{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T04:09:59Z","timestamp":1775102999289,"version":"3.50.1"},"reference-count":104,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T00:00:00Z","timestamp":1575849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006533","name":"Ministerstvo Zem\u011bd\u011blstv\u00ed","doi-asserted-by":"publisher","award":["QK1820389"],"award-info":[{"award-number":["QK1820389"]}],"id":[{"id":"10.13039\/501100006533","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006533","name":"Ministerstvo Zem\u011bd\u011blstv\u00ed","doi-asserted-by":"publisher","award":["QK1720289"],"award-info":[{"award-number":["QK1720289"]}],"id":[{"id":"10.13039\/501100006533","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006533","name":"Ministerstvo Zem\u011bd\u011blstv\u00ed","doi-asserted-by":"publisher","award":["MZE-RO2018"],"award-info":[{"award-number":["MZE-RO2018"]}],"id":[{"id":"10.13039\/501100006533","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001824","name":"Grantov\u00e1 Agentura \u010cesk\u00e9 Republiky","doi-asserted-by":"publisher","award":["17-27726S"],"award-info":[{"award-number":["17-27726S"]}],"id":[{"id":"10.13039\/501100001824","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The image spectral data, particularly hyperspectral data, has been proven as an efficient data source for mapping of the spatial variability of soil organic carbon (SOC). Multispectral satellite data are readily available and cost-effective sources of spectral data compared to costly and technically demanding processing of hyperspectral data. Moreover, their continuous acquisition allows to develop a composite from time-series, increasing the spatial coverage of SOC maps. In this study, an evaluation of the prediction ability of models assessing SOC using real multispectral remote sensing data from different platforms was performed. The study was conducted on a study plot (1.45 km2) in the Chernozem region of South Moravia (Czechia). The adopted methods included field sampling and predictive modeling using satellite multispectral Sentinel-2, Landsat-8, and PlanetScope data, and multispectral UAS Parrot Sequoia data. Furthermore, the performance of a soil reflectance composite image from Sentinel-2 data was analyzed. Aerial hyperspectral CASI 1500 and SASI 600 data was used as a reference. Random forest, support vector machine, and the cubist regression technique were applied in the predictive modeling. The prediction accuracy of models using multispectral data, including Sentinel-2 composite, was lower (RPD range from 1.16 to 1.65; RPIQ range from 1.53 to 2.17) compared to the reference model using hyperspectral data (RPD = 2.26; RPIQ = 3.34). The obtained results show very similar prediction accuracy for all spaceborne sensors (Sentinel-2, Landsat-8, and PlanetScope). However, the spatial correlation between the reference mapping results obtained from the hyperspectral data and other maps using multispectral data was moderately strong. UAS sensors and freely available satellite multispectral data can represent an alternative cost-effective data source for remote SOC mapping on the local scale.<\/jats:p>","DOI":"10.3390\/rs11242947","type":"journal-article","created":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T11:22:51Z","timestamp":1575890571000},"page":"2947","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":115,"title":["Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7685-7604","authenticated-orcid":false,"given":"Daniel","family":"\u017d\u00ed\u017eala","sequence":"first","affiliation":[{"name":"Research Institute for Soil and Water Conservation, \u017dabov\u0159esk\u00e1 250, CZ 156 27 Prague, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5654-1522","authenticated-orcid":false,"given":"Robert","family":"Mina\u0159\u00edk","sequence":"additional","affiliation":[{"name":"Research Institute for Soil and Water Conservation, \u017dabov\u0159esk\u00e1 250, CZ 156 27 Prague, Czech Republic"}]},{"given":"Tereza","family":"Z\u00e1dorov\u00e1","sequence":"additional","affiliation":[{"name":"Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kam\u00fdck\u00e1 129, CZ 165 00 Prague, Czech Republic"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,9]]},"reference":[{"key":"ref_1","unstructured":"Batjes, N.H. (1995). World Soil Carbon Stocks and Global Change, ISRIC."},{"key":"ref_2","unstructured":"Lal, R., Kimble, J.M., Levine, E.R., and Stewart, B.A. (1995). Changes in the storage of terrestrial carbon since 1850. Soils and Global Change, CRC Press."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.5194\/bg-10-1717-2013","article-title":"Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations","volume":"10","author":"Randerson","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_4","unstructured":"Hiederer, R., and K\u00f6chy, M. (2011). Global Soil Organic Carbon Estimates and the Harmonized World Soil Database, Publications Office of the European Union. EUR 25225."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Banwart, S.A., Noellemeyer, E., and Milne, E. (2015). The Global Challenge for Soil Carbon. Soil Carbon: Science, Management and Policy for Multiple Benefits, CAB International.","DOI":"10.1079\/9781780645322.0000"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.envdev.2014.11.005","article-title":"Soil carbon, multiple benefits","volume":"13","author":"Milne","year":"2015","journal-title":"Environ. Dev."