Chlorophyll a (Chla) is an important indicator of phytoplankton biomass in waters, and its concentration can reflect the degree of eutrophication. This paper is aimed to develop a highly accurate and universally applicable retrieval model for the concentration of Chla in rivers using remote sensing data. Taking the middle and lower reaches of the Han River as the study area, the Chla retrieval model (VIP-BP model) is established by combining the Variable Importance Projection Index and BP neural algorithm and then calibrated by the measured data from 2012 to 2013. This model uses the VIP index for selection of the appropriate spectrum transformation form and input bands. Then, the BP neural network algorithm is integrated to estimate Chla concentration. After validation and comparison with the three-band model, the results suggest that the VIP-BP model could more accurately and really reflect the changes in Chla concentration than the three-band model in the study area. When Chla concentration decreases, the retrieval error of both models increases, while the error of the VIP-BP model is significantly lower than that of the three-band model, which indicates that the VIP-BP model is more stable and preferred.
Seasonality of the vertical distribution of chlorophyll-a in the sublatitudinal zones of the Antarctic part of the Atlantic according to direct and remote sensing data
