Improved cloud detection over sea ice and snow during Arctic summer using MERIS data

• Published in: Atmospheric Measurement Techniques
• Main Authors: Istomina, Larysa, Marks, Henrik, Huntemann, Marcus, Heygster, Georg, Spreen, Gunnar
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2020
• Subjects: article; Verlagsveröffentlichung; albedo; Sea ice
• Online Access: https://doi.org/10.5194/amt-13-6459-2020; https://noa.gwlb.de/receive/cop_mods_00054864; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00054515/amt-13-6459-2020.pdf; https://amt.copernicus.org/articles/13/6459/2020/amt-13-6459-2020.pdf

The historic MERIS (Medium Resolution Imaging Spectrometer) sensor on board Envisat (Environmental Satellite, operation 2002–2012) provides valuable remote sensing data for the retrievals of summer sea ice in the Arctic. MERIS data together with the data of recently launched successor OLCI (Ocean and Land Colour Instrument) on board Sentinel 3A and 3B (2016 onwards) can be used to assess the long-term change of the Arctic summer sea ice. An important prerequisite to a high-quality remote sensing dataset is an accurate separation of cloudy and clear pixels to ensure lowest cloud contamination of the resulting product. The presence of 15 visible and near-infrared spectral channels of MERIS allows high-quality retrievals of sea ice albedo and melt pond fraction, but it makes cloud screening a challenge as snow, sea ice and clouds have similar optical features in the available spectral range of 412.5–900 nm. In this paper, we present a new cloud screening method MECOSI (MERIS Cloud Screening Over Sea Ice) for the retrievals of spectral albedo and melt pond fraction (MPF) from MERIS. The method utilizes all 15 MERIS channels, including the oxygen A absorption band. For the latter, a smile effect correction has been developed to ensure high-quality screening throughout the whole swath. A total of 3 years of reference cloud mask from AATSR (Advanced Along-Track Scanning Radiometer) (Istomina et al., 2010) have been used to train the Bayesian cloud screening for the available limited MERIS spectral range. Whiteness and brightness criteria as well as normalized difference thresholds have been used as well. The comparison of the developed cloud mask to the operational AATSR and MODIS (Moderate Resolution Imaging Spectroradiometer) cloud masks shows a considerable improvement in the detection of clouds over snow and sea ice, with about 10 % false clear detections during May–July and less than 5 % false clear detections in the rest of the melting season. This seasonal behavior is expected as the sea ice surface is generally ...