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Remote sensing monitoring of particulate organic carbon (POC) concentration is essential for understanding phytoplankton productivity, carbon storage, and water quality in global lakes. Some algorithms have been proposed, but only for regional eutrophic lakes. Based on in-situ data (N = 1269) in 49 lakes across China, we developed a blended POC algorithm by distinguishing Type-I and Type-II waters. Compared to Type-I, Type-II waters had higher reflectance peak around 560 nm (>0.0125 sr -1 ) and mean POC (4.65 ± 4.11 vs. 2.66 ± 3.37 mg/L). Furthermore, because POC was highly related to algal production (r = 0.85), a three-band index (R 2  = 0.65) and the phytoplankton fluorescence peak height (R 2  = 0.63) were adopted to estimate POC in Type-I and Type-II waters, respectively. The novel algorithm got a mean absolute percent difference (MAPD) of 35.93 % and outperformed three state-of-the-art formulas with MAPD values of 40.56-76.42 %. Then, the novel algorithm was applied to OLCI/Sentinel-3 imagery, and we first obtained a national map of POC in 450 Chinese lakes (> 20 km 2 ), which presented an apparent spatial pattern of "low in the west and high in the east". In brief, water classification should be considered when remotely monitoring lake POC concentration over a large area. Moreover, a process-oriented method is required when calculating water column POC storage from satellite-derived POC concentrations in type-II waters. Our results contribute substantially to advancing the dynamic observation of the lake carbon cycle using satellite data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Dissolved organic matter (DOM) plays important roles in the aquatic biogeochemical cycle and the global carbon cycle. However, it is highly spatially and temporally varied due to complex sources from the catchment (allochthonous) and from within the system (autochthonous). Satellite remote sensing provides the ability to monitor DOM and identify the spatio-temporal variations in lakes on a global or regional scale. In this study, field work was conducted in 55 lakes in August 2012 along the middle and lower reaches of the Yangtze River (MLR-YR), where most lakes were characterized by eutrophication due to intense human activities. The results showed that both colored DOM (CDOM) and total DOM differed significantly by and were linearly related to the human-induced trophic state index (TSI), with R 2  = 0.41 and 0.61, respectively. Autochthonous substances by phytoplankton contributed to 38.5% of CDOM and 35.2% of DOM, and allochthonous terrestrial substance indexed by land cover change and aquaculture contributed to almost half, with 49.7% of CDOM and 49.8% of DOM. In total, human activities explained as much as 81.7% and 87.5% of the variations in CDOM and DOM, respectively. Finally, a flowchart for estimating DOM from satellite-derived TSI was proposed. This study has great significance for synchronously monitoring and managing aquatic environment quality in regional eutrophic lakes around the world., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

• A novel algorithm was proposed to remotely observe POC storage in eutrophic lakes. • POC profile could be identified and parameterized by surface Chl-a, POC, and wind. • Spatiotemporal variations in POC storage were determined by temperature and depth. Particulate organic carbon (POC) storage play an essential role in determining carbon sinks and water quality in shallow eutrophic lakes. Although satellite data have been adopted to observe surface POC content, the previous remote sensing results cannot indicate water column-integrated POC storage in shallow eutrophic lakes with vertically nonuniform POC profiles. Based on 272 in-situ POC profiles collected from 19 shallow eutrophic lakes in China's Yangtze-Huai Plain (YHP), this study developed a novel process-oriented method to derive water column-integrated POC storage from OLCI/Sentinel-3A satellite data. The method included three critical steps: remotely estimating surface POC contents, identifying POC profile types (uniform, exponential decay, or power decay) through a binary decision tree, and parameterizing POC profiles using surface POC concentrations. The developed algorithms showed a bias of -25.79% for in-situ match-ups from 15 lakes (N = 88). The POC in the eutrophic lakes was mainly produced by phytoplankton (R 2 = 0.87). As a result, the surface POC content presented a unimodal peak in summer. However, in addition to air temperature regulations (0.52 – 68.53%), the water level also showed apparent effects on increasing POC storage, with relative contributions of 29.29 – 95.51% for the six largest lakes. Therefore, multisource satellite data are needed to derive POC storage in global eutrophic lakes remotely. This study is the first attempt to three-dimensionally observe lake POC contents from space and is important for understanding the carbon cycle in shallow eutrophic lakes. [Display omitted] [ABSTRACT FROM AUTHOR]