Khole et al. (2025) Soil Moisture Index (SMI) Estimation Using Raw Landsat-8 OLI Data, NDVI and Land Surface Temperature for Agricultural Drought Assessment

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Geography Environment and Earth Science International
• Year: 2025
• Date: 2025-09-08
• Authors: Monika S. Khole, Sandip Maruti Anpat, S. B. Sayyad, Sanjay K. Tupe
• DOI: 10.9734/jgeesi/2025/v29i9941

Research Groups

Not specified in the provided text.

Short Summary

This study outlines a procedure to calculate the Soil Moisture Index (SMI) using Landsat-8 OLI and TIRS data (Land Surface Temperature and Normalized Difference Vegetation Index) during the summer season, finding that the selected study area is under drought conditions with SMI values ranging from 0 to 0.3.

Objective

• To outline a procedure for calculating the Soil Moisture Index (SMI) using Landsat-8 OLI and TIRS data (Land Surface Temperature and Normalized Difference Vegetation Index) to monitor agricultural drought conditions.

Study Configuration

• Spatial Scale: Regional scale, implied by the use of Landsat-8 data, which typically covers areas of several tens to hundreds of square kilometers per scene with a spatial resolution of 30 meters for OLI bands and 100 meters for TIRS bands (resampled to 30 meters). The specific study area is not detailed.
• Temporal Scale: Summer season.

Methodology and Data

• Models used: QGIS software for calculating Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Soil Moisture Index (SMI).
• Data sources: Landsat-8 OLI (Bands 4, 5) and Thermal Infrared Sensor (TIRS) (Band 10) satellite data, sourced via the Earth Explorer platform.

Main Results

• The Soil Moisture Index (SMI) values for the selected study area ranged from 0 to 0.3.
• This range of SMI values indicates significant water scarcity, confirming that the study area is under drought conditions.
• The developed technique is affirmed as reliable for estimating SMI using Landsat data, providing an effective method for monitoring agricultural drought.
• Normalized Difference Vegetation Index (NDVI) scores were within the range of -1 to 1.
• Land Surface Temperature (LST) readings were measured in degrees Celsius.

Contributions

• Provides a reliable and effective technique for estimating the Soil Moisture Index (SMI) using readily available Landsat-8 satellite data.
• Offers a practical method for monitoring agricultural drought conditions, enhancing existing literature on remote sensing applications for drought assessment.

Funding

Not specified in the provided text.

Citation

@article{Khole2025Soil,
  author = {Khole, Monika S. and Anpat, Sandip Maruti and Sayyad, S. B. and Tupe, Sanjay K.},
  title = {Soil Moisture Index (SMI) Estimation Using Raw Landsat-8 OLI Data, NDVI and Land Surface Temperature for Agricultural Drought Assessment},
  journal = {Journal of Geography Environment and Earth Science International},
  year = {2025},
  doi = {10.9734/jgeesi/2025/v29i9941},
  url = {https://doi.org/10.9734/jgeesi/2025/v29i9941}
}