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Banwart, S.A., Noellemeyer, E., and Milne, E. (2015). Climate Change and Soil Carbon Impacts. Soil Carbon: Science, Management and Policy for Multiple Benefits, CAB International.","DOI":"10.1079\/9781780645322.0000"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1111\/j.1365-2486.2005.001075.x","article-title":"Projected changes in mineral soil carbon of European croplands and grasslands, 1990\u20132080","volume":"11","author":"Smith","year":"2005","journal-title":"Glob. Chang. Biol."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Ravi Shankar, D. (2017). Remote Sensing of Soils, Springer.","DOI":"10.1007\/978-3-662-53740-4"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Castaldi, F., Chabrillat, S., Jones, A., Vreys, K., Bomans, B., and van Wesemael, B. (2018). Soil organic carbon Estimation in croplands by hyperspectral remote APEX data using the LUCAS topsoil database. Remote Sens., 10.","DOI":"10.3390\/rs10020153"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Steinberg, A., Chabrillat, S., Stevens, A., Segl, K., and Foerster, S. (2016). Prediction of common surface soil properties based on Vis-NIR airborne and simulated EnMAP imaging spectroscopy Data: Prediction accuracy and influence of spatial resolution. Remote Sens., 8.","DOI":"10.3390\/rs8070613"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.geoderma.2012.05.023","article-title":"Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis-NIR data","volume":"189\u2013190","author":"Gomez","year":"2012","journal-title":"Geoderma"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"\u017d\u00ed\u017eala, D., Z\u00e1dorov\u00e1, T., and Kapi\u010dka, J. (2017). Assessment of soil degradation by erosion based on analysis of soil properties using aerial hyperspectral images and ancillary data, Czech Republic. Remote Sens., 9.","DOI":"10.3390\/rs9010028"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kanning, M., Siegmann, B., and Jarmer, T. (2016). Regionalization of uncovered agricultural soils based on organic carbon and soil texture estimations. Remote Sens., 8.","DOI":"10.3390\/rs8110927"},{"key":"ref_16","first-page":"24","article-title":"Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra","volume":"49","author":"Vaudour","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","first-page":"358","article-title":"Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy","volume":"38","author":"Franceschini","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Matarrese, R., Ancona, V., Salvatori, R., Muolo, M.R., Uricchio, V.F., and Vurro, M. (2014, January 13\u201318). Detecting soil organic carbon by CASI hyperspectral images. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947181"},{"key":"ref_19","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\u2013227","author":"Denis","year":"2014","journal-title":"Geoderma"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1111\/ejss.12203","article-title":"Estimation of soil organic carbon from airborne hyperspectral thermal infrared data: A case study","volume":"65","author":"Pascucci","year":"2014","journal-title":"Eur. J. Soil Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2174","DOI":"10.2136\/sssaj2012.0054","article-title":"Soil organic carbon predictions by airborne imaging spectroscopy: Comparing cross-validation and validation","volume":"76","author":"Stevens","year":"2012","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.geoderma.2012.01.017","article-title":"Airborne hyperspectral imaging of spatial soil organic carbon heterogeneity at the field-scale","volume":"175\u2013176","author":"Hbirkou","year":"2012","journal-title":"Geoderma"},{"key":"ref_23","first-page":"1","article-title":"Soil organic carbon mapping using remote sensing techniques and multivariate regression model","volume":"6049","author":"Bhunia","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_24","first-page":"61","article-title":"Spatial soil organic carbon (SOC) prediction by regression kriging using remote sensing data","volume":"20","author":"Mondal","year":"2017","journal-title":"Egypt. J. Remote Sens. Sp. Sci."},{"key":"ref_25","first-page":"96","article-title":"Predicting soil organic carbon content in Cyprus using remote sensing and Earth observation data","volume":"Volume 9229","author":"Hadjimitsis","year":"2014","journal-title":"Proceedings of the Second International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2014)"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"85","DOI":"10.14358\/PERS.76.1.85","article-title":"Mapping topsoil organic carbon in non-agricultural semi-arid and arid ecosystems of Israel","volume":"76","author":"Jarmer","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_27","unstructured":"Ray, S.S., Singh, J.P., Das, G., Panigrahy, S., Group, A.R., Centre, S.A., and Potato, C. (2004, January 12\u201323). Use of high resolution remote sensing data for generatin site-specific soil mangement plan. Proceedings of the XX ISPRS Congress, Commission VII, Working Group VII\/2, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Istanbul, Turkey."