Water quality observation using Principal Component Analysis

The aim of the present study is to define and tentatively to interpret the distribution of polluted water released from Lake Sihwa into Yellow Sea using Landsat TM. Since the region is an extreme case 2 water, empirical algorithms for chlorophyll-a and suspended sediments have limitations. This work focuses on the use of multi-temporal Landsat TM. We applied PCA to detect evolution of spatial feature of polluted water after release from the lake. The PCA results were compared with in situ data, such as chlorophyll-a, suspended sediments, Secchi disk depth (SDD), surface temperature, radiance reflectance at six bands. The in situ remote sensing reflectance was analysed with PCA. On the basis of these In situ data we found good correlation between first Principal Component and Secchi disk depth ($R^2$=0.7631), although other variables did not result in such a good correlation. The problems in applying PCA techniques to multi-spectral remote sensed data are also discussed.