{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T03:08:46Z","timestamp":1772852926601,"version":"3.50.1"},"reference-count":141,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,19]],"date-time":"2021-12-19T00:00:00Z","timestamp":1639872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the K. C. Wong Education Foundation and Special Foundation for Basic Research Program in wild China of CAS","award":["XDA23070501"],"award-info":[{"award-number":["XDA23070501"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil organic matter (SOM) plays a critical role in agroecosystems and the terrestrial carbon cycle. Thus, accurately mapping SOM promotes sustainable agriculture and estimations of soil carbon pools. However, few studies have analyzed the changing trends in multi-period SOM prediction accuracies for single cropland soil types and mapped their spatial SOM patterns. Using time series 7 MOD09A1 images during the bare soil period, we combined the pixel dates of training samples and precipitation data to explore the variation in SOM accuracy for two typical cropland soil types. The advantage of using single soil type data versus the total dataset was evaluated, and SOM maps were drawn for the northern Songnen Plain. When almost no precipitation occurred on or near the optimal pixel date, the accuracies increased, and vice versa. SOM models of the two soil types achieved a lower root mean squared error (RMSE = 0.55%, 0.79%) and mean absolute error (MAE = 0.39%, 0.58%) and a higher coefficient of determination (R2 = 0.65, 0.75) than the model using the total dataset and resulted in a mean relative improvement (RI) of 30.21%. The SOM decreased from northeast to southwest. The results provide reference data for the accurate management of cultivated soil and determining carbon sequestration.<\/jats:p>","DOI":"10.3390\/rs13245162","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T02:40:32Z","timestamp":1639968032000},"page":"5162","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Mapping Soil Organic Matter and Analyzing the Prediction Accuracy of Typical Cropland Soil Types on the Northern Songnen Plain"],"prefix":"10.3390","volume":"13","author":[{"given":"Meiwei","family":"Zhang","sequence":"first","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huanjun","family":"Liu","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"School of Public Administration and Law, Northeast Agricultural University, Harbin 150030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meinan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Tsinghua University, Beijing 100089, China"},{"name":"Institute of Forest Ecology, Environment and Nature Conservation, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoxuan","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanliang","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Environment, Tsinghua University, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Han","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haitao","family":"Tang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinle","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,19]]},"reference":[{"key":"ref_1","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_2","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_3","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_4","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_5","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","author":"Mishra","year":"2012","journal-title":"Geoderma"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15","DOI":"10.5194\/soil-5-15-2019","article-title":"Global meta-analysis of the relationship between soil organic matter and crop yields","volume":"5","author":"Oldfield","year":"2019","journal-title":"Soil"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1016\/j.envsci.2010.07.004","article-title":"Soil loss and conservation in the black soil region of Northeast China: A retrospective study","volume":"13","author":"Xu","year":"2010","journal-title":"Environ. Sci. Policy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.catena.2018.08.001","article-title":"Erosion-induced carbon losses and CO2 emissions from Loess and Black soil in China","volume":"171","author":"Gao","year":"2018","journal-title":"Catena"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107081","DOI":"10.1016\/j.agee.2020.107081","article-title":"Response of soil OC, N and P to land-use change and erosion in the black soil region of the Northeast China","volume":"302","author":"Li","year":"2020","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.geoderma.2019.02.019","article-title":"Mapping soil organic matter contents at field level with Cubist, Random Forest and kriging","volume":"342","author":"Pouladi","year":"2019","journal-title":"Geoderma"},{"key":"ref_13","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_14","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_15","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","author":"Zhang","year":"2012","journal-title":"Geoderma"},{"key":"ref_16","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_17","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_18","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_19","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_20","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_21","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1111\/j.1365-2389.1992.tb00128.x","article-title":"Sample adequately to estimate variograms of soil properties","volume":"43","author":"Webster","year":"1992","journal-title":"J. Soil Sci."