Understanding Soil Indices from Multispectral Imagery and their Formulas and Functions in Python, MATLAB, and R

Learn about common soil indices used in remote sensing and how to implement them in popular programming languages.

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Multispectral imagery can be used to derive several soil indices, which provide information about soil properties and characteristics.

Some commonly used soil indices include:

1. Normalized Difference Soil Index (NDSI): This index uses the ratio of the difference between the near-infrared (NIR) and red bands to the sum of the NIR and red bands. NDSI is sensitive to soil moisture, organic matter content, and texture.
2. Soil Adjusted Vegetation Index (SAVI): This index takes into account the background soil reflectance and is more sensitive to vegetation cover. It is calculated as (NIR-Red)/(NIR+Red+L), where L is a constant that reduces the influence of soil brightness.
3. Soil-Adjusted Total Vegetation Index (SATVI): Similar to SAVI, this index accounts for soil brightness, but it also considers the blue band to reduce the effect of atmospheric interference. It is calculated as (NIR-Red)/(NIR+Red+L*Blue), where L is a constant.
4. Visible Atmospherically Resistant Index (VARI): This index uses the ratio of the difference between…