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Lake Taihu, an inland lake, frequently experiences Cyanobacterial blooms that have historically posed severe threats to its aquatic ecosystem. Combining field observations and satellite remote-sensed data, factors that influence algal bloom intensity in Lake Taihu over an eight-year period, from 2016 to 2023, are examined, and changes in phytoplankton community composition, climate, water quality, economic activities, and food web dynamics are reported. Sentinel-2 MSI data analysis reveals a dramatic decrease in Cyanobacterial blooms in 2023, with a reduction in the annual maximum bloom area of 76.90 % from 2016 values. From 2016 to 2022, the ratio of Cyanobacteria to other phytoplankton ranged 82.09 %-98.29 %, but in 2023, this dropped to 60.98 %. Concurrently, Cyanobacteria density dropped to an historic low of 2.29 × 10 7 cells/L (16.4 % of 2021 peak values). Redundancy and random forest analyses indicated that nitrogen has a greater influence on phytoplankton than phosphorus, with temperature and permanganate index being the important parameters to affect phytoplankton community structure. We attribute the decrease in Cyanobacterial blooms in Lake Taihu in 2023 to be largely caused by shifts in phytoplankton community structure, particularly the sharp decline in Microcystis sp. density, a genus often linked to bloom formation. Meteorological conditions such as reduced rainfall and wind speed during the winter and spring of 2023 also contributed to diminishing Cyanobacterial blooms. Ongoing improvements in water quality, reduced economic activities because of pandemic restrictions, and implementation of a fishing ban since 2020 have likely further contributed to reductions in bloom frequency. These results improve our understanding of the processes that affect algal blooms in Lake Taihu, and potentially other eutrophic inland lakes., 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 © 2024 Elsevier B.V. All rights reserved.)

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 MLB L , along with increasing trends in SUVA 254 and AI mod , 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., 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 © 2024. Published by Elsevier Ltd.)

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 (R 2 = 0.83, RMSE = 1.08 mg/L, and MAPE = 19.49 %) was developed to estimate the average autumnal TSM in large TP lakes (≥50 km 2 ) 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., 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 © 2022 Elsevier B.V. All rights reserved.)

Dissolved organic matter (DOM), a heterogeneous mixture of diverse compounds with different molecular weights, is crucial for the lake carbon cycle. The properties and concentration of DOM in lakes are closely related to anthropogenic activities, terrigenous input, and phytoplankton growth. Thus, the lake's trophic state, along with the above factors, has an important effect on DOM. We determined the DOM sources and molecular composition in six lakes along a trophic gradient during and after phytoplankton bloom by combining optical techniques and the Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). CDOM pools in eutrophic lakes may be more biologically refractory than in oligotrophic and mesotrophic lakes. Molecular formulas of DOM were positively correlated with the TSI (trophic state index) value (R 2  = 0.73), with the nitrogen-containing compounds (CHON) being the most abundant formulas in all studied lakes. Eutrophication modified the molecular formulas of DOM to have less CHO% and more heteroatom S-containing compounds (CHOS% and CHNOS%), and this was the synactic result of the anthropogenic perturbation and phytoplankton proliferation. In eutrophic lakes, summer DOM showed higher molecular lability than in autumn, which was related to the seasonal phytoplankton community succession. Although the phytoplankton-derived DOM is highly bioavailable, we detected a simpler and more fragile phytoplankton community ecosystem in autumn, which may be accompanied by a lower phytoplankton production and metabolic activity. Therefore, we concluded that the lake eutrophication increased the allochthonous DOM accumulation along with sewage and nutrient input, and subsequently increased its release with phytoplankton bloom. Eutrophication and phytoplankton growth are accompanied by more highly unsaturated compounds, O 3 S+O 5 S compounds, and carboxylic-rich alicyclic compounds (CRAMs), which are the biotransformation product of phytoplankton-derived DOM. Eutrophication may be a potential source of refractory DOM compounds for biodegradation and photodegradation. Our results can clarify the potential role of water organic matter in the future global carbon cycle processes, considering the increasing worldwide eutrophication of inland waters., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests that could have appeared to influence the work reported in this manuscript., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Dissolved organic matter (DOM) plays an essential role in the global carbon biogeochemical cycle for aquatic ecosystems. The complexity of DOM compounds contributes to the accurate monitoring of its sources and compositions from large-scale patterns to microscopic molecular groups. Here, this study demonstrates the diverse sources and compositions for humic-rich lakes and protein-rich lakes for large-scale regions across China with the linkage to optical components and molecular high-resolution mass spectrometry properties. The total fluorescence intensity of colored DOM (CDOM) for humic-rich lake regions (0.176 Raman unit; R.U.) is significantly (p<0.05) higher than that of the protein-rich lake region (0.084 R.U.). The combined percentages of CDOM absorption variance explained by the anthropogenic and climatic variables across the five lake regions of Northeastern lake region (NLR), Yungui Plateau lake region (YGR), Inner Mongolia-Xinjiang lake region (MXR), Eastern lake region (ELR), and Tibetan-Qinghai Plateau lake region (TQR) were 86.25%, 82.57%, 80.23%, 88.55%, and 87.72% respectively. The averaged relative intensity percentages of CHOS and CHONS formulas from humic-rich lakes (90.831‰, 10.561‰) were significantly higher than that from the protein-like lakes (47.484‰, 5.638‰), respectively. The more complex molecular composition with higher aromaticity occurred in the humic-rich lakes than in the protein-rich lakes. The increasing anthropogenic effects would significantly enhance the sources, transformation, and biodegradation of terrestrial DOM and link to the greenhouse gas emission and the carbon cycle in inland waters., (Copyright © 2022. Published by Elsevier Ltd.)

The Trophic state index (TSI) is a vital parameter for aquatic ecosystem assessment. Estimating TSI by remote sensing is still a challenge due to the multivariate complexity of the eutrophication process. A comprehensive in situ spectral-biogeochemical dataset for 7 lakes in Northeast China was collected in October 2020. The dataset covers trophic states from oligotrophic to eutrophic, with a wide range of total phosphorus (TP, 0.07-0.2 mg L -1 ), Secchi disk depth (SDD, 0.1-0.78 m), and chlorophyll a (Chla, 0.11-20.41 μg L -1 ). Here, we propose an empirical method to estimate TSI from remote sensing data. First, TP, SDD, and Chla were estimated by band ratio/band combination models. Then TSI was estimated using the Carlson model with a high R 2 (0.88), a low RMSE (3.87), and a low MRE (6.83%). Synergistic effects between TP, SDD, and Chla dominated the trophic state, changed the distribution of light in the water column, affected the spectral characteristics. Furthermore, the contribution of each parameter for eutrophication were different among the studied lakes from ternary plot. High Chla concentration was the main reason for eutrophication in HMT Lake with 45.4% of contribution more than the other two parameters, However, in XXK Lake, high TP concentrations were the main reason for eutrophication with 66.8% of contribution rather than Chla and SDD. Overall, the trophic state was dominated by TP, and SDD accounted for 85.6% of contribution in all sampled lakes. Additionally, we found using one-parameter index to evaluate the lake trophic state will lead to a great deviation, even with two levels of difference. Therefore, multi-parameter TSI is strongly recommended for the lake trophic state assessment. Summarily, our findings provide a theoretical and methodological basis for future large-scale estimations of lake TSI using satellite image data, help with water quality monitoring and management.

