Assessing Spatial Patterns of Surface Soil Moisture and Vegetation Cover in Batifa, Kurdistan Region-Iraq: Machine Learning Approach

Abstract

The accurate quantification of surface soil moisture (SSM) and vegetation cover using remote sensing techniques is essential for effective environmental management. This study investigated the spatial variations in SSM and vegetation cover in the Batifa region of the Kurdistan Region of Iraq. Landsat-8 images of the study area were classified using a support vector machine (SVM), and the soil land type was subsequently extracted. A random forest (RF) algorithm was developed to retrieve SSM using Landsat data in conjunction with in situ measurements. The results demonstrated that the RF algorithm achieved a high coefficient of determination ( $\text{R}^{2}=0.80$ ) for the SSM retrieval. The study area exhibited distinct distributions of SSM and normalized difference vegetation index (NDVI) values across different ranges. The low range of SSM (2.21%–3.34%) and NDVI (−0.020–0.172) values occupied approximately 25% of the soil area, whereas the moderate range of SSM (3.34%–4.05%) and NDVI (0.172–0.238) values covered approximately 50% of the soil area. A high range of SSM (4.05%–6.49%) and NDVI (0.238–0.935) values was found in approximately 25% of the region. The southern part of Batifa experienced drought conditions, whereas the northern part exhibited higher SSM levels. Anthropogenic resources caused a decrease in vegetation and SSM in Batifa. These findings have significant implications for sustainable management of water and soil resources in the Batifa area.

Keywords

• Surface soil moisture
• random forest
• landsat 8
• NDVI
• image classification