Repozytorium Uniwersytetu Jagiellońskiego

Modeling and mapping of soil salinity with reflectance spectroscopy and Landsat data using two quantitative methods (PLSR and MARS)

Modeling and mapping of soil salinity with reflectance ...

Metadane (Dublin Core)

dc.contributor.author Nawar, Said [USOS139993] pl
dc.contributor.author Buddenbaum, Henning pl
dc.contributor.author Hill, Joachim pl
dc.contributor.author Kozak, Jacek [SAP11014091] pl
dc.date.accessioned 2015-03-10T09:18:51Z
dc.date.available 2015-03-10T09:18:51Z
dc.date.issued 2014 pl
dc.identifier.uri http://ruj.uj.edu.pl/xmlui/handle/item/3640
dc.language eng pl
dc.title Modeling and mapping of soil salinity with reflectance spectroscopy and Landsat data using two quantitative methods (PLSR and MARS) pl
dc.type JournalArticle pl
dc.description.physical 10813-10834 pl
dc.description.additional Bibliogr. s. 10830-10834 pl
dc.abstract.en The monitoring of soil salinity levels is necessary for the prevention and mitigation of land degradation in arid environments. To assess the potential of remote sensing in estimating and mapping soil salinity in the El-Tina Plain, Sinai, Egypt, two predictive models were constructed based on the measured soil electrical conductivity (EC_{e}) and laboratory soil reflectance spectra resampled to Landsat sensor's resolution. The models used were partial least squares regression (PLSR) and multivariate adaptive regression splines (MARS). The results indicated that a good prediction of the soil salinity can be made based on the MARS model (R^{2} = 0.73, RMSE = 6.53, and ratio of performance to deviation (RPD) = 1.96), which performed better than the PLSR model (R^{2} = 0.70, RMSE = 6.95, and RPD = 1.82). The models were subsequently applied on a pixel-by-pixel basis to the reflectance values derived from two Landsat images (2006 and 2012) to generate quantitative maps of the soil salinity. The resulting maps were validated successfully for 37 and 26 sampling points for 2006 and 2012, respectively, with R^{2} = 0.72 and 0.74 for 2006 and 2012, respectively, for the MARS model, and R^{2} = 0.71 and 0.73 for 2006 and 2012, respectively, for the PLSR model. The results indicated that MARS is a more suitable technique than PLSR for the estimation and mapping of soil salinity, especially in areas with high levels of salinity. The method developed in this paper can be used for other satellite data, like those provided by Landsat 8, and can be applied in other arid and semi-arid environments. pl
dc.subject.en soil salinity pl
dc.subject.en reflectance spectra pl
dc.subject.en Landsat pl
dc.subject.en PLSR pl
dc.subject.en MARS pl
dc.subject.en Egypt pl
dc.description.volume 6 pl
dc.description.number 11 pl
dc.identifier.doi 10.3390/rs61110813 pl
dc.identifier.eissn 2072-4292 pl
dc.title.journal Remote Sensing pl
dc.language.container eng pl
dc.affiliation Wydział Biologii i Nauk o Ziemi : Instytut Geografii i Gospodarki Przestrzennej pl
dc.subtype Article pl
dc.rights.original CC-BY; otwarte czasopismo; ostateczna wersja wydawcy; w momencie opublikowania; 0; pl
dc.pbn.affiliation USOS139993:UJ.WBl; pl
.pointsMNiSW [2014 A]: 35


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