In this study, we empirically developed a robust model (the Root Mean Square Error (RMSE), bias, NSE and RE were 26.63 mg/L, -4.86 mg/L, 0.47 and 16.47%, respectively) for estimating the total suspended solids (TSS) concentrations in lakes and reservoirs (Hereinafter referred to as lakes) across the Eastern Plain Lake (EPL) Zone. The model was based on 700 in-situ TSS samples collected during 2007-2020 and logarithmic transformed red band reflectance of Landsat data. Based on the Google Earth Engine (GEE), the TSS concentrations in 16,804 lakes were mapped from 1984 to 2019. The results demonstrated a decreasing tendency of TSS in 82.2% of the examined lakes (72.5% of the basins) indicating that the pollutants carried by TSS flowing into the lakes were decreasing. Statistically significant variation (p < 0.05) was found in half of these lakes (28.6% of the basins). High TSS level (>100 mg/L) was observed in 0.31% of lakes (1.1% of the basins). The changing rates of TSS in 47.8% of the lakes (52.7% of the basins) ranged between -50 mg/L/yr and 0. We found high and significantly increased relative spatial heterogeneity of TSS in 4.6% and 6.5% of lakes, respectively. Likewise, the environmental factors, i.e., fertilizer usage, domestic wastewater, industrial wastewater, precipitation, wind speed and Normalized Difference Vegetation Index (NDVI) exhibited a significant correlation with interannual TSS in 38, 21, 20, 11, 17 and 15 of the 91 basins, respectively. This analysis indicated that only precipitation and fertilizer usage were significantly (p < 0.05) related to the spatial distribution of TSS. The relative contributions of the six factors to the interannual TSS changes were varied in different basins. Overall, the NDVI (the representation of vegetation cover) had a high mean contribution to the interannual TSS changes with an average contribution of 7.2%, and contributions of fertilizer were varied greatly among the basins (0.01%-68%). Human activities (fertilizer usage, domestic wastewater, industrial wastewater) and natural factors (precipitation, wind speed and NDVI) played relatively important roles to TSS changes in 14 and 15 of the 91 basins, respectively. Beyond the six factors in this study, other unanalyzed factors (such as lake depth and soil texture) also had some impacts on the distribution of TSS in the study area., 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 © 2021 Elsevier B.V. All rights reserved.)

Lake eutrophication has attracted the attention of the government and general public. Chlorophyll-a (Chl-a) is a key indicator of algal biomass and eutrophication. Many efforts have been devoted to establishing accurate algorithms for estimating Chl-a concentrations. In this study, a total of 273 samples were collected from 45 typical lakes across China during 2017-2019. Here, we proposed applicable machine learning algorithms (i.e., linear regression model (LR), support vector machine model (SVM) and Catboost model (CB)), which integrate a broad scale dataset of lake biogeochemical characteristics using Multispectral Imager (MSI) product to seamlessly retrieve the Chl-a concentration. A K-means clustering approach was used to cluster the 273 normalized water leaving reflectance spectra [Rrs (λ)] extracted from MSI imagery with Case 2 Regional Coast Colour (CR2CC) processor into three groups. The pH, electrical conductivity (EC), total suspended matter (TSM) and dissolved organic carbon (DOC) from three clustering groups had significant differences (p < 0.05**), indicating that water quality parameters have an integrated impact on Rrs(λ)-spectra. The results of machine learning algorithms integrating demonstrated that SVM obtained a better degree of measured- and derived- fitting (calibration: slope = 0.81, R 2  = 0.91; validation: slope = 1.21, R 2  = 0.88). On the contrary, the documented nine Chl-a algorithms gave poor results (fitting 1:1 linear slope < 0.4 and R 2  < 0.70) with synchronous train and test datasets. It demonstrated that machine learning provides a robust model for quantifying Chl-a concentration. Further, considering three Rrs(λ) clustering groups by k-means, Chl-a SVM model indicated that cluster 1 group gave a better retrieving performance (slope = 0.71, R 2  = 0.78), followed by cluster 3 group (slope = 0.77, R 2  = 0.64) and cluster 2 group (slope = 0.67, R 2  = 0.50). These are related to the low TSM and high DOC levels for cluster-1 and cluster-3 Rrs(λ) spectra, which reduce the influence of particle in red bands for Rrs(λ) signal. Our results highlighted the quantification of lake Chl-a concentrations using MSI imagery and SVM, which can realize the large-scale monitoring and more appropriate for medium/low Chl-a level. The remote estimation of Chl-a based on artificial intelligence can provide an effective and robust way to monitor the lake eutrophication on a macro-scale; and offer a better approach to elucidate the response of lake ecosystems to global change., 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 © 2021 Elsevier B.V. All rights reserved.)

As important components of dissolved organic matter (DOM) in an aquatic environment, colored DOM (CDOM) and dissolved organic carbon (DOC) play an essential role in the carbon cycle of an inland aquatic system. Traditionally, CDOM and DOC in inland waters have been primarily determined using in situ observations and laboratory measurements. Most of past lake investigations on CDOM and DOC focused on easily accessible regions and covered a small fraction of lakes worldwide. To our knowledge, little is known about lakes in less accessible areas like the Qinghai-Tibet Plateau (QTP). To address this challenge, optical satellite remote sensing might be useful for capturing a synoptic view of CDOM and DOC with high frequency at large scales, complementing in situ sampling methods for inland waters. In this study, 216 samples collected from 36 lakes across the QTP (2014-2017) were examined to determine the relationships between CDOM absorption coefficient at 350 nm (a 350 ) and Sentinel-2A Multi Spectral Instrument (MSI) imagery reflectance data. A strong positive linear correlation with a 350 was observed with B4/B2 (R 2  = 0.78, p < 0.01) and with B4/B3 (R 2  = 0.62). A multi-step regression model was established for estimating a 350 with B4/B2 and B4/B3 as input variables (R 2  = 0.81, p < 0.01). A scattered CDOM-DOC relationship was revealed (R 2  = 0.34, p < 0.05) using a pooled dataset. By dividing the inland waters into four separate groups in accordance with their salinity gradients, we were able to develop much stronger relationships (R 2  > 0.8, p < 0.01) for CDOM-DOC. Significant differences between fresh and saline waters were demonstrated using satellite-derived CDOM and DOC, where high CDOM (0.86 ± 0.67 m -1 ) and low DOC (3.76 ± 4.92 mg L -1 ) concentrations were observed for freshwaters, while inverse trends of CDOM (0.53 ± 0.72 m -1 ) and DOC (15.76 ± 17.07 mg L -1 ) were demonstrated for saline lakes in the Tibetan Plateau. This study confirmed that satellite optical imagery can be used for the monitoring of CDOM and DOC of the lakes of the Tibetan Plateau, which are sensitive to a changing climate and are infrequently investigated due to the harsh environment and poor accessibility. Moreover, it highlighted the importance of combining salinity and remote sensing data in the process of estimating lake DOC., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The proliferation of algal blooms (ABs) in lakes and reservoirs (L&Rs) poses a threat to water quality and the ecological health of aquatic communities. With global climate change, there is a concern that the frequency and geographical expansion of ABs in L&Rs could increase. China has experienced rapid economic growth and major land-use changes over the last several decades and therefore provides an excellent context for such an analysis. About 289,600 Landsat images were used to examine the spatiotemporal distribution of ABs in L&Rs (>1 km 2 ) across China (1983-2017). Results showed significant changes in the temporal slope of the sum of normalized area (0.26), frequency (2.28), duration (6.14), and early outbreak (-3.48) of AB events in L&Rs across China. Specifically, AB-impacted water bodies expanded longitudinally, and the time range of AB observation has expanded starting in the 2000s. Spearman correlation and random forest regression analyses further indicated that, among climatic factors, wind speed and temperature contributed the most to AB expansion. Overall, anthropogenic forces have overridden the imprints of climatic factors on the temporal evolution of ABs in China's L&Rs and therefore could inform policy decisions for the management of these resources.

