[Remote Sensing Estimation of Chlorophyll-a Concentration in Inland Lakes Based on GOCI Image and Optical Classification of Water Body].

Chlorophyll-a as one of the important water quality parameters is often used as a measure of the level of water eutrophication. The 326 measured data collected from Lake Taihu and Lake Dongting were classified based on their measured values of remote sensing reflectance spectra using an automatic clustering algorithm-two-step method, and three water types were finally classified. According to the location and width of GOCI satellite bands, the specific algorithm to estimate chlorophyll-a concentration for different water body types was developed. The bands at 490 nm and 555 nm were used for water body type I , while bands at 660 nm and 443 nm were selected for water body type II and bands at 745 nm and 680 nm were applied for water body type III. The accuracy assessment showed that the mean relative error decreased from 49. 78% to 38. 91% , 24. 19% and 22. 90% for water body type I , II and III, respectively, while the root mean square error decreased from 14.10 µg · L(-1) to 4.87 µg · L(-1), 8.13 µg · L(-1) and 11.66 µg · L(-1) for water body type I, II and III, respectively. The overall mean relative error decreased from 49. 78% to 29. 59% after classification, while the overall root mean square error was reduced from 14.10 µg · L(-1) to 9.29 µg · L(-1) after classification. The retrieval accuracy was significantly improved after classification. The chlorophyll-a concentration in Lake Taihu was retrieved using the GOCI image on May 13, 2013. The results showed that there was a significantly diurnal variation in the concentration of chllorophyll-a on May 13, 2013, and the regions with higher chlorophyll-a concentration were mainly distributed in the Zhushan Bay, Meiliang Bay and Gonghu Bay, while the regions with lower values were mainly located in the centre of the lake and the southern region. The chlorophyll-a concentration reduced significantly after 10:00 in the southwestern region of Lake Taihu. This method of retrieving, after classification played an important role in improving the model retrieval accuracy of case 2 water.