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 (C_endPOC) and between the nonalgal particulate absorption coefficient and terrestrial POC concentration (C_terPOC). Second, based on the valid relationships, semianalytical retrieval models were established to estimate C_endPOC and C_terPOC. Third, the proportions of endogenous POC (R_endPOC) and terrestrial POC (R_terPOC) to the total POC concentration (C_POC) were derived using a three-band empirical model. Finally, C_POC was obtained by dividing C_endPOC by R_endPOC (R_endPOC ≥ 0.5) or dividing C_terPOC by R_terPOC (R_endPOC < 0.5). Validation with field data shows that our proposed algorithm can accurately derive C_POC (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.

从卫星图像中估计颗粒有机碳（POC）浓度可以为碳循环和生态环境治理提供重要的时空连续观测数据。在这里，我们开发了一种基于遥感和地球化学同位素推导内陆水域 POC 的新反演算法，总结如下。首先，我们建立了浮游植物吸收系数与内源 POC 浓度 ( C _endPOC ) 之间以及非藻类颗粒吸收系数与陆地 POC 浓度 ( C _terPOC ) 之间的经验关系。其次，基于有效关系，建立半解析检索模型来估计C _endPOC和C _terPOC。第三，使用三波段经验模型推导出内源性POC（ R _endPOC）和陆地POC（R _terPOC）占总POC浓度（C _{POC ）的比例。}最后，通过 C endPOC 除以R _endPOC ( R _endPOC ≥ 0.5 ₎ 或C _terPOC除以R _terPOC ( R _{endPOC < 0.5) 得到}C _POC  。现场数据验证表明，我们提出的算法可以准确推导出C _POC(0–20 mg/L)，均方根偏差 (RMSD)、中位偏差 (MB)、中位绝对百分比差 (MAPD)​​ 和中位比率 (MR) 为 1.15 mg/L、-0.05 mg/L 、24% 和 0.98，分别。基于 Sentinel-3/OLCI 图像的同步验证证实了准确性，RMSD、MB、MAPD 和 MR 值分别为 0.41 mg/L、-0.16 mg/L、28% 和 0.91。将该算法应用于海洋和陆地颜色传感器（OLCI）图像，揭示太湖POC的时空变化。