{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T06:19:16Z","timestamp":1774505956700,"version":"3.50.1"},"reference-count":97,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T00:00:00Z","timestamp":1628380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry of Food and Agriculture (BMEL)","award":["281B301816"],"award-info":[{"award-number":["281B301816"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>For food security issues or global climate change, there is a growing need for large-scale knowledge of soil organic carbon (SOC) contents in agricultural soils. To capture and quantify SOC contents at a field scale, Earth Observation (EO) can be a valuable data source for area-wide mapping. The extraction of exposed soils from EO data is challenging due to temporal or permanent vegetation cover, the influence of soil moisture or the condition of the soil surface. Compositing techniques of multitemporal satellite images provide an alternative to retrieve exposed soils and to produce a data source. The repeatable soil composites, containing averaged exposed soil areas over several years, are relatively independent from seasonal soil moisture and surface conditions and provide a new EO-based data source that can be used to estimate SOC contents over large geographical areas with a high spatial resolution. Here, we applied the Soil Composite Mapping Processor (SCMaP) to the Landsat archive between 1984 and 2014 of images covering Bavaria, Germany. Compared to existing SOC modeling approaches based on single scenes, the 30-year SCMaP soil reflectance composite (SRC) with a spatial resolution of 30 m is used. The SRC spectral information is correlated with point soil data using different machine learning algorithms to estimate the SOC contents in cropland topsoils of Bavaria. We developed a pre-processing technique to address the issue of combining point information with EO pixels for the purpose of modeling. We applied different modeling methods often used in EO soil studies to choose the best SOC prediction model. Based on the model accuracies and performances, the Random Forest (RF) showed the best capabilities to predict the SOC contents in Bavaria (R\u00b2 = 0.67, RMSE = 1.24%, RPD = 1.77, CCC = 0.78). We further validated the model results with an independent dataset. The comparison between the measured and predicted SOC contents showed a mean difference of 0.11% SOC using the best RF model. The SCMaP SRC is a promising approach to predict the spatial SOC distribution over large geographical extents with a high spatial resolution (30 m).<\/jats:p>","DOI":"10.3390\/rs13163141","type":"journal-article","created":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T21:35:40Z","timestamp":1628458540000},"page":"3141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7178-0476","authenticated-orcid":false,"given":"Simone","family":"Zepp","sequence":"first","affiliation":[{"name":"German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Muenchener Str. 20, 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3865-1912","authenticated-orcid":false,"given":"Uta","family":"Heiden","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), Muenchener Str. 20, 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8381-7662","authenticated-orcid":false,"given":"Martin","family":"Bachmann","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Muenchener Str. 20, 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3981-5461","authenticated-orcid":false,"given":"Martin","family":"Wiesmeier","sequence":"additional","affiliation":[{"name":"Bavarian State Research Center for Agriculture, Institute for Organic Farming, Soil and Resource Management, Lange Point 6, 85354 Freising, Germany"}]},{"given":"Michael","family":"Steininger","sequence":"additional","affiliation":[{"name":"Mitteldeutsches Institut f\u00fcr Angewandte Standortkunde und Bodenschutz (MISB), 06114 Halle, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4007-0241","authenticated-orcid":false,"given":"Bas","family":"van Wesemael","sequence":"additional","affiliation":[{"name":"Georges Lema\u00eetre Centre for Earth and Climate Research, Earth and Life Institute, Universit\u00e9 Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1097\/ss.0b013e31815cc498","article-title":"Soil carbon sequestration to mitigate climate change and advance food security","volume":"172","author":"Lal","year":"2007","journal-title":"Soil Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/s41561-020-0612-3","article-title":"Persistence of Soil Organic Carbon Caused by Functional Complexity","volume":"13","author":"Lehmann","year":"2020","journal-title":"Nat. Geosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1890\/1051-0761(2000)010[0423:TVDOSO]2.0.CO;2","article-title":"The vertical distribution of soil organic carbon and its relation to climate and vegetation","volume":"10","author":"Jackson","year":"2000","journal-title":"Ecol. Appl."