The influence of weather on geological remote sensing
- Suggested elective course
- Q4: Weather Impact Analysis
- Additional remarks
- Earlier work on this topic focused on the role of vegetation over time (Akhil Sampatirao, in 2019) , weather influences in arid Australia (Chamidu Gunaratne, in 2022) and correlation to atmospheric conditions (Jordan Nikolov, 2023). Processing multi-temporal Sentinel-2 data is best done in Google Earth Engine, for which scripting in Python or JavaScript will be necessary.
Rocks and minerals do not change by day or by season. However, the environment in which remote sensing images are acquired changes continuously. Major influences are the sun (illumination) , the atmosphere (weather) and resulting parameters such as soil moisture. How difficult is it to ignore rapidly changing external changing factors and to only monitor slow changes in surface mineralogy?
We would like to review the above concept by analyzing a long-term image time-series, thereby measuring a supposedly invariable surface (i.e., a rock or soil surface). The main question in this research is: How stable are mineral spectra over time? Does the key assumption, that the environment changes over time but the “rock signal” should not change, hold?
The following research question could be considered:
- which geological spectral indices are robust over time and which are not?
- can we distinguish which conditions interfere with mineral spectra most?
- does a standardized atmospheric correction help to reduce weather influence?
- do different climate zones require different approaches to data handling?
Multiple years of Sentinel-2 images are directly available in the Google Earth Engine. Spectral indices will be calculated to map exposed minerals. By analyzing the temporal behavior on different geographic locations, you can deduce how spectral indices change and what environmental parameter (sun angle, atmospheric composition, weather, soil moisture) may be the cause of that change. If needed, original Sentinel-2 data could be downloaded to determine the influence of ESA’s standard atmospheric correction. Also, weather model databases can be consulted as an independent source of information.
- Sampatirao, Akhil (2020). Incorporation of time in data analysis for surface mineralogical mapping with multispectral remote sensing. MSc thesis, University of Twente. (http://essay.utwente.nl/85173/).
- van der Werff, H.M.A., Ettema, J., Sampatirao, A. & Hewson, R. D. (2022). How weather affects over time the repeatability of spectral indices used for geological remote sensing. Remote Sensing.