Machine Learning Application for Improving Cloud Detection and Phase Determination Over Sunglint Regions for Geostationary Satellites

Cloud detection and phase determination over sunglint regions has been a challenge, especially for geostationary (GEO) satellites. Sunglint is observed when the sunlight specular reflection is at the same viewing angle of the satellite sensor. This intense reflection in the visible channels (VIS) is often comparable to that from optically thick clouds. It also contaminates the shortwave infrared channels (SWIR). Consequently, VIS and SWIR channels become less useful - or not useful- when they are saturated, hampering the detection of cloudy and clear-sky pixels. Sunglint contamination happens frequently and exists nearly in every daytime GEO full disk satellite images. However, sunglint intensity and region are difficult to model due to variable viewing geometry and ocean surface conditions. Moreover, existing physical models do not meet the accuracy required for operational GEO satellite cloud detection.

We developed a machine learning algorithm to improve cloud detection in sunglint conditions for the NASA Langley’s Satellite ClOud and radiation Property retrieval System (SatCORPS). This poster presents our recent progress in the algorithm development, validation and applications. The algorithm is validated using collocated SatCORPS GOES-East and GOES-West cloud products. We demonstrate that the machine learning cloud detection in sunglint regions is superior to the traditional approach by improving temporal consistency between sunglint and non-sunglint conditions.