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.geoderma.2018.07.026","article-title":"Soil organic carbon storage as a key function of soils\u2014A review of drivers and indicators at various scales","volume":"333","author":"Wiesmeier","year":"2019","journal-title":"Geoderma"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0167-1987(02)00139-3","article-title":"Is there a critical level of organic mattes in the agricultural soils of temperate regions: A review","volume":"70","author":"Loveland","year":"2003","journal-title":"Soil Tillage Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1002\/fes3.96","article-title":"Soil Health and carbon management","volume":"5","author":"Lal","year":"2016","journal-title":"Food Energy Secur."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"367","DOI":"10.4141\/cjss94-051","article-title":"Towards a minimum data set to assess soil organic matter quality in agricultural soils","volume":"74","author":"Gregorich","year":"1994","journal-title":"Can. J. Soil Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3285","DOI":"10.1111\/gcb.14054","article-title":"Digging deeper: A holistic perspective of factors affecting soil organic carbon sequestration in agroecosystems","volume":"24","author":"Lal","year":"2018","journal-title":"Glob. Chang. Biol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1002\/ldr.3270","article-title":"Soil organic carbon stock as an indicator for monitoring land and soil degradation in relation to United Nations\u2019 Sustainable Development Goals","volume":"30","author":"Lorenz","year":"2019","journal-title":"Land Degrad. Dev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"579","DOI":"10.2134\/jeq2018.05.0213","article-title":"Measurements and models to identify agroecosystem practices that enhance soil organic carbon under changing climate","volume":"47","author":"Gollany","year":"2018","journal-title":"J. Environ. Qual."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1080\/17583004.2019.1633231","article-title":"Quantifying carbon for agricultural soil management: From the current status toward a global soil information system","volume":"10","author":"Paustian","year":"2019","journal-title":"Carbon Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.scitotenv.2013.08.026","article-title":"Current status, uncertainty and future needs in soil organic carbon monitoring","volume":"468\u2013469","author":"Jandl","year":"2014","journal-title":"Sci. Total. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.geoderma.2014.04.020","article-title":"The historical role of base maps in soil geography","volume":"230\u2013231","author":"Miller","year":"2014","journal-title":"Geoderma"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1111\/j.1365-2389.2005.00728.x","article-title":"Estimating organic carbon in the soils of Europe for policy support","volume":"56","author":"Jones","year":"2005","journal-title":"Eur. J. Soil Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1111\/ejss.12193","article-title":"A Map of the Topsoil Organic Carbon Content of Europe Generated by a Generalized Additive Model","volume":"66","author":"Ballabio","year":"2015","journal-title":"Eur. J. Soil Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Crucil, G., Castaldi, F., Aldana-Jague, E., van Wesemael, B., Macdonald, A., and Van Oost, K. (2019). Assessing the performance of UAS-compatible multispectral and hyperspectral sensors for soil organic carbon prediction. Sustainability, 11.","DOI":"10.3390\/su11071889"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"S38","DOI":"10.1016\/j.rse.2008.09.019","article-title":"Using imaging spectroscopy to study soil properties","volume":"113","author":"Chabrillat","year":"2009","journal-title":"Remote. Sens. Environ."},{"key":"ref_19","first-page":"81","article-title":"Soil organic carbon mapping of partially vegetated agricultural fields with imaging spectroscopy","volume":"13","author":"Bartholomeus","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3997","DOI":"10.1109\/JSTARS.2016.2585674","article-title":"Combining field and imaging spectroscopy to map soil organic carbon in a semiarid environment","volume":"9","author":"Bayer","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10712-019-09524-0","article-title":"Imaging spectroscopy for soil mapping and monitoring","volume":"40","author":"Chabrillat","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2019.01.006","article-title":"Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems","volume":"223","author":"Vaudour","year":"2019","journal-title":"Remote. Sens. Environ."