The particle size distribution (PSD) slope (ξ) can indicate the predominant particle size, material composition, and inherent optical properties (IOPs) of inland waters. However, few semi-analytical methods have been proposed for deriving ξ from the surface remote sensing reflectance due to the variable optical state of inland waters. A semi-analytical algorithm was developed for inland waters having a wide range of turbidity and ξ in this study. Application of the proposed model to Ocean and Land Color Instrument (OLCI) imagery of the water body resulted in several important observations: (1) the proposed algorithm (754 nm and 779 nm combination) was capable of retrieving ξ with R2 being 0.72 (p < 0.01, n = 60), and MAPE and RMSE being 4.37% and 0.22 (n = 30) respectively; (2) the ξ in HZL was lower in summer than other seasons during the period considered, this variation was driven by the phenological cycle of algae and the runoff caused by rainfall; (3) the band optimization proposed in this study is important for calculating the particle backscattering slope (η) and deriving ξ because it is feasible for both algae dominant and sediment governed turbid inland lakes. These observations help improve our understanding of the relationship between IOPs and ξ, which are affected by different bio-optic processes and algal phenology in the lake environment., 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 © 2020 Elsevier Ltd. All rights reserved.)

Lake ice is an essential and integral part of the cryosphere and freshwater systems. The formation of lake ice affects the physical, hydrological, and biological conditions of ecological systems. Global warming may contribute to even shorter periods of ice cover in the lakes of the Frigid Zone, which adversely affects the growth of phytoplankton and primary productivity. This study was conducted for the purpose of evaluating the growth of phytoplankton and factors involved, in 28 ice-covered lakes across the Songnen Plain, in the Northeast of China, to understand how they take part in the whole-ecosystem functioning. A total of 1026 water samples were collected in April, September, and January during the period 2014-2018. In the frozen period, the concentration levels of dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP) were all comparable with the spring and autumn. Despite the limited light availability and low temperature, the phytoplankton survived in sub-ice waters during winter with a low concentration of chlorophyll a (Chla). Its average concentration was positively correlated with the concentration observed in the previous autumn (r p  = 0.563, p < 0.01). According to the regression tree analysis, during the winter period, Chla was mainly related to the concentration of TN in sub-ice water (TN water ) and with the difference of concentration of TP between water and ice (TP cd ). Furthermore, either in ice or in sub-ice water, the concentration of Chla was also significantly affected by total suspended matter (TSM) (p < 0.05). The levels of TN water , TP cd , and TSM could explain the 77.8% of the variance in the concentration of Chla during winter with contributions in the ranges of 25.5%-35.0%, 9.2%-11.3%, and 21.5%-34.0%, respectively (p < 0.05). This research substantially contributes to comprehending how the existing conditions under-ice affect the whole ecosystem when the ice cover is reduced lakes or rivers., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The Tibetan Plateau and Siberia are both crucial regions in which the vegetation dynamics are sensitive to climate change. The variabilities in the normalized difference vegetation index (NDVI) over the two regions have been explored previously, but there have been few studies on the relationship of the NDVI in the two regions. Using the GIMMS-NDVI, GHCN-CAMS and NCEP reanalysis datasets and statistical and physical diagnostic methods, we show that the summer (June, July and August) NDVI over the eastern Tibetan Plateau and Lake Baikal and its adjacent eastern region of Siberia have an in-phase co-variability, especially on an interannual timescale (with a correlation coefficient of 0.69 during the time period 1982-2014). Further analyses show that precipitation and the related cloud cover and solar radiation are responsible for the variability in the NDVI over the eastern Tibetan Plateau, whereas temperature has the more important role in modulating the variability in the NDVI over the Lake Baikal region. A dipole pattern prevails over the Tibetan Plateau-Lake Baikal region and reflects the anomalies in the intensity and location of the South Asian high and the northeast Asian blocking high. This dipole pattern simultaneously modulates precipitation over the eastern Tibetan Plateau and the temperature over the Lake Baikal region and leads to the co-variability of the NDVI between the two regions. A synergistic sea surface temperature index, which reflects sea surface temperature anomalies in the eastern tropical Pacific Ocean, the northwest Pacific Ocean, the northern Indian Ocean and the subtropical north Atlantic Ocean, appears to adjust this Tibetan Plateau-Lake Baikal dipole pattern and is therefore closely related to the co-variability of the NDVI between the eastern Tibetan Plateau and the Lake Baikal region. Our results suggest that vegetation dynamics may not be only a local phenomenon in some areas, but are also likely to remotely link with variations in vegetation over other regions., Competing Interests: The authors have declared that no competing interests exist.

