Spatiotemporal dynamics of chlorophyll-a in a large reservoir as derived from Landsat 8 OLI data: understanding its driving and restrictive factors.

Chlorophyll-a (Chla) is an important indicator of water quality and eutrophication status. Monitoring Chla concentration (C ) and understanding the interactions between C and related environmental factors (hydrological and meteorological conditions, nutrients enrichment, etc.) are necessary for assessing and managing water quality and eutrophication. An acceptable Landsat 8 OLI-based empirical algorithm for C has been developed and validated, with a mean absolute percentage error of 14.05% and a root mean square error of 1.10 μg L. A time series of remotely estimated C was developed from 2013 to 2015 and examined the relationship of C to inflow rate, rainfall, temperature, and sunshine duration. Spatially, C values in the riverine zone were higher than in the transition and lacustrine zones. Temporally, mean C value were ranked as spring > summer > autumn > winter. A significant positive correlation [Pearson correlation coefficient (r) = 0.88, p < 0.001] was observed between the inflow rate and mean C in the northwest segment of the Xin'anjiang Reservoir. However, no significant relation was observed between mean C and meteorological conditions. Mean (± standard deviation) value for the ratio of total nitrogen concentration to total phosphorus concentration in our in situ dataset is 75.75 ± 55.72. This result supports that phosphorus is the restrictive factor to algal growth in Xin'anjiang Reservoir. In addition, the response of nutrients to Chla has spatial variabilities. Current results show the potential of Landsat 8 OLI data for estimating Chla in slight turbid reservoir and indicate that external pollution loading is an important driving force for the Chla spatiotemporal variability.