{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T14:37:12Z","timestamp":1784126232101,"version":"3.55.0"},"reference-count":28,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["BR28712473"],"award-info":[{"award-number":["BR28712473"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The paper presents a hybrid machine learning model for the spatial segmentation of soils by salinity using multispectral satellite data from Sentinel-2 and climate parameters of the ERA5-Land model. The proposed method aims to solve the problem of accurate soil cover segmentation under climate change and high spatial heterogeneity of data. The approach includes the sequential application of unsupervised learning algorithms (K-Means, hierarchical clustering, DBSCAN), the XGBoost model, and a multitasking neural network that performs simultaneous classification and regression. At the first stage, pseudo-labels are formed using K-Means, then a probabilistic assessment of object membership in classes and ensemble voting of clustering algorithms are carried out. The final model is trained on an extended feature space and demonstrates improved results compared to traditional approaches. Experiments on a sample of 33,624 observations (23,536\u2014training sample, 10,088\u2014test sample) showed an increase in the Silhouette Score value from 0.7840 to 0.8156 and a decrease in the Davies\u2013Bouldin Score from 0.3567 to 0.3022. The classification accuracy was 99.99%, with only one error in more than 10,000 test objects. The results confirmed the proposed method\u2019s high efficiency and applicability for remote monitoring, environmental analysis, and sustainable land management.<\/jats:p>","DOI":"10.3390\/a18050285","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T08:44:58Z","timestamp":1747212298000},"page":"285","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Development of a Model for Soil Salinity Segmentation Based on Remote Sensing Data and Climate Parameters"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4953-0737","authenticated-orcid":false,"given":"Gulzira","family":"Abdikerimova","sequence":"first","affiliation":[{"name":"Department of Information Systems, L.N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dana","family":"Khamitova","sequence":"additional","affiliation":[{"name":"Department of Information Systems, L.N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akmaral","family":"Kassymova","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Zhangir Khan University, Uralsk 090000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Assyl","family":"Bissengaliyeva","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Zhangir Khan University, Uralsk 090000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gulsara","family":"Nurova","sequence":"additional","affiliation":[{"name":"Academy of Public Administration Under the President of the Republic of Kazakhstan in the Kyzylorda Region, Kyzylorda 120000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8397-8914","authenticated-orcid":false,"given":"Murat","family":"Aitimov","sequence":"additional","affiliation":[{"name":"Faculty of Natural Sciences, Educational Program of Informatics and Information and Communication Technologies, Korkyt Ata Kyzylorda University, Kyzylorda 120000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8412-434X","authenticated-orcid":false,"given":"Yerlan Alimzhanovich","family":"Shynbergenov","sequence":"additional","affiliation":[{"name":"Kyzylorda Regional Branch Within the Academy of Public Administration Under the President of the Republic of Kazakhstan, Kyzylorda 120000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moldir","family":"Yessenova","sequence":"additional","affiliation":[{"name":"Department of Information Systems, L.N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roza","family":"Bekbayeva","sequence":"additional","affiliation":[{"name":"Department of Automation, Information Technology, Urban Development of Non-Profit Limited Company Semey University Named After Shakarim, Semey 070000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1080\/01431161.2024.2412804","article-title":"Present Knowledge and Future Challenges in Remote Sensing for Soil Salinization Monitoring: A Review of Bibliometric Analysis","volume":"46","author":"Jiang","year":"2025","journal-title":"Int. J. 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Stud."},{"key":"ref_6","first-page":"1430","article-title":"Impact of Cattle Grazing on Degradation of Mountain Pastelands in South-East of Kazakhstan","volume":"27","author":"Sadyrova","year":"2025","journal-title":"ES Energy Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"19902","DOI":"10.1109\/ACCESS.2024.3361046","article-title":"Analysis of Formal Concepts for Verification of Pests and Diseases of Crops Using Machine Learning Methods","volume":"12","author":"Tussupov","year":"2024","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"71323","DOI":"10.1109\/ACCESS.2024.3397843","article-title":"Analyzing Disease and Pest Dynamics in Steppe Crop Using Structured Data","volume":"12","author":"Tussupov","year":"2024","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2465452","DOI":"10.1080\/10106049.2025.2465452","article-title":"Bare Ground Classification Using a Spectral Index Ensemble and Machine Learning Models Optimized across 12 International Study Sites","volume":"40","author":"Becker","year":"2025","journal-title":"Geocarto Int."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tashpolat, N., and Reheman, A. 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