Nutrient estimation by HJ-1 satellite imagery of Xiangxi Bay, Three Gorges Reservoir, China

Since its impoundment in 2003, the Three Gorges Reservoir (TGR) has brought far-reaching economic and other benefits to China. However, the resulting negative ecological and environmental impacts such as eutrophication and algal bloom have been problematic. This paper describes a study on nutrient estimation concerning eutrophication and algal bloom based on Huan Jing-1 (HJ-1) satellite imagery of the Xiangxi Bay (XXB), a tributary of the TGR in China. Models of nutrient concentration were established by correlation analysis between field measurement of nutrient concentration and the values obtained from the simultaneous satellite images of the study area. The results showed that total nitrogen (TN) had the best correlation with B1/(B3 + B4), with a maximal Spearman correlation coefficient of 0.885. Both nitrate (NO3−–N) and dissolved silicon (D-Si) had the best correlation with B1/(B2 + B4), with the maximal Spearman correlation coefficients of 0.818 and 0.756, respectively. All band combinations had little correlation with NH4+–N, TP, and PO43−–P. The single variable quadratic models were established for predicting the concentration of TN and D-Si, while, a single variable cubic model was for the concentration of NH4+–N. Robustness analysis and validation were undertaken for the feasibility of the models, which showed that the predicted models were acceptable. Through the models, the levels of TN and NO3−–N were predicted on Jan 12, May 2, July 30 and Oct 8, 2010. The results showed that the water in the XXB was eutrophic mostly. The nitrogen level was higher on Jan 12 than that of May 2, July 30 and Oct 8. Compared with the upstream, the downstream of the XXB had higher nitrogen level on Jan 12 and May 2, whereas, on July 30, it was higher for the upstream. The nitrogen level was lowest on Oct 8, with the uniform distribution in the upstream and downstream of the XXB. The predicted results coincided with the field observation. The results will help to estimate the eutrophication and algal bloom through large-scale remote sensing monitoring and water quality management in the TGR.