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Shadow

Understanding the impact of shadows in satellite remote sensing of aerosols and clouds

Doctoral Thesis (2025)
Author(s)

V. Trees (TU Delft - Atmospheric Remote Sensing)

Contributor(s)

AP Siebesma – Promotor (TU Delft - Geoscience and Remote Sensing)

Stephan De Roode – Copromotor (TU Delft - Atmospheric Remote Sensing)

Research Group
Atmospheric Remote Sensing
More Info
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Publication Year
2025
Language
English
Related content
Research Group
Atmospheric Remote Sensing
Bibliographical Note
Met dank aan: Dr. P.Wang, KNMI, external advisor and daily supervisor Dr. K.F. Boersma, KNMI/WUR, external advisor Dr. ir. C.C. van Heerwaarden, WUR, external advisor Prof. dr. ir. H.W.J. Russchenberg, TU Delft, external advisor Dr. D.M. Stam, Leiden University, external advisor@en
ISBN (print)
978-90-835486-7-8
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Abstract

Earth observation satellites rely on sunlight reflected by the Earth to retrieve information about atmospheric constituents and clouds. However, radiative transfer models used in satellite algorithms often neglect shadow effects, introducing potential biases. This thesis investigates the impact of both lunar (solar eclipse) and cloud shadows on satellite-based atmospheric measurements and proposes correction strategies to improve data accuracy.
We develop a method to correct for lunar shadow effects during solar eclipses by calculating wavelength-dependent solar obscuration, including solar limb darkening. Applying this method to TROPOMI/S5P data, we correct ultraviolet Aerosol Absorbing Index (AAI) anomalies during the annular eclipses of December 2019 and June 2020, restoring geophysical features like sunglint and desert dust. No evidence of eclipse-induced aerosol changes is found. Similarly, we apply the correction to cloud retrievals, revealing that shallow cumulus clouds begin to dissipate at solar obscurations as low as ~15%, a phenomenon supported by large-eddy simulations. Ignoring this effect may lead to overestimates of eclipse-induced radiative forcing.
We also introduce DARCLOS, the first cloud shadow detection algorithm for a spaceborne spectrometer. Using geometric and spectral criteria, DARCLOS identifies potential and actual cloud shadow pixels in TROPOMI data. We validate this method using VIIRS imagery, though cloud evolution limits validation accuracy. Applying DARCLOS to eight months of TROPOMI AAI data over Europe, we quantify cloud shadow signatures and simulate them with our new 3D radiative transfer model, MONKI. Despite cloud shadows significantly altering the UV reflectance ratio, the AAI computation inherently compensates for most effects. Remaining second-order biases are small and dependent on cloud properties, making a full correction challenging and possibly unnecessary.
This work improves our understanding of shadow impacts on satellite retrievals and provides tools to mitigate or study their influence, enhancing the reliability of atmospheric observations from space.