{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T18:48:53Z","timestamp":1783363733281,"version":"3.54.6"},"reference-count":140,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:00:00Z","timestamp":1693353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon H2020 research and innovation European Joint Programme","award":["862695"],"award-info":[{"award-number":["862695"]}]},{"name":"European Union\u2019s Horizon H2020 research and innovation European Joint Programme","award":["ANR-22-PEAE-0010"],"award-info":[{"award-number":["ANR-22-PEAE-0010"]}]},{"name":"European Union\u2019s Horizon H2020 research and innovation European Joint Programme","award":["44324WB\/20G093"],"award-info":[{"award-number":["44324WB\/20G093"]}]},{"name":"French National Research Agency","award":["862695"],"award-info":[{"award-number":["862695"]}]},{"name":"French National Research Agency","award":["ANR-22-PEAE-0010"],"award-info":[{"award-number":["ANR-22-PEAE-0010"]}]},{"name":"French National Research Agency","award":["44324WB\/20G093"],"award-info":[{"award-number":["44324WB\/20G093"]}]},{"name":"French\u2013Tunisian project PHC-Utique IPASS","award":["862695"],"award-info":[{"award-number":["862695"]}]},{"name":"French\u2013Tunisian project PHC-Utique IPASS","award":["ANR-22-PEAE-0010"],"award-info":[{"award-number":["ANR-22-PEAE-0010"]}]},{"name":"French\u2013Tunisian project PHC-Utique IPASS","award":["44324WB\/20G093"],"award-info":[{"award-number":["44324WB\/20G093"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding spatial and temporal variability in soil organic carbon (SOC) content helps simultaneously assess soil fertility and several parameters that are strongly associated with it, such as structural stability, nutrient cycling, biological activity, and soil aeration. Therefore, it appears necessary to monitor SOC regularly and investigate rapid, non-destructive, and cost-effective approaches for doing so, such as proximal and remote sensing. To increase the accuracy of predictions of SOC content, this study evaluated combining remote sensing time series with laboratory spectral measurements using machine and deep-learning algorithms. Partial least squares (PLS) regression, random forest (RF), and deep neural network (DNN) models were developed using Sentinel-2 (S2) time series of 58 sampling points of bare soil and according to three approaches. In the first approach, only S2 bands were used to calibrate and compare the performance of the models. In the second, S2 indices, Sentinel-1 (S1) indices, and S1 soil moisture were added separately during model calibration to evaluate their effects individually and then together. In the third, we added the laboratory indices incrementally and tested their influence on model accuracy. Using only S2 bands, the DNN model outperformed the PLS and RF models (ratio of performance to the interquartile distance RPIQ = 0.79, 1.36 and 1.67, respectively). Additional information improved performances only for model calibration, with S1 soil moisture yielding the most stable improvement among three iterations. Including equivalent indices of the S2 indices calculated using soil spectra obtained under laboratory conditions improved prediction of SOC, and the use of only two indices achieved good validation performances for the RF and DNN models (mean RPIQ = 2.01 and 1.77, respectively).<\/jats:p>","DOI":"10.3390\/rs15174264","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T10:09:49Z","timestamp":1693390189000},"page":"4264","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7415-3443","authenticated-orcid":false,"given":"Hayfa","family":"Zayani","sequence":"first","affiliation":[{"name":"SAS, Institut Agro, INRAE, 65 Rue de St Brieuc, 35000 Rennes, France"},{"name":"Universit\u00e9 de Carthage, Institut National Agronomique de Tunisie, LR 17AGR01 (Lr GREEN-TEAM), 43 Avenue Charles Nicolle, Tunis 1082, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3344-7928","authenticated-orcid":false,"given":"Youssef","family":"Fouad","sequence":"additional","affiliation":[{"name":"SAS, Institut Agro, INRAE, 65 Rue de St Brieuc, 35000 Rennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Didier","family":"Michot","sequence":"additional","affiliation":[{"name":"SAS, Institut Agro, INRAE, 65 Rue de St Brieuc, 35000 Rennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeineb","family":"Kassouk","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Carthage, Institut National Agronomique de Tunisie, LR 17AGR01 (Lr GREEN-TEAM), 43 Avenue Charles Nicolle, Tunis 1082, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9461-4120","authenticated-orcid":false,"given":"Nicolas","family":"Baghdadi","sequence":"additional","affiliation":[{"name":"CIRAD, CNRS, INRAE, TETIS, Universit\u00e9 de Montpellier, AgroParisTech, CEDEX 5, 34093 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4703-3702","authenticated-orcid":false,"given":"Emmanuelle","family":"Vaudour","sequence":"additional","affiliation":[{"name":"INRAE, Universit\u00e9 Paris-Saclay, AgroParisTech, UMR EcoSys, 91120 Palaiseau, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zohra","family":"Lili-Chabaane","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Carthage, Institut National Agronomique de Tunisie, LR 17AGR01 (Lr GREEN-TEAM), 43 Avenue Charles Nicolle, Tunis 1082, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4395-4942","authenticated-orcid":false,"given":"Christian","family":"Walter","sequence":"additional","affiliation":[{"name":"SAS, Institut Agro, INRAE, 65 Rue de St Brieuc, 35000 Rennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1079\/SUM2002134","article-title":"Managing soil organic matter\u2014Implications for soil structure on organic farms","volume":"18","author":"Shepherd","year":"2002","journal-title":"Soil Use Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2003GB002204","article-title":"Effects of level and quality of organic matter input on carbon storage and biological activity in soil: Synthesis of a long-term experiment","volume":"18","author":"Kirchmann","year":"2004","journal-title":"Glob. 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