{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T05:03:35Z","timestamp":1775711015489,"version":"3.50.1"},"reference-count":107,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Key R&amp;D projects in Hubei Province","award":["2021BCA220"],"award-info":[{"award-number":["2021BCA220"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate mapping of farmland soil organic carbon (SOC) provides valuable information for evaluating soil quality and guiding agricultural management. The integration of natural factors, agricultural activities, and landscape patterns may well fit the high spatial variation of SOC in low-relief farmlands. However, commonly used prediction methods are global models, ignoring the stratified heterogeneous relationship between SOC and environmental variables and failing to reveal the determinants of SOC in different subregions. Using 242 topsoil samples collected from Jianghan Plain, China, this study explored the stratified heterogeneous relationship between SOC and natural factors, agricultural activities, and landscape metrics, determined the dominant factors of SOC in each stratum, and predicted the spatial distribution of SOC using the Cubist model. Ordinary kriging, stepwise linear regression (SLR), and random forest (RF) were used as references. SLR and RF results showed that land use types, multiple cropping index, straw return, and percentage of water bodies are global dominant factors of SOC. Cubist results exhibited that the dominant factors of SOC vary in different cropping systems. Compared with the SOC of paddy fields, the SOC of irrigated land was more affected by irrigation-related factors. The effect of straw return on SOC was diverse under different cropping intensities. The Cubist model outperformed the other models in explaining SOC variation and SOC mapping (fitting R2 = 0.370 and predicted R2 = 0.474). These results highlight the importance of exploring the stratified heterogeneous relationship between SOC and covariates, and this knowledge provides a scientific basis for farmland zoning management. The Cubist model, integrating natural factors, agricultural activities, and landscape metrics, is effective in explaining SOC variation and mapping SOC in low-relief farmlands.<\/jats:p>","DOI":"10.3390\/rs14153575","type":"journal-article","created":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:17:27Z","timestamp":1658794647000},"page":"3575","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Mapping Soil Organic Carbon in Low-Relief Farmlands Based on Stratified Heterogeneous Relationship"],"prefix":"10.3390","volume":"14","author":[{"given":"Zihao","family":"Wu","sequence":"first","affiliation":[{"name":"School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Research Center for Land Use and Ecological Security Governance in Mining Area, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7442-3239","authenticated-orcid":false,"given":"Yiyun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Yang","sequence":"additional","affiliation":[{"name":"Qingdao Geotechnical Investigation and Surveying Institute, Qingdao 266000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanli","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Research Center for Land Use and Ecological Security Governance in Mining Area, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiran","family":"Han","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,26]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"114447","DOI":"10.1016\/j.geoderma.2020.114447","article-title":"Mapping soil organic carbon at a terrain unit resolution across South Africa","volume":"373","author":"Schulze","year":"2020","journal-title":"Geoderma"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2017.01.002","article-title":"Soil carbon 4 per mille","volume":"292","author":"Minasny","year":"2017","journal-title":"Geoderma"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"115542","DOI":"10.1016\/j.geoderma.2021.115542","article-title":"Response of global farmland soil organic carbon to nitrogen application over time depends on soil type","volume":"406","author":"Ni","year":"2022","journal-title":"Geoderma"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107639","DOI":"10.1016\/j.agee.2021.107639","article-title":"Changing soil organic carbon with land use and management practices in a thousand-year cultivation region","volume":"322","author":"Niu","year":"2021","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1038\/s41467-022-30037-9","article-title":"Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery","volume":"13","author":"Qiu","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1038\/nature04514","article-title":"Temperature sensitivity of soil carbon decomposition and feedbacks to climate change","volume":"440","author":"Davidson","year":"2006","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"115600","DOI":"10.1016\/j.geoderma.2021.115600","article-title":"Revealing the scale- and location-specific relationship between soil organic