Some of the water indexing methods are,

• Automated Water Extraction Index (AWEI)
• Modified Normalized Difference Water Index (MNDWI)
• Normalized Difference Infrared Index (NDII)
• Normalized Difference Laplacian of Water Index (NDLWI)
• Normalized Difference Moisture Index (NDMI)
• Normalized Difference Water Index (NDWI)
• Water Band Index (WBI)
• Water Cloud Index (WCI)
• Water Index (WI)

U P B Herath

The most common and established methods for remotely sensing surface water bodies using multispectral and SAR data include reflectance measurements, radiative transfer modeling, feature extraction, classification, and water body detection (WBD) techniques.

For reflectance measurements, the most commonly used techniques are based on the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (NDWI).

These techniques are used to identify water bodies by calculating the ratios between specific bands of a multispectral image.

Radiative transfer modeling is another established method for remotely sensing surface water bodies.

This technique involves the simulation of the interaction between the electromagnetic radiation in the atmosphere and the surface of the Earth. Feature extraction is a method for extracting geometric features from remotely sensed images, such as the shape, size, and texture of water bodies.

These features can then be used to classify water bodies in an image.

Classification is another established method for remotely sensing surface water bodies.

This technique involves the use of supervised or unsupervised classification algorithms to identify water bodies in an image. Finally, water body detection (WBD) techniques are used to detect and map water bodies in an image. The most commonly

Multispectral and Synthetic Aperture Radar (SAR) data are commonly used for remote sensing surface water bodies. Here are some established methods that utilize these data:

Methods:

1 - Reflectance measurements: This method involves measuring the reflectance of water bodies at different wavelengths. This can be done using sensors such as Landsat, Sentinel-2, or MODIS.

2 - Radiative transfer modeling: This method involves modeling the way light interacts with water and other materials in the environment. This can be used to estimate the amount of water present in a given area.

3 - Feature extraction: This method involves identifying specific features of water bodies in multispectral or SAR data, such as their shape or texture.

4 - Classification: This method involves using machine learning algorithms to classify pixels in multispectral or SAR data as water or non-water.

Water Body Detection (WBD): This method involves detecting water bodies in SAR data based on their backscatter properties.

Indexes:

1 - Normalized Difference Water Index (NDWI): This index uses near-infrared and green wavelengths to highlight water bodies in multispectral data.

2 - Modified Normalized Difference Water Index (MNDWI): This index is similar to NDWI, but uses green and mid-infrared wavelengths.

3 - Automatic Water Extraction Model in Complex Environment (AWECE): This index is designed specifically for SAR data and uses a combination of backscatter intensity and texture to detect water bodies.

Some references that may be helpful for further reading include:

• Gao, B. (1996). NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257-266.
• Xu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025-3033.
• Lin, H., Chen, X., & Wang, C. (2019). A review of water extraction from SAR images. Remote Sensing, 11(6), 704.
• Zhang, C., & Chen, Y. (2019). Automatic water extraction from TerraSAR-X images using AWECE and threshold-based approaches. Remote Sensing, 11(14), 1633.

I hope this helps!

Often a simple threshold of one of several indexes will do it.