Eutrophication and lakes dynamic conditions control the endogenous and terrestrial POC observed by remote sensing: Modeling and application

Abstract The sources of particulate organic carbon (POC) determine its conversion, thereby playing an important role in the carbon cycle of lakes. Accurate estimation of the sources and dynamic characteristics of POC is important for understanding the migration and transformation of organic carbon. However, the synchronous observation of POC sources with large areas through remote sensing is still challenging because of the complex composition of POC and the optical conditions of inland lakes. In this study, a three-band (1/ Rrs (689)–1/ Rrs (717)) × Rrs (697)) empirical algorithm of POC sources was constructed based on remote sensing reflectance (Rrs(λ)) and the proportion of endogenous POC estimated from the field-measured stable isotope (δ13CPOC) values. The validation and calibration results of the three-band algorithm showed robust performance, with MAPE and RMSE of estimated values and measured values of 10% and 0.07, respectively. The three-band algorithm had good simulation results for the Ocean and Land Color Instrument (OLCI), Moderate Imaging Spectroradiometer (MODIS), Geostationary Ocean Color Imager (GOCI), and Medium Resolution Imaging Spectrometer (MERIS) spectra. The POC sources estimated by the three-band algorithm suggest that the endogenous POC of Taihu Lake in August showed a decreasing trend from 2006 to 2019. The variation in terrestrial POC was slow and stable for both annual and monthly variations. The analysis of POC sources with total phosphorus (TP), total nitrogen (TN), water temperature, and wind speed indicated that terrestrial POC was closely related to wind speed (r = 0.33, P