Brief Overview

Understanding the sub surface and near surface properties of MARS is critical to many exploration attempts to the planet. EMM gives us access to explore the changes in thermal inertia on MARS and explore the variations seasonally, annually to identify sub surface properties. 

Challenge

How reliable is the thermal inertia calculated using EMM surface temperature? EMM gives an unprecedented global coverage of MARS over multiple years at all local times. Can we use this data to monitor thermal inertia across seasons and years giving us insights into sub surface ice which can help identify ideal landing sites on MARS for future missions and bases?

Solution

Build an EMM thermal inertia pipeline for EMIRS data using the KRC model. We compare the gridwise maps first with THEMIS and TES (Mellon) Maps to establish reliability (Increased correlation between maps by 40% identifying multiple sources)

Build year on year / season on season thermal inertia maps of MARS using the EMM thermal inertia pipeline using the unprecedented advantage afforded by EMM. The variability is analyzed to determine areas of sub-surface ice. 

New thermal inertia maps based on EMM data to explore landing sites for future missions and bases

Reliable, re-usable pipelines to extract EMM thermal inertia data

Sub-surface ice maps with global coverage

Topics: 

Data Ingestion, Data Sourcing, API, Geospatial Data Analysis, Data Mining, Statistical Analysis, Image Data processing

Tools:

python, EMM SDC api, pandas, matplotlib, seaborn, astropy, numpy, KRC - Thermal Inertia Model, bash scripting, job arrays, HPC Jubail, NASA pdr library

Data:

Large Format Image Data (Themis), Mellon TI Map - .img files, Data from KRC model - TI output stored as csv

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