Why do I need Earth observation data processing?
While our eyes only detect a fraction of all light available, satellite sensors can actually capture – and send back – much more information. Furthermore, this information is relayed back to us in a format quite different from the photographs we are used to. For each band, satellites capture the spectral reflectance of the area within a specific narrow band of the light spectrum.
True-colour composite images use the red, green, and blue bands gathered by satellites to mimic the range of vision for the human eye, showing us images closer to what we would expect to see in a normal photograph.
Satellites also capture information in the non-visible part of the light spectrum. Different features: rock, bare soil, vegetation, burned ground, snow, sediment-rich water, etc. all have different reflectance properties in each band. This a called a 'spectral signature'.
To highlight specific features, one or more of the RGB bands can be substituted for another, such as infrared, or near infrared, which are not visible to the human eye. These images are referred to as false-colour images.
Additionally, to better discriminate between features and highlight changes in time, mathematical models can be applied to the data to produce a new kind of processed image. These are referred to as indexes.
When should I use MSAVI2?
The Second Modified Soil Adjusted Vegetation Index (MSAVI2) algorithm was was developed by Qi et al. (1994) as a recursion of MSAVI.
The difference between the Soil Adjusted Vegetation Index (SAVI) and MSAVI/MSAVI is the integration of a soil adjustment factor, determined by the relationship between the red and infrared bands. This new formula increases vegetation sensitivity and reduces the soil background effects.
Data processed using MSAVI/MSAVI2 represents vegetation greenness with values ranging from -1 (absence of green vegetation) to +1, with -1 (very green vegetation).
MSAVI/MSAVI2 are often used to look at vegetation cover, biomass and to determine leaf area index. They can also be used as an input layer for mapping land cover or vegetation classes.
In a study from Liu and Wang (2005), MSAVI was used to monitor desertification in China. Phillips et al. (2009) used biomass estimates derived from MSAVI and field data to estimate grazing capacity in northern U.S. prairies.
How to obtain MSAVI2 data using EarthCache
Note: If you do not have an account, you can sign up for one here.
To obtain a true-colour image using EarthCache, simply select MSAVI2 processing as an output when creating or editing a pipeline through the dashboard.
To obtain a true-colour image using code, you can create or edit a pipeline using the following parameters: