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@Article{MoreiraTeixGalv:2015:PoMuHy,
               author = "Moreira, L. C. J. and Teixeira, A. D. S. and Galv{\~a}o, 
                         L{\^e}nio Soares",
          affiliation = "{Universidade Federal do Cear{\'a} (UFC)} and {Universidade 
                         Federal do Cear{\'a} (UFC)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Potential of multispectral and hyperspectral data to detect 
                         saline-exposed soils in Brazil",
              journal = "GIScience and Remote Sensing",
                 year = "2015",
               volume = "52",
               number = "4",
                pages = "416--436",
                month = "July",
             keywords = "electrical conductivity, hyperion, hyperspectral, NDVI, 
                         OLI/Landsat-8, soil salinity.",
             abstract = "Irrigation-induced soil salinization is an important land 
                         degradation process in northeastern Brazil. We used multispectral 
                         and hyperspectral sensors to detect saline-exposed soils in an 
                         area cultivated with irrigated rice. Spectral mixture analysis 
                         (SMA) was applied to Operational Land Imager (OLI)/Landsat-8 data 
                         to identify exposed soils. By measuring the electrical 
                         conductivity (EC) of soil samples from 53 sites, we classified 
                         them into saline and non-saline. The surface reflectance Thematic 
                         Mapper /Landsat-5 product was used to inspect the normalized 
                         difference vegetation index (NDVI) variations over time 
                         (1984-2011) at the sites. Using OLI/Landsat-8 and Hyperion/Earth 
                         Observing One, we obtained five salinity indices and scores from 
                         principal component analysis applied to exposed soil pixels. These 
                         indices along with the first principal component (PC1) were 
                         regressed against EC to estimate soil salinization. Different 
                         metrics and support vector machine (SVM) were tested to 
                         discriminate saline and non-saline soils. The results showed that 
                         exposed soils detected by SMA had NDVI with a lower mean and 
                         standard deviation over time in the saline areas due to vegetation 
                         growth limitation. NaCl absorption bands were not observed in the 
                         Hyperion spectra due to atmospheric water vapor. Therefore, soil 
                         salinity detected by OLI or Hyperion was due to soil brightness 
                         rather than absorption bands. Because most salinity indices and 
                         scores expressed brightness to some extent, they were correlated 
                         with EC, especially the Salinity Index and PC1. However, compared 
                         with OLI, the narrow-band salinity indices of Hyperion produced a 
                         lower root mean square error for EC estimates, better 
                         discrimination between saline and non-saline soils using the 
                         Euclidean distance and spectral angle metrics, and higher SVM 
                         classification accuracy.",
                  doi = "10.1080/15481603.2015.1040227",
                  url = "http://dx.doi.org/10.1080/15481603.2015.1040227",
                 issn = "1548-1603",
             language = "en",
        urlaccessdate = "27 nov. 2020"
}


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