Tracking spatio-temporal dynamics of POC sources in eutrophic lakes by remote sensing

Estimating the proportions of particulate organic carbon (POC) endmembers is essential to fully understand the carbon cycle, the function of aquatic ecosystems, and the migration of contaminants in eutrophic lakes. There is currently no effective remote sensing optical algorithm in the literature to solve this problem. In this study, a POC-source color index (S_POC) was constructed based on the terrestrial and endogenous POC ratios calculated from field-measured stable isotope (δ¹³C_POC) values. The S_POC algorithm traces the sources of POC by utilizing three spectral bands centered approximately at 560 nm, 674 nm, and 709 nm, covering the intrinsic optical information of different POC sources. At the same time, the S_POC algorithm shows good applicability to Ocean and Land Color Instrument (OLCI), Medium-Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Geostationary Ocean Color Imager (GOCI) image data. The POC sources estimated using the algorithm and monthly OLCI data showed that from March 2018 to January 2019, the POC at the surface of Lake Taihu was mainly terrigenous. In addition, due to multiple factors such as algal blooms, plant physiology, river transport, regional rainfall, and carbon cycling, the distribution of POC sources exhibited strong spatial and temporal heterogeneity. Compared with other methods, it is more convenient to use remote sensing to identify the proportion of POC in different endmembers, which offers a more comprehensive understanding of the energy flows and material circulation in lakes.