},{"key":"ref_22","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_23","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_24","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_25","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_26","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_27","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_28","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_29","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_30","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_31","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_32","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_33","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.geoderma.2013.04.007","article-title":"Spatially-explicit regional-scale prediction of soil organic carbon stocks in cropland using environmental variables and mixed model approaches","volume":"204","author":"Doetterl","year":"2013","journal-title":"Geoderma"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s10533-018-0424-3","article-title":"Beyond clay: Towards an improved set of variables for predicting soil organic matter content","volume":"137","author":"Rasmussen","year":"2018","journal-title":"Biogeochemistry"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1080\/01621459.1961.10482095","article-title":"Note on stepwise least squares","volume":"56","author":"Goldberger","year":"1961","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/0895-4356(88)90119-9","article-title":"Assessing the importance of an independent variable in multiple regression: Is stepwise unwise?","volume":"41","author":"Leigh","year":"1988","journal-title":"J. Clin. Epidemiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.11.004","article-title":"Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984\u20132014)","volume":"205","author":"Rogge","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1900","DOI":"10.13031\/2013.31816","article-title":"Evaluation of reflectance methods for soil organic matter sensing","volume":"34","author":"Sudduth","year":"1991","journal-title":"Trans. ASAE"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.2136\/sssaj2005.0071","article-title":"IKONOS imagery to estimate surface soil property variability in two Alabama physiographies","volume":"69","author":"Sullivan","year":"2005","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1080\/00288230709510399","article-title":"Hyperspectral extraction of soil organic matter content based on principal component regression","volume":"50","author":"Yanli","year":"2007","journal-title":"N. Z. J. Agric. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1366\/13-07288","article-title":"Visible, near-infrared, and mid-infrared spectroscopy applications for soil assessment with emphasis on soil organic matter content and quality: State-of-the-art and key issues","volume":"67","author":"Gholizadeh","year":"2013","journal-title":"Appl. Spectrosc."},{"key":"ref_43","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_44","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_45","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/S1002-0160(15)60029-7","article-title":"Estimation of organic matter content in coastal soil using reflectance spectroscopy","volume":"26","author":"Zheng","year":"2016","journal-title":"Pedosphere"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1097\/00010694-196508000-00009","article-title":"Reflection of Radiant Energy from Soils","volume":"100","author":"Bower","year":"1965","journal-title":"Soil Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hong, Y., Yu, L., Chen, Y., Liu, Y., Liu, Y., Liu, Y., and Cheng, H. (2018). Prediction of soil organic matter by VIS\u2013NIR spectroscopy using normalized soil moisture index as a proxy of soil moisture. Remote Sens., 10.","DOI":"10.3390\/rs10010028"},{"key":"ref_48","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."},{"key":"ref_49","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","author":"Guo","year":"2015","journal-title":"Geoderma"},{"key":"ref_50","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_51","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_52","doi-asserted-by":"crossref","unstructured":"Muro, J., Canty, M., Conradsen, K., H\u00fcttich, C., Nielsen, A.A., Skriver, H., Remy, F., Strauch, A., Thonfeld, F., and Menz, G. (2016). Short-term change detection in wetlands using Sentinel-1 time series. Remote Sens., 8.","DOI":"10.3390\/rs8100795"},{"key":"ref_53","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_54","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","author":"Liu","year":"2012","journal-title":"Geoderma"},{"key":"ref_55","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_56","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_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","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, M., Yang, H., Jin, Y., Zhang, X., and Liu, H. (2021). Mapping Regional Soil Organic Matter Based on Sentinel-2A and MODIS Imagery Using Machine Learning Algorithms and Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13152934"},{"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":"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_61","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_62","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_63","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_64","unstructured":"IUSS Working Group WRB (2006). World Reference Base for Soil Resources 2006, FAO. World Soil Resources Reports No. 103."},{"key":"ref_65","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_66","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.still.2017.12.022","article-title":"Tillage erosion and its effect on spatial variations of soil organic carbon in the black soil region of China","volume":"178","author":"Zhao","year":"2018","journal-title":"Soil Tillage Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1007\/s11442-012-0959-5","article-title":"Soil loss tolerance in the black soil region of Northeast China","volume":"22","author":"Duan","year":"2012","journal-title":"J. Geogr. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.geomorph.2012.04.019","article-title":"Using 137Cs technique to quantify soil erosion and deposition rates in an agricultural catchment in the black soil region, Northeast China","volume":"169","author":"Fang","year":"2012","journal-title":"Geomorphology"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"104259","DOI":"10.1016\/j.catena.2019.104259","article-title":"Hyper-temporal remote sensing data in bare soil period and terrain attributes for digital soil mapping in the Black soil regions of China","volume":"184","author":"Yang","year":"2020","journal-title":"Catena"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1016\/S2095-3119(16)61559-9","article-title":"Chemical fertilizers could be completely replaced by manure to maintain high maize yield and soil organic carbon (SOC) when SOC reaches a threshold in the Northeast China Plain","volume":"16","author":"Hui","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_71","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_72","first-page":"539","article-title":"Total carbon, organic carbon, and organic matter","volume":"9","author":"Nelson","year":"1983","journal-title":"Methods Soil Anal. Part 2 Chem. Microbiol. Prop."