{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:24:07Z","timestamp":1774542247413,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Research Foundation (DFG)","award":["3150"],"award-info":[{"award-number":["3150"]}]},{"name":"German Research Foundation (DFG)","award":["SCHO 739\/21-1"],"award-info":[{"award-number":["SCHO 739\/21-1"]}]},{"name":"German Research Foundation (DFG)","award":["390727645"],"award-info":[{"award-number":["390727645"]}]},{"name":"MLTRANS-Transferability of Machine Learning Models in Digital Soil Mapping","award":["3150"],"award-info":[{"award-number":["3150"]}]},{"name":"MLTRANS-Transferability of Machine Learning Models in Digital Soil Mapping","award":["SCHO 739\/21-1"],"award-info":[{"award-number":["SCHO 739\/21-1"]}]},{"name":"MLTRANS-Transferability of Machine Learning Models in Digital Soil Mapping","award":["390727645"],"award-info":[{"award-number":["390727645"]}]},{"name":"Machine Learning for Science","award":["3150"],"award-info":[{"award-number":["3150"]}]},{"name":"Machine Learning for Science","award":["SCHO 739\/21-1"],"award-info":[{"award-number":["SCHO 739\/21-1"]}]},{"name":"Machine Learning for Science","award":["390727645"],"award-info":[{"award-number":["390727645"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil organic carbon (SOC) contents and stocks provide valuable insights into soil health, nutrient cycling, greenhouse gas emissions, and overall ecosystem productivity. Given this, remote sensing data coupled with advanced machine learning (ML) techniques have eased SOC level estimation while revealing its patterns across different ecosystems. However, despite these advances, the intricacies of training reliable and yet certain SOC models for specific end-users remain a great challenge. To address this, we need robust SOC uncertainty quantification techniques. Here, we introduce a methodology that leverages conformal prediction to address the uncertainty in estimating SOC contents while using remote sensing data. Conformal prediction generates statistically reliable uncertainty intervals for predictions made by ML models. Our analysis, performed on the LUCAS dataset in Europe and incorporating a suite of relevant environmental covariates, underscores the efficacy of integrating conformal prediction with another ML model, specifically random forest. In addition, we conducted a comparative assessment of our results against prevalent uncertainty quantification methods for SOC prediction, employing different evaluation metrics to assess both model uncertainty and accuracy. Our methodology showcases the utility of the generated prediction sets as informative indicators of uncertainty. These sets accurately identify samples that pose prediction challenges, providing valuable insights for end-users seeking reliable predictions in the complexities of SOC estimation.<\/jats:p>","DOI":"10.3390\/rs16030438","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T04:17:40Z","timestamp":1705983460000},"page":"438","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Uncertainty Quantification of Soil Organic Carbon Estimation from Remote Sensing Data with Conformal Prediction"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5526-6725","authenticated-orcid":false,"given":"Nafiseh","family":"Kakhani","sequence":"first","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"CRC 1070 RessourceCultures, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}]},{"given":"Setareh","family":"Alamdar","sequence":"additional","affiliation":[{"name":"School of Environmental Sciences, University of Guelph, 50 Stone Rd East, Guelph, ON N1G 2W1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9246-1987","authenticated-orcid":false,"given":"Ndiye Michael","family":"Kebonye","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"DFG Cluster of Excellence \u201cMachine Learning\u201d, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9495-4010","authenticated-orcid":false,"given":"Meisam","family":"Amani","sequence":"additional","affiliation":[{"name":"WSP Environment and Infrastructure Canada Limited, Ottawa, ON K2E 7L5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-2602","authenticated-orcid":false,"given":"Thomas","family":"Scholten","sequence":"additional","affiliation":[{"name":"Department of Geosciences, Soil Science and Geomorphology, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"CRC 1070 RessourceCultures, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"},{"name":"DFG Cluster of Excellence \u201cMachine Learning\u201d, University of T\u00fcbingen, 72070 T\u00fcbingen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,23]]},"reference":[{"key":"ref_1","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. 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