LAUSR

Owing to limitations in monitoring technologies, monitoring the algae content index of water has lagged behind the conventional water quality index. As a result, sample monitoring in many rivers has been too sparse, and the monitoring data have been inconsistent; thus the evolution of water eutrophication has not been fully reflected. This study focused on the middle and lower reaches of the Hanjiang River, China, and correlated moderate-resolution imaging spectroradiometer (MODIS) remote sensing data with measured chlorophyll-a concentrations. Algorithm settings for chlorophyll-a inversion in the middle and lower reaches of the Hanjiang River were established via the trial and error method. The algorithm model for the middle and lower reaches of the Hanjiang River chlorophyll-a concentration inversion, and the results of the inversion analysis for the spatiotemporal evolution characteristics were subsequently used to determine the influence of various environmental factors on changes in the chlorophyll-a concentration. The results indicate that (1) the band combinations B7/(B6 + B5), B7/B5, B4-B2, and B4/(B3 + B2) are well-correlated with the chlorophyll-a concentration; (2) the back propagation (BP) neural network model inversion achieved a better fit and more accurate inversion results than the band ratio model; (3) temporally, algal outbreaks were mostly concentrated occurring in February and March, with higher chlorophyll-a concentrations in the water column during 2000, 2006, 2007, and 2008; (4) spatially, high chlorophyll-a concentrations were observed in the Zhongxiang, the Shayang, and upper Xiantao sections; and (5) increases in the water temperature and decreases in the water level and flow rate could lead to higher chlorophyll-a concentrations; similarly, nutrient salts were identified to be a major factor contributing to changes in the chlorophyll-a concentrations.