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.rse.2018.04.047","article-title":"Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images","volume":"212","author":"Fongaro","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Gallo, B.C., Dematt\u00ea, J.A., Rizzo, R., Safanelli, J.L., Mendes, W.d.S., Lepsch, I.F., Sato, M.V., Romero, D.J., and Lacerda, M.P. (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_75","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_76","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_77","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_78","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.geoderma.2014.01.011","article-title":"VisNIR spectra of dried ground soils predict properties of soils scanned moist and intact","volume":"221","author":"Ge","year":"2014","journal-title":"Geoderma"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.molstruc.2006.03.053","article-title":"Progress in two-dimensional (2D) correlation spectroscopy","volume":"799","author":"Noda","year":"2006","journal-title":"J. Mol. Struct."},{"key":"ref_80","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":"2020","journal-title":"Catena"},{"key":"ref_81","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_82","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_83","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.envpol.2013.04.033","article-title":"Influence of plant root morphology and tissue composition on phenanthrene uptake: Stepwise multiple linear regression analysis","volume":"179","author":"Zhan","year":"2013","journal-title":"Environ. Pollut."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1080\/00401706.1989.10488486","article-title":"Applied Regression Analysis and Other Multivariable Methods","volume":"31","author":"Walker","year":"1989","journal-title":"Technometrics"},{"key":"ref_85","unstructured":"Kleinbaum, D.G., Kupper, L.L., Nizam, A., and Rosenberg, E.S. (2013). Applied Regression Analysis and Other Multivariable Methods, Cengage Learning."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"115263","DOI":"10.1016\/j.geoderma.2021.115263","article-title":"A regional-scale hyperspectral prediction model of soil organic carbon considering geomorphic features","volume":"403","author":"Bao","year":"2021","journal-title":"Geoderma"},{"key":"ref_87","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_88","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":"Minasny","year":"2018","journal-title":"Geoderma"},{"key":"ref_89","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_90","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_91","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_92","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2015.04.007","article-title":"A linear physically-based model for remote sensing of soil moisture using short wave infrared bands","volume":"164","author":"Sadeghi","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"722","DOI":"10.2136\/sssaj2002.7220","article-title":"Moisture effects on soil reflectance","volume":"66","author":"Lobell","year":"2002","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/S0034-4257(01)00347-9","article-title":"Relating soil surface moisture to reflectance","volume":"81","author":"Weidong","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.compag.2009.10.006","article-title":"On-the-go VisNIR: Potential and limitations for mapping soil clay and organic carbon","volume":"70","author":"Bricklemyer","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.13031\/2013.21717","article-title":"Effects of soil moisture content on absorbance spectra of sandy soils in sensing phosphorus concentrations using UV-VIS-NIR spectroscopy","volume":"49","author":"Bogrekci","year":"2006","journal-title":"Trans. ASABE"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1071\/SR09005","article-title":"Evaluating near infrared spectroscopy for field prediction of soil properties","volume":"47","author":"Minasny","year":"2009","journal-title":"Soil Res."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.13031\/2013.28498","article-title":"Soil organic matter, CEC, and moisture sensing with a portable NIR spectrophotometer","volume":"36","author":"Sudduth","year":"1993","journal-title":"Trans. ASAE"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Prudnikova, E., and Savin, I. (2021). Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data. Remote Sens., 13.","DOI":"10.3390\/rs13122313"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.geoderma.2011.09.008","article-title":"Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon","volume":"167","author":"Minasny","year":"2011","journal-title":"Geoderma"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/S1002-0160(09)60146-6","article-title":"Quantitative analysis of moisture effect on black soil reflectance","volume":"19","author":"Zhang","year":"2009","journal-title":"Pedosphere"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1016\/S0883-2927(03)00045-3","article-title":"Spatial distribution of soil organic carbon concentrations in grassland of Ireland","volume":"18","author":"McGrath","year":"2003","journal-title":"Appl. Geochem."},{"key":"ref_103","first-page":"189","article-title":"Soil organic matter and available water capacity","volume":"49","author":"Hudson","year":"1994","journal-title":"J. Soil Water Conserv."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1071\/SR01064","article-title":"Organic carbon and soil porosity","volume":"41","author":"Emerson","year":"2003","journal-title":"Soil Res."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1111\/j.1365-2389.1977.tb02291.x","article-title":"Influence of organic matter on the physical properties of some East Anglian soils of high silt content","volume":"28","author":"Hamblin","year":"1977","journal-title":"J. Soil Sci."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1097\/00010694-193905000-00005","article-title":"Effect of organic matter on the water-holding capacity and the wilting point of mineral soils","volume":"47","author":"Bouyoucos","year":"1939","journal-title":"Soil Sci."