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<p>Soil moisture is a critical component of the global water cycle, and its accurate estimation is essential for agricultural planning, water resource management, and drought monitoring. However, soil moisture estimation remains a significant challenge, particularly in semi-arid regions. Against this backdrop, this study demonstrates a geospatial technology approach for soil moisture estimation in Kurnool District, Andhra Pradesh, using Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data. The methodology integrates remote sensing data from Landsat 8 to develop a Soil Moisture Index (SMI) model which enables a comprehensive analysis of soil moisture dynamics in the region.A case study methodology is employed, focusing on the application of NDVI and LST to estimate soil moisture levels. The findings provide valuable insights into the spatial distribution and temporal variations of soil moisture, facilitating informed decision-making in agriculture, water resource management, and environmental conservation in Kurnool District. Rising temperatures and shifting precipitation patterns, attributed to climate change, have exacerbated extreme weather events such as drought, significantly impacting agricultural production. Irregular rainfall and declining soil moisture levels have profound economic implications for nations. Soil moisture serves as a critical indicator for monitoring agricultural drought, influencing hydrological, ecological, and meteorological processes globally. Remote Sensing (RS) and Geographic Information System (GIS) techniques offer valuable insights into climate change analysis and soil moisture estimation through Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) calculations. Leveraging multispectral satellite data, particularly from LANDSAT 8, alongside GIS, this study assesses LST and NDVI to derive the Soil Moisture Index (SMI). The SMI quantifies soil moisture levels relative to field capacity and residual moisture, ranging from 0 (extreme dryness) to 1 (extreme wetness). The results show a strong correlation between NDVI, LST, and soil moisture. The study’s findings indicate that the integration of NDVI and LST data can improve soil moisture estimation accuracy, addressing a critical need in the context of global water scarcity and climate change. The novelty of this study lies in its application of geospatial technology for soil moisture estimation in a semi-arid region of India, providing valuable insights for agricultural planning, water resource management, and drought monitoring in the region.</p>

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Geospatial Technology Approach for Soil Moisture Estimation in Kurnool District, Andhra Pradesh, Using NDVI and LST

  • 期刊论文

  • 发表日期：

    2025年5月15日

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  • Sunandana Reddy Machireddy

摘要

Soil moisture is a critical component of the global water cycle, and its accurate estimation is essential for agricultural planning, water resource management, and drought monitoring. However, soil moisture estimation remains a significant challenge, particularly in semi-arid regions. Against this backdrop, this study demonstrates a geospatial technology approach for soil moisture estimation in Kurnool District, Andhra Pradesh, using Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data. The methodology integrates remote sensing data from Landsat 8 to develop a Soil Moisture Index (SMI) model which enables a comprehensive analysis of soil moisture dynamics in the region.A case study methodology is employed, focusing on the application of NDVI and LST to estimate soil moisture levels. The findings provide valuable insights into the spatial distribution and temporal variations of soil moisture, facilitating informed decision-making in agriculture, water resource management, and environmental conservation in Kurnool District. Rising temperatures and shifting precipitation patterns, attributed to climate change, have exacerbated extreme weather events such as drought, significantly impacting agricultural production. Irregular rainfall and declining soil moisture levels have profound economic implications for nations. Soil moisture serves as a critical indicator for monitoring agricultural drought, influencing hydrological, ecological, and meteorological processes globally. Remote Sensing (RS) and Geographic Information System (GIS) techniques offer valuable insights into climate change analysis and soil moisture estimation through Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) calculations. Leveraging multispectral satellite data, particularly from LANDSAT 8, alongside GIS, this study assesses LST and NDVI to derive the Soil Moisture Index (SMI). The SMI quantifies soil moisture levels relative to field capacity and residual moisture, ranging from 0 (extreme dryness) to 1 (extreme wetness). The results show a strong correlation between NDVI, LST, and soil moisture. The study’s findings indicate that the integration of NDVI and LST data can improve soil moisture estimation accuracy, addressing a critical need in the context of global water scarcity and climate change. The novelty of this study lies in its application of geospatial technology for soil moisture estimation in a semi-arid region of India, providing valuable insights for agricultural planning, water resource management, and drought monitoring in the region.