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/S0034-4257(00)00146-2","article-title":"Mapping complex patterns of erosion and stability in dry mediterranean ecosystems","volume":"74","author":"Hill","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.geoderma.2016.04.012","article-title":"UAS-based soil carbon mapping using VIS-NIR (480\u20131000nm) multi-spectral imaging: Potential and limitations","volume":"275","author":"Heckrath","year":"2016","journal-title":"Geoderma"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Crucil, G., Castaldi, F., Aldana-Jague, E., van Wesemael, B., Macdonald, A., and Oos, K. (2019). Van Assessing the performance of UAS-Compatible multispectral and hyperspectral sensors for soil organic carbon prediction. Sustainability, 11.","DOI":"10.3390\/su11071889"},{"key":"ref_31","first-page":"105","article-title":"Estimation des Teneurs en Carbone Organique des Sols Agricoles par T\u00e9l\u00e9d\u00e9tection par Drone","volume":"213\u2013214","author":"Gilliot","year":"2017","journal-title":"Rev. Fr. Photogramm. Teledetect."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1007\/s11119-009-9123-3","article-title":"Estimating soil organic carbon from soil reflectance: A review","volume":"11","author":"Ladoni","year":"2010","journal-title":"Precis. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.catena.2012.01.001","article-title":"On the use of remote sensing techniques for monitoring spatio-temporal soil organic carbon dynamics in agricultural systems","volume":"94","author":"Croft","year":"2012","journal-title":"Catena"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G., and Bochtis, D. (2019). Remote sensing techniques for soil organic carbon estimation: A review. Remote Sens., 11.","DOI":"10.3390\/rs11060676"},{"key":"ref_35","unstructured":"Themistocleous, K., Hadjimitsis, D.G., Michaelides, S., Ambrosia, V., and Papadavid, G. (2018). Soil Organic Carbon and Clay Monitoring and Mapping using Airborne and Sentinel-2 Spectral Imaging. Proceedings of SPIE, vol. 10733, Proceedings of the Sixth International Conference on Remote Sensing and Geoinformation of Environment(RSCy2018), International Society for Optics and Photonics, Aliathon, Cyprus, 6 August 2018, SPIE."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.isprsjprs.2018.11.026","article-title":"Van 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_37","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_38","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/S0065-2113(07)00008-9","article-title":"Imaging spectrometry for soil applications","volume":"97","author":"Taylor","year":"2008","journal-title":"Adv. Agron."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.geoderma.2008.06.011","article-title":"Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study","volume":"146","author":"Gomez","year":"2008","journal-title":"Geoderma"},{"key":"ref_40","unstructured":"Sparks, D.L. (2015). Fusion of soil and remote sensing data to model soil properties. Advances in Agronomy, Elsevier."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"63","DOI":"10.4236\/ars.2015.41006","article-title":"Prediction modeling and mapping of soil carbon content using artificial neural network, hyperspectral satellite data and field spectroscopy","volume":"4","author":"Tiwari","year":"2015","journal-title":"Adv. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1111\/ejss.12202","article-title":"Estimation of soil properties at the field scale from satellite data: A comparison between spatial and non-spatial techniques","volume":"65","author":"Castaldi","year":"2014","journal-title":"Eur. J. Soil Sci."},{"key":"ref_43","first-page":"47","article-title":"Modeling soil parameters using hyperspectral image reflectance in subtropical coastal wetlands","volume":"33","author":"Anne","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_44","first-page":"501","article-title":"Temporal-spatial variability of soil organic carbon stocks in a rehabilitating ecosystem","volume":"14","author":"Zhang","year":"2013","journal-title":"Pedosphere"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.gexplo.2013.04.003","article-title":"Prediction of soil properties using laboratory VIS-NIR spectroscopy and Hyperion imagery","volume":"132","author":"Lu","year":"2013","journal-title":"J. Geochemical Explor."},{"key":"ref_46","first-page":"1753","article-title":"Mapping soil organic matter using hyperion images","volume":"4","author":"Nowkandeh","year":"2013","journal-title":"Int. J. Agron. Plant. Prod."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5077","DOI":"10.1080\/01431161.2010.494637","article-title":"Estimating spatial variations in soil organic carbon using satellite hyperspectral data and map algebra","volume":"32","author":"Jaber","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2010.12.018","article-title":"The use of remote sensing in soil and terrain mapping - A review","volume":"162","author":"Mulder","year":"2011","journal-title":"Geoderma"},{"key":"ref_50","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_51","doi-asserted-by":"crossref","unstructured":"\u017d\u00ed\u017eala, D., Ju\u0159icov\u00e1, A., Z\u00e1dorov\u00e1, T., Zelenkov\u00e1, K., and Mina\u0159\u00edk, R. (2018). Mapping soil degradation using remote sensing data and ancillary data: South-East Moravia, Czech Republic. Eur. J. Remote Sens., 1\u201315.","DOI":"10.1080\/22797254.2018.1482524"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6059","DOI":"10.3390\/rs70506059","article-title":"Soil Clay Content Mapping Using a Time Series of Landsat TM Data in Semi-Arid Lands","volume":"7","author":"Shabou","year":"2015","journal-title":"Remote Sens."