Internship: Correction and evaluation of the topography induced-biases on satellite observations of the snow surface temperature
5/6 months – February to July 2022
Context and goal
The Trishna satellite, which will be launched at the end of 2024, will soon provide high resolution thermal infrared images at a global scale every three days, halving both the resolution and the revisit time of the satellites currently in orbit. These images represent a major opportunity to accurately and extensively monitor the surface temperature of snow, which is a fundamental parameter of the surface energy budget of snow covered areas. However, the retrieval of the snow surface temperature in mountainous areas is challenging because of the modifications induced by topography on the radiative fluxes and on the snow emissivity. The goal of this internship is to implement multiple topographic corrections into an atmospheric correction algorithm for the retrieval of the surface temperature over mountainous, snow-covered areas. The relevance and relative importance of the topography-induced biases will be evaluated by comparing corrected satellite products with in-situ observations of the snow surface temperature.
Missions
- The candidate will implement an atmospheric correction algorithm for the surface temperature retrieval from the thermal infrared top-of-atmosphere radiance measurements from satellite. The algorithm will account for the modifications of the downwelling longwave radiance, the atmospheric composition and emissivity induced by topography with respect to a flat surface. The algorithm will be developed for Landsat 8 and 9 TIRS images and eventually adapted to Landsat 7’s ETM+ and Ecostress.
- The algorithm will be applied to thermal infrared radiances acquired by space-borne sensors during different stages of the winter season. The results will be compared to in-situ observations of the surface temperature acquired using thermal infrared cameras and radiometers. The candidate will use in-situ data to evaluate the relative importance of the topography induced biases.
Highlights : autonomy in the use of Python, knowledge of radiative transfer and geospatial data processing
Research unit : IGE
Supervisor : Sara Arioli
Co-supervisor : Ghislain Picard
Contact : sara.arioli@univ-grenoble-alpes.fr
Place : Grenoble
Education : Master in atmospheric sciences or any other related topic
Keywords : Remote sensing, Atmosphere, Snow, Temperature