carbon and environmental factors in China\u2019s north-south transition zone","volume":"409","author":"Tian","year":"2022","journal-title":"Geoderma"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e12246","DOI":"10.7717\/peerj.12246","article-title":"Land-use change influence soil quality parameters at an ecologically fragile area of YongDeng County of Gansu Province, China","volume":"9","author":"Adingo","year":"2021","journal-title":"Peerj"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nijbroek, R., Piikki, K., Soderstrom, M., Kempen, B., Turner, K.G., Hengari, S., and Mutua, J. (2018). Soil Organic Carbon Baselines for Land Degradation Neutrality: Map Accuracy and Cost Tradeoffs with Respect to Complexity in Otjozondjupa, Namibia. Sustainability, 10.","DOI":"10.3390\/su10051610"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Suleymanov, A., Abakumov, E., Suleymanov, R., Gabbasova, I., and Komissarov, M. (2021). The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes. Isprs Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10040243"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.geoderma.2015.07.017","article-title":"Digital soil mapping: A brief history and some lessons","volume":"264","author":"Minasny","year":"2016","journal-title":"Geoderma"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.geoderma.2019.01.018","article-title":"Land parcel-based digital soil mapping of soil nutrient properties in an alluvial-diluvia plain agricultural area in China","volume":"340","author":"Dong","year":"2019","journal-title":"Geoderma"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"142120","DOI":"10.1016\/j.scitotenv.2020.142120","article-title":"Mapping farmland soil organic carbon density in plains with combined cropping system extracted from NDVI time-series data","volume":"754","author":"Wu","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.still.2014.12.002","article-title":"Comparing geospatial techniques to predict SOC stocks","volume":"148","author":"Liu","year":"2015","journal-title":"Soil Tillage Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5241","DOI":"10.1002\/ldr.4105","article-title":"Optimization of tillage rotation and fertilization increased the soil organic carbon pool and crop yield in a semiarid region","volume":"32","author":"Zhang","year":"2021","journal-title":"Land Degrad. Dev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104381","DOI":"10.1016\/j.still.2019.104381","article-title":"Estimating soil organic carbon density in plains using landscape metric-based regression Kriging model","volume":"195","author":"Wu","year":"2019","journal-title":"Soil Tillage Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Guo, L., Chen, Y., Shi, T., Luo, M., Ju, Q., Zhang, H., and Wang, S. (2019). Prediction of soil organic carbon based on landsat 8 monthly NDVI data for the Jianghan Plain in Hubei Province, China. Remote Sens., 11.","DOI":"10.3390\/rs11141683"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.geoderma.2019.01.015","article-title":"Predicting soil organic carbon content in croplands using crop rotation and Fourier transform decomposed variables","volume":"340","author":"Yang","year":"2019","journal-title":"Geoderma"},{"key":"ref_20","first-page":"102428","article-title":"A deep learning method to predict soil organic carbon content at a regional scale using satellite-based phenology variables","volume":"102","author":"Yang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"105442","DOI":"10.1016\/j.catena.2021.105442","article-title":"Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images","volume":"205","author":"He","year":"2021","journal-title":"CATENA"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dvorakova, K., Shi, P., Limbourg, Q., and van Wesemael, B. (2020). Soil organic carbon mapping from remote sensing: The effect of crop residues. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-8253"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1080\/01431161.2018.1513180","article-title":"Quantitative assessment of soil salinity using multi-source remote sensing data based on the support vector machine and artificial neural network","volume":"40","author":"Jiang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nguyen, K.A., Chen, W., Lin, B.-S., and Seeboonruang, U. (2021). Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements. Isprs. Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10010042"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"528441","DOI":"10.3389\/fdata.2020.528441","article-title":"Ensemble Machine Learning Approach Improves Predicted Spatial Variation of Surface Soil Organic Carbon Stocks in Data-Limited Northern Circumpolar Region","volume":"3","author":"Mishra","year":"2020","journal-title":"Front. Big Data"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e00444","DOI":"10.1016\/j.geodrs.2021.e00444","article-title":"Assessment of the soil fertility status in Benin (West Africa)-Digital soil mapping using machine learning","volume":"28","author":"Hounkpatin","year":"2022","journal-title":"Geoderma Reg."