Estimating the Soil Moisture Index using Normalized Difference Vegetation Index (NDVI) And Land Surface Temperature (LST) for Bidar and Kalaburagi District, Karnataka

Volume 8 Issue 2 March - May 2018

Case Study

Estimating the Soil Moisture Index using Normalized Difference Vegetation Index (NDVI) And Land Surface Temperature (LST) for Bidar and Kalaburagi District, Karnataka

R. Reshma*, S. Emilyprabha Jothi**, G. S. Srinivasa Reddy***

* M.Tech Scholar, Department of Geoinformtics, University of Madras, Chennai, Tamil Nadu, India.

** Project Scientist, Karnataka State Natural Disaster Monitoring Center, Bengaluru, Karnataka, India.

*** Director, Karnataka State Natural Disaster Monitoring Center, Bengaluru, Karnataka, India.

Reshma, R., Jothi, E, S., and Reddy, S, J, S. (2018). Estimating the Soil Moisture Index using Normalized Difference Vegetation Index (NDVI) And Land Surface Temperature (LST) for Bidar and Kalaburagi District, Karnataka. i-manager’s Journal on Civil Engineering, 8(2), 35-42. https://doi.org/10.26634/jce.8.2.14553

Abstract

The soil moisture is a significant analysis of understanding the moisture content in soil that are moist. The Soil moisture Index measures the moisture condition at different levels in the soil. It is mostly determined by the rainfall via the method of penetration. To bring out the geospatial data that allows to generate suitable information relating to Soil Moisture content, authors used the remote sensing method and GIS software's that depend on the use of Soil Moisture Index (SMI) such as Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Landsat 8 satellite images that are provided with visible (red band) and infrared bands (near infrared bands) are important for calculating NDVI and the Band 10, Band 11 along with NDVI is provided as the input for LST analysis. The Soil moisture index (SMI) is based on the observed parameters and the relationship between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI). The SMI condition is done for Bidar and Kalaburgi districts, Karnataka, India for the month of April in 2017. Soil moisture data's are collected from under the surface from a long period as well as at higher spatiotemporal resolutions data that are very important in assessing the severity and also level of drought is relatively accurate.