Remote sensing of CDOM and DOC in alpine lakes across the Qinghai-Tibet Plateau using Sentinel-2A imagery data

Spatial variations of DOM in a diverse range of lakes across various frozen ground zones in China: Insights into molecular composition

Dissolved organic matter (DOM) plays a significant role in aquatic biogeochemical processes and the carbon cycle. As global climate warming continues, it is anticipated that the composition of DOM in lakes will be altered. This could have significant ecological and environmental implications, particularly in frozen ground zones. However, there is limited knowledge regarding the spatial variations and molecular composition of DOM in lakes within various frozen ground zones. In this study, we examined the spatial variations of in-lake DOM both quantitatively, focusing on dissolved organic carbon (DOC), and qualitatively, by evaluating optical properties and conducting molecular characterization using Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). Lakes in cold regions retained more organic carbon compared to those in warmer regions, the comparison of the mean value of DOC concentration of all sampling sites in the same frozen ground zone showed that the highest mean lake DOC concentration found in the permafrost zone at 21.4 ± 19.3 mg/L. We observed decreasing trends in E2:E3 and MLBL, along with increasing trends in SUVA254 and AImod, along the gradually warming ground. These trends suggest lower molecular weight, reduced aromaticity, and increased molecular lability of in-lake DOM in the permafrost zone compared to other frozen ground zones. Further FT-ICR MS characterization revealed significant molecular-level heterogeneity of DOM, with the lowest abundance of assigned DOM molecular formulas found in lakes within permafrost zones. In all studied zones, the predominant molecular formulas in-lake DOM were compounds consisted by CHO elements, accounting for 40.1 % to 63.1 % of the total. Interestingly, the percentage of CHO exhibited a gradual decline along the warming ground, while there was an increasing trend in nitrogen-containing compounds (CHON%). Meanwhile, a substantial number of polyphenols were identified, likely due to the higher rates of DOM mineralization and the transport of terrestrial DOM derived from vascular plants under the elevated temperature and precipitation conditions in the warming region. In addition, sulfur-containing compounds (CHOS and CHNOS) associated with synthetic surfactants and agal derivatives were consistently detected, and their relative abundances exhibited higher values in seasonal and short-frozen ground zones. This aligns with the increased anthropogenic disturbances to the lake's ecological environment in these two zones. This study reported the first description of in-lake DOM at the molecular level in different frozen ground zones. These findings underline that lakes in the permafrost zone serve as significant hubs for carbon processing. Investigating them may expand our understanding of carbon cycling in inland waters.

Dynamic monitoring and analysis of chlorophyll-a concentrations in global lakes using Sentinel-2 images in Google Earth Engine

Remote estimation of Chlorophyll-a (Chl-a) has long been used to investigate the responses of aquatic ecosystems to global climate change. High-spatiotemporal-resolution Sentinel-2 satellite images make it possible to routinely monitor and trace the spatial distributions of lake Chl-a if reliable retrieval algorithms are available. In this study, Sentinel-2 images and in-situ measured data were used to develop a Chl-a retrieval algorithm based on 13 optical water types (OWTs) with a satisfying performance (R2 = 0.74, RMSE = 0.42 mg/m3, MAE = 0.33 mg/m3, and MAPE = 55.56 %). After removing the disturbance of algal blooms and other factors, the distribution of Chl-a in 3067 of the largest global lakes (≥50 km2) was mapped using the Google Earth Engine (GEE). From 2019 to 2021, the average Chl-a concentration was 16.95 ± 5.95 mg/m3 for the largest global lakes. During the COVID-19 pandemic, global lake-averaged Chl-a concentration reached its lowest value in 2020. From the perspective of spatial distribution, lakes with low Chl-a concentrations were mainly distributed in high-latitude, high-elevation, or economically underdeveloped areas. Among all the potential influencing factors, lake surface temperature had the largest contribution to Chl-a and showed a positive correlation with Chl-a in approximately 92.39 % of the lakes. Conversely, factors such as precipitation and tree cover area around the lake were negatively correlated with Chl-a concentration in nearly 61.44 % of the lakes.

