Abstract
Information on soil attributes still largely relies on traditional methods of point sampling and subsequent laboratory test which are time and resource consuming. Thus, this study tested the applicability of Kauth-Thomas Tasseled-Cap Transformation (TCT) to soil textural mapping on the main campus of Usmanu Danfodiyo University, Sokoto as a faster method. We hypothesized that the TCT-Brightness image had no relationship individually with soil particle size and Land use/ Land Cover (LUC). Landsat 8 of 22-03-2019 was preprocessed with QGIS and subjected to TCT in Idrisi Terrset to produce the TCT-Brightness image. Soil samples were collected at 91 points based on stratified random sampling at 0-15cm depth. Soil particle size was determined by Bouyoucos Hydrometer method. Simple linear regression analysis was used to model soil particle sizes from the TCT-Brightness image, while soil textural map was produced in SAGA. LUC of the area was mapped at Level III within the Google Earth Engine (GEE). Cross map-tabulation was carried out to test for the relationship between LUC and soil texture. Four textural classes were obtained namely sandy-clay-loam, loamy sand, sand and sandy-loam, with sand being dominant. Soil particle sizes were modeled at 99.85% accuracy, while soil textural mapping yielded 95% accuracy. Five LUC classes namely built-up area, wetland, upland forest, bare surface and riparian forest were mapped at 98.3% accuracy, with bare surface being dominant. A significant (p<0.01) relationship between LUC and soil texture was obtained at 0.85 Kappa Index of Agreement. The study concluded that the TCT is sufficient for predicting soil texture in a largely sandy semi-arid environment. A repeat of this study for the wet season was recommended.
Highlights
The coupling of anthropogenic pressure and climate change on the landscape in the 21st century has necessitated good and sustainable soil management which is critical to successful agriculture (Zribi et al, 2011)
A standard deviation of 11.81 suggests high spatial variability in soil texture of the area. This reasonably hints of the heterogeneity of Land Use/Land Cover (LUC) types in the area as opposed to homogeneity that generally characterizes such a semi-arid environment when vegetation is not in the high biomass period (Liu et al, 2015)
The study tested the applicability of Kauth-Thomas Tasseled-Cap Transformation (TCT) for soil textural mapping in the study area
Summary
The coupling of anthropogenic pressure and climate change on the landscape in the 21st century has necessitated good and sustainable soil management which is critical to successful agriculture (Zribi et al, 2011). Understanding soil attributes and types within an area in a short time will be critical to making best land-use decisions as changes in landscape are expected to accelerate with time (Menezes et al, 2014; Silva et al, 2014). The traditional way of getting soil data in most countries of the world requires recurrence of point sampling and subsequent laboratory determination of both the physical and chemical properties. This procedure is time and resource consuming especially where information is needed urgently and resources are limited
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