Abstract
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 (ECe) 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 (R2 = 0.73, RMSE = 6.53, and ratio of performance to deviation (RPD) = 1.96), which performed better than the PLSR model (R2 = 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 R2 = 0.72 and 0.74 for 2006 and 2012, respectively, for the MARS model, and R2 = 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.
Highlights
Salinization is a worldwide problem that affects the physical and chemical properties of soil, leading to the loss of crop productivity [1,2]
The trends in the spectral reflectance derived from ETM+ bands and the resampled measured reflectance spectra were consistent with variations in the soil salinity values (Figure 3)
The results indicate that the multivariate adaptive regression splines (MARS) model using Landsat reflectance data is stable, and slightly better than the partial least squares regression (PLSR) model for mapping and monitoring the soil salinity
Summary
Salinization is a worldwide problem that affects the physical and chemical properties of soil, leading to the loss of crop productivity [1,2]. To manage salt-affected soils better, soil salinity must be first monitored and mapped [5]. Several studies have shown the visible, near infrared, or short-wave infrared spectral bands from the optical sensors to be promising for the detection of surface soil salinity [11,12,13,14,15]. Hyperspectral data have been successfully used in several studies on soil salinity, enabling quantitative assessment of salt-affected soils [16,17,18,19,20,21,22]. Practical limitations associated with hyperspectral imagery, including the availability of orbital data and the limited spatial coverage of the existing satellite sensors, still limit its potential for regional monitoring of salt-affected soils [23]. The recent launch of Landsat 8 has extended opportunities to map soil salinity, with Landsat data having been freely available for several years
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