{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T16:56:10Z","timestamp":1792688170273,"version":"4.5.0"},"reference-count":29,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,6]],"date-time":"2017-01-06T00:00:00Z","timestamp":1483660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil salinization due to irrigation affects agricultural productivity in the semi-arid region of Brazil. In this study, the performance of four computational models to estimate electrical conductivity (EC) (soil salinization) was evaluated using laboratory reflectance spectroscopy. To investigate the influence of bandwidth and band positioning on the EC estimates, we simulated the spectral resolution of two hyperspectral sensors (airborne ProSpecTIR-VS and orbital Hyperspectral Infrared Imager (HyspIRI)) and three multispectral instruments (RapidEye\/REIS, High Resolution Geometric (HRG)\/SPOT-5, and Operational Land Imager (OLI)\/Landsat-8)). Principal component analysis (PCA) and the first-order derivative analysis were applied to the data to generate metrics associated with soil brightness and spectral features, respectively. The three sets of data (reflectance, PCA, and derivative) were tested as input variable for Extreme Learning Machine (ELM), Ordinary Least Square regression (OLS), Partial Least Squares Regression (PLSR), and Multilayer Perceptron (MLP). Finally, the laboratory models were inverted to a ProSpecTIR-VS image (400\u20132500 nm) acquired with 1-m spatial resolution in the northeast of Brazil. The objective was to estimate EC over exposed soils detected using the Normalized Difference Vegetation Index (NDVI). The results showed that the predictive ability of the linear models and ELM was better than that of the MLP, as indicated by higher values of the coefficient of determination (R2) and ratio of the performance to deviation (RPD), and lower values of the root mean square error (RMSE). Metrics associated with soil brightness (reflectance and PCA scores) were more efficient in detecting changes in the EC produced by soil salinization than metrics related to spectral features (derivative). When applied to the image, the PLSR model with reflectance had an RMSE of 1.22 dS\u00b7m\u22121 and an RPD of 2.21, and was more suitable for detecting salinization (10\u201320 dS\u00b7m\u22121) in exposed soils (NDVI &lt; 0.30) than the other models. For all computational models, lower values of RMSE and higher values of RPD were observed for the narrowband-simulated sensors compared to the broadband-simulated instruments. The soil EC estimates improved from the RapidEye to the HRG and OLI spectral resolutions, showing the importance of shortwave intervals (SWIR-1 and SWIR-2) in detecting soil salinization when the reflectance of selected bands is used in data modelling.<\/jats:p>","DOI":"10.3390\/rs9010042","type":"journal-article","created":{"date-parts":[[2017,1,6]],"date-time":"2017-01-06T10:08:12Z","timestamp":1483697292000},"page":"42","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation"],"prefix":"10.3390","volume":"9","author":[{"given":"Od\u00edlio","family":"Rocha Neto","sequence":"first","affiliation":[{"name":"Departamento de Engenharia Agr\u00edcola, Universidade Federal do Cear\u00e1 (UFC), Caixa Postal 12.168, Fortaleza CE 60450-760, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1480-0944","authenticated-orcid":false,"given":"Adunias","family":"Teixeira","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia Agr\u00edcola, Universidade Federal do Cear\u00e1 (UFC), Caixa Postal 12.168, Fortaleza CE 60450-760, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raimundo","family":"Le\u00e3o","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia Agr\u00edcola, Universidade Federal do Cear\u00e1 (UFC), Caixa Postal 12.168, Fortaleza CE 60450-760, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis","family":"Moreira","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia Agr\u00edcola, Universidade Federal do Cear\u00e1 (UFC), Caixa Postal 12.168, Fortaleza CE 60450-760, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L\u00eanio","family":"Galv\u00e3o","sequence":"additional","affiliation":[{"name":"Instituto Nacional de Pesquisas Espaciais (INPE), Divis\u00e3o de Sensoriamento Remoto, Caixa Postal 515, S\u00e3o Jos\u00e9 dos Campos SP 12245-970, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,6]]},"reference":[{"key":"ref_1","unstructured":"Eswaran, H., Lal, R., and Reich, P.F. 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