{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T05:29:46Z","timestamp":1789190986473,"version":"build-2803163510"},"reference-count":91,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T00:00:00Z","timestamp":1652140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005908","name":"German Federal Ministry of Food and Agriculture (BMEL)","doi-asserted-by":"publisher","award":["281B301816"],"award-info":[{"award-number":["281B301816"]}],"id":[{"id":"10.13039\/501100005908","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at the field scale for food security and monitoring long-term changes related to soil health and climate change. In Germany, SOC maps are mostly available with a spatial resolution of 250 m to 1 km2. The nationwide availability of both digital elevation models at various spatial resolutions and multi-temporal satellite imagery enables the derivation of multi-scale terrain attributes and (here: Landsat-based) multi-temporal soil reflectance composites (SRC) as explanatory variables. In the example of a Bavarian test of about 8000 km2, relations between 220 SOC content samples as well as different aggregation levels of the explanatory variables were analyzed for their scale-specific predictive power. The aggregation levels were generated by applying a region-growing segmentation procedure, and the SOC content prediction was realized by the Random Forest algorithm. In doing so, established approaches of (geographic) object-based image analysis (GEOBIA) and machine learning were combined. The modeling results revealed scale-specific differences. Compared to terrain attributes, the use of SRC parameters leads to a significant model improvement at field-related scale levels. The joint use of both terrain attributes and SRC parameters resulted in further model improvements. The best modeling variant is characterized by an accuracy of R2 = 0.84 and RMSE = 1.99.<\/jats:p>","DOI":"10.3390\/rs14102295","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T21:52:11Z","timestamp":1652219531000},"page":"2295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Scale-Specific Prediction of Topsoil Organic Carbon Contents Using Terrain Attributes and SCMaP Soil Reflectance Composites"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1918-7747","authenticated-orcid":false,"given":"Markus","family":"M\u00f6ller","sequence":"first","affiliation":[{"name":"Julius K\u00fchn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Bundesallee 58, 38116 Braunschweig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7178-0476","authenticated-orcid":false,"given":"Simone","family":"Zepp","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), M\u00fcnchener Str. 20, 82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5042-8548","authenticated-orcid":false,"given":"Heike","family":"Gerighausen","sequence":"additional","affiliation":[{"name":"Julius K\u00fchn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Crop and Soil Science, Bundesallee 58, 38116 Braunschweig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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), M\u00fcnchener Str. 20, 82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2017.01.002","article-title":"Soil carbon 4 per mille","volume":"292","author":"Minasny","year":"2017","journal-title":"Geoderma"},{"key":"ref_2","unstructured":"Lorenz, K., and Lal, R. 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