{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T21:39:18Z","timestamp":1783719558158,"version":"3.55.0"},"reference-count":72,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T00:00:00Z","timestamp":1700611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007599","name":"Agropolis Fondation","doi-asserted-by":"publisher","award":["2001-032"],"award-info":[{"award-number":["2001-032"]}],"id":[{"id":"10.13039\/100007599","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Centre National d\u2019Etudes Spatiales (CNES)","award":["2001-032"],"award-info":[{"award-number":["2001-032"]}]},{"name":"IRD (Institut de Recherche pour le D\u00e9veloppement)","award":["2001-032"],"award-info":[{"award-number":["2001-032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Several recent studies have evidenced the relevance of machine-learning for soil salinity mapping using Sentinel-2 reflectance as input data and field soil salinity measurement (i.e., Electrical Conductivity-EC) as the target. As soil EC monitoring is costly and time consuming, most learning databases used for training\/validation rely on a limited number of soil samples, which can affect the model consistency. Based on the low soil salinity variation at the Sentinel-2 pixel resolution, this study proposes to increase the learning database\u2019s number of observations by assigning the EC value obtained on the sampled pixel to the eight neighboring pixels. The method allowed extending the original learning database made up of 97 field EC measurements (OD) to an enhanced learning database made up of 691 observations (ED). Two classification machine-learning models (i.e., Random Forest-RF and Support Vector Machine-SVM) were trained with both OD and ED to assess the efficiency of the proposed method by comparing the models\u2019 outcomes with EC observations not used in the models\u00b4 training. The use of ED led to a significant increase in both models\u2019 consistency with the overall accuracy of the RF (SVM) model increasing from 0.25 (0.26) when using the OD to 0.77 (0.55) when using ED. This corresponds to an improvement of approximately 208% and 111%, respectively. Besides the improved accuracy reached with the ED database, the results showed that the RF model provided better soil salinity estimations than the SVM model and that feature selection (i.e., Variance Inflation Factor-VIF and\/or Genetic Algorithm-GA) increase both models\u00b4 reliability, with GA being the most efficient. This study highlights the potential of machine-learning and Sentinel-2 image combination for soil salinity monitoring in a data-scarce context, and shows the importance of both model and features selection for an optimum machine-learning set-up.<\/jats:p>","DOI":"10.3390\/s23239328","type":"journal-article","created":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T10:41:51Z","timestamp":1700649711000},"page":"9328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Towards the Improvement of Soil Salinity Mapping in a Data-Scarce Context Using Sentinel-2 Images in Machine-Learning Models"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2938-4936","authenticated-orcid":false,"given":"J. W.","family":"Sirpa-Poma","sequence":"first","affiliation":[{"name":"ESPACE-DEV, Univ Montpellier, IRD, Univ Antilles, Univ Guyane, Univ R\u00e9union, 34093 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F.","family":"Satg\u00e9","sequence":"additional","affiliation":[{"name":"ESPACE-DEV, Univ Montpellier, IRD, Univ Antilles, Univ Guyane, Univ R\u00e9union, 34093 Montpellier, France"},{"name":"Universidad Mayor de San Andr\u00e9s, Instituto de Hidr\u00e1ulica e Hidrolog\u00eda, La Paz, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E.","family":"Resongles","sequence":"additional","affiliation":[{"name":"HSM, Univ Montpellier, CNRS, IRD, 34093 Montpellier, France"},{"name":"Universidad Mayor de San Andr\u00e9s, Instituto de Investigaciones Qu\u00edmicas, La Paz, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Pillco-Zol\u00e1","sequence":"additional","affiliation":[{"name":"Universidad Mayor de San Andr\u00e9s, Instituto de Hidr\u00e1ulica e Hidrolog\u00eda, La Paz, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4324-5772","authenticated-orcid":false,"given":"J.","family":"Molina-Carpio","sequence":"additional","affiliation":[{"name":"Universidad Mayor de San Andr\u00e9s, Instituto de Hidr\u00e1ulica e Hidrolog\u00eda, La Paz, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. G.","family":"Flores Colque","sequence":"additional","affiliation":[{"name":"Universidad Mayor de San Andr\u00e9s, Instituto de Investigaciones Qu\u00edmicas, La Paz, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Ormachea","sequence":"additional","affiliation":[{"name":"Universidad Mayor de San Andr\u00e9s, Instituto de Investigaciones Qu\u00edmicas, La Paz, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.","family":"Pacheco Mollinedo","sequence":"additional","affiliation":[{"name":"ESPACE-DEV, Univ Montpellier, IRD, Univ Antilles, Univ Guyane, Univ R\u00e9union, 34093 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3950-4041","authenticated-orcid":false,"given":"M.-P.","family":"Bonnet","sequence":"additional","affiliation":[{"name":"ESPACE-DEV, Univ Montpellier, IRD, Univ Antilles, Univ Guyane, Univ R\u00e9union, 34093 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/bs.agron.2021.03.001","article-title":"Critical Knowledge Gaps and Research Priorities in Global Soil Salinity","volume":"169","author":"Hopmans","year":"2021","journal-title":"Adv. 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