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New satellite-based techniques open up new horizons to researchers and local communities. Concurrently, however, requirements and expectations with regard to satel-lite-based remote sensing products are increasingly higher. By relying on satellite-derived information, environmental observations can cover areas of a few to several metres resolution. Here we are dealing with free-of-charge and generally available sources of satellite-based information. The Piaśnica River mouth area was selected as an observation site representing a highly dynamic morphological transect. The paper compares products of cloud cover detection, supplied with data and available in the Copernicus database for a local area in the coastal zone of the Baltic Sea. The absolute difference did not exceed 5%, which confirms a high efficiency of the solutions offered. More than 96% of the clouded area determined for the Sentinel-2/MSI (Multispectral Instrument) was correctly identified when compared with supervised observations. The rate was lower (92%) for the Sentinel-3/OLCI (Ocean and Land Colour Instrument). It was eventually concluded that, at the local level, successful observations can be conducted using the cloud cover map supplied with the satellite data. At the same time, the analyses presented do not rule out further efforts to, e.g., increase the accuracy and speed of the analyses. [ABSTRACT FROM AUTHOR]

A very simple algorithm with which to assess cloudiness over the Baltic Sea has been developed. The algorithm involves analysis of cloud longwave emissivity, supplemented with available information on shortwave emissions. The simple classification method is based on the split-window technique and compares a signal recorded in the neighbouring spectral bands with results of cloud-free atmosphere modelling. The algorithm was tested using data from the SEVIRI (Spinning Enhanced Visible and InfraRed Imager) satellite radiometer, but the method can be easily implemented for other data sources. Compatibility with instant shipborne radiation measurements was obtained. The effectiveness of the comparison was determined from the Hanssen–Kuiper score. The CM SAF (Satellite Application Facility on Climate Monitoring) data comparison shows that the system correctly classified areas in 88%, the satellite map accuracy was 46% with a 54% overvaluation error. The accuracy of the detection system was 44%, and the underestimation error was 56%. The comparison of modelling results showed 71% of correctly classified areas, the accuracy of the satellite map was 35%, and the repricing error was 65%. The accuracy of the detection system was 36%, and the underestimation error was 64%. The concordance of satellite measurements (Cohen's kappa coefficient) was at a level of 98% and 99% between the satellite and model. Statistically, the analysis is based on the results of measurements for the Baltic Sea basin in 2015. The obtained data on the monthly cloudiness was compared with the operational, model and lidar data. A difference between the solution proposed and the approaches derived from prognostic models is demonstrated. The regional scale solution is shown to be a better representation of the true state of the atmosphere, compared to global-scale models. The results obtained show cloudiness assessments to differ depending on the model applied (CM SAF, UMPL – Unifed Model for Poland). The average annual cloudiness of the Baltic Sea is around 58.5%. It is a repeatable value by other solutions based on various methods and sources (MODIS – Moderate Resolution Imaging Spectroradiometer), including lidar ones (CALIPSO – Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation). In addition, a potential for applying the regional cloudiness assessment to estimate the top-down components of the radiation budget is illustrated. [ABSTRACT FROM AUTHOR]

The search for clouds in satellite images is a challenging subject which still attracts a lot of attention due to the amount and quality of data, which is growing at a tremendous pace, the development of satellite techniques and methods, inexpensive equipment, and automation of satellite imaging processes. This paper presents a new approach to the assessment of cloudiness based on the use of the theory of moments with invariants. The values of moments with invariants, determined on the basis of the available cloudiness maps, create a new, valuable set of data, which are the geometrical parameters of the scene representing the cloud cover. In further research, the obtained data sets will be used in machine learning methods, deep machine learning methods, etc. The method is used for different conditions, including different angular positions of the Sun and time periods. The effectiveness of the method is checked on the basis of comparing the entropy results of the input maps after subtracting clouds masked by various methods. The obtained results additionally indicate the potential of the moments method as a support for the existing methods of estimating cloudiness over the sea surface. [ABSTRACT FROM AUTHOR]