Water indices for surface water extraction using geospatial techniques: a brief review

Water is essential for global water cycle. This natural wealth is invaluable. Water is used for drinking, making food, to make a living, and to maintain good health. The life quality of the global natural ecosystem and the regulatory frameworks of the climate depend on water bodies like rivers, river basins, lakes, glacial lakes, ponds, watersheds, reservoirs, dams, aquifers, and wetlands. Recent population developments have been accelerating urbanization. The present natural resources are under extreme strain due to the rising demand of water. Therefore, monitoring, regulating, and conserving our water supplies becomes vital, including map and manage the current water bodies. Remote sensing (RS) and Geographical Information System (GIS) techniques could be applied to accomplish this. The present study’s research methodology is to examine existing water extraction indices and their evaluation utilized to determine the spatial distribution and alterations of water bodies. It has been discovered that novel spectral water indices are extracting water bodies more effectively than conventional approaches since they use multispectral, multiband satellite data, which can provide a more precise result. The most popular traditional spectral water indices for extracting water bodies are Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Enhance Water Index (EWI) and Automated Water Extraction Index (AWEI). We conclude that there are various algorithms existing for surface water extraction and their effectiveness varies on spectral indices that uses satellite bands available in various resolutions. These methods have its own high-quality benefits, including low operational expense, straightforward in significance, absolutely modifiable and efficient in delivering satisfactory results. However, these techniques are not universally recognized because they are reliant on the characteristics of the land classes. New methods that have been developed recently are producing more accurate results than those that were produced by the traditional methods.