},{"key":"ref_24","first-page":"102182","article-title":"Predicting soil organic carbon content in Spain by combining landsat TM and ALOS PALSAR images","volume":"92","author":"Wang","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Safanelli, J.L., Chabrillat, S., Ben-Dor, E., and Dematt\u00ea, J.A.M. (2020). Multispectral models from bare soil composites for mapping topsoil properties over Europe. Remote Sens., 12.","DOI":"10.3390\/rs12091369"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.rse.2018.04.047","article-title":"Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images","volume":"212","author":"Fongaro","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4461","DOI":"10.1038\/s41598-020-61408-1","article-title":"Bare Earth\u2019s surface spectra as a proxy for soil resource monitoring","volume":"10","author":"Safanelli","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1080\/01431161.2010.519002","article-title":"Continuous fields of land cover for the conterminous United States using Landsat data: First results from the Web-Enabled Landsat Data (WELD) project","volume":"2","author":"Hansen","year":"2011","journal-title":"Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1080\/07038992.2014.945827","article-title":"Pixel-based image compositing for large-area dense time series applications and science","volume":"40","author":"White","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.rse.2014.11.005","article-title":"An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites","volume":"158","author":"Hermosilla","year":"2015","journal-title":"Remote. Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.031","article-title":"Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping","volume":"220","author":"Griffiths","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Adams, B., Iverson, L., Matthews, S., Peters, M., Prasad, A., and Hix, D.M. (2020). Mapping forest composition with Landsat time series: An evaluation of seasonal composites and harmonic regression. Remote Sens., 12.","DOI":"10.3390\/rs12040610"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2015.11.032","article-title":"The global Landsat archive: Status, consolidation, and direction","volume":"185","author":"Wulder","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_36","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_37","first-page":"102277","article-title":"Temporal mosaicking approaches of Sentinel-2 images for extending organic carbon content mapping in croplands","volume":"96","author":"Vaudour","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","first-page":"102065","article-title":"Spatial and semantic effects of LUCAS samples on fully automated land use\/land cover classification in high-resolution Sentinel-2 data","volume":"88","author":"Weigand","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.rse.2016.03.025","article-title":"Evaludation 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_40","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_41","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_42","doi-asserted-by":"crossref","unstructured":"Castaldi, F., Chabrillat, S., and van Wesemael, B. (2019). Sampling strategies for soil property mapping using multispectral Sentinel-2 and hyperspectral EnMAP satellite data. Remote Sens., 11.","DOI":"10.3390\/rs11030309"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Dvorakova, K., Heiden, U., and van Wesemael, B. (2021). Sentinel-2 exposed soil composite for soil organic carbon prediction. Remote Sens., 13.","DOI":"10.3390\/rs13091791"},{"key":"ref_44","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_45","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_46","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_47","doi-asserted-by":"crossref","unstructured":"Vaudour, E., Gomez, C., Loiseau, T., Baghdadi, N., Loubet, B., Arrouays, D., Ali, L., and Lagacherie, P. (2019). The impact of acquisition date on the prediction performance of topsoil organic carbon from Sentinel-2 for croplands. Remote Sens., 11.","DOI":"10.3390\/rs11182143"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"\u017d\u00ed\u017eala, D., Mina\u0159\u00edk, R., and Z\u00e1dorov\u00e1, T. (2019). Soil organic carbon mapping using multispectral remote sensing data: Prediction ability of data with different spatial and spectral resolutions. Remote Sens., 11.","DOI":"10.3390\/rs11242947"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.agee.2013.05.012","article-title":"Amount, distribution and driving factors of soil organic carbon and nitrogen in cropland and grassland soils of Southeast Germany (Bavaria)","volume":"176","author":"Wiesmeier","year":"2013","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_50","first-page":"128","article-title":"World reference base for soil resources 2015","volume":"103","author":"Wrb","year":"2015","journal-title":"World Soil Resour. Rep."