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.13031\/2013.33720","article-title":"Estimation of soil water properties","volume":"25","author":"Rawls","year":"1982","journal-title":"Trans. ASAE"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.geoderma.2003.08.018","article-title":"A generic framework for spatial prediction of soil variables based on regression-kriging","volume":"120","author":"Hengl","year":"2004","journal-title":"Geoderma"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Tziolas, N., Tsakiridis, N., Ben-Dor, E., Theocharis, J., and Zalidis, G. (2020). Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data. Remote Sens., 12.","DOI":"10.3390\/rs12091389"},{"key":"ref_110","first-page":"125622","article-title":"Soil moisture memory and soil properties: An analysis with the stored precipitation fraction","volume":"593","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_111","first-page":"132","article-title":"Soil classification based on maximum likelihood method and features of multi-temporal remote sensing images in bare soil period","volume":"34","author":"Liu","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_112","first-page":"105","article-title":"Hyperspectral reflectance characteristics paramter extraction for soil classification model","volume":"21","author":"Huanjun","year":"2017","journal-title":"J. Remote Sens."},{"key":"ref_113","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_114","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_115","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\u2013near infrared hyperspectral images","volume":"76","author":"Holden","year":"2012","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_116","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_117","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_118","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_119","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_120","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.catena.2014.09.004","article-title":"Laboratory-based Vis\u2013NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content","volume":"124","author":"Conforti","year":"2015","journal-title":"Catena"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1111\/ejss.12272","article-title":"Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis\u2013NIR spectral library","volume":"66","author":"Shi","year":"2015","journal-title":"Eur. J. Soil Sci."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.geoderma.2017.11.014","article-title":"Towards prediction of soil erodibility, SOM and CaCO3 using laboratory Vis-NIR spectra: A case study in a semi-arid region of Iran","volume":"314","author":"Ostovari","year":"2018","journal-title":"Geoderma"},{"key":"ref_123","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_124","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.catena.2017.05.016","article-title":"A comparative study between popular statistical and machine learning methods for simulating volume of landslides","volume":"157","author":"Shirzadi","year":"2017","journal-title":"Catena"},{"key":"ref_125","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_126","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.catena.2015.10.010","article-title":"Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran","volume":"137","author":"Rahmati","year":"2016","journal-title":"Catena"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1080\/01431160110071897","article-title":"Effects of farming works on soil surface bidirectional reflectance measurements and modelling","volume":"23","author":"Cierniewski","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Vaudour, E., Gomez, C., Loiseau, T., Baghdadi, N., Loubet, B., Arrouays, D., Ali, L., and Lagacherie, P. (2019). The impact of acquisition date on the prediction performance of topsoil organic carbon from Sentinel-2 for croplands. Remote Sens., 11.","DOI":"10.3390\/rs11182143"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.geoderma.2014.02.015","article-title":"Soil organic carbon assessment by field and airborne spectrometry in bare croplands: Accounting for soil surface roughness","volume":"226","author":"Denis","year":"2014","journal-title":"Geoderma"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/S1002-0160(10)60016-1","article-title":"Long-term effect of no-tillage on soil organic carbon fractions in a continuous maize cropping system of Northeast China","volume":"20","author":"Huang","year":"2010","journal-title":"Pedosphere"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"409","DOI":"10.5194\/bg-7-409-2010","article-title":"Soil organic carbon dynamics under long-term fertilizations in arable land of northern China","volume":"7","author":"Zhang","year":"2010","journal-title":"Biogeosciences"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"364","DOI":"10.2136\/sssaj1995.03615995005900020014x","article-title":"Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties","volume":"59","author":"Banin","year":"1995","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_133","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_134","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/S0034-4257(00)00198-X","article-title":"Modeling soil moisture-reflectance","volume":"76","author":"Muller","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Bouma, J. (1989). Using soil survey data for quantitative land evaluation. Advances in Soil Science, Springer.","DOI":"10.1007\/978-1-4612-3532-3_4"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.geoderma.2009.06.003","article-title":"Multi-criteria characterization of recent digital soil mapping and modeling approaches","volume":"152","author":"Grunwald","year":"2009","journal-title":"Geoderma"},{"key":"ref_137","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_138","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_139","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_140","unstructured":"Lagacherie, P., McBratney, A., and Voltz, M. (2006). Digital Soil Mapping: An Introductory Perspective, Elsevier."},{"key":"ref_141","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5162\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:51:59Z","timestamp":1760169119000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,19]]},"references-count":141,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13245162"],"URL":"https:\/\/doi.org\/10.3390\/rs13245162","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,19]]}}}