Soil Moisture Index (SMI) Estimation Using Raw Landsat-8 OLI Data, NDVI and Land Surface Temperature for Agricultural Drought Assessment

Abstract

This study outlines the procedure for calculating the Soil Moisture Index (SMI) using data from Landsat-8 OLI during the summer season, along with Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), both obtained from analysis of thermal imagery from Landsat-8. The satellite data are sourced through the Earth Explorer platform. NDVI and LST serve as fundamental variables in determining SMI. To estimate LST, Band-10 from the Thermal Infrared Sensor (TIRS) is used, in combination with Bands 4 and 5 from the Operational Land Imager (OLI). SMI is computed by utilising both LST and NDVI values. NDVI scores fall within a range of -1 to 1. The QGIS software is used to calculate LST, NDVI and SMI. LST readings are measured in degrees Celsius. SMI is classified from no drought to extreme drought. Results show SMI values range between 0 to 0.3, suggesting significant water scarcity; therefore, it is observed that the selected study area is under drought conditions. Findings affirm that this technique is reliable for estimating SMI using Landsat data, providing an effective method for monitoring agricultural drought conditions.