Integration of VIS–NIR Spectroscopy and Multivariate Technique for Soils Discrimination Under Different Land Management - Mohamed S. Shokr; Abdel-rahman A. Mustafa; Talal Alharbi; Jose Emilio Meroño de Larriva; Abdelbaset S. El-Sorogy; Khaled Al-Kahtany; Elsayed A. Abdelsamie (Land)
Proximal sensing has become increasingly popular due to developments in soil observation technologies and the demands of timely information gathering through contemporary methods. By utilizing the morphological, physical, and chemical characteristics of representative pedogenetic profiles established in various soils of the Sohag governorate, Egypt, the current research addresses the characterization of surface reflectance spectra and links them with the corresponding soil classification. Three primary areas were identified: recently cultivated, old cultivated, and bare soils. For morphological analysis, a total of 25 soil profiles were chosen and made visible. In the dark room, an ASD Fieldspec portable spectroradiometer (350–2500 nm) was used to measure the spectrum. Based on how similar their surface spectra were, related soils were categorized. Ward’s method served as the basis for the grouping. Despite the fact that the VIS–NIR spectra of the surface soils from various land uses have a similar reflectance shape, it is still possible to compare the soil reflectance curves and the effects of the surface soils. As a result, three groups of soil curves representing various land uses were observed. Cluster analysis was performed on the reflectance data in four ranges (350–750, 751–1150, 1151–1850, and 1851–2500 nm). The groups derived from the soil surface ranges of 350–750 nm and 751–1150 nm were not the same as those derived from the ranges of 1151–1850 nm and 1851–2500 nm. The last two categories are strikingly comparable to various land uses with marginally similar features. Based on the ranges of 1151–1850 nm and 1851–2500 nm in surface spectral data, the dendrogram effectively separated and combined the profiles into two separate clusters. These clusters matched different land uses exactly. The results can be used to promote the widespread usage of in situ hyperspectral data sets for the investigation of various soil characteristics.