Bacterioplankton diversity and potential health risks in volcanic lakes: A study from Arxan Geopark, China

Bacterioplankton play a vital role in maintaining the functions and services of lake ecosystems. Understanding the diversity and distribution patterns of bacterioplankton, particularly the presence of potential pathogenic bacterial communities, is crucial for safeguarding human health. In this study, we employed 16S rRNA gene amplicon sequencing to investigate the diversity and geographic patterns of bacterioplankton communities, as well as potential pathogens, in eight volcanic lakes located in the Arxan UNESCO Global Geopark (in the Greater Khingan Mountains of China). Our results revealed that the bacterial communities primarily comprised Bacteroidota (45.3%), Proteobacteria (33.1%), and Actinobacteria (9.0%) at the phylum level. At the genus level, prominent taxa included Flavobacterium (31.5%), Acinetobacter (11.0%), Chryseobacterium (7.9%), and CL500-29 marine group (5.6%). Among the bacterioplankton, we identified 34 pathogen genera (165 amplicon sequence variants [ASVs]), with Acinetobacter (59.8%), Rahnella (18.3%), Brevundimonas (9.6%), and Pseudomonas (5.8%) being the most dominant. Our findings demonstrated distinct biogeographic patterns in the bacterial communities at the local scale, driven by a combination of dispersal limitation and environmental factors influenced by human activities. Notably, approximately 15.3% of the bacterioplankton reads in the Arxan lakes were identified as potential pathogens, underscoring the potential risks to public health in these popular tourist destinations. This study provides the first comprehensive insight into the diversity of bacterioplankton in mountain lake ecosystems affected by high tourist activity, laying the groundwork for effective control measures against bacterial pathogens.

Remote sensing for chromophoric dissolved organic matter (CDOM) monitoring research 2003-2022: A bibliometric analysis based on the web of science core database

Chromophoric dissolved organic matter (CDOM) occupies a critical part in biogeochemistry and energy flux of aquatic ecosystems. CDOM research spans in many fields, including chemistry, marine environment, biomass cycling, physics, hydrology, and climate change. In recent years, a series of remarkable research milestone have been achieved. On the basis of reviewing the research process of CDOM, combined with a bibliometric analysis, this study aims to provide a comprehensive review of the development and applications of remote sensing in monitoring CDOM from 2003 to 2022. The findings show that remote sensing data plays an important role in CDOM research as proven with the increasing number of publications since 2003, particularly in China and the United States. Primary research areas have gradually changed from studying absorption and fluorescence properties to optimization of remote sensing inversion models in recent years. Since the composition of oceanic and freshwater bodies differs significantly, it is important to choose the appropriate inversion method for different types of water body. At present, the monitoring of CDOM mainly relies on a single sensor, but the fusion of images from different sensors can be considered a major research direction due to the complex characteristics of CDOM. Therefore, in the future, the characteristics of CDOM will be studied in depth inn combination with multi-source data and other application models, where inversion algorithms will be optimized, inversion algorithms with low dependence on measured data will be developed, and a transportable inversion model will be built to break the regional limitations of the model and to promote the development of CDOM research in a deeper and more comprehensive direction.

Global modeling of lake-water indirect photochemistry based on the equivalent monochromatic wavelength approximation: The case of the triplet states of chromophoric dissolved organic matter

Chromophoric dissolved organic matter (CDOM) plays key role as photosensitizer in sunlit surface-water environments, and it is deeply involved in the photodegradation of contaminants. It has recently been shown that sunlight absorption by CDOM can be conveniently approximated based on its monochromatic absorption at 560 nm. Here we show that such an approximation allows for the assessment of CDOM photoreactions on a wide global scale and, particularly, in the latitude belt between 60°S and 60°N. Global lake databases are currently incomplete as far as water chemistry is concerned, but estimates of the content of organic matter are available. With such data it is possible to assess global steady-state concentrations of CDOM triplet states (³CDOM*), which are predicted to reach particularly high values at Nordic latitudes during summer, due to a combination of high sunlight irradiance and elevated content of organic matter. For the first time to our knowledge, we are able to model an indirect photochemistry process in inland waters around the globe. Implications are discussed for the phototransformation of a contaminant that is mainly degraded by reaction with ³CDOM* (clofibric acid, lipid regulator metabolite), and for the formation of known products on a wide geographic scale.

Integrated Omics Approach to Discover Differences in the Metabolism of a New Tibetan Desmodesmus sp. in Two Types of Sewage Treatments

Microalgae are now widely applied in municipal (YH_3) and industrial sewage (YH_4) treatments. Through integrated omics analysis, we studied the similarities and differences at the molecular level between the two different types of sewage treatment processes. The most significantly enriched gene ontology (GO) terms in both types of sewage treatments were the ribosome, photosynthesis, and proteasome pathways. The results show that the pathways of differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) were enriched for photosynthesis, glyoxylate and dicarboxylate metabolism, and carbon fixation in photosynthetic organisms. Considering YH_3 vs. YH_4, the metabolism of citrate, sedoheptulose-7P, and succinate was significantly upregulated. In addition, the results showed that the pathways of DEGs and DAMs were enriched in terms of amino acid metabolism and carotenoid biosynthesis in YH_4 vs. YH_3. The metabolism of S-Adenosyl-L-homocysteine was significantly downregulated, 2-oxobutanoate was significantly upregulated and downregulated, and the metabolism of abscisic acid glucose ester (ABA-GE) was also significantly upregulated. Overall, the results of this paper will help to improve the basic knowledge of the molecular response of microalgae to sewage treatments, and help design a response strategy based on microalgae for complex, mixed sewage treatments.