In urban settings, one may find (i) lakes that are non-treated (NT) and impacted by recurrent discharges of pollutants and nutrients, and (ii) lakes that, through restoration measures and active management, are treated (T) from external inputs. The optical properties of chromophoric dissolved organic matter (CDOM) have been used to assess the anthropogenic impact on lakes ecology, but their application in comparative assessments of urban lakes has not been attempted. For 2 years, we measured nutrients and CDOM properties in water samples collected from NT and T lakes in the city of Changchun, China. Significant differences in CDOM properties were found between the two types of lakes, and these results were supported by redundancy analysis. The NT lakes were eutrophic while the T lakes were mesotrophic, with mean trophic status index (TSI) of 74.2 and 50.3, respectively. The CDOM absorption coefficient at 350 nm, a(350), was 2-fold higher in NT than in T lakes (6.59 vs 3.21 m -1 ). In the NT lakes, CDOM components predominantly comprised large molecular weight (MW > 1000-Da) humus-like substances of allochthonous origin, whereas in the T lakes CDOM was dominated by low MW (<1000-Da) substances from autochthonous production. Seasonal fluctuation has a great influence on the CDOM concentration, but a little influence on its molecular composition. The CDOM concentration were higher in summer than in other seasons. Weather conditions (rainfall, temperature) and biophysical processes (biodegradation, photo-bleaching) likely contributed to these variations. We found the water quality of the treated lakes was getting better from 2016 to 2018. In summary, the study results, not only revealed seasonal effects, but most importantly documented the impact of human activities on the characteristics of CDOM in urban lakes. Most specifically, the sharp difference between the lakes in regard to a(350) (2-fold lower in T than in NT lakes) demonstrated the suitability CDOM absorption coefficient as an early indicator of the impact of treatment measures on the hydrochemistry of DOM in urban lakes., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The comprehensive analysis of the relationships between the attenuation of photosynthetic active radiation (K d (PAR)) and light absorption is an imperative requirement to retrieve K d (PAR) from remote sensing data for aquatic environments. The spatial distributions of the K d (PAR) and light absorption of optically active components (a OACs ) were routinely estimated in China lakes and reservoirs. Spatial K d (PAR) was relatively dependent on the inorganic particles (average relative contribution of 57.95%). The a OACs could explain 70-87% of K d (PAR) variations. A linear model is used to predict K d (PAR), as a function of light absorption coefficient of phytoplankton (a phy ), colored dissolved organic matter (a CDOM ), and inorganic particles (a NAP ): K d (PAR) = 0.41 + 0.57 × a CDOM + 0.96 × a NAP + 0.57 × a phy (R 2 = 0.87, n = 741, p < 0.001). In the lakes with low TSM concentration and non-eutrophic lakes with high TSM, a CDOM was the most powerful predicting factor on K d (PAR). In eutrophic lakes with high TSM, a NAP had the most significant impact on K d (PAR). This study allowed K d (PAR) to be predicted from a OACs values in the inland waters.

High-frequency and reliable data on cyanobacteria blooming over a long time period is crucial to identify the outbreak mechanism of blooms and to forecast future trends. However, in cloudy and rainy areas, it is difficult to retrieve useful satellite images, especially in the rainy season. To address this problem, we used data from the HJ-1/CCD (Chinese environment and disaster monitoring and forecasting satellite/charge coupled device), GF-1/WFV (Chinese high-resolution satellite/wide field of view), and Landsat-8/OLI (Operational Land Imager) satellites to generate a time series of the bloom area from 2009 to 2016 in Dianchi Lake, China. We then correlated the responses of bloom dynamics to meteorological factors. Several findings can be drawn: (1) a higher bloom frequency and a larger bloom area occurred in 2011, 2013, and 2016, compared to the other years; (2) the frequency of blooms peaked in April, August, and November each year and expanded from north to south starting in July; (3) air temperature in spring and sunshine hours in summer greatly correlated to the yearly bloom area; (4) wind speed and sunshine hours strongly affected the short-term expansion of blooms and thereafter influenced the monthly bloom scale; and (5) rainfall had a strong short-term influence on the occurrence of blooms. Cyanobacteria blooms often occurred when wind speeds were less than 2.35 ± 0.78 m/s in the dry season and 2.01 ± 0.75 m/s in the rainy season, when there were 48 to 72 h of sunshine in the dry season and 35 to 57 h of sunshine in the rainy season, and when there was more than 10 mm of daily precipitation.

Colored dissolved organic matter (CDOM) is a major component of DOM in waters, and plays a vital role in carbon cycling in inland waters. In this study, the light absorption and three-dimensional excitation-emission matrix spectra (EEMs) of CDOM of 936 water samples collected in 2014-2017 from 234 lakes in five regions across China were examined to determine relationships between lake water sources (fresh versus saline) and their fluorescence/absorption characteristics. Results indicated significant differences regarding DOC concentration and a CDOM (254) between freshwater (6.68 mg C L -1 , 19.55 m -1 ) and saline lakes (27.4 mg C L -1 , 41.17 m -1 ). While humic-like (F 5 ) and fulvic-like (F 3 ) compounds contributed to CDOM fluorescence in all lake waters significantly, their contribution to total fluorescence intensity (F T ) differed between saline and freshwater lakes. Significant negative relationships were also observed between lake altitude with either F 5 (R 2  = 0.63, N = 306) or F T (R 2  = 0.64, N = 306), suggesting that the abundance of humic-like materials in CDOM tends to decrease with increased in lakes altitude. In high-altitude lakes, strong solar irradiance and UV exposure may have induced photo-oxidation reactions resulting in decreased abundance of humic-like substances and the formation of low molecular weight compounds. These findings have important implications regarding our understanding of C dynamics in lacustrine systems and the contribution of these ecosystems to the global C cycle., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Chlorophyll-a (Chl-a) concentrations are usually measured as the proxy of phytoplankton biomass and used to evaluate the trophic status of inland waters. Based on 49 in situ samples taken from two measurement campaigns in Lake Hongze in 2016, we evaluate the performance of five Chl-a estimation algorithms (including the band ratio, three-band, FLH algorithm, MCI, and UMOC algorithms). The results showed that the UMOC model was the most suitable model for the estimation of Chl-a in Lake Hongze. The mean relative error (MRE) of UMOC was 32.30%, much lower than the band ratio algorithm (75.17%), three-band algorithm (62.44%), FLH algorithm (45.87%), and MCI algorithm (56.95%). The best-performing UMOC model was applied to the atmospherically corrected 689 MERIS images between 2002-2012 and long time series MERIS Chl-a concentration estimation products were acquired. Between 2002 and 2012, the mean Chl-a concentration in Lake Hongze was 19.560 mg·m -3 with substantial spatial and temporal variability. Based on the variability of monthly mean Chl-a concentrations in each pixel, the Lake Hongze waterbody was divided into three water types, Region A, Region B, and Region C. The annual mean Chl-a concentrations of Region B and Region C showed no significant changes, while the concentrations in Region A increased markedly. The analysis of the meteorological factors showed that the fluctuations of the annual mean Chl-a concentrations in Region B and Region C were mainly affected by annual precipitation, suggesting that the Chl-a concentrations of these two regions are dominated by the intensity of the lake flow. The annual mean Chl-a concentrations of Region A showed a strong negative correlation with the annual mean wind speed. The descending trend of the annual wind speed may enhance the eutrophication degree of this region, threatening the safety of the water quality of the South-North Water Transfer Project. The Chl-a concentrations showed a strong positive correlation with the distance from the Huaihe Estuary in the wet season suggesting that the Huaihe River has an obvious inhibitory effect on algal biomass in Lake Hongze during this period.

