Regional water quality mapping through the assimilation of spaceborne remote sensing data to ship‐based transect observations

Abstract

Satellite observations and surface‐observed transect data are combined to assess the spatial characteristics of water quality in the Baltic Sea, northern Europe. The technique that optimally assimilates the two data sources is based on Bayes' theorem. The method considers the spatial accuracy characteristics of both transect and satellite data. The mathematical formulation of the method is generally applicable. Hence it can be applied to other environmental monitoring problems as well. This investigation demonstrates its feasibility for water quality mapping. The characteristic under investigation is chlorophyll (chl a) concentration, which is currently operationally monitored in the Baltic Sea at shipborne Alg@line transects, using flow‐through fluorometer sampling systems. In order to optimally extrapolate transect observations the developed method benefits from the full spatial coverage of spaceborne optical spectroradiometer data. The results show that the estimation accuracy of regional chl a concentration during the spring bloom period substantially improves as satellite observations are assimilated to transect data. The assimilation results show significantly higher accuracies than those obtained using either extrapolated transect data or stand‐alone retrieval algorithms for satellite data.