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.isprsjprs.2021.06.015","article-title":"The influence of vegetation index thresholding on EO-based assessments of exposed soil masks in Germany between 1984 and 2019","volume":"178","author":"Zepp","year":"2021","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2019.02.015","article-title":"Current status of Landsat program, science, and applications","volume":"225","author":"Wulder","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detectionn for Landsat 4-7, 8 and Sentinel-2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_55","unstructured":"Richter, R., and Schl\u00e4pfer, D. (2014). Atmospheric\/Topographic Correction for Satellite Imagery\/ATCOR-2\/3 User Guide, Version 8.3.1, ReSe Applications Schl\u00e4pfer Langeggweg."},{"key":"ref_56","first-page":"87","article-title":"Using thematic mapper data to identify contrasting soil plains and tillage practices","volume":"63","author":"Ward","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/0273-1177(89)90481-X","article-title":"Remote sensing of arid soil surface color with Landsat thematic mapper","volume":"9","author":"Escadafal","year":"1989","journal-title":"Adv. Space Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lutes, D.C., Keane, R.E., Caratti, J.F., Key, C.H., Benson, N.C., Sutherland, S., and Gangi, L.J. (2006). Landscape Assessment (LA). FIREMON: Fire Effects Monitoring and Inventory System, US Department of Agriculture, Forest Service, Rocky Mountain Research Station. Gen. Tech. Rep. RMRS-GTR-164-CD.","DOI":"10.2737\/RMRS-GTR-164"},{"key":"ref_60","first-page":"730","article-title":"External factor consideration in vegetation index development","volume":"723","author":"Qi","year":"1994","journal-title":"Proc. Phys. Meas. Signat. Remote Sens. ISPRS"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.rse.2004.03.010","article-title":"Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data","volume":"91","author":"Xiao","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1080\/01431160310001618103","article-title":"Reducing signature variability in unmixing coastal marsh thematic mapper scenes using spectral indices","volume":"25","author":"Rogers","year":"2004","journal-title":"Int. J. Remote. Sens."},{"key":"ref_63","unstructured":"Pouget, M., Madeira, J., Le Floch, E., and Kamal, S. (1990). Caracteristiques spectrales des surfaces sableuses de la Region Cotiere Nord-Ouest de l\u2019Egypte. Appl. Aux Donnees Satell. SPOT, 4\u20136."},{"key":"ref_64","unstructured":"Chen, W., Liu, L., Zhang, C., Wang, J., Wang, J., and Pan, Y. (2004, January 20\u201324). Monitoring the seasonal bare soil areas in Beijing using multitemporal TM images. Proceedings of the IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"93","DOI":"10.2307\/3628024","article-title":"Transformed vegetation index for measuring spatial variation in drought impacted biomass on Konza Prairie, Kansas","volume":"95","author":"Nellis","year":"1992","journal-title":"Trans. Kans. Acad. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of leaf-area index from quality of light on the forest floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"530","DOI":"10.2111\/05-201R.1","article-title":"Remote sensing for grassland management in the arid southwest","volume":"59","author":"Marsett","year":"2006","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_70","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973, January 10\u201314). Monitoring vegetation systems in the great plains with ERTS proceeding. Proceedings of the Third Earth Reserves Technology Satellite Symposium, Washington, DC, USA."},{"key":"ref_71","first-page":"355","article-title":"Monitoring soluble sugar, total nitrogen & its ratio in wheat leaves with canopy spectral reflectance","volume":"31","author":"Tian","year":"2005","journal-title":"Zuo Wu Xue Bao"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_73","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_74","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_75","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_76","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.catena.2016.01.001","article-title":"Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions","volume":"139","author":"Bhering","year":"2016","journal-title":"Catena"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Jiang, Q., Chen, Y., Guo, L., Fei, T., and Qi, K. (2016). Estimating soil organic carbon of cropland soil at different levels of soil moisture using VIS-NIR spectroscopy. Remote Sens., 8.","DOI":"10.3390\/rs8090755"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.geoderma.2019.07.010","article-title":"A remote sensing adapted approach for soil organic carbon prediction based on the spectrally clustered LUCAS soil database","volume":"353","author":"Ward","year":"2019","journal-title":"Geoderma"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"106925","DOI":"10.1016\/j.ecolind.2020.106925","article-title":"Comparison of random forest and multiple linear regression models for estimation of soil extracellular enzyme activities in agricultural reclaimed coastal saline land","volume":"120","author":"Xie","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_80","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1097\/00010694-200202000-00003","article-title":"Near-infrared reflectance spectroscopic analysis of soil C and N","volume":"167","author":"Chang","year":"2002","journal-title":"Soil Sci."