},{"key":"ref_53","first-page":"1","article-title":"Is it possible to classify topsoil texture using a sensor located 800 km away from the surface?","volume":"40","author":"Alves","year":"2016","journal-title":"Rev. Bras. Cienc. Solo"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2015.01.012","article-title":"Multitemporal soil pattern analysis with multispectral remote sensing data at the field-scale","volume":"113","author":"Blasch","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_55","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_56","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 re fl ectance from satellite images","volume":"212","author":"Fongaro","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Gallo, B.C., Dematt\u00ea, J.A.M., Rizzo, R., Safanelli, J.L., Mendes, W.D.S., Lepsch, I.F., Sato, M.V., Romero, D.J., and Lacerda, M.P.C. (2018). Multi-temporal satellite images on topsoil attribute quantification and the relationship with soil classes and geology. Remote Sens., 10.","DOI":"10.3390\/rs10101571"},{"key":"ref_58","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_59","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_60","first-page":"101905","article-title":"Satellite data integration for soil clay content modelling at a national scale","volume":"82","author":"Loiseau","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_61","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_62","unstructured":"Hrabal\u00edkov\u00e1, M., Huislov\u00e1, P., Ure\u0161, J., Holub\u00edk, O., \u017d\u00ed\u017eala, D., and Kumh\u00e1lov\u00e1, J. (2016, January 22\u201326). Assessment of changes in topsoil depth redistribution in relation to different tillage technologies. Proceedings of the 3rd WASWAC Conference, Belgrade, Serbia."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"47","DOI":"10.17221\/57\/2013-SWR","article-title":"Relating extent of colluvial soils to topographic derivatives and soil variables in a Luvisol sub-catchment, Central Bohemia, Czech Republic","volume":"9","year":"2014","journal-title":"Soil Water Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1111\/ejss.12673","article-title":"Formation, morphology and classification of colluvial soils: A review","volume":"69","year":"2018","journal-title":"Eur. J. Soil Sci."},{"key":"ref_65","unstructured":"ESA (2015). SENTINEL-2 User Handbook, ESA. Revision 2; ESA Standard Document."},{"key":"ref_66","unstructured":"(2019, November 10). ESA Radiometric\u2014Resolutions\u2014Sentinel-2 MSI\u2014User Guides\u2014Sentinel Online. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/user-guides\/sentinel-2-msi\/resolutions\/radiometric."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2011.08.026","article-title":"The next Landsat satellite: The Landsat data continuity mission","volume":"122","author":"Irons","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_68","unstructured":"Planet team (2018). Planet Imagery Product Specifications, Planet Team."},{"key":"ref_69","unstructured":"Pandey, P. Personal communication."},{"key":"ref_70","unstructured":"Hanu\u0161, J. Personal communication."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_73","unstructured":"Planet team (2017). Planet application program interface: In space for life on Earth, Planet team."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1080\/01431160050021295","article-title":"Determination of surface reflectance from raw hyperspectral data without simultaneous ground data measurements: A case study of the GER 63-channel sensor data acquired over Naan, Israel","volume":"21","author":"Levin","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1002\/arp.399","article-title":"Taking computer vision aloft\u2014Archaeological three-dimensional reconstructions from aerial photographs with photoscan","volume":"18","author":"Verhoeven","year":"2011","journal-title":"Archaeol. Prospect."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1016\/j.cageo.2005.12.009","article-title":"A conditioned Latin hypercube method for sampling in the presence of ancillary information","volume":"32","author":"Minasny","year":"2006","journal-title":"Comput. Geosci."},{"key":"ref_77","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_78","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., and Smola, A.J. (2001). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Cristianini, N., and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines: And Other kernel-Based Learning Methods, Cambridge University Press.","DOI":"10.1017\/CBO9780511801389"},{"key":"ref_81","unstructured":"Kuhn, M., Weston, S., Keefer, C., Coulter, N., and Ross, Q. (2019, November 10). Cubist: Rule- and Instance-Based Regression Modeling, 2013. R package version 0.2.1. Available online: https:\/\/topepo.github.io\/Cubist."