A state-of-art algorithm to retrieve particulate organic carbon concentration in optically complex waters via multiple satellite missions

• Published in: Remote sensing of environment Vol. 329; p. 114914
• Main Authors: Xue, Kun, Ma, Ronghua, Wang, Menghua, Wei, Xiaoqi, Liu, Haoze, Hu, Minqi, Jiang, Lide, Shen, Ming, Cao, Zhigang
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.11.2025
• Subjects: Absorption coefficient; Multi-sensor; Optically complex waters; Particulate organic carbon; Semi-analytical model; Multi-sensor; Absorption coefficient; Semi-analytical model; Particulate organic carbon; Optically complex waters
• ISSN: 0034-4257
• DOI: 10.1016/j.rse.2025.114914

The complex sources of particulate organic carbon (POC) in lakes introduce substantial variability in its correlation with optically active constituents (OACs) like chlorophyll-a and suspended minerals. This variability poses a significant challenge in developing long-term POC concentration records from multi-satellite observations, particularly when bridging historical and modern sensors in optically complex waters. The Medium Resolution Imaging Spectrometer (MERIS) and Ocean and Land Colour Instrument (OLCI) perform well in retrieving the bio-optical parameters related to POC in global inland waters. In this study, we utilize data from the Geostationary Ocean Color Imager (GOCI), which has similar spectral bands, as a connection to validate the consistency of the atmospherically corrected remote sensing reflectance (Rrs(λ)) of MERIS and OLCI using field measurements. An inter-sensor validation of Rrs(λ) has also been conducted based on the match-up pairs of satellite data. A blended POC model suitable for both non-algal particles (NAP)-dominated (Type 1) and phytoplankton-dominated (Type 2) waters has been proposed and developed using the multi-sensor Rrs(λ) data. POC concentrations of 110 lakes in the middle and lower reaches of the Yangtze River and Huai River (MLYHR) basin in China are retrieved based on multi-sensor Rrs(λ) data from 2003 to 2023. The results demonstrate that: 1) the Rrs(λ) of MERIS, GOCI, and OLCI show good consistency from the green to near-infrared (NIR) bands, 2) the proposed POC inversion model has been validated using field-measured POC with good performance (root mean square error (RMSE) of 1.49 mg/L for Type 1 and 4.52 mg/L for Type 2), and 3) significant seasonal variations in POC in the region have been observed, with high values in summer and low values in winter. Small lakes with areas of ∼10–50 km2 have higher mean POC values (7.63 ± 0.69 mg/L) than those in large lakes (e.g., in lakes ranging from 100 to 500 km2, the POC concentration is 5.91 ± 0.81 mg/L). The multi-sensor strategy for estimating POC in optically complex waters provides a practical way to fully utilize the spectral bands and long-term observations from multiple satellites. •Consistent Rrs(λ) of MERIS, GOCI, and OLCI were built in optically complex waters.•A blended POC model for NAP and phytoplankton dominated waters was developed.•Spatial and temporal variations of POC in 110 lakes were illustrated in two decades.•Small lakes have higher POC values than those in large lakes in the MLYHR basin.