BOREAL ENVIRONMENT RESEARCH 8: 251–261 ISSN 1239-6095 Helsinki 29 September 2003 © 2003 Empirical algorithms for Secchi disk depth using optical and microwave remote sensing data from the Gulf of Finland and the Archipelago Sea Yuanzhi Zhang, Jouni Pulliainen, Sampsa Koponen and Martti Hallikainen Laboratory of Space Technology, Helsinki University of Technology, P.O. Box 3000, FIN-02015 HUT, Finland Zhang, Y., Pulliainen, J., Koponen, S. & Hallikainen, M. 2003: Empirical algo- rithms for Secchi disk depth using optical and microwave remote sensing data from the Gulf of Finland and the Archipelago Sea. Boreal Env. Res. 8: 251– 261. ISSN 1239-6095 In this paper empirical algorithms for determining the Secchi disk depth (SDD) are developed and employed using optical (e.g., Landsat TM) and microwave (e.g., ERS-2 SAR) remote sensing data from the Gulf of Finland and the Archipelago Sea. The SDD is an important optical measure of water quality in the study area, where the coastal water considerably attenuates light because of the presence of phytoplankton, suspended matter and yellow substance. The results show that the accuracy of SDD estimation using a neural network-based method is much higher than that of a semi- empirical or multivariate approach. On the other hand, the additional use of SAR data only slightly improved SDD estimation when compared with the use of TM data only. Although the improvement is marginal, the results suggest that there may be some SAR backscattering signatures correlated to SDD measurements in the area. However, such a small improvement is not very helpful for the practical estimation of SDD. In the future, the technique of using combined optical and microwave data still needs to be refined using, e.g., MERIS and ASAR data. Introduction Up to the present, the digital evaluation of satel- lite sensorsʼ information at visible and near infra- red (NIR) wavelengths has been used to estimate water quality variables (see e.g. Klemas et al. 1974, Alföldi and Munday 1978, Moore 1980, Shih and Gervin 1980, Carpenter and Carpenter 1983, Verdin 1985, Ferrari et al. 1996). These investigations suggest that Landsat TM can pro- vide relatively low-cost, simultaneous informa- tion on surface water conditions from numerous lakes and coastal areas situated within a large geographic area (Lathrop and Lillesand 1986,