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_83","unstructured":"Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., and Benesty, M. (2019, December 09). Caret: Classification and Regression Training; 2015. R Package Version 6.0-82. Available online: https:\/\/CRAN.R-project.org\/package=caret."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1016\/j.camwa.2013.09.006","article-title":"Data partition methodology for validation of predictive models","volume":"66","author":"Morrison","year":"2013","journal-title":"Comput. Math. Appl."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_86","first-page":"14","article-title":"Why you don\u2019t need to use RPD By Budiman Minasny & Alex. McBratney University of Sydney Why you don\u2019t need to use RPD","volume":"33","author":"Minasny","year":"2013","journal-title":"Pedometron"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/j.trac.2010.05.006","article-title":"Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy","volume":"29","author":"Palagos","year":"2010","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.geoderma.2019.05.031","article-title":"Digital soil mapping algorithms and covariates for soil organic carbon mapping and their implications: A review","volume":"352","author":"Lamichhane","year":"2019","journal-title":"Geoderma"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.geoderma.2015.06.024","article-title":"Mapping soil organic carbon content by geographically weighted regression: A case study in the Heihe River Basin, China","volume":"261","author":"Song","year":"2016","journal-title":"Geoderma"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/j.catena.2015.01.028","article-title":"Spatial variability in soil organic carbon and its influencing factors in a hilly watershed of the Loess Plateau, China","volume":"137","author":"Xin","year":"2016","journal-title":"Catena"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/soil-4-1-2018","article-title":"Evaluation of digital soil mapping approaches with large sets of environmental covariates","volume":"4","author":"Nussbaum","year":"2018","journal-title":"SOIL"},{"key":"ref_92","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_93","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.geoderma.2007.11.004","article-title":"Extrapolating regional soil landscapes from an existing soil map: Sampling intensity, validation procedures, and integration of spatial context","volume":"143","author":"Grinand","year":"2008","journal-title":"Geoderma"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2015). Remote Sensing of Soil in the Optical Domains. Land Resources Monitoring, Modeling, and Mapping with Remote Sensing, CRC Press.","DOI":"10.1201\/b19322"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.geoderma.2015.05.008","article-title":"Van Detecting and quantifying field-related spatial variation of soil organic carbon using mixed-effect models and airborne imagery","volume":"259\u2013260","author":"Stevens","year":"2015","journal-title":"Geoderma"},{"key":"ref_96","first-page":"64","article-title":"Modeling soil organic matter (SOM) from satellite data using VISNIR-SWIR spectroscopy and PLS regression with step-down variable selection algorithm: Case study of Campos Amazonicos National Park savanna enclave, Brazil","volume":"Volume 10421","author":"Neale","year":"2017","journal-title":"Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/S0065-2113(10)07005-7","article-title":"Visible and near infrared spectroscopy in soil science","volume":"107","author":"Bertsch","year":"2010","journal-title":"Advances in Agronomy"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2005.03.007","article-title":"Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties","volume":"131","author":"Walvoort","year":"2006","journal-title":"Geoderma"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1016\/j.soilbio.2011.02.019","article-title":"Near-infrared (NIR) and mid-infrared (MIR) spectroscopic techniques for assessing the amount of carbon stock in soils\u2014Critical review and research perspectives","volume":"43","author":"McBratney","year":"2011","journal-title":"Soil Biol. Biochem."},{"key":"ref_100","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_101","first-page":"111","article-title":"Soil Reflectante","volume":"3","author":"Rencz","year":"1999","journal-title":"Manual of Remote Sensing: Remote Sensing for Earth Science"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Ch\u00e9nier, R., Faucher, M.-A., and Ahola, R. (2018). Satellite-derived bathymetry for improving Canadian hydrographic service charts. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7080306"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2012\/868090","article-title":"Spatially explicit estimation of clay and organic carbon content in agricultural soils using multi-annual imaging spectroscopy data","volume":"2012","author":"Gerighausen","year":"2012","journal-title":"Appl. Environ. Soil Sci."},{"key":"ref_104","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/24\/2947\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:40:44Z","timestamp":1760190044000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/24\/2947"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,9]]},"references-count":104,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["rs11242947"],"URL":"https:\/\/doi.org\/10.3390\/rs11242947","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,9]]}}}