Water Chlorophyll Concentration Estimation Using Landsat TM Data with Empirical Algorithms in Chagan Lake, China

摘要

one of important parameters used for monitoring inland water quality is the concentration of chlorophyll in surface water. Remote sensing provides a convenient and synoptic method for determining water chlorophyll concentration from upwelling radiances. In this study, a scene of Landsat TM data and concurrent in situ water sample were acquired. As neural networks has been proven successful in modeling a variety of geophysical transfer functions. So in this paper, both regression model and empirical neural network were established to model the relationship between water chlorophyll concentration and satellite-received radiances. It was found that neural network model play much better in accuracy than regression model with Landsat TM visible and near infrared bands as inputs. The relative RMSE for the neural network were < 7%, while the RMSE for regression analysis were less than 25% in general. The dynamic characteristic of Chagan Lake water chlorophyll concentration was simply analyzed, but more work still need to be done to explore the reason for Chagan water chlorophyll content, and the algorithms developed in this study need to be tested with future collected imagery data and in situ samples data.