Total suspended matter is an important water quality parameter. Firstly, a semi-analytical model for inversing concentration of total suspended matter was constructed by simplifying the bio-optical model, using the in-situ data of Lake Taihu in spring and autumn and that of Lake Chaohu in summer. And then, its applicability was validated by MERIS and HJ-1 HSI data. The results showed that: (1) The optical range for inversing total suspended matter in Lake Taihu and Chaohu was between 730 nm and 832 nm except the oxygen absorption bands. (2) According to MERIS data, bands centered at 754 nm and 779 nm were both suitable for inversing total suspended matter in Lake Taihu whereas the band centered at 761 nm was not suitable due to the influence of oxygen absorption. (3) According to MERIS data, the relative error of this model increased with the time difference after satellite transit time. The relative errors of inversion results of this model were below 50% when the time difference was less than 3 hours, while they increased to more than 50% when the time difference exceeded 3 hours. (4) The seventeen bands of HJ-1 HSI data were all suitable for inversing total suspended matter in Lake Chaohu and its result was better than that in Lake Taihu using MERIS data.

Lake Xingkai is a large turbid lake composed of two parts, Small Lake Xingkai and Big Lake Xingkai, on the border between Russia and China, where it represents a vital source of water, fishing, water transport, recreation, and tourism. Chlorophyll-a (Chla) is a prominent phytoplankton pigment and a proxy for phytoplankton biomass, reflecting the trophic status of waters. Regularly monitoring Chla concentrations is vital for issuing timely warnings of this lake's eutrophication. Owing to its higher spatial and temporal coverages, remote sensing can provide a synoptic complement to traditional measurement methods by targeting the optical Chla absorption signals, especially for the lakes that lack regular in situ sampling cruises, like Lake Xingkai. This study calibrated and validated several commonly used remote sensing Chla retrieval algorithms (including the two-band ratio, three-band method, four-band method, and baseline methods) by applying them to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in Lake Xingkai. Among these algorithms, the four-band model (FBA), which removes the absorption signal of detritus and colored dissolved organic matter, was the best-performing model with an R2 of 0.64 and a mean absolute percentage difference of 38.26%. With the FBA model applied to OLCI images, the monthly and spatial distributions of Chla in Lake Xingkai were studied from 2016 to 2022. The results showed that over the seven years, the Chla concentrations in Small Lake Xingkai were higher than in Big Lake Xingkai. Unlike other eutrophic lakes in China (e.g., Lake Taihu and Lake Chaohu), Lake Xingkai did not display a stable seasonal Chla variation pattern. We also found uncertainties and limitations of the Chla algorithm models when using a larger satellite zenith angle or applying it to an algal bloom area. Recent increases in anthropogenic nutrient loading, water clarity, and warming temperatures may lead to rising phytoplankton biomass in Lake Xingkai, and the results of this study can be applied for the satellite-based monitoring of its water quality. [ABSTRACT FROM AUTHOR]

Cyanobacterial blooms represent a significant environmental problem, threatening aquatic ecosystems worldwide. Caused by the eutrophication of water bodies and global climate change, these blooms have altered freshwater ecosystems worldwide during recent decades. Although cyanobacterial blooms are typically caused by blue-green cyanobacteria, which derive their color from the phycocyanin pigment, other pigmented cyanobacterial blooms have been frequently observed in water bodies. These blooms pose a serious environmental threat to inland waters, endangering global public health and aquatic ecosystems. Therefore, comprehending the mechanism of color variation in cyanobacterial blooms is crucial for revealing the outbreak mechanism and implementing effective prevention and control measures. This study developed a human–machine interactive workflow for extracting cyanobacterial blooms and recognizing their colors based on the Forel-Ule index and Sentinel-2 MultiSpectral Instrument data. Using this workflow, the authors conducted spatiotemporal analysis and statistical analysis of bloom color for cyanobacterial blooms in four typical eutrophic lakes from 2019 to 2022. The findings indicated a declining trend in cyanobacterial blooms across the four studied lakes over the years, among which Hulun Lake experienced an annual increase in cyanobacterial blooms and emerged as the lake with the most severe outbreak of such blooms in 2022. The yellowing status of cyanobacterial blooms varied among the four lakes, with Taihu Lake and Dianchi Lake exhibiting a relatively high proportion of green-yellow and yellow cyanobacterial blooms, followed by Chaohu Lake, whereas Hulun Lake had the lowest occurrence. The workflow developed in this study was implemented in Google Earth Engine and provided an automated, integrated, and rapid monitoring solution for the long-term monitoring and color recognition of cyanobacterial blooms. [ABSTRACT FROM AUTHOR]

Lake turbidity, representing a general indicator of water 'cloudiness', is a key parameter in many monitoring programs. It is not possible to cover all lakes with frequent in situ monitoring. Sentinel-2 MultiSpectral Imager (MSI) can help to fill the gaps if a robust turbidity retrieval methodology is developed. Previously published results demonstrated the usefulness of MSI at a limited regional scale, while our aim was to develop methodology that allows monitoring turbidity over the whole of China. We proposed methodology with a reflectance that can be classified into optical water types (OWTs), and then a back propagation neural network model (BP-TURB) is used to estimate turbidity. The reflectance of in situ lake samples extracted from MSI imagery was clustered as three OWTs, and validation performance was satisfactory: R2 > 0.81, RMSE < 17.54, and MAE < 11.20. This allowed us to map turbidity in all Chinese lakes, of which the area is larger than 1 km2. A larger percentage of clear lakes (53.26%) with low turbidity levels (<10 NTU) was found in 2020 than in 2015 (37.43%). Lakes in the plateau regions generally exhibited lower turbidity than those situated in the plains regions, for which the turbidity patterns were determined by lake volume, averaged depth, and elevation. We demonstrated that the Sentinel-2 MSI data with the novel approach proposed by us allows for mapping lake turbidity over a large variety of lakes and extensive geographic conditions, as well as for revealing temporal changes in these lakes and their links to lake abiotic characteristics. [ABSTRACT FROM AUTHOR]

The intensification of anthropogenic activities has led to the infiltration of enormous quantities of pollutants into rivers and lakes, resulting in significant deterioration in water quality and a more prominent occurrence of eutrophication. Poyang Lake, the largest freshwater lake in China, is facing a severe challenge related to eutrophication, which seriously threatens the delivery of the ecosystem service and the safety of drinking water. To address this challenge, Landsat-8 Operational Land Imager (OLI) data for the Poyang Lake Basin (PLB) from May 2013 to December 2020 were used. Since inland water bodies with complex optical characteristics, we developed a semi-analytical algorithm to assess the trophic state of the water based on two cruise field measurements in 2016 and 2019. Combining the semi-analytical trophic level index (TLI) with an atmospheric correction model is the most suitable model for OLI images of the PLB, this model was then applied to Landsat-8 time series observations. The trends of the trophic state of water bodies in PLB were revealed, and the annual, quarterly and monthly percentages of eutrophic water bodies were calculated. Natural and anthropogenic factors were then used to explain the changes in the trophic state of the PLB waters. The main findings are as follows: (1) From the 8-year observation results, it can be seen that the variation of trophic level of water in PLB showed obviously spatial and temporal variations, characterized by higher in the north than in the south and higher in winter than in summer. (2) Temperature promoted the growth of harmful algae and plays an essential role in affecting changes in the trophic level of the water. (3) Changes in the trophic level of water bodies in PLB were mainly affected by human activities. The results of spatial and temporal variation of the trophic level of water and the driving factors in PLB can extend our knowledge of water quality degradation and provide essential references for relevant policy-making institutions. [ABSTRACT FROM AUTHOR]

