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One of the most promising new applications of remote observation satellite systems (RO) is the near-shore bathymetry estimation based on spaceborn multispectral imageries. In recent years, many experiments aiming to estimate bathymetry in optically shallow water with the use of remote optical observations have been presented. In this paper, optimal models of satellite derived bathymetry (SDB) for relatively turbid waters of the South Baltic Sea were presented. The obtained results were analysed in terms of depth error estimation, spatial distribution, and overall quality. The models were calibrated based on sounding (in-situ) data obtained by a single-beam echo sounder, which was retrieved from the Maritime Office in Gdynia, Poland. The remote observations for this study were delivered by the recently deployed European Space Agency Sentinel-2 satellite observation system. A detailed analysis of the obtained results has shown that the tested methods can be successfully applied for the South Baltic region at depths of 12-18 meters. However, significant limitations were observed. The performed experiments have revealed that the error of model calibration, expressed in meters (RMSE), equals up to 10-20% of the real depth and is, generally, case dependent. To overcome this drawback, a novel indicator of determining the maximal SDB depth was proposed. What is important, the proposed SDB quality indicator is derived only on the basis of remotely registered data and therefore can be applied operationally
Over the past few years considerable advances in sonar technology, spatial positioning capabilities and computer processing power have lead to significant improvements in mapping, imaging and technologies of seafloor exploration. Recently, modern multibeam echosounder systems (MBES) capable of recording backscatter data for the whole water column, not just for the seabed, have become available thus providing data allowing for visualization and analysis of objects other than the seabed such as single fish, fish schools or pollution. Unlike bathymetric sonars, which only capture the seafloor, multibeam systems produce very large amounts of data during surveys. Because of this, storing the data collected during hydrographic or scientific cruises becomes a crucial problem. In this context, the paper proposes a new approach for efficient reduction and storage of MBES records. The results of a sample implementation of the algorithm being tested on several different sets of MBES data are also discussed
Estimation of surface temperature using multispectral imagery retrieved from satellite sensors constitutes several problems in terms of accuracy, accessibility, quality and evaluation. In order to obtain accurate results, currently utilized methods rely on removing atmospheric fluctuations in separate spectral windows, applying atmospheric corrections or utilizing additional information related to atmosphere or surface characteristics like atmospheric water vapour content, surface effective emissivity correction or transmittance correction. Obtaining accurate results of estimation is particularly critical for regions with fairly non-uniform distribution of surface effective emissivity and surface characteristics such as coastal zone areas. The paper presents the relationship between retrieved land surface temperature, air temperature, sea surface temperature and vegetation indices (VI) calculated based on remote observations in the coastal zone area. An indirect comparison method between remotely estimated surface temperature and air temperature using LST/VI feature space characteristics in an operational Geographic Information System is also presented
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