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105791","DOI":"10.1016\/j.catena.2021.105791","article-title":"Clay content mapping and uncertainty estimation using weighted model averaging","volume":"209","author":"Zhao","year":"2022","journal-title":"Catena"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"115446","DOI":"10.1016\/j.geoderma.2021.115446","article-title":"Machine learning techniques for acid sulfate soil mapping in southeastern Finland","volume":"406","author":"Estevez","year":"2022","journal-title":"Geoderma"},{"key":"ref_29","first-page":"809","article-title":"Soil organic carbon and the response to climate change in Hebei Plains","volume":"37","author":"Zhong","year":"2016","journal-title":"Res. Agric. Mod."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.1002\/ldr.3075","article-title":"An assessment of the variation of soil properties with landscape attributes in the highlands of Cameroon","volume":"29","author":"Takoutsing","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.catena.2019.05.015","article-title":"Soil salinity and land use-land cover interactions with soil carbon in a salt-affected irrigation canal command of Indo-Gangetic plain","volume":"180","author":"Bhardwaj","year":"2019","journal-title":"Catena"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.catena.2018.07.005","article-title":"Soil carbon sequestration potential as affected by soil physical and climatic factors under different land uses in a semiarid region","volume":"171","author":"Alidoust","year":"2018","journal-title":"Catena"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1080\/03650340.2019.1576171","article-title":"Predicting soil organic matter content in a plain-to-hill transition belt using geographically weighted regression with stratification","volume":"65","author":"Yang","year":"2019","journal-title":"Arch. Agron. Soil Sci."},{"key":"ref_34","unstructured":"Zhou, S. (2016). Analysis of Influencing Factors and Prediction of Soil Organic Carbon at Agricultural Landscape in Hilly Area. [Master\u2019s Thesis, Southwest University]."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Quinlan, J.R. (1993, January 27\u201329). Combining Instance-Based and Model-Based Learning. Proceedings of the Tenth International Conference on International Conference on Machine Learning, Amherst, MA, USA.","DOI":"10.1016\/B978-1-55860-307-3.50037-X"},{"key":"ref_36","unstructured":"Kuhn, M., and Quinlan, R. (2020, October 01). Cubist: Rule- And Instance-Based Regression Modeling. Available online: https:\/\/cran.r-project.org\/web\/packages\/Cubist\/vignettes\/cubist.html."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"137703","DOI":"10.1016\/j.scitotenv.2020.137703","article-title":"Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran","volume":"721","author":"Fathololoumi","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"114901","DOI":"10.1016\/j.geoderma.2020.114901","article-title":"Effect of multi-temporal satellite images on soil moisture prediction using a digital soil mapping approach","volume":"385","author":"Fathololoumi","year":"2021","journal-title":"Geoderma"},{"key":"ref_39","first-page":"1","article-title":"A Regional Legacy Soil Dataset for Prediction of Sand and Clay Content with Vis-Nir-Swir, in Southern Brazil","volume":"43","author":"Silva","year":"2019","journal-title":"Rev. Bras. De. Cienc. Do. Solo"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"114132","DOI":"10.1016\/j.geoderma.2019.114132","article-title":"Tropical soil pH and sorption complex prediction via portable X-ray fluorescence spectrometry","volume":"361","author":"Faria","year":"2020","journal-title":"Geoderma"},{"key":"ref_41","first-page":"456","article-title":"A rapid and accurate procedure for estimation of organic carbon in soils","volume":"84","author":"Nelson","year":"1974","journal-title":"Proc. Indiana Acad. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e00306","DOI":"10.1016\/j.geodrs.2020.e00306","article-title":"Precocious 19th century soil carbon science","volume":"22","author":"Minasny","year":"2020","journal-title":"Geoderma Reg."},{"key":"ref_43","unstructured":"FAO\/IIASA\/ISRIC\/ISSCAS\/JRC (2020, October 01). Harmonized World Soil Database (Version 1.2). Available online: http:\/\/www.fao.org\/home\/en\/."},{"key":"ref_44","unstructured":"CAS (2019, September 01). ASTER DEM. Available online: http:\/\/www.gscloud.cn."