":"Supervised classification of multispectral remote sensing image using BP neural network","volume":"2","author":"Yong","year":"1998","journal-title":"J. Infrared Millim. Waves"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.geoderma.2017.10.018","article-title":"Open digital mapping as a cost-effective method for mapping peat thickness and assessing the carbon stock of tropical peatlands","volume":"313","author":"Rudiyanto","year":"2018","journal-title":"Geoderma"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.geoderma.2008.06.011","article-title":"Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study","volume":"146","author":"Gomez","year":"2008","journal-title":"Geoderma"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"104257","DOI":"10.1016\/j.catena.2019.104257","article-title":"Prediction of soil organic matter in northwestern China using fractional-order derivative spectroscopy and modified normalized difference indices","volume":"185","author":"Zhang","year":"2019","journal-title":"Catena"},{"key":"ref_107","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_108","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_109","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ecolind.2013.12.015","article-title":"Mapping soil organic matter in low-relief areas based on land surface diurnal temperature difference and a vegetation index","volume":"39","author":"Zhao","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2013.02.029","article-title":"Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors","volume":"134","author":"Jain","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"111624","DOI":"10.1016\/j.rse.2019.111624","article-title":"Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine","volume":"239","author":"Liu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"111265","DOI":"10.1016\/j.rse.2019.111265","article-title":"Mapping Moso bamboo forest and its on-year and off-year distribution in a subtropical region using time-series Sentinel-2 and Landsat 8 data","volume":"231","author":"Li","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_113","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_114","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.patcog.2012.09.005","article-title":"Stratified sampling for feature subspace selection in random forests for high dimensional data","volume":"46","author":"Ye","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Menze, B.H., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., and Hamprecht, F.A. (2009). A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinform., 10.","DOI":"10.1186\/1471-2105-10-213"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.geoderma.2013.09.016","article-title":"Predictive soil parent material mapping at a regional-scale: A Random Forest approach","volume":"214\u2013215","author":"Heung","year":"2014","journal-title":"Geoderma"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"112294","DOI":"10.1016\/j.rse.2021.112294","article-title":"Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression","volume":"255","author":"Kuter","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Zhang, C., and Ma, Y. (2012). Ensemble Machine Learning: Methods and Applications, Springer.","DOI":"10.1007\/978-1-4419-9326-7"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1002\/widm.1072","article-title":"Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics","volume":"2","author":"Boulesteix","year":"2012","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"114210","DOI":"10.1016\/j.geoderma.2020.114210","article-title":"Development of pedotransfer functions by machine learning for prediction of soil electrical conductivity and organic carbon content","volume":"366","author":"Benke","year":"2020","journal-title":"Geoderma"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1038\/538020a","article-title":"Can we open the black box of AI?","volume":"538","author":"Castelvecchi","year":"2016","journal-title":"Nature"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A Tutorial on Support Vector Machines for Pattern Recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"4319","DOI":"10.1109\/TGRS.2019.2963075","article-title":"On the Value of Available MODIS and Landsat8 OLI Image Pairs for MODIS Fractional Snow Cover Mapping Based on an Artificial Neural Network","volume":"58","author":"Hou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_124","unstructured":"Suykens, J.A. (2003). Advances in Learning Theory: Methods, Models, and Applications, IOS Press."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.021","article-title":"Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine","volume":"202","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2018). Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. Remote Sens., 10.","DOI":"10.3390\/rs10101509"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"\u017d\u00ed\u017eala, D., Mina\u0159\u00edk, R., and Z\u00e1dorov\u00e1, T. (2019). Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions. Remote Sens., 11.","DOI":"10.3390\/rs11242947"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Gallo, B.C., Dematt\u00ea, J.A.M., Rizzo, R., Safanelli, J.L., Mendes, W.D.S., Lepsch, I.F., Sato, M.V., Romero, D.J., and Lacerda, M.P.C. (2018). Multi-Temporal Satellite Images on Topsoil Attribute Quantification and the Relationship with Soil Classes and Geology. Remote Sens., 10.","DOI":"10.3390\/rs10101571"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Diek, S., Fornallaz, F., Schaepman, M.E., and De Jong, R. (2017). Barest Pixel Composite for Agricultural Areas Using Landsat Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9121245"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"11125","DOI":"10.3390\/rs70911125","article-title":"Organic Matter Modeling at the Landscape Scale Based on Multitemporal Soil Pattern Analysis Using RapidEye Data","volume":"7","author":"Blasch","year":"2015","journal-title":"Remote Sens."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"586","DOI":"10.2136\/sssaj2011.0053","article-title":"Determination of Soil Organic Matter and Carbon Fractions in Forest Top Soils using Spectral Data Acquired from Visible-Near Infrared Hyperspectral Images","volume":"76","author":"Holden","year":"2012","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_133","first-page":"137","article-title":"Estimation of Soil Organic Carbon using Artificial Neural Network and Multiple Linear Regression Models based on Color Image Processing","volume":"8","author":"Ataieyan","year":"2018","journal-title":"J. Agric. Mach."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.geoderma.2006.03.050","article-title":"High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures","volume":"136","author":"Selige","year":"2006","journal-title":"Geoderma"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"112117","DOI":"10.1016\/j.rse.2020.112117","article-title":"Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison","volume":"252","author":"Silvero","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_136","first-page":"102094","article-title":"The refined spatiotemporal representation of soil organic matter based on remote images fusion of Sentinel-2 and Sentinel-3","volume":"89","author":"Lin","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.geoderma.2016.06.033","article-title":"Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method","volume":"281","author":"Zeng","year":"2016","journal-title":"Geoderma"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1007\/s11442-013-1010-1","article-title":"Estimating the spatial distribution of organic carbon density for the soils of Ohio, USA","volume":"23","author":"Kumar","year":"2013","journal-title":"J. Geogr. Sci."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.geoderma.2014.08.009","article-title":"Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach","volume":"237\u2013238","author":"Guo","year":"2015","journal-title":"Geoderma"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.1016\/S2095-3119(13)60395-0","article-title":"Spatial Interpolation of Soil Texture Using Compositional Kriging and Regression Kriging with Consideration of the Characteristics of Compositional Data and Environment Variables","volume":"12","author":"Zhang","year":"2013","journal-title":"J. Integr. Agric."},{"key":"ref_141","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_142","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.ecolind.2013.08.009","article-title":"Estimation of soil organic matter by geostatistical methods: Use of auxiliary information in agricultural and environmental assessment","volume":"36","author":"Piccini","year":"2014","journal-title":"Ecol. Indic."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2934\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:35:09Z","timestamp":1760164509000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2934"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,26]]},"references-count":142,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13152934"],"URL":"https:\/\/doi.org\/10.3390\/rs13152934","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,26]]}}}