Long-term POC Trend and Variability in a Eutrophic Inland Waters from MODIS Imagery: Human-induced or Climate-driven?

Lakes and inland water bodies are active, changing, and important regulators of the carbon cycle and global climate. Collectively, nearly half as much organic carbon is buried in lakes globally as in the world’s oceans. Particulate organic carbon (POC) is generally the form of carbon that most readily undergoes sedimentation and in-ecosystem loss. Even though POC is a small fraction of total organic carbon (TOC) present in most lakes (with respect to dissolved organic carbon, DOC), it plays an important role in sequestering carbon and associated compounds downward as part of the biological pump. To better explore carbon cycling in the freshwater ecosystems and understand the fate of the main organic components, it is important to quantify POC as well as DOC effectively.

Satellite remote sensing observations provide a suitable means to explore temporal and spatial properties of inland lakes. However, the estimate of total dissolved and particulate carbon presents a larger challenge, as optical properties of these two carbon pools can vary significantly in relation to their sources and sinks. The objective of the present study is to: (1) develop an algorithm to estimate surface water POC concentrations from MODIS imagery; (2) establish long-term POC in a eutrophic inland water (Lake Chaohu); (3) interpret spatial and temporal POC trend and variability in relation to human-induced and climate variability between 2000 and 2014.

In this study, an approach based on Empirical Orthogonal Function (EOF) analysis has been developed and validated to estimate POC concentrations in surface waters of Lake Chaohu with hypereutrophic state. The EOF algorithm between POC and the atmospherically Rayleigh-corrected reflectance (Rrc) data at 469, 555, 645, and 859 nm was developed (R²=0.90, RMSE_rel = 79.01%, N=35) and validated (R²=0.76, RMSE_rel = 26.94%, N=73) using concurrent MODIS and field measurements. This algorithm was applied to a 15-year series of MODIS data to determine the spatial and temporal distribution of POC in Lake Chaohu. Anomaly and EOF analyses revealed strong spatial gradients, seasonality, and inter-annual changes in the satellite-based POC. These changes were highly correlated with satellite-based chlorophyll-a and human-induced nutrients.