INVESTIGATING THE SPATIAL RELATIONSHIP BETWEEN VEGETATION COVER AND SOIL MOISTURE USING INTEGRATED SENTINEL 1 (SAR) AND SENTINEL 2 (MSI) IMAGERIES: A CASE STUDY OF GIDABO SUB- BASIN, ETHIOPIA

ABSTRACT Soil moisture has a clear influence on surface‐atmosphere interactions and subsequently has the potential of strongly affecting weather patterns, hydrological variations and vegetation development. Hence, identifying the relationship between soil moisture and vegetation cover in certain areas of Ethiopia is challenging in terms of time and cost for sustainable agricultural production problem. Therefore, this study used integrated optical and Radar remote sensing to investigate the relationship between soil moisture and vegetation cover in Gidabo sub basin, Ethiopia. The two high resolution (10m) sentinel families; sentinel 1A (SAR) and sentinel 2A (MSI) images were used. Digital elevation model (DEM) was downloaded from Alaska satellite facility and used for thematic layer preparation. These layers were soil moisture, normalized difference vegetation indices (NDVI), normalized difference moisture index (NDMI), slope, elevation and aspect. Delta index model were applied as a change detection method to extract soil moisture from dry season image and wet season images and analyzed through GIS environment. The result revealed that soil moisture ranges from 0% to 28.69%. This shows high value was observed in southeastern and northeastern and the lower value at the central and Northwestern part. The maximum and minimum value of NDVI was -0.52 to 1 which indicates from bare land to densely vegetated area. The relationship between soil moisture and NDVI was positive correlation that is 0.76 values and the correlation between soil moisture and NDMI which is used to validate SM was 0.703 this shows there is a positive correlation. Since SM is influenced by topography of the area elevation, Aspect and slope has a profound effect on SM so has effect on vegetation cover. In addition, the results from zonal statistics tool showed at maximum SM majority of the area covered by vegetation. It is concluded that integrated optical and RADAR remote sensing with GIS techniques are very efficient, useful, timely and cost effective tool for resolving problems related to soil moisture and vegetation cover. Generally, this research has provide promising result that could help to boost crop production especially agroforestry system.