Algal blooms in lakes have become a common global environmental problem. Nowadays, remote sensing is widely used to monitor algal blooms in lakes due to the macroscopic, fast, real-time characteristics. However, it is often difficult to distinguish between algal blooms and aquatic vegetation due to their similar spectral characteristics. In this paper, we used modified vegetation presence frequency index (VPF) based on Moderate-resolution Imaging Spectroradiometer (MODIS) imagery to distinguish algal blooms from aquatic vegetation, and analyzed the spatial and temporal variations of algal blooms and aquatic vegetation from a phenological perspective for five large natural lakes with frequent algal bloom outbreaks in China from 2019 to 2020. We simplified the VPF method to make it with a higher spatial transferability so that it could be applied to other lakes in different climatic zones. Through accuracy validation, we found that the modified VPF method can effectively distinguish between algal blooms and aquatic vegetation, and the results vary from lake to lake. The highest accuracy of 97% was achieved in Hulun Lake, where the frequency of algal outbreaks is low and the extent of aquatic vegetation is stable, while the lowest accuracy of 76% was achieved in Dianchi Lake, which is rainy in summer and the lake is small. Analyses suggests that the time period when algal blooms occur most frequently might not coincide with that when they have the largest area. However, in most cases these two are close in terms of time period. The modified VPF method has a broad scope of application, is easy to implement, and has a high practical value. Furthermore, the method could be established using only a small amount of measured data, which is useful for water quality monitoring on large spatial scales. [ABSTRACT FROM AUTHOR]

Algal blooms frequently occur in numerous lakes in China, risking human health and the environment. In contrast, aquatic vegetation contributes to water purification. Due to the similar spectral characteristics shared by algal and aquatic vegetation, both are hardly distinguishable in remote sensing imaging, especially in turbid water bodies. To address this challenge, this study constructed a method to effectively extract algal blooms and aquatic vegetation from the turbid water bodies using Sentinel 2 images with high spatial resolution. Our results showed that the accuracy of the extraction of vegetation information could reach 96.1%. Since this method combined the vegetation extraction results from multiple indices, it effectively tackled the mis-extraction when only the Floating Algae Index (FAI) or the Normalized Difference Vegetation Index (NDVI) is used in water with high turbidity. By combining the image time series information with the natural phenological characteristics of the aquatic vegetation and algal blooms, an improved Vegetation Presence Frequency (VPF) was developed. It effectively distinguished algal blooms and aquatic vegetation without actual measurement data. Based on the above method and process, the information of algal blooms and aquatic vegetation was sufficiently distinguished in five typical lakes in China (Lake Hulun, Lake Hongze, Lake Chaohu, Lake Taihu, and Lake Dianchi), and the spatial distribution was reasonably mapped. The overall identification accuracy of aquatic vegetation and algal blooms using the improved VPF ranged 71.8–84.3%. The spatial transferability test of the method in the independent lakes with the various optical properties indicated the prospects of its application in other turbid water bodies. This study should provide strong methodological and theoretical support for future monitoring of algal blooms in turbid water bodies with vigorous aquatic vegetation, especially in the absence of actual measurement data. This should have practical relevance for water environment management and governance departments. [ABSTRACT FROM AUTHOR]

Water clarity serves as a sensitive tool for understanding the spatial pattern and historical trend in lakes' trophic status. Despite the wide availability of remotely sensed data, this metric has not been fully explored for long-term environmental monitoring. To this end, we utilized Landsat top-of-atmosphere reflectance products within Google Earth Engine in the period 1984–2018 to retrieve the average Secchi disk depth (SDD) for each lake in each year. Three SDD datasets were used for model calibration and validation from different field campaigns mainly conducted during 2004–2018. The red / blue band ratio algorithm was applied to map SDD for lakes (>0.01 km 2) based on the first SDD dataset, where R2=0.79 and relative RMSE (rRMSE) =61.9 %. The other two datasets were used to validate the temporal transferability of the SDD estimation model, which confirmed the stable performance of the model. The spatiotemporal dynamics of SDD were analyzed at the five lake regions and individual lake scales, and the average, changing trend, lake number and area, and spatial distribution of lake SDDs across China were presented. In 2018, we found the number of lakes with SDD <2 m accounted for the largest proportion (80.93 %) of the total lakes, but the total areas of lakes with SDD of <0.5 and >4 m were the largest, both accounting for about 24.00 % of the total lakes. During 1984–2018, lakes in the Tibetan–Qinghai Plateau region (TQR) had the clearest water with an average value of 3.32±0.38 m, while that in the northeastern region (NLR) exhibited the lowest SDD (mean 0.60±0.09 m). Among the 10 814 lakes with SDD results for more than 10 years, 55.42 % and 3.49 % of lakes experienced significant increasing and decreasing trends, respectively. At the five lake regions, except for the Inner Mongolia–Xinjiang region (MXR), more than half of the total lakes in every other region exhibited significant increasing trends. In the eastern region (ELR), NLR and Yungui Plateau region (YGR), almost more than 50 % of the lakes that displayed increase or decrease in SDD were mainly distributed in the area range of 0.01–1 km 2 , whereas those in the TQR and MXR were primarily concentrated in large lakes (>10 km 2). Spatially, lakes located in the plateau regions generally exhibited higher SDD than those situated in the flat plain regions. The dataset is freely available at the National Tibetan Plateau Data Center (10.11888/Hydro.tpdc.271571, Tao et al., 2021). [ABSTRACT FROM AUTHOR]

Water clarity provides a sensitive tool to examine spatial pattern and historical trend in lakes trophic status. Yet, this metric has insufficiently been explored despite the availability of remotely-sensed data. We used three Secchi disk depth (SDD) datasets for model calibration and validation from different field campaigns mainly conducted during 2004-2018. The red/blue band ratio algorithm was applied to map SDD for lakes (> 1 ha) based on the first SDD dataset, where R2 = 0.79, RMSE = 100.3 cm, rRMSE = 61.9 %, MAE = 57.7 cm. The other two datasets were used to validate the SDD estimation model, which were indicated the model had a stable performance of temporal transferability. The annual mean SDD of lakes were retrieved across China using Landsat top of air reflectance products in GEE from 1984 to 2018. The spatiotemporal dynamics of SDD were analysed at the five lake regions and individual lake scales, and the average, changing trend, lake number and area, and spatial distribution of lake SDDs across China were presented. In 2018, we found that the lakes with SDDs < 2 m accounted for the largest proportion (80.93 %) of the total lakes, but the total area of lakes with SDD between 0-0.5 m and > 4 m were the largest, accounting for 48.28 % of the total lakes. During 1984-2018, lakes in the Tibetan-Qinghai Plateau lake region (TQR) had the clearest water with an average value of 3.32 ± 0.38 m, while that in the Northeastern lake region (NLR) exhibited the lowest SDD (mean: 0.60 ± 0.09 m). Among the 10,814 lakes with SDD results more than 10 years, 55.42 % and 3.49 % of lakes experienced significant increasing and decreasing trends, respectively. At the five lake regions, except for the Inner Mongolia-Xinjiang lake region (MXR), more than half of the total lakes in every other lake region exhibited significant increasing trends. In the Eastern lake region (ELR), NLR and Yungui Plateau lake region (YGR), almost more than 50 % of the lakes that displayed an increase or decrease in SDD were mainly distributed in an area of 0.01-1 km2, whereas that in the TQR and MXR were primarily concentrated in large lakes (> 10 km2). Spatially, lakes located in the plateau regions generally exhibited higher SDD than those situated in the flat plain regions. The dataset can now be accessed through the website of the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn): DOI: 10.11888/Hydro.tpdc.271571. [ABSTRACT FROM AUTHOR]