An ensemble machine learning model for water quality estimation in coastal area based on remote sensing imagery

The accurate estimation of coastal water quality parameters (WQPs) is crucial for decision-makers to manage water resources. Although various machine learning (ML) models have been developed for coastal water quality estimation using remote sensing data, the performance of these models has significant uncertainties when applied to regional scales. To address this issue, an ensemble ML-based model was developed in this study. The ensemble ML model was applied to estimate chlorophyll-a (Chla), turbidity, and dissolved oxygen (DO) based on Sentinel-2 satellite images in Shenzhen Bay, China. The optimal input features for each WQP were selected from eight spectral bands and seven spectral indices. A local explanation strategy termed Shapley Additive Explanations (SHAP) was employed to quantify contributions of each feature to model outputs. In addition, the impacts of three climate factors on the variation of each WQP were analyzed. The results suggested that the ensemble ML models have satisfied performance for Chla (errors = 1.7%), turbidity (errors = 1.5%) and DO estimation (errors = 0.02%). Band 3 (B3) has the highest positive contribution to Chla estimation, while Band Ration Index2 (BR2) has the highest negative contribution to turbidity estimation, and Band 7 (B7) has the highest positive contribution to DO estimation. The spatial patterns of the three WQPs revealed that the water quality deterioration in Shenzhen Bay was mainly influenced by input of terrestrial pollutants from the estuary. Correlation analysis demonstrated that air temperature (Temp) and average air pressure (AAP) exhibited the closest relationship with Chla. DO showed the strongest negative correlation with Temp, while turbidity was not sensitive to Temp, average wind speed (AWS), and AAP. Overall, the ensemble ML model proposed in this study provides an accurate and practical method for long-term Chla, turbidity, and DO estimation in coastal waters.

Variation of satellite-derived total suspended matter in large lakes with four types of water storage across the Tibetan Plateau, China

Total suspended matter (TSM), as an indicator of the concentration of fine materials in the water column including particulate nutrients, pollutants, and heavy metals, is widely used to monitor aquatic ecosystems. However, the long-term spatiotemporal variations of TSM in lakes across the Tibetan Plateau (TP) and their response to environmental factors are rarely explored. Accordingly, taking advantage of the Landsat top-of-atmosphere reflectance and in-situ data, an empirical model (R² = 0.83, RMSE = 1.08 mg/L, and MAPE = 19.49 %) was developed to estimate the average autumnal TSM in large TP lakes (≥50 km²) during the 1990-2020 period. For analyzing the spatiotemporal variability in TP lakes TSM, the examined lakes were classified into four types (Type A-D) based on their water storage changing in different periods. The results showed that the lakes in the southern and some northeastern parts of the TP exhibited lower TSM values than those situated in other regions. The assessment of TSM in each of these four lake types showed that more than half of them had a TSM value of <20 mg/L. Apart from Type D, the lakes with the TSM showing significantly decreasing trends were dominantly Types A-C. A relative contribution analysis involving five driving factors indicated that they contributed by >50 % to lake TSM interannual variation in 73 out of 114 watersheds, and the lakes area change demonstrated the greatest contribution (82.2 %), followed by wind speed (11.0 %). Further comparison between the entire lake and the non-expansive regions suggested that the expansive region played an indispensable role in determining the TSM value of the whole lake. This study can help to better understand the water quality condition and provide valuable information for policy-makers to maintain sustainable development in the TP region.

Using CDOM spectral shape information to improve the estimation of DOC concentration in inland waters: A case study of Andean Patagonian Lakes

