Monitoring and understanding the water transparency changes of fifty large lakes on the Yangtze Plain based on long-term MODIS observations

Various aspects of the water qualities of the large lakes in the Yangtze Plain (YP) have been studied, while the efforts have been spent on one of the most important parameters-water transparency were only limited to several large lakes. Using in situ remote sensing reflectance and Secchi-disk depth (Z(sd)) datasets, we assessed the performance of a semi-analytical model recently proposed by Lee et al. (2015) for remote sensing of the Z(sd) (Z(sd), (Lee)). The results show that a linear scaling correction over Z(sd), (Lee) (Z(sd)', (Lee)) could lead to improved agreement between remote sensing estimates and field measurements (root mean square error < 35%) in the study region. The Z(sd)'(,) (Lee) scheme was then applied to MODIS/Aqua observations between 2003 and 2016 to obtain the spatial and temporal dynamics of water transparency in 50 large lakes in the YP. The long-term mean Z(sd) of the entire region was 0.39 +/- 1.17 m during the observation period, with high and low values occurring in warm and cold seasons, respectively. Of the 50 examined lakes, half demonstrated decreasing or increasing Z(sd) trends, and the number of lakes exhibiting significantly decreasing trends also comparable to the number exhibiting increasing trends. The relative contributions of the seven potential driving factors (from both human activities and natural processes) to the interannual changes in water clarity were quantified for each lake using a multiple general linear model regression analysis. The responses of Z(sd) to these drivers showed considerable differences among lakes, and human activities demonstrated significant roles in more lakes than those affected by natural variability, accounting for 50% (25/50) and 20% (10/50) of the lakes, respectively. This study provides the first comprehensive basin-scale estimate of the water transparency in the lakes in the YP, and the Z(sd) results and analysis of driving forces can provide important information for local water quality conservation and restoration.