Simultaneous inversion of concentrations of POC and its endmembers in lakes: A novel remote sensing strategy

Mapping particulate organic carbon in lakes across China using OLCI/Sentinel-3 imagery

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 (R2 = 0.65) and the phytoplankton fluorescence peak height (R2 = 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 km2), 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.

Source, composition and molecular diversity of dissolved and particulate organic matter varied with riparian land use in tropical coastal headstreams

Source, composition and molecular diversity determine the reactivity and stabilization of organic matter (OM, dissolved [DOM]/particulate [POM]), affecting its behavior and fate. Here, multiple spectral and mass spectrometry techniques were applied to examine how riparian land-use shaped the source, composition and molecular diversity of POM and DOM (HDOM) in adjacent headstreams. Compared to HDOM with abundant lignins, microbially-transformed heteroatoms and carboxyl-rich alicyclic acids (CRAMs), POM exhibited higher allochthonous characteristics and more bioactive components, but lower molecular weight and diversity in different land-use-dominated streams. Compared to wetland-dominated headstreams, both POM and HDOM exhibited more terrestrial origin and condensed aromatics/tannins molecules for agriculture-impacted headstreams and bio-labile lipids, proteins and carbohydrates for forest-impacted headstreams. Structural equation mode (SEM) showed that soil-derived DOM (SDOM) showed the most prominent influence on the source, composition and molecular diversity of POM and the source of HDOM. The molecular composition and diversity of HDOM were mainly influenced by soil properties/SDOM and aquatic microorganisms, respectively. Redundancy analysis (RDA) revealed that autochthonous, bio-labile compositions of POM in forest and wetland streams were positively related to aquatic Bacteroidetes/Cyanobacteria, and carbohydrates/biogenic index of SDOM, while that of HDOM were positively linked with aquatic Bacteroidetes/Cyanobacteria, and SDOM molecular diversity. Terrestrial and aromatic POM in agricultural headstreams were associated with aquatic total nitrogen/Actinobacteria, and humification degree, aromatic/phenolic substances of SDOM, while that of HDOM were mainly regulated by aquatic nitrate/total nitrogen/Actinobacteria, and aromatic/carboxylic-containing moieties of SDOM. Noteworthily, the molecular diversity of agricultural OM increased along the soil-stream continuum due to the input of soil condensed aromatics and tannins. The opposite trend was observed in forest and wetland streams due to the input of bioactive carbohydrates and the microbial-degradation in-stream. These results are helpful to predict the behavior and fate of OM and determine effective management strategies in tropical coastal regions undergoing intense anthropogenic alterations.

A classification-based approach to mapping particulate organic matter (POM) in inland water using OLCI images

Particulate organic matter (POM) plays a major role in freshwater ecosystems by serving as a bridge for the conversion of various nutrients. The composition and sources of POM in inland lakes are complex, making it difficult to estimate its concentration accurately via remote sensing. Therefore, a classification-based method based on the sources and composition of POM is proposed for estimating POM concentrations in inland lakes. In this study, 379 samples were collected from ten lakes in the Yangtze River Delta (YRD) at different times. A water-type classification method based on OLCI [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] was developed for POM estimation based on biological and optical characteristics. Water type 1 is relatively clear, and POM may originate from aquatic vegetation or sediment. Water type 2 was dominated by inorganic suspended matter, and POM mainly originated from the attachment and entrainment of inorganic minerals. Water type 3 is an algae-dominated water body, and POM is mainly derived from fresh algal particles and the microbial degradation of phytoplankton. Therefore, specific POM estimation algorithms were developed for each water type. OLCI [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] were used for water type 1; [Formula: see text], [Formula: see text], and [Formula: see text] were adopted for water type 2; and [Formula: see text], [Formula: see text], and [Formula: see text] were selected for water type 3. Using an independent dataset to evaluate the estimation accuracy of the developed algorithm, the results show that the estimation performance of this algorithm is significantly improved compared to the two other algorithms used; the mean absolute percentage errors (MAPE) decreased from 72.56% and 52.21% to 32.61%, and the root mean square errors (RMSE) decreased from 3.05 mg/L and 2.24 mg/L to 1.75 mg/L. A random error analysis of the atmospheric correction demonstrated that this algorithm is robust and can still perform well within a random error of 30%. Finally, this method was successfully applied to map the POM concentrations in the YRD using OLCI images acquired on November 12, 2020.

A hybrid remote sensing approach for estimating chemical oxygen demand concentration in optically complex waters: A case study in inland lake waters in eastern China

Chemical oxygen demand concentration (C_COD) is widely used to indicate the degree of organic pollution of lakes, reservoirs and rivers. Mastering the spatiotemporal distribution of C_COD is imperative for understanding the variation mechanism and controlling of organic pollution in water. In this study, a hybrid approach suitable for Sentinel 3A/Ocean and Land Colour Instrument (OLCI) data was developed to estimate C_COD in inland optically complex waters embedding the interaction between C_COD and the absorption coefficients of optically active constituents (OACs). Based on in-situ sampling in different waters, the independent validations of the proposed model performed satisfactorily in Lake Taihu (MAPE = 23.52 %, RMSE = 0.95 mg/L, and R² = 0.81), Lake Qiandaohu (MAPE = 21.63 %, RMSE = 0.50 mg/L and R² = 0.69), and Yangtze River (MAPE = 29.34 %, RMSE = 0.83 mg/L, and R² = 0.64). In addition, the approach not only showed significant superiority compared with previous algorithms, but also was suitable for other common satellite sensors equipped same or similar bands. The hybrid approach was applied to OLCI images to retrieve C_COD of Lake Taihu from 2016 to 2020 and reveals substantial interannual and seasonal variations. The above results indicate that the proposed approach is effective and stable for studying spatiotemporal dynamic of C_COD in optically complex waters, and that satellite-derived products can provide reliable information for lake water quality management.

Monitoring the particulate phosphorus concentration of inland waters on the Yangtze Plain and understanding its relationship with driving factors based on OLCI data

Tracking the spatiotemporal dynamics of particulate phosphorus concentration (C_PP) and understanding its regulating factors is essential to improve our understanding of its impact on inland water eutrophication. However, few studies have assessed this in eutrophic inland lakes, owing to a lack of suitable bio-optical algorithms allowing the use of remote sensing data. Herein, a novel semi-analytical algorithm of C_PP was developed to estimate C_PP in lakes on the Yangtze Plain, China. The independent validations of the proposed algorithm showed a satisfying performance with the mean absolute percentage error and root mean square error less than 27% and 27 μg/L, respectively. The Ocean and Land Color Instrument observations revealed a remarkable spatiotemporal heterogeneity of C_PP in 23 lakes on the Yangtze Plain from 2016 to 2020, with the lowest value in December (62.91 ± 34.59 μg/L) and the highest C_PP in August (114.9 ± 51.69 μg/L). Among the 23 examined lakes, the highest mean C_PP was found in Lake Poyang (124.58 ± 44.71 μg/L), while the lowest value was found in Lake Qiandao (33.51 ± 4.71 μg/L). Additionally, 13 lakes demonstrated significant decreasing or increasing trends (P < 0.05) of annual mean C_PP during the observation period. The driving factor analysis revealed that four natural factors (wind speed, air temperature, precipitation, and sunshine duration) and two anthropogenic factors (the normalized difference vegetation index and nighttime light) combined explained more than 91% of the variation in C_PP, while the impacts of these factors on C_PP showed considerable differences among lakes. This study offered a novel and scalable algorithm for the study of the spatiotemporal variation of C_PP in inland waters and provided new insights into the regulating factors in water eutrophication.