Survey and Classification of Soil Texture on both Banks of Sirwan River, Kurdistan Region, Iraq Using Remote Sensing and GIS.

Salim  Neimat Azeez

doi:10.25098/9.2.27

Authors

Salim Neimat Azeez Protected Cultivation Department, Bakrajo Technical Institute, Sulaimani Polytechnic University, Sulaymaniyah, Iraq.

DOI:

https://doi.org/10.25098/9.2.27

Keywords:

Survey and Classification, Sirwan River, Kurdistan Region, Geographic Information System, Remote Sensing

Abstract

This study uses integrated remote sensing (RS) and geographic information system (GIS) methods to analyze the survey and classification of soil texture on both sides of the Sirwan River. The main problem of this study is that there is no accurate description of the soil texture of the study area through GIS. The study region, which spans 79. 7 km between Darbandikhan and Kalar in the Kurdistan Region of Iraq, was examined using field sampling and laboratory analysis of 30 soil sites. Using the United States Department of Agriculture (USDA) Soil Texture Triangle, soil texture was classified according to the ratios of sand, silt, and clay. Spatial modeling in ArcMap was supported by Landsat 9 satellite imagery. The main objectives of this study is to classify soil texture, to show the impact of fluvial dynamics on the difference in soil textures between the two river banks and to mapping soil texture of the study area. The results showed that the region's major soil textures are loamy (66. 5%) and silty loam (33. 5%), which are caused by the river's fluvial environment's sediment deposition patterns. Because of continuous quarrying operations, the left riverbank displayed more variation in soil particle distribution, whereas the right bank was more stable. The sediment sorting process was confirmed by statistical analysis, which revealed significant correlations between soil elements. The combination of remote sensing and geographic information systems proved useful for mapping soil heterogeneity and provided significant information for land use planning, erosion control, and sustainable agriculture. The study emphasizes the promise of digital soil mapping methods and suggests further investigation of unmanned aerial vehicles (UAVs) and hyperspectral imaging to improve accuracy.

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