Estimation of chlorophyll-a Concentration of lakes based on SVM algorithm and Landsat 8 OLI images.

Chlorophyll-a (Chl-a) is the main component of phytoplankton and an important index of water quality. Pearson correlation analysis is conducted on measured Chl-a concentration and band reflectance to determine the sensitive bands or multiband combinations of the Chl-a to input to a support vector machine (SVM) model. An indicator ß is defined to evaluate the model performance of fitting and prediction. The model performs well with the lowest ß (decision coefficient, (R2) = 0.774; root mean square error (RMSE) = 22.636 µg/L) of the validation set. The model test results prove that the model performs well. We analyze the impact factors of the model. The seasonal factor affects the model performance significantly; thus, samples from different seasons should be combined to train the model and inverse the water quality. Noise points reduce the model accuracy significantly; therefore, obvious outliers must be excluded at first. Additionally, the sampling method affects model accuracy, and systematic sampling in the descending order of Chl-a concentration is recommended. The combination of SVM algorithm and remote sensing technology provides a convenient, scientific, and real-time method to monitor and control water quality.

Eutrophication assessment of seasonal urban lakes in China Yangtze River Basin using Landsat 8-derived Forel-Ule index: A six-year (2013-2018) observation.

Lakes eutrophication have been a complex and serious problem for China's Yangtze River Basin. A series of algorithms based on different remote sensing dataset have been proposed to simulate the lakes trophic state. However, these algorithms are often targeted at a particular lake and cannot be applied to a watershed management. In this study, a Forel-Ule index (FUI) method based on Landsat 8 OLI image is proposed to simulate trophic state index (TSI) in three typical urban lakes (Dianchi, Donghu, and Chaohu) from 2013 to 2018. The results show that the Landsat 8 derived FUI can well represent the lake TSI with an accuracy of R2 = 0.6464 for the in situ experimental TSI dataset (N = 115) and R2 = 0.8065 for the lake average TSI dataset (N = 315). In the study period 2013-2018, the order of the simulated TSI is Dianchi > Chaohu > Donghu. Seasonal dynamics show differences where the percentage of eutrophic area in summer is significantly lower than the other seasons for Lake Dianchi and Chaohu. However, the percentage of eutrophic area for Lake Donghu is highest in summer and lowest in winter. To further detect the driving factors of eutrophication in study lakes, the Pearson correlation and multiple linear regression analyses were conducted. The results show that sunshine and temperature are, respectively, the most and the second most significant factors for Lake Dianchi with explanations of 14.8% and 22.0%; temperature and pollution are the main influencing factors for Lake Donghu (39.2% and 10.9% explanation, respectively) and Chaohu (57.2% and 60.7% explanations, respectively). In addition, the wind is another negatively significant factor for Lake Chaohu with an explanation of 31.3%. Our results serve as an example for other lakes in the Yangtze River Basin and support the formulation of effective strategies to reduce seasonal eutrophication.