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yang, R., Luo, X., Xu, Q., Zhang, X., and Wu, J. (2021). Measuring the Impact of the Multiple Cropping Index of Cultivated Land during Continuous and Rapid Rise of Urbanization in China: A Study from 2000 to 2015. Land, 10.","DOI":"10.3390\/land10050491"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/07038992.2017.1252906","article-title":"Extracting the Spatiotemporal Pattern of Cropping Systems From NDVI Time Series Using a Combination of the Spline and HANTS Algorithms: A Case Study for Shandong Province","volume":"43","author":"Liang","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"120312","DOI":"10.1016\/j.jclepro.2020.120312","article-title":"Spatial-temporal dynamics of grain yield and the potential driving factors at the county level in China","volume":"255","author":"Pan","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1080\/07038992.1996.10874649","article-title":"Identification of Agricultural Tillage Practices from C-Band Radar Backscatter","volume":"22","author":"Mcnairn","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_50","first-page":"474","article-title":"Remote sensing retrieval of maize residue cover on soil heterogeneous background","volume":"31","author":"Huang","year":"2020","journal-title":"Ying Yong Sheng Tai Xue Bao J. Appl. Ecol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Memon, M.S., Jun, Z., Sun, C., Jiang, C., Xu, W., Hu, Q., Yang, H., and Ji, C. (2019). Assessment of Wheat Straw Cover and Yield Performance in a Rice-Wheat Cropping System by Using Landsat Satellite Data. Sustainability, 11.","DOI":"10.3390\/su11195369"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.rse.2011.09.016","article-title":"Remote sensing of crop residue cover using multi-temporal Landsat imagery","volume":"117","author":"Zheng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_53","unstructured":"Mcgarigal, K. (2020, October 01). FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. Computer Software Program Produced by the Authors at the University of Massachuse-tts, Amherst. Available online: Www.umass.edu\/landeco\/research\/fragstats\/fragstats.html."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"439","DOI":"10.2307\/1425829","article-title":"The Intrinsic Random Functions and Their Applications","volume":"5","author":"Matheron","year":"1973","journal-title":"Adv. Appl. Probab."},{"key":"ref_55","unstructured":"Webster, R. (2001). Geostatistics for Environmental Scientists, John Wiley & Sons."},{"key":"ref_56","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_57","doi-asserted-by":"crossref","unstructured":"Forkuor, G., Hounkpatin, O.K.L., Welp, G., and Thiel, M. (2017). High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0170478"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"114761","DOI":"10.1016\/j.geoderma.2020.114761","article-title":"Improvement of soil property mapping in the Great Clay Belt of northern Ontario using multi-source remotely sensed data","volume":"381","author":"Pittman","year":"2021","journal-title":"Geoderma"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Quinlan, J.R. (1993). Combining Instance-Based and Model-Based Learning, Morgan Kaufmann.","DOI":"10.1016\/B978-1-55860-307-3.50037-X"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.envsoft.2017.12.021","article-title":"Improving predictions of hydrological low-flow indices in ungaged basins using machine learning","volume":"101","author":"Worland","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1111\/j.1467-8640.1989.tb00315.x","article-title":"Instancebased Prediction of Real-valued Attributes","volume":"5","author":"Kibler","year":"1989","journal-title":"Comput. Intell."},{"key":"ref_62","first-page":"324","article-title":"A note on the concordance correlation coefficient","volume":"56","author":"Lin","year":"2000","journal-title":"Biometrics"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2307\/2532051","article-title":"A concordance correlation coefficient to evaluate reproducibility","volume":"45","author":"Lin","year":"1989","journal-title":"Biometrics"},{"key":"ref_64","unstructured":"Mcbride, G.B. (2005). A Proposal for Strength-of-Agreement Criteria for Lin\u2019s Concordance Correlation Coefficient, National Institute of Water & Atmospheric Research Ltd."},{"key":"ref_65","unstructured":"Wilding, L.P. (December, January 30). Spatial variability: Its documentation, accommodation and implication to soil survey. Proceedings of the Soil Spatial Variability, Las Vegas, NV, USA."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.ecoleng.2015.06.030","article-title":"Soil organic carbon as a function of land use and topography on the Loess Plateau of China","volume":"83","author":"Sun","year":"2015","journal-title":"Ecol. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.geomorph.2010.11.008","article-title":"Linking spatial patterns of soil organic carbon to topography\u2014A case study from south-eastern Spain","volume":"126","author":"Schwanghart","year":"2011","journal-title":"Geomorphology"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ecohyd.2020.08.006","article-title":"Terrain indices control the quality of soil total carbon stock within water erosion-prone environments","volume":"21","author":"Mohseni","year":"2021","journal-title":"Ecohydrol. Hydrobiol."