The water clarity of many inland water bodies in the Inner Mongolian Plateau of China is under threat due to intensifying land use pressures and climate variability. Total suspended matter (TSM), one of the main factors determining water clarity, is a parameter that can be remotely derived. Two data-gathering expeditions were conducted in Hulun Lake, the largest inland lake of this plateau, in 2013 and 2015. Based on the in situ TSM data, we evaluated commonly used TSM algorithms that can be applied to Landsat images. The algorithm using the single near infrared (NIR) band had the best performance. Time series of TSM maps of Hulun Lake from 1986 to 2017 were obtained applying this algorithm to 258 cloudless Landsat images and the relationship of TSM to water level, water area, precipitation, and wind speed were analysed. We found that annual mean TSM was negatively correlated with water level and water area, with R2 of 0.46 and 0.60, respectively; TSM was positively correlated with precipitation and wind speed, with R2 of 0.32 and 0.34, respectively. [ABSTRACT FROM AUTHOR]

Abstract Eutrophication of lakes has become one of the world's most serious environmental problems, resulting in an urgent need to monitor and provide safeguards to control water quality. Results from analysis of lake trophic status based on calculated throphic state index (TSI) showed that 69.5% of the surveyed 277 lakes were in a state of eutrophication. Significant logarithmic relationships between light absorption of optically active components (a OACs) and TSI (R2 = 0.78) existed: TSI = 13.64 × ln(a OACs)+43.24, and the regression relationship between a OACs and TSI had a better degree of fit (R2) than the currently used reflectance-TSI relationship. a OACs appeared to be a good predictor of TSI estimation in lake ecosystems. The relationship coefficient (a OACs -TSI) slightly varied with lake type, and relationships in saline lakes and phy-type lakes were shown to be more robust than the relationship with the total lake data. This study highlights the quantification of the trophic status in lakes using a OACs , which realized the monitoring of trophic status in lakes using inherent optical properties on a large-scale. To our knowledge this is the first investigation to assess the variability of trophic status in lakes across China. The assessment trophic state of lakes based on a OACs provides a new way to monitor the trophic status of lakes, and findings may have applications for monitoring large-scale and long-term trophic patterns in lakes using remote sensing techniques. Graphical abstract Image 1 Highlights • Describe the variability of trophic status in lakes across China. • Quantify the lake trophic status by light absorption of optically active components. • Analyze the relationships between a OACs and trophic state index in different lakes. • a OACs appeared to be a powerful predictor of TSI M estimation in lakes. Quantify the lake trophic status by light absorption of optically active components, and a OACs appeared to be a powerful predictor of TSI estimation in lakes. [ABSTRACT FROM AUTHOR]

Knowledge of tempo-spatial dynamics of water quality and its response to river flow is important for the management of lake water quality because river discharge associated with rainstorms can be an important source of pollutants to the estuary. Total phosphorus (TP), chlorophyll a (Chl-a), and total suspended matter (TSM) are important indexes of water quality and important factors influencing eutrophication and algal blooms. In this study, remote sensing was used to monitor these indexes to investigate the effects of river discharge on the estuary of Taihu Lake by the largest inflow river which is Chendong River using a total of 136 Geostationary Ocean Color Images (GOCI). In situ datasets collected during the four cruise experiments on Taihu Lake between 2011 and 2015 were used to develop the TP, Chl-a, and TSM inversion models based on simple empirical algorithms: 154 points for TP (mg/L), 114 for Chl-a (μg/L), and 181 for TSM (mg/L). The spatial and temporal changes of the concentration of the three parameters in the Chendong River estuary were analyzed by combining the GOCI data, the flow of the Chendong River, and meteorological data throughout the year in 2014. The several key findings are as follows: (1) In summer and autumn, TP, Chl-a, and TSM contents were significantly higher than in winter and spring. TP and Chl-a have a few similar distribution characteristics. And organic suspended matter in summer was the main reason for the increase of the TSM concentration. (2) The severe surface erosion in the rivers cannot be ignored; the high erodibility is an important factor in the increase of TP and TSM concentrations in the estuary. The concentration of the water quality parameter showed exponential decay with distance from the shore. The concentration decreased slowly after 12 km and then remained essentially constant. (3) TP content in the Chendong River estuary decreased under steady flow inputs and dramatically increased when the flow became large. The increase in Chl-a content was linked to higher levels of TP and good weather conditions after the rain event. Higher flow rates mainly play a dilution role for the Chl-a concentration. Erosion of the surface soil via rainfall is a major source of TSM to the estuary. This paper firstly analyzes tempo-spatial dynamics of water quality and its response to river flow in estuary of Taihu Lake, helps to further understand the impact of river input on lake water quality, and is important for lake eutrophication. [ABSTRACT FROM AUTHOR]

Land target-based atmospheric correction methods over inland waters are an effective alternative method for when the standard or improved atmospheric correction method that is based on water is invalid. The type of aerosol model that is selected and land dark target that is distinguished are two key factors that could determine the atmospheric correction’s accuracy. We present an improved land target-based iterative method [denoted as dense dark vegetation (DDV)-WC] for the atmospheric correction of Moderate Resolution Imaging Spectroradiometer (MODIS) over Lake Taihu. The improvements include the use of aerosol models in Optical Properties of Aerosols and Clouds (OPAC), which fully consider the impact of relative humidity (RH) on aerosol properties, and a rigorous land dark target selection rule (DDV-WC-Selection). The DDV-WC algorithm includes three steps: the selection of DDV pixels from lakeshore areas, the retrieval of the aerosol optical thickness (AOT) at 550 nm (AOT550) for the selected DDV pixels, and the retrieval of the remote sensing reflectance (Rrs) over water. The AOT550 inversion accuracy of different aerosol models in OPAC is tested in Lake Taihu. The “continental clean model” has the best performance compared with five other aerosol models for every month of the year. Therefore, the aerosol model of the DDV-WC algorithm is fixed to the “continental clean model” in Lake Taihu because of its stable and excellent performance. When using the “continental clean model” to perform these retrievals, the DDV pixels selection (DDV-WC-Selection) in the DDV-WC method (\text MRE(\text Mean\;\text Relative\;\text Error) = 14.63\% ) is more reliable than the DDV pixels selection (DDV-Selection) in the MODIS V5.2 method (\text MRE = 17.24\% ). The atmospheric correction of MODIS indicates that this approach produces superior performance in the green and red bands but poor results in the near-infrared (NIR) and blue bands. The MRE is only 13.15% at 645 nm. These findings illustrate that the DDV-WC algorithm is a practical MODIS atmospheric correction method for inland turbid waters. [ABSTRACT FROM PUBLISHER]