For the last two decades different scientific disciplines have focused on lacustrine dissolved organic matter (DOM) given its importance in the biogeochemistry of carbon and in ecosystem functioning. New satellites supply the appropriate resolutions to evaluate chromophoric dissolved organic matter (CDOM) in inland waters, opening the possibility to estimate DOM at appropriate spatiotemporal scales. This requires, however, a robust relationship between CDOM and dissolved organic carbon (DOC). In this work, we evaluated the use of CDOM as a proxy of DOC in 7 Andean Patagonian lakes. Considering the entire data set, CDOM absorption coefficients (a₃₅₅ and a₄₄₀) were linearly related with DOC. Shallow lakes, however, drove this relationship showing a moderate relationship, whereas, deep lakes with lower colour presented a weaker relationship. Therefore, we assessed the use of CDOM spectral shape information to improve DOC estimates regardless of observed DOM differences due to climatic seasonality and lakes' morphometry. The use of well-known CDOM spectral shape metrics (i.e., S_275-295 and a₂₅₀:a₃₆₅ ratio) significantly improved DOC estimation. Particularly, using a Gaussian decomposition approach we found that much of the variation in the spectral shape, associated with the variability of CDOM:DOC ratio, was explained by differences in two dynamic regions centred at 270 and 320 nm. A strong nonlinear relationship was found between the a₂₇₀:a₃₂₀ ratio and the DOC-specific absorption coefficients a*₃₅₅ and a*₄₄₀. This was translated into a further improvement in DOC estimation yielding the higher R² and lower mean absolute differences (MAPD < 16%), either considering the entire data set or shallow and deep lakes separately. Our results highlight that incorporating the CDOM spectral shape information improves the characterization of the DOC pool of inland waters, which is particularly relevant for remote and/or inaccessible sites and has significant implications for the environmental management, biogeochemical studies and future remote sensing applications.

Monitoring water transparency, total suspended matter and the beam attenuation coefficient in inland water using innovative ground-based proximal sensing technology

Long-term and high-frequency observations are vital to reveal water quality dynamics and responses to climate change and human activities. However, the datasets collected from traditional in situ and satellite observations may miss the rapid dynamics of water quality in the short term due to low temporal-spatial monitoring frequency and cloudy or rainy weather. To address this shortage, innovative ground-based proximal sensing (GBPS) technology was proposed to monitor water quality and identify emergencies with a wavelength range of 400-1000 nm, a spectral resolution of 1 nm and a minimal observation interval of 30 s. The GBPS was equipped with a hyperspectral imager placed 4-5 m above the water surface to minimize the impacts of the atmosphere and clouds. In this study, combined with 583 water samples obtained from four field samplings, GBPS datasets were first applied to estimate the total suspended matter (TSM), Secchi disk depth (SDD) and beam attenuation coefficient at 550 nm (C(550)) in Taihu Lake (TL), Liangxi River (LR) and Funchunjiang Reservoir (FR). The results demonstrated good performance with the TSM (R² = 0.83, RMSE = 8.35 mg/L, MAPE = 24.0%), SDD (R² = 0.88, RMSE = 0.09 m, MAPE = 14.7%), and C(550) (R² = 0.79, RMSE = 3.55 m^-1, MAPE = 35.8%). The time series of TSM and C(550) at the second-minute level showed consistent changes, but they were opposite to those of SDD. Taking TSM as an example, the datasets captured two mutations in TL with an 853.6% increase in 65 min and a rapid change from 40.3 mg/L to 256.9 mg/L and then to 51.0 mg/L in 224 min on November 1 and 3, respectively. Meanwhile, a significant decreasing trend (r = -0.83, p < 0.01) in LR from November 7 to 9 and a periodic diurnal increasing trend of TSM in FR during November 11 to 13 (0.46 ≤ R² ≤ 0.70, p < 0.01) were observed. GBPS, with the advantages of high-frequency observations and the applicability of complex weather conditions, compensates for the in situ, aircraft and satellite observation deficiencies. Therefore, GBPS allows us to capture more detailed water quality information and episodic events, which is an important part of an integrated air-space-ground monitoring system in the future.

Optimization and Evaluation of Widely-Used Total Suspended Matter Concentration Retrieval Methods for ZY1-02D’s AHSI Imagery

Total suspended matter concentration (CTSM) is an important parameter in aquatic ecosystem studies. Compared with multispectral satellite images, the Advanced Hyperspectral Imager (AHSI) carried by the ZY1-02D satellite can capture finer spectral features, and the potential for CTSM retrieval is enormous. In this study, we selected seven typical Chinese inland water bodies as the study areas, and recalibrated and validated 11 empirical models and two semi-analytical models for CTSM retrieval using the AHSI data. The results showed that the semi-analytical algorithm based on the 697 nm AHSI-band achieved the highest retrieval accuracy (R2 = 0.88, average unbiased relative error = 34.43%). This is because the remote sensing reflectance at 697 nm was strongly influenced by CTSM, and the AHSI image spectra were in good agreement with the in-situ spectra. Although further validation is still needed in highly turbid waters, this study shows that AHSI images from the ZY1-02D satellite are well suited for CTSM retrieval in inland waters.