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Wisniewski, P., and Maerker, M. (2021). Comparison of Topsoil Organic Carbon Stocks on Slopes under Soil-Protecting Forests in Relation to the Adjacent Agricultural Slopes. Forests, 12.","DOI":"10.3390\/f12040390"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"104960","DOI":"10.1016\/j.still.2021.104960","article-title":"Long-term crop rotation and different tillage practices alter soil organic matter composition and degradation","volume":"209","author":"Man","year":"2021","journal-title":"Soil Tillage Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"105102","DOI":"10.1016\/j.catena.2020.105102","article-title":"Long term impact of different tillage systems on carbon pools and stocks, soil bulk density, aggregation and nutrients: A field meta-analysis","volume":"199","author":"Topa","year":"2021","journal-title":"Catena"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1007\/s11629-014-3213-z","article-title":"Spatial variability of soil organic carbon in different hillslope positions in Toshan area, Golestan Province, Iran: Geostatistical approaches","volume":"12","author":"Bameri","year":"2015","journal-title":"J. Mt. Sci."},{"key":"ref_73","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_74","doi-asserted-by":"crossref","first-page":"110091","DOI":"10.1016\/j.jenvman.2020.110091","article-title":"Lateral mobilization of soil carbon induced by runoff along karstic slopes","volume":"260","author":"Gaspar","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"110319","DOI":"10.1016\/j.jenvman.2020.110319","article-title":"A review of soil carbon dynamics resulting from agricultural practices","volume":"268","author":"Abbas","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.still.2018.10.009","article-title":"Cropland soils in China have a large potential for carbon sequestration based It on literature survey","volume":"186","author":"Tao","year":"2019","journal-title":"Soil Tillage Res."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1016\/S1002-0160(17)60383-7","article-title":"Environmental and anthropogenic factors driving changes in paddy soil organic matter: A case study in the middle and lower Yangtze River Plain of China","volume":"27","author":"Guo","year":"2017","journal-title":"Pedosphere"},{"key":"ref_78","first-page":"71","article-title":"Soil organic matter stabilization and carbon-cycling enzyme activity are affected by land management","volume":"63","author":"Blonska","year":"2020","journal-title":"Ann. For. Res."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"107112","DOI":"10.1016\/j.agee.2020.107112","article-title":"Effects of human activities on soil organic carbon redistribution at an agricultural watershed scale on the Chinese Loess Plateau","volume":"303","author":"Zeng","year":"2020","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"105056","DOI":"10.1016\/j.catena.2020.105056","article-title":"Vertical distributions of organic carbon fractions under paddy and forest soils derived from black shales: Implications for potential of long-term carbon storage","volume":"198","author":"Qin","year":"2021","journal-title":"Catena"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1080\/00103624.2013.741949","article-title":"Effects of Land-Use Change on Soil Organic Carbon and Nitrogen","volume":"44","author":"Jafarian","year":"2013","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"103351","DOI":"10.1016\/j.apsoil.2019.09.001","article-title":"Land use change from upland to paddy field in Mollisols drives soil aggregation and associated microbial communities","volume":"146","author":"Li","year":"2020","journal-title":"Appl. Soil Ecol."},{"key":"ref_83","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_84","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.5194\/bg-12-1635-2015","article-title":"Soil organic carbon in the Sanjiang Plain of China: Storage, distribution and controlling factors","volume":"12","author":"Mao","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1139\/cjss-2018-0094","article-title":"Effects of different wheat straw returning modes on soil organic carbon sequestration in a rice-wheat rotation","volume":"99","author":"Hu","year":"2019","journal-title":"Can. J. Soil Sci."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"e200","DOI":"10.1002\/fes3.200","article-title":"Effect of straw returning on soil organic carbon in rice-wheat rotation system: A review","volume":"9","author":"Jin","year":"2020","journal-title":"Food Energy Secur."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Zou, H., Ye, X., Li, J., Lu, J., Fan, Q., Yu, N., Zhang, Y., Dang, X., and Zhang, Y. (2016). Effects of Straw Return in Deep Soils with Urea Addition on the Soil Organic Carbon Fractions in a Semi-Arid Temperate Cornfield. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0153214"},{"key":"ref_88","first-page":"412","article-title":"Effects of Straw Incorporation on Soil Organic Carbon Density and the Carbon Pool Management Index under Long-Term Continuous Cotton","volume":"48","author":"Liu","year":"2017","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_89","first-page":"3491","article-title":"Effect of straw-returning on the storage and distribution of different active fractions of soil organic carbon","volume":"25","author":"Wang","year":"2014","journal-title":"Ying Yong Sheng Tai Xue Bao J. Appl. Ecol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.agwat.2016.04.009","article-title":"Irrigation regime affected SOC content rather than plow layer thickness of rice paddies: A county level survey from a river basin in lower Yangtze valley, China","volume":"172","author":"Li","year":"2016","journal-title":"Agric. Water Manag."