{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T19:44:58Z","timestamp":1771271098705,"version":"3.50.1"},"reference-count":77,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Exploring soil organic carbon (SOC) mapping is crucial for addressing critical challenges in environmental sustainability and food security. This study evaluates the suitability of the synergistic use of multi-temporal and high-resolution radar and optical remote sensing data for SOC prediction in the Kaffrine region of Senegal, covering over 1.1 million hectares. For this purpose, various scenarios were developed: Scenario 1 (Sentinel-1 data), Scenario 2 (Sentinel-2 data), Scenario 3 (Sentinel-1 and Sentinel-2 combination), Scenario 4 (topographic features), and Scenario 5 (Sentinel-1 and -2 with topographic features). The findings from comparing three different algorithms (Random Forest (RF), XGBoost, and Support Vector Regression (SVR)) with 671 soil samples for training and 281 samples for model evaluation highlight that RF outperformed the other models across different scenarios. Moreover, using Sentinel-2 data alone yielded better results than using only Sentinel-1 data. However, combining Sentinel-1 and Sentinel-2 data (Scenario 3) further improved the performance by 6% to 11%. Including topographic features (Scenario 5) achieved the highest accuracy, reaching an R2 of 0.7, an RMSE of 0.012%, and an RPIQ of 5.754 for the RF model. Applying the RF and XGBoost models under Scenario 5 for SOC mapping showed that both models tended to predict low SOC values across the study area, which is consistent with the predominantly low SOC content observed in most of the training data. This limitation constrains the ability of ML models to capture the full range of SOC variability, particularly for less frequent, slightly higher SOC values.<\/jats:p>","DOI":"10.3390\/rs16111871","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T05:36:06Z","timestamp":1716528966000},"page":"1871","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Synergistic Use of Multi-Temporal Radar and Optical Remote Sensing for Soil Organic Carbon Prediction"],"prefix":"10.3390","volume":"16","author":[{"given":"Sara","family":"Dahhani","sequence":"first","affiliation":[{"name":"Faculty of Sciences Ben M\u2019sik, Hassan II University of Casablanca, Sidi Othmane, Casablanca P.O. Box 7955, Morocco"}]},{"given":"Mohamed","family":"Raji","sequence":"additional","affiliation":[{"name":"Faculty of Sciences Ben M\u2019sik, Hassan II University of Casablanca, Sidi Othmane, Casablanca P.O. Box 7955, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3666-1850","authenticated-orcid":false,"given":"Yassine","family":"Bouslihim","sequence":"additional","affiliation":[{"name":"National Institute of Agricultural Research (INRA), CRRA Tadla, Rabat P.O. Box 415, Morocco"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1126\/science.1097396","article-title":"Soil Carbon Sequestration Impacts on Global Climate Change and Food Security","volume":"304","author":"Lal","year":"2004","journal-title":"Science"},{"key":"ref_2","first-page":"1","article-title":"Continental-scale controls on soil organic carbon across sub-Saharan Africa","volume":"2020","author":"Hoyt","year":"2020","journal-title":"Soil Discuss."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.isprsjprs.2022.04.026","article-title":"Modelling soil organic carbon stock distribution across different land-uses in South Africa: A remote sensing and deep learning approach","volume":"188","author":"Odebiri","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.geoderma.2015.06.023","article-title":"Mapping of soil properties and land deg-radation risk in Africa using MODIS reflectance","volume":"263","author":"Winowiecki","year":"2016","journal-title":"Geoderma"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1007\/s40808-021-01329-8","article-title":"Use of machine learning in Moroccan soil fertility prediction as an alternative to laborious analyses","volume":"8","author":"Bouslihim","year":"2022","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103359","DOI":"10.1016\/j.earscirev.2020.103359","article-title":"Machine learning for digital soil mapping: Applications, challenges and suggested solutions","volume":"210","author":"Wadoux","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nenkam Mentho, A., Wadoux, A.M.C., Minasny, B., Silatsa, F.B., Yemefack, M., Ugbaje, S., Akpa, S., van Zijl, G.M., Bouslihim, Y., and Chabala, L. (2024, March 15). Applications and Challenges of Digital Soil Mapping in Africa. Available online: https:\/\/ssrn.com\/abstract=4725182.","DOI":"10.2139\/ssrn.4725182"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hengl, T., Heuvelink, G.B., Kempen, B., Leenaars, J.G., Walsh, M.G., Shepherd, K.D., Sila, A., MacMillan, R.A., de Jesus, J.M., and Tamene, L. (2015). Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0125814"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6130","DOI":"10.1038\/s41598-021-85639-y","article-title":"African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning","volume":"11","author":"Hengl","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1080\/10095020.2022.2026743","article-title":"Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks","volume":"25","author":"Bouasria","year":"2022","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1080\/19475683.2024.2309868","article-title":"The effect of covariates on Soil Organic Matter and pH variability: A digital soil mapping approach using random forest model","volume":"30","author":"Bouslihim","year":"2024","journal-title":"Ann. GIS"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7801","DOI":"10.1080\/01431161.2020.1763512","article-title":"Assessing the use of cross-orbit Sentinel-1 images in land cover clas-sification","volume":"41","author":"Sayedain","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Urbina-Salazar, D., Vaudour, E., Baghdadi, N., Ceschia, E., Richer-de-Forges, A.C., Lehmann, S., and Arrouays, D. (2021). Using sentinel-2 images for soil organic carbon content mapping in croplands of southwestern france. The usefulness of sentinel-1\/2 derived moisture maps and mismatches between sentinel images and sampling dates. Remote Sens., 13.","DOI":"10.3390\/rs13245115"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102299","DOI":"10.1016\/j.apgeog.2020.102299","article-title":"Digital soil mapping of nitrogen, phosphorus, potassium, organic carbon and their crop response thresholds in smallholder managed escarpments of Malawi","volume":"124","author":"Mponela","year":"2020","journal-title":"Appl. Geogr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/02571862.2022.2059115","article-title":"Farm-scale digital soil mapping of soil classes in South Africa","volume":"39","author":"Flynn","year":"2022","journal-title":"S. Afr. J. Plant Soil"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2018.09.015","article-title":"Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging","volume":"218","author":"Gholizadeh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Castaldi, F., Chabrillat, S., Don, A., and van Wesemael, B. (2019). Soil Organic Carbon Mapping Using LUCAS Topsoil Database and Sentinel-2 Data: An Approach to Reduce Soil Moisture and Crop Residue Effects. Remote Sens., 11.","DOI":"10.3390\/rs11182121"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.isprsjprs.2018.11.026","article-title":"Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands","volume":"147","author":"Castaldi","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhou, M., Zhuang, Q., and Guo, L. (2021). Prediction Potential of Remote Sensing-Related Variables in the Topsoil Organic Carbon Density of Liaohekou Coastal Wetlands, Northeast China. Remote Sens., 13.","DOI":"10.3390\/rs13204106"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1786","DOI":"10.1016\/j.asr.2021.08.007","article-title":"Utilisation of spaceborne C-band dual pol Sentinel-1 SAR data for simplified regres-sion-based soil organic carbon estimation in Rupnagar, Punjab, India","volume":"69","author":"Tripathi","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s11104-022-05506-1","article-title":"Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel-2 and GIS using Gaussian processes regression","volume":"479","author":"Izurieta","year":"2022","journal-title":"Plant Soil"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106077","DOI":"10.1016\/j.catena.2022.106077","article-title":"Synergetic use of multi-temporal Sentinel-1, Sentinel-2, NDVI, and topographic factors for estimating soil organic carbon","volume":"212","author":"Minaei","year":"2022","journal-title":"Catena"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"138244","DOI":"10.1016\/j.scitotenv.2020.138244","article-title":"High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms","volume":"729","author":"Zhou","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_25","unstructured":"FAO (2019). Standard Operating Procedure for Soil Organic Carbon Walkley-Black Method Titration and Colorimetric Method, Food & Agriculture Organization."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dahhani, S., Raji, M., Hakdaoui, M., and Lhissou, R. (2022). Land cover mapping using sentinel-1 time-series data and ma-chine-learning classifiers in agricultural sub-saharan landscape. Remote Sens., 15.","DOI":"10.3390\/rs15010065"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2006.09.002","article-title":"Generation of geometrically and radiometrically terrain corrected SAR image products","volume":"106","author":"Loew","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_28","first-page":"37","article-title":"Sen2Cor for sentinel-2","volume":"Volume 10427","author":"Pflug","year":"2017","journal-title":"Image and Signal Processing for Remote Sensing XXIII"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0034-4257(89)90035-7","article-title":"Munsell soil color and soil reflectance in the visible spectral bands of landsat MSS and TM data","volume":"27","author":"Escadafal","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_30","unstructured":"Escadafal, R., Belghith, A., and Ben Moussa, H. (1994, January 17\u201321). Indices spectraux pour la t\u00e9l\u00e9d\u00e9tection de la d\u00e9gradation des milieux naturels en Tunisie aride. Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, Val d\u2019Is\u00e8re, France."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"The MERIS terrestrial chlorophyll index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/02757259509532298","article-title":"A review of vegetation indices","volume":"13","author":"Bannari","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Darst, B.F., Malecki, K.C., and Engelman, C.D. (2018). Using recursive feature elimination in random forest to account for correlated variables in high dimensional data. BMC Genet., 19.","DOI":"10.1186\/s12863-018-0633-8"},{"key":"ref_37","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_38","first-page":"1","article-title":"Comparing Pan-sharpened Landsat-9 and Sentinel-2 for Land-Use Classification Using Machine Learning Classifiers","volume":"6","author":"Bouslihim","year":"2022","journal-title":"J. Geovisualization Spat. Anal."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"11209","DOI":"10.1080\/10106049.2022.2048091","article-title":"Assessing the impact of sampling strategy in random forest-based predicting of soil nutrients: A study case from northern Morocco","volume":"37","author":"John","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102294","DOI":"10.1016\/j.ecoinf.2023.102294","article-title":"Predictive performance of machine learning model with varying sampling designs, sample sizes, and spatial extents","volume":"78","author":"Bouasria","year":"2023","journal-title":"Ecol. Inform."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_42","unstructured":"Drucker, H., Burges, C.J., Kaufman, L., Smola, A., and Vapnik, V. (1996, January 2\u20135). Support vector regression machines. Proceedings of the Advances in Neural Information Processing Systems 9, NIPS, Denver, CO, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bisong, E. (2019). Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, Apress.","DOI":"10.1007\/978-1-4842-4470-8"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.ecolmodel.2008.05.006","article-title":"How to evaluate models: Observed vs. predicted or predicted vs. observed?","volume":"216","author":"Perelman","year":"2008","journal-title":"Ecol. Model."},{"key":"ref_45","unstructured":"Smith, J., Smith, P., and Addiscott, T. (1996). Evaluation of Soil Organic Matter Models: Using Existing Long-Term Datasets, Springer."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.rse.2016.03.025","article-title":"Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon","volume":"179","author":"Castaldi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pastor-Guzman, J., Brown, L., Morris, H., Bourg, L., Goryl, P., Dransfeld, S., and Dash, J. (2020). The Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI): Algorithm Improvements, Spatiotemporal Consistency and Continuity with the MERIS Archive. Remote Sens., 12.","DOI":"10.3390\/rs12162652"},{"key":"ref_48","first-page":"559","article-title":"Comparative study of NDVI and SAVI vegetation indices in Anantapur district semi-arid areas","volume":"8","author":"Vani","year":"2017","journal-title":"Int. J. Civ. Eng. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.geoderma.2015.05.017","article-title":"Soil mapping, classification, and pedologic modeling: History and future directions","volume":"264","author":"Brevik","year":"2016","journal-title":"Geoderma"},{"key":"ref_50","unstructured":"Ngatia, L.W., Moriasi, D., Grace, J.M., Fu, R., Gardner, C.S., and Taylor, R.W. (2021). Environmental Health, Books on Demand."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"161676","DOI":"10.1016\/j.scitotenv.2023.161676","article-title":"Spatial predictors and temporal forecast of total organic carbon levels in boreal lakes","volume":"870","author":"Crapart","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1080\/03650340.2019.1626983","article-title":"Applying statistical methods to map soil organic carbon of agricultural lands in northeastern coastal areas of China","volume":"66","author":"Bian","year":"2019","journal-title":"Arch. Agron. Soil Sci."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Kaya, F., Keshavarzi, A., Francaviglia, R., Kaplan, G., Ba\u015fayi\u011fit, L., and Dedeo\u011flu, M. (2022). Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus. Agriculture, 12.","DOI":"10.3390\/agriculture12071062"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"150187","DOI":"10.1016\/j.scitotenv.2021.150187","article-title":"A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and SAR data fusion","volume":"804","author":"Nguyen","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhuang, Q., Jin, X., Yang, Z., and Liu, H. (2020). Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data. Remote Sens., 12.","DOI":"10.3390\/rs12071115"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, K., Qi, Y., Guo, W., Zhang, J., and Chang, Q. (2021). Retrieval and Mapping of Soil Organic Carbon Using Sentinel-2A Spectral Images from Bare Cropland in Autumn. Remote Sens., 13.","DOI":"10.3390\/rs13061072"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Liu, T., Zhang, H., and Shi, T. (2020). Modeling and Predictive Mapping of Soil Organic Carbon Density in a Small-Scale Area Using Geographically Weighted Regression Kriging Approach. Sustainability, 12.","DOI":"10.3390\/su12229330"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sodango, T.H., Sha, J., Li, X., Noszczyk, T., Shang, J., Aneseyee, A.B., and Bao, Z. (2021). Modeling the Spatial Dynamics of Soil Organic Carbon Using Remotely-Sensed Predictors in Fuzhou City, China. Remote Sens., 13.","DOI":"10.3390\/rs13091682"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.ecolind.2009.10.005","article-title":"Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods","volume":"10","author":"Pei","year":"2010","journal-title":"Ecol. Indic."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","first-page":"81","DOI":"10.4155\/cmt.13.77","article-title":"Global soil carbon: Understanding and managing the largest terrestrial carbon pool","volume":"5","author":"Scharlemann","year":"2014","journal-title":"Carbon Manag."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.catena.2018.03.007","article-title":"Examining soil organic carbon distribution and dynamic change in a hickory plantation region with Landsat and ancillary data","volume":"165","author":"Lu","year":"2018","journal-title":"Catena"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11806-009-0160-x","article-title":"Spectral features of soil organic matter","volume":"12","author":"He","year":"2009","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1023\/A:1013376922354","article-title":"Farmer\u2019s view on soil organic matter depletion and its management in Bangladesh","volume":"61","author":"Hossain","year":"2001","journal-title":"Nutr. Cycl. Agroecosyst."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s12524-021-01459-7","article-title":"Integrated use of hyperspectral remote sensing and geostatistics in spatial pre-diction of soil organic carbon content","volume":"50","author":"Saha","year":"2022","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5219","DOI":"10.1109\/JSTARS.2023.3281732","article-title":"Prediction of Soil Organic Carbon Content Using Sentinel-1\/2 and Machine Learning Algorithms in Swamp Wetlands in Northeast China","volume":"16","author":"Zhang","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_67","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_68","doi-asserted-by":"crossref","unstructured":"Wang, L., and Zhou, Y. (2022). Combining Multitemporal Sentinel-2A Spectral Imaging and Random Forest to Improve the Accuracy of Soil Organic Matter Estimates in the Plough Layer for Cultivated Land. Agriculture, 13.","DOI":"10.3390\/agriculture13010008"},{"key":"ref_69","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_70","doi-asserted-by":"crossref","first-page":"142661","DOI":"10.1016\/j.scitotenv.2020.142661","article-title":"Prediction of soil organic carbon and the C: N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images","volume":"755","author":"Zhou","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.catena.2017.09.026","article-title":"Topographic metric predictions of soil redistribution and organic carbon in Iowa cropland fields","volume":"160","author":"Li","year":"2018","journal-title":"Catena"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"115106","DOI":"10.1016\/j.geoderma.2021.115106","article-title":"Assessing digital elevation model resolution for soil organic carbon prediction","volume":"398","author":"Gibson","year":"2021","journal-title":"Geoderma"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/22797254.2022.2045226","article-title":"Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic","volume":"55","author":"Duarte","year":"2022","journal-title":"Eur. J. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.5194\/bg-15-1663-2018","article-title":"High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: A case study in a sub-Arctic peatland environment","volume":"15","author":"Siewert","year":"2018","journal-title":"Biogeosciences"},{"key":"ref_75","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_76","doi-asserted-by":"crossref","first-page":"e00256","DOI":"10.1016\/j.geodrs.2020.e00256","article-title":"Digital mapping of soil organic carbon using ensemble learning model in Mollisols of Hyrcanian forests, northern Iran","volume":"20","author":"Tajik","year":"2020","journal-title":"Geoderma Reg."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s10705-023-10310-z","article-title":"Advances and applications of multivariate statistics and soil-crop sensing to improve nutrient use efficiency and monitor carbon cycling","volume":"127","author":"Pullanagari","year":"2023","journal-title":"Nutr. Cycl. Agroecosyst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1871\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:47:50Z","timestamp":1760107670000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1871"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,24]]},"references-count":77,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16111871"],"URL":"https:\/\/doi.org\/10.3390\/rs16111871","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,24]]}}}