Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity | ||||
Arab Universities Journal of Agricultural Sciences | ||||
Article 4, Volume 29, Issue 3, December 2021, Page 943-952 PDF (745.74 K) | ||||
Document Type: Original Article | ||||
DOI: 10.21608/ajs.2021.87863.1402 | ||||
View on SCiNiTO | ||||
Authors | ||||
Abdelrahman Medhat Saleh 1; Mohammed Abd-Elwahed 1; Yasser Metwally2; Sayed Arafat3 | ||||
1Soil Science Department, Faculty of Agriculture, Ain Shams University, Cairo Egypt | ||||
2Soil Science Department, Faculty of Agriculture, Ain Shams University, Cairo, Egypt | ||||
3Agric. appl., Soils and Marine Division, National Authority for Remote Sensing and Space Sci., (NARSS), Cairo, Egypt | ||||
Abstract | ||||
The objectives of the current study were to investigate the opportunity of estimating soil salinity from hyperspectral data and identifying the most informative spectral zones for estimation. Electrical conductivity (EC) measurements of ninety topsoil samples (0–30 cm) collected from Toshka, Egypt, were used as data set. Analytical spectral device was employed to collect the reflectance spectral signatures of soil samples. Both linear regression and HSD Tukey’s analyses displayed that the SWIR1 and SWIR2 zones are the most suitable for soil salinity prediction while, blue, green and NIR were the wickedest. Moreover, EC estimation was better in case of lower soil salinity (0-2 dS m-1) than higher levels (8-1). Partial-least-squares-regression (ΡLSR) was employed to establish soil salinity prediction model using the training set of soil samples (n=75). The PLSR model was set up using the most informative wave bands (SWIR1 and SWIR2). The result showed that PLSR linear model gave a precise prediction of soil salinity (R2 = 0.93). The results revealed that employing reflectance values in SWIR in the model variables increases the precision of soil EC prediction. | ||||
Keywords | ||||
Soil salinity; Hyperspectral; Remote sensing; PLSR model | ||||
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