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s13593-013-0134-0","article-title":"Irrigation, soil organic carbon and N2O emissions. A review","volume":"33","author":"Trost","year":"2013","journal-title":"Agron. Sustain. Dev."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"144372","DOI":"10.1016\/j.scitotenv.2020.144372","article-title":"Irrigation alters source-composition characteristics of groundwater dissolved organic matter in a large arid river basin, Northwestern China","volume":"767","author":"Hu","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"3655","DOI":"10.5194\/bg-12-3655-2015","article-title":"Soil moisture influence on the interannual variation in temperature sensitivity of soil organic carbon mineralization in the Loess Plateau","volume":"12","author":"Zhang","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"138330","DOI":"10.1016\/j.scitotenv.2020.138330","article-title":"Patterns and trends of topsoil carbon in the UK: Complex interactions of land use change, climate and pollution","volume":"729","author":"Thomas","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"125","DOI":"10.17221\/817\/2016-PSE","article-title":"Organic carbon content and its liable components in paddy soil under water-saving irrigation","volume":"63","author":"Ma","year":"2017","journal-title":"Plant Soil Environ."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1007\/s10333-017-0591-1","article-title":"Effect of water management on soil respiration and NEE of paddy fields in Southeast China","volume":"15","author":"Yang","year":"2017","journal-title":"Paddy Water Environ."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.agee.2016.03.009","article-title":"The influence of the type of crop residue on soil organic carbon fractions: An 11-year field study of rice-based cropping systems in southeast China","volume":"223","author":"Chen","year":"2016","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1080\/713608318","article-title":"Crop management for soil carbon sequestration","volume":"22","author":"Jarecki","year":"2003","journal-title":"Crit. Rev. Plant Sci."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Tziachris, P., Aschonitis, V., Chatzistathis, T., Papadopoulou, M., and Doukas, I.D. (2020). Comparing Machine Learning Models and Hybrid Geostatistical Methods Using Environmental and Soil Covariates for Soil pH Prediction. Isprs Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9040276"},{"key":"ref_100","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_101","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.2136\/sssaj1994.03615995005800050033x","article-title":"Field-Scale Variability of Soil Properties in Central Iowa Soils","volume":"58","author":"Cambardella","year":"1994","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_102","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_103","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1071\/SR18319","article-title":"Digital mapping of topsoil pH by random forest with residual kriging (RFRK) in a hilly region","volume":"57","author":"Wang","year":"2019","journal-title":"Soil Res."},{"key":"ref_104","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_105","doi-asserted-by":"crossref","first-page":"105258","DOI":"10.1016\/j.catena.2021.105258","article-title":"Evaluation and Prediction of Topsoil organic carbon using Machine learning and hybrid models at a Field-scale","volume":"202","author":"Matinfar","year":"2021","journal-title":"Catena"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Guo, Z., Han, J., Li, J., Xu, Y., and Wang, X. (2019). Effects of long-term fertilization on soil organic carbon mineralization and microbial community structure. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0216006"},{"key":"ref_107","unstructured":"Xiaosheng, Q., and Amahmid, O. (2020). Short-term effects of different fertilization measures on water-stable aggregates and carbon and nitrogen of tea garden soil. E3S Web of Conferences, Proceedings of the 2020 2nd International Conference on Water Resources and Environmental Engineering, Shanghai, China, 23\u201324 October 2020, EDP Sciences."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3575\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:56:24Z","timestamp":1760140584000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3575"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,26]]},"references-count":107,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153575"],"URL":"https:\/\/doi.org\/10.3390\/rs14153575","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,26]]}}}