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Remote sensing technology is a crucial asset in assessing and monitoring the condition of surface and groundwater, offering a cost-effective and time-efficient way to gather data over vast areas. This chapter investigates the application of remote sensing techniques in evaluating and tracking water quality over large regions, with a particular emphasis on surface and groundwater conditions, and water and salinity indicators. Essential, indices like the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Normalized Difference Turbidity Index (NDTI) are vital for locating and outlining water bodies and identifying water quality characteristics such as turbidity. Additionally, the chapter explores indices designed for salinity assessment, including the Normalized Difference Salinity Index (NDSI) and other Salinity Indices (SIs), utilizing satellite data. Advanced tools like Google Earth Engine are employed for processing and analyzing spatio-temporal remote sensing datasets. A practical case study is presented to demonstrate the real-world utility of these indices, highlighting their effectiveness in evaluating water quality and salinity levels.

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Remote Sensing and Google Earth Engine for Surface and Groundwater Quality Assessment

  • 章节

  • 发表日期：

    2025年

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  • Nobin Chandra Paul,
  • Rahul Banerjee,
  • P. Navyasree,
  • Bharti,
  • Pankaj Das,
  • Tauqueer Ahmad

摘要

Remote sensing technology is a crucial asset in assessing and monitoring the condition of surface and groundwater, offering a cost-effective and time-efficient way to gather data over vast areas. This chapter investigates the application of remote sensing techniques in evaluating and tracking water quality over large regions, with a particular emphasis on surface and groundwater conditions, and water and salinity indicators. Essential, indices like the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Normalized Difference Turbidity Index (NDTI) are vital for locating and outlining water bodies and identifying water quality characteristics such as turbidity. Additionally, the chapter explores indices designed for salinity assessment, including the Normalized Difference Salinity Index (NDSI) and other Salinity Indices (SIs), utilizing satellite data. Advanced tools like Google Earth Engine are employed for processing and analyzing spatio-temporal remote sensing datasets. A practical case study is presented to demonstrate the real-world utility of these indices, highlighting their effectiveness in evaluating water quality and salinity levels.