Soil Moisture Estimation Using Landsat˗8 Satellite Data: A Case Study the Karshi Steppe, Uzbekistan

Abstract

This study presents the results of soil moisture estimation using Landsat˗8 for the Karshi Steppe territory (Uzbekistan). Soil moisture estimation was carried out using the soil moisture index (SMI) calculated based on surface temperature (LST) and the normalized difference vegetation index (NDVI).  Thermal Infrared Sensor (TIRS) and Red, Near-Infrared (NIR) bands of Landsat˗8 were used to calculate LST and NDVI. Observation shows that NDVI and LST are considered essential data to obtain SMI calculation. The study made it possible to categorized 4 class results of SMI from very wet to very dry of the soil edge. The final result is obtainable with the values range from 0 to 1. The results indicate that this method from Landsat images is valuable for monitoring agricultural drought and flood disaster assessment. It is shown that the method can efficiently be applied to estimate soil moisture using remote sensing, which has some advantages over traditional methods and can be effectively used to monitoring soil moisture in agricultural areas of the Karshi Steppe.