Satellite Imagery Analysis in Python Part III: Land Surface Temperature and The National Land Cover Database (NLCD)

The third entry of the satellite imagery analysis in Python uses land surface temperature (LST) as the data variable along with land cover information from the national (U.S.) database. The land cover information will allow us to create a relationship between land cover type and its respective heating (or cooling) contribution to the earth’s surface. Land cover is used in many applications ranging from algorithm development to military applications and crop surveying, not to mention applications in water management and drought awareness.

Satellite Imagery Analysis in Python Part I: GOES-16 Data, netCDF Files, and The Basemap Toolkit

In this tutorial series, Python’s Basemap toolkit and several other libraries are utilized to explore the publicly-available Geostationary Operational Environmental Satellite-16 (GOES-16). In this first entry, the following will be introduced: acquisition of satellite data, understanding of satellite data files, mapping of geographic information in Python, and plotting satellite land surface temperature (LST) on a map.

GOES-R Satellite Latitude and Longitude Grid Projection Algorithm

Calculating latitude and longitude from a GOES-R L1b data file. The GOES-R L1b radiance files contain radiance data and geometry scan information in radians. This information is not enough to plot geographic radiance data right from the file, however, after some geometric manipulation harnessing satellite position and ellipsoid parameters, we can derive latitude and longitude values from the one-dimensional scan angles and plot our data in projected formats familiar to many geographic information tools.