Today many different indices exist and each one has its own significance in the study. Here are the most popular indices that be calculated from free data satellites.
The Normalized Difference Water Index (NDWI) is remote sensing derived index estimating the leaf water content at the canopy level.
EVI is similar to Normalized Difference Vegetation Index (NDVI) and can be used to quantify vegetation greenness. However, EVI corrects for some atmospheric conditions and canopy background noise and is more sensitive in areas with dense vegetation.
Normalized Difference Vegetation Index (NDVI) quantifies vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs).
There are several ways to use GEE and each one has its advantages and disadvantages. In this example, GEE is used for web mapping with python Django.
Extract Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset using Google earth engine API and convert it into. Excel and charts using Python Pandas data frame.
The simplest way to convert shapefile, CSV it into ee.FeatureCollection is to infuse the Geopandas data frame with GEE python API.
Variation in the reflectance within the same land cover type can be seen in mountain areas caused due to sun position, slope, and aspect of the landform.
While using remote sensing data several pre-processing steps can be applied that aim at removing artifacts from the images that are not related to the actual reflectance of the land cover such as sensor effects, atmospheric, and illumination conditions.