A novel semianalytical remote sensing retrieval strategy and algorithm for particulate organic carbon in inland waters based on biogeochemical-optical mechanisms

Abstract

The estimation of particulate organic carbon (POC) concentrations from satellite images can provide crucial spatiotemporal continuous observation data for the carbon cycle and ecological environmental governance. Here, we developed a novel inversion algorithm for deriving POC in inland water based on remote sensing and geochemical isotopes, which is summarized as follows. First, we developed empirical relationships between the phytoplankton absorption coefficient and endogenous POC concentration (CendPOC) and between the nonalgal particulate absorption coefficient and terrestrial POC concentration (CterPOC). Second, based on the valid relationships, semianalytical retrieval models were established to estimate CendPOC and CterPOC. Third, the proportions of endogenous POC (RendPOC) and terrestrial POC (RterPOC) to the total POC concentration (CPOC) were derived using a three-band empirical model. Finally, CPOC was obtained by dividing CendPOC by RendPOC (RendPOC ≥ 0.5) or dividing CterPOC by RterPOC (RendPOC < 0.5). Validation with field data shows that our proposed algorithm can accurately derive CPOC (0–20 mg/L), with a root mean square deviation (RMSD), median bias (MB), median absolute percent difference (MAPD), and median ratio (MR) of 1.15 mg/L, −0.05 mg/L, 24%, and 0.98, respectively. Synchronous validation based on Sentinel-3/OLCI images confirmed the accuracy, with RMSD, MB, MAPD, and MR values of 0.41 mg/L, −0.16 mg/L, 28%, and 0.91, respectively. The algorithm was applied to ocean and land color sensor (OLCI) images to reveal the temporal and spatial variations in POC in Lake Taihu.