Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity
Medhat Saleh, A., Abd-Elwahed, M., Metwally, Y., Arafat, S. (2021). Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity. EKB Journal Management System, 29(3), 943-952. doi: 10.21608/ajs.2021.87863.1402
Abdelrahman Medhat Saleh; Mohammed Abd-Elwahed; Yasser Metwally; Sayed Arafat. "Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity". EKB Journal Management System, 29, 3, 2021, 943-952. doi: 10.21608/ajs.2021.87863.1402
Medhat Saleh, A., Abd-Elwahed, M., Metwally, Y., Arafat, S. (2021). 'Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity', EKB Journal Management System, 29(3), pp. 943-952. doi: 10.21608/ajs.2021.87863.1402
Medhat Saleh, A., Abd-Elwahed, M., Metwally, Y., Arafat, S. Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity. EKB Journal Management System, 2021; 29(3): 943-952. doi: 10.21608/ajs.2021.87863.1402

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  XML PDF (745.74 K)
Document Type: Original Article
DOI: 10.21608/ajs.2021.87863.1402
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Authors
Abdelrahman Medhat Saleh email orcid 1; Mohammed Abd-Elwahedorcid 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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