The chlorophyll-a (Chl-a) concentration of eutrophic lakes fluctuates significantly due to the disturbance of wind and anthropogenic activities on the water body. Consequently, estimation of the Chl-a concentration has become an immense challenge. Due to urgent demand and rapid development in high-resolution earth observation systems, it has become crucial to assess hyperspectral satellite imagery capabilities on inland water monitoring. The Orbita hyperspectral (OHS) satellite is the latest hyperspectral sensor with both high spectral and spatial resolution (2.5 nm and 10 m, respectively), which could provide great potential for remotely estimating the concentration of Chl-a for inland waters. However, there are still some deficiencies that are mainly manifested in the Chl-a concentration remote sensing retrieval model assessment and accuracy validation, as well as signal-to-noise ratio (SNR) estimation of OHS imagery for inland waters. Therefore, the radiometric performance of OHS imagery for water quality monitoring is evaluated in this study by comparing different atmospheric correction models and the SNR with several remote sensing images. Several crucial findings can be drawn: (1) the three-band model ((1/B15-1/B17)B19) developed by OHS imagery is most suitable for estimating the Chl-a concentration in Dianchi Lake, with the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE) of 15.55 µg/L and 16.31%, respectively; (2) the applicability of the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model for OHS imagery in a eutrophic plateau lake (Dianchi Lake) was better than the 6S (Second Simulation of Satellite Signal in the Solar Spectrum) model, and QUAC (Quick Atmospheric Correction) model, as well as the dark pixel method; (3) the SNR of the OHS imagery was similar to that of Hyperion imagery and was significantly higher than SNR of the HSI imagery; (4) the spatial resolution showed slight influence on the SNR of the OHS imagery. The results show that OHS imagery could be applied to remote sensing retrieval of Chl-a in eutrophic plateau lakes and presents a new tool for dynamic hyperspectral monitoring of water quality. [ABSTRACT FROM AUTHOR]

The eutrophication of lake and reservoir (hereafter referred to as lakes) has attracted concerns from the public and government in China. Water clarity is a reliable indicator for quantifying eutrophic status because of its strong association with chlorophyll-a, total suspended matter, and nutrients. Traditionally, water clarity is measured using Secchi disk depth (SD). By linking the spectral signal from water surface with in situ measured SD, remote sensing provides a useful tool for SD estimation at a large scale in a repetitive manner. Remote sensing derived water clarity has been reported in many regions with specific models established for different satellite overpasses concurrent with in situ measured SD, but national water clarity remained unknown in China. In this study, 2152 samples were collected from 34 field campaigns in 2013–2018, of which 1792 samples were gathered within ± 7 days of Landsat OLI overpasses. We used Landsat 8 OLI bands 1–4 to develop regression models (n = 1016), and 782 samples to validate model performances. We further collected three additional in situ SD datasets to validate the best performance model, and eventually used it to map SD at a national scale with OLI images mainly acquired in 2016. Our results indicated that the entire dataset of SD has a strong association with Landsat reflectance, yielding low root mean square error between measured and estimated SD (RMSE = 63 cm) for lakes in China. The national water clarity was averaged to 176 cm in 2016 with large spatial variability (S.D: 216 cm) due to the marked variation between turbid waters in the east plain area and clean water across the Tibet Plateau. Lakes in the northeastern (75 cm) and eastern (84 cm) China had low clarity due to shallow water depth combined with high suspended matter and algal abundance. Lakes in the Yungui Plateau (91 cm), Inner Mongolia and Xinjiang autonomous regions (114 cm) exhibited intermediate clarity; while lakes in the Tibet Plateau (294 cm) displayed the highest clarity. This investigation demonstrated that Landsat imagery with Rayleigh scattering correction reflectance combined with in situ observation can provide quantitative information about the lake clarity with surface area >8 ha. Moreover, this method has the potential to retrieve SD with archived Landsat imagery to determine the temporal variation of SD at national or continental scale which can be used to support inland water management and decision-maker for improving water quality. • The rhos method was the best of four atmospheric correction methods tested. • Model based on red and blue band performed stable for national clarity estimate. • Model based on the rhos method demonstrated spatiotemporal transferability. • China's water clarity of lake and reservoir (area > 8 ha) was mapped nationally. • Lake SDD in the TQR and NLR demonstrated the max and min clarity in China. [ABSTRACT FROM AUTHOR]

Total suspended matter (TSM) has a crucial impact on light propagation in the water column, and often co-varies with nutrients, heavy metal and micropollutant fluxes. The objective of this study was to explore the feasibility of mapping TSM in lakes (area > 1 ha) across China using Landsat surface reflectance product embedded in Google Earth Engine. We conducted 44 sampling campaigns at 423 natural and manmade lakes across China, and determined TSM from 2036 water samples collected during 2011–2020. Landsat surface reflectance was matched with water sampling events within ±7 days of satellite overpasses, yielding 1908 matched pairs. We divided the TSM dataset into nine subsets, with eight subsets dedicated to building of simple regression and Random Forest (RF) models, and the remaining subset used to validate model performance. Regression analysis indicated strong associations between TSM concentration and both the Red band (Band 4; R2 = 0.76, RMSE = 21.4 mg/L) and the suspended matter index [(Nir + Red)/2); R2 = 0.71, RMSE = 17.1 mg/L]. The RF model outperformed the Red band model as indicated by higher coefficient of determination for model calibration (R2 = 0.95) and validation (R2 = 0.81). We also assembled samples (N = 1703) from lakes in different continents, and both the RF (RMSE = 16.04 mg/L) and Red band (RMSE = 18.9 mg/L) models exhibited acceptable performance. The models were further evaluated using a time series of TSM records from six large lakes in the USA and Japan (in situ TSM collected during 1988–2018). Finally, we used 460 scenes of Landsat/OLI images mainly acquired in 2019 to map TSM in lakes across China. Results showed considerable regional variability (TSM range: 0.12–860 mg/L), with lakes in Northeast China (44.6 mg/L), East China (38.8 mg/L) and Xinjiang-Inner Mongolia (39.7 mg/L) exhibiting much higher TSM concentration than lakes in the Yungui Plateau (17.62 mg/L) and the Tibetan Plateau (5.31 mg/L). Results confirmed the stability