Chlorophyll-A Estimation in Turbid Waters Using Combined SAR Data with Hyperspectral Reflectance Data: A Case Study in Lake Taihu, China

Abstract

© 2008-2012 IEEE. The estimation of chlorophyll-A (chl-A) concentration remains a great challenge in turbid waters due to their complex optical conditions. To improve chl-A estimation, this study aims to determine whether combined use of polarimetric synthetic-Aperture radar (SAR) data has potential for improving the chl-A estimation from hyperspectral sensing reflectance for turbid waters such as those found in Lake Taihu, China. In situ measurements of hyperspectral reflectance data and water samples were collected over the lake corresponding to ENVISAT ASAR data. Semiempirical (two-band and three-band models) and empirical [multiple linear regression (MLR) and multilayer perceptron network (MLP)] models are compared to estimate the chl-A concentration from in situ hyperspectral reflectance and SAR data. The results show that there is a general underestimation of chl-A for concentrations higher than 26 ug/L, which is probably caused by the large spatial variation of chl-A in the study area. The results also demonstrate that the MLR model performs in a more stable manner than the MLP network does, while MLP underestimates low and high areas of chl-A concentrations in the lake. On the other hand, due to the availability of one scenic SAR data on the same day, our results show that the additional use of SAR data improved chl-A estimation very slightly in this case study, although the performance of vertical/vertical polarization SAR data was better than that of horizontal/horizontal polarization data in chl-A estimation. Potential future work in this subject could explore other measures of mutual information between SAR and hyperspectral optical data beyond the correlation and regression techniques described. Therefore, it is still necessary to apply more SAR data in varied turbid waters in the near future to determine how SAR data can be useful in the improvement of chl-A estimation.