Remote Sensing Data Fusion for Earth Science Application Using Python

It is important to monitor Earth system data for both research and scientific reasons — analysis of such data furthers understanding of the planet and better informs political, economic, and policy decisions. Thus, in an effort to aid the Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, this project developed Python code for fusing six satellite Level 2 aerosol data (three are from geostationary satellites (GEO), and other three are from low earth orbital satellites (LEO)) from Dark Target Aerosol Retrieval Algorithm. This work emulated existing IDL workflow, which was originally created with IDL (interactive data language) by the science team. The Python code used packages such as numpy, matplotlib, nctoolkit, and gdal among others for calculation, manipulation, and visualization processes. Upon receiving a list of individual sensor data and user-specified parameters, data would be gridded and "fused" along a generated time dimension in accordance to user specification. There are two types of output format that may be selected: NetCDF or GeoTIFF. Quality checking was done by comparison to Panoply results and netCDF output matching through Python. Fused datasets generated by the program allow for visualization and analysis of the global aerosol data record over specific time periods. It also aids in research and analysis as users can better manipulate and work with satellite and sensor data. By making such code, and the accompanying functionality, open source and scalable, the scientific community is granted easier access to aerosol data processing resources.