A topographically fragmental archipelago with dynamic waters set the
preconditions for assessing coherent remotely sensed information. We
generated a turbidity dataset for an archipelago coast in the Baltic Sea
from MERIS data (FSG L1b), using CoastColour L1P, L2R and L2W
processors. We excluded land and mixed pixels by masking the imagery
with accurate (1:10 000) shoreline data. Using temporal linear averaging
(TLA), we produced satellite-imagery datasets applicable to temporal
composites for the summer seasons of three years. The turbidity
assessments and temporally averaged data were compared to in situ
observations obtained with coastal monitoring programs. The ability of
TLA to estimate missing pixel values was further assessed by
cross-validation with the leave-one-out method. The correspondence
between L2W turbidity and in situ observations was good (r = 0.89), and even after applying TLA the correspondence remained acceptable (r = 0.78).
The datasets revealed spatially divergent temporal water
characteristics, which may be relevant to the management, design of
monitoring and habitat models. Monitoring observations may be spatially
biased if the temporal succession of water properties is not taken into
account in coastal areas with anisotropic dispersion of waters and
asynchronous annual cycles. Accordingly, areas of varying turbidity may
offer a different habitat for aquatic biota than areas of static
turbidity, even though they may appear similar if water properties are
measured for short annual periods.