{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T18:49:56Z","timestamp":1784054996107,"version":"3.55.0"},"reference-count":61,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T00:00:00Z","timestamp":1707436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52379047"],"award-info":[{"award-number":["52379047"]}]},{"name":"National Natural Science Foundation of China","award":["52209067"],"award-info":[{"award-number":["52209067"]}]},{"name":"National Natural Science Foundation of China","award":["2021YFD1900804"],"award-info":[{"award-number":["2021YFD1900804"]}]},{"name":"National Key Research and Development Program of China","award":["52379047"],"award-info":[{"award-number":["52379047"]}]},{"name":"National Key Research and Development Program of China","award":["52209067"],"award-info":[{"award-number":["52209067"]}]},{"name":"National Key Research and Development Program of China","award":["2021YFD1900804"],"award-info":[{"award-number":["2021YFD1900804"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based \u201cpoint\u201d salinity data and remote sensing pixel-based \u201cspatial\u201d data often limit the accuracy of remote sensing monitoring of soil salinity, and (2) the same salinity RS monitoring model usually provides inconsistent or sometimes conflicting explanations for different data. Therefore, based on Landsat 8 imagery and synchronously collected ground-sampling data of two typical study regions (denoted as N and S, respectively) of the Yichang Irrigation Area in the Hetao Irrigation District for May 2013, this study used geostatistical methods to obtain \u201crelative truth values\u201d of salinity corresponding to the Landsat 8 pixel scale. Additionally, based on Landsat 8 multispectral data, 14 salinity indices were constructed. Subsequently, the Correlation-based Feature Selection (CFS) method was used to select sensitive features, and a strategy similar to the concept of ensemble learning (EL) was adopted to integrate the single-feature-sensitive Bayesian classification (BC) model in order to construct an RS monitoring model for soil salinization (Nonsaline, Slightly saline, Moderately saline, Strongly saline, and Solonchak). The research results indicated that (1) soil salinity exhibits moderate to strong variability within a 30 m scale, and the spatial heterogeneity of soil salinity needs to be considered when developing remote sensing models; (2) the theoretical models of salinity variance functions in the N and S regions conform to the exponential model and the spherical model, with R2 values of 0.817 and 0.967, respectively, indicating a good fit for the variance characteristics of salinity and suitability for Kriging interpolation; and (3) compared to a single-feature BC model, the soil salinization identification model constructed using the concept of EL demonstrated better potential for robustness and effectiveness.<\/jats:p>","DOI":"10.3390\/rs16040642","type":"journal-article","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T08:12:03Z","timestamp":1707466323000},"page":"642","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Monitoring Soil Salinity Classes through Remote Sensing-Based Ensemble Learning Concept: Considering Scale Effects"],"prefix":"10.3390","volume":"16","author":[{"given":"Huifang","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwei","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi","family":"Xu","sequence":"additional","affiliation":[{"name":"Changjiang Institute of Survey, Planning, Design and Research, Wuhan 430010, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(02)00188-8","article-title":"Remote sensing of soil salinity: Potentials and constraints","volume":"85","author":"Metternicht","year":"2003","journal-title":"Remote Sens. 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