Surface water quality estimation using remote sensing in the Gulf of Finland and the Finnish Archipelago Sea

Abstract

This thesis deals with surface water quality estimation using remote sensing in the Gulf of Finland and the Archipelago Sea. Satellite remote sensing of water and empirical algorithms for surface water quality variables in coastal waters in the Gulf of Finland and the Archipelago Sea are explained and results from the studies in the area are presented.

Concurrent in situ surface water measurements, AISA data, Landsat TM data, ERS-2 SAR data, AVHRR and MODIS data were obtained for selected locations in the Gulf of Finland and the Archipelago Sea in August 1997 and from April to May 2000, respectively. The AISA, TM, SAR, AVHRR and MODIS data from locations of water samples were extracted and digital data were examined. Significant correlations were observed between digital data and surface water quality variables. Semi-empirical, simple and multivariate regression analyses, and neural network algorithms were developed and applied in the study area. Application of neural networks appears to yield a superior performance in modelling radiative transfer functions describing the relation between satellite observations and surface water characteristics. The results show that the estimated accuracy for major characteristics of surface waters using the neural network method is much better than retrieval by using regression analysis. Since radar observations of water are strongly affected by surface geometry but not by water quality, radar data should be useful to eliminate the effects of surface roughness from the results when combined with optical observations. However, our results suggest that microwave data improve estimation of water quality very little or not at all. The technique, however, should be examined with new data sets obtained under various weather and water quality conditions in order to estimate its feasibility for estimating surface water quality parameters in the Finnish coastal waters.