},{"key":"ref_82","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_83","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.chemolab.2004.12.011","article-title":"Performance of some variable selection methods when multicollinearity is present","volume":"78","author":"Chong","year":"2005","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s11104-015-2380-1","article-title":"Drivers of soil organic carbon storage and vertical distribution in Eastern Australie","volume":"390","author":"Hobley","year":"2015","journal-title":"Plant. Soil"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.agee.2016.03.004","article-title":"Environmental and human influences on organic carbon fractions down the soil profile","volume":"223","author":"Hobley","year":"2016","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_86","unstructured":"K\u00fchnel, A., Wiesmeier, M., K\u00f6gel-Knabner, I., and Sp\u00f6rlein, P. (2020). Ver\u00e4nderungen der Humusqualit\u00e4t und -Quantit\u00e4t Bayerischer B\u00f6den im Klimawandel, Bayerisches Landesamt f\u00fcr Umwelt. Umwelt Spezial."},{"key":"ref_87","unstructured":"T\u00f3th, G., Jones, A., and Montanarella, L. (2013). LUCAS Topsoil Survey: Methodology, Data and Results, Publications Office."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.1111\/j.1365-2486.2012.02699.x","article-title":"Soil organic carbon stocks in Southeast Germany (Bavaria) as affected by land use, soil type and sampling depth","volume":"18","author":"Wiesmeier","year":"2012","journal-title":"Glob. Chang. Biol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1111\/ejss.12499","article-title":"LUCAS soil, the largest expandable soil dataset for Europe: A review","volume":"69","author":"Origazzi","year":"2018","journal-title":"Eur. J. Soil Sci."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.agee.2013.12.028","article-title":"Quantification of functional soil organic carbon pools for majow soil units and land uses in southeast Germany (Bavaria)","volume":"185","author":"Wiesmeier","year":"2014","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_91","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_92","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/01431160701294695","article-title":"Surface soil moisture quantification models from reflectance data under field conditions","volume":"29","author":"Haubrock","year":"2008","journal-title":"Int. J. Remote. Sens."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.geoderma.2012.07.020","article-title":"Prediction of soil organic carbon for different levels of soil moisture using vis-NIR spectroscopy","volume":"199","author":"Nocita","year":"2013","journal-title":"Geoderma"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"15561","DOI":"10.3390\/rs71115561","article-title":"Reducing the influence of soil moisture on the estimation of clay from hyperspectral data: A case study using simulated PRISMA data","volume":"7","author":"Castaldi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Mzid, N., Pignatti, S., Huang, W., and Casa, R. (2021). An analysis of bare soil occurrence in arable croplands for remote sensing topsoil applications. Remote Sens., 13.","DOI":"10.3390\/rs13030474"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Hengl, T., de Jesus, J.M., Heuvelink, G.B.M., Gonzalez, M.R., Kilibarda, M., Blagot\u00edc, A., Shangguan, W., Wright, M.N., Geng, X., and Bauer-Marschallinger, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169748"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"113911","DOI":"10.1016\/j.geoderma.2019.113911","article-title":"Drained organic soils under agriculture \u2014The more degraded the soil the higher the specific basal respiration","volume":"355","author":"Tiemeyer","year":"2019","journal-title":"Geoderma"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3141\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:42:35Z","timestamp":1760164955000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,8]]},"references-count":97,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163141"],"URL":"https:\/\/doi.org\/10.3390\/rs13163141","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,8]]}}}