Surface water quality estimation using remote sensing in the Gulf of Finland and the Finnish Archipelago Sea
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Doctoral thesis (article-based)
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Date
2005-06-17
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Language
en
Pages
82, [app]
Series
Report / Helsinki University of Technology, Laboratory of Space Technology, 55
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.Description
Keywords
water quality monitoring, remote sensing retrieval, coastal waters, regression and neural network algorithms
Other note
Parts
- Zhang, Y., Pulliainen, J., Koponen, S., and Hallikainen, M., 2003. Empirical algorithms for Secchi disk depth using optical and microwave remote sensing data from the Gulf of Finland and the Archipelago Sea. Boreal Environment Research, vol. 8, no. 3, pp. 251-261.
- Zhang, Y., Koponen, S., Pulliainen, J., and Hallikainen, M., 2003. Application of empirical neural networks to chlorophyll-a estimation in coastal waters using remote optosensors. IEEE Sensors Journal, vol. 3, no. 4, pp. 376-382.
- Zhang, Y., Pulliainen, J., Koponen, S., and Hallikainen, M., 2003. Water quality retrievals from combined Landsat TM data and ERS-2 SAR data in the Gulf of Finland. IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 3, pp. 622-629.
- Zhang, Y., Pulliainen, J., Koponen, S., and Hallikainen, M., 2002. Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data. Remote Sensing of Environment, vol. 81, no. 2-3, pp. 327-336.
- Zhang, Y., Pulliainen, J., Koponen, S., and Hallikainen, M., 2002. Water quality studies of combined optical, thermal infrared, and microwave remote sensing. Microwave and Optical Technology Letters, vol. 34, no. 4, pp. 281-285.
- Zhang, Y., Pulliainen, J., Koponen, S., and Hallikainen, M., 2002. Detection of sea surface temperature (SST) using infrared band data of Advanced Very High Resolution Radiometer (AVHRR) in the Gulf of Finland. International Journal of Infrared and Millimeter Waves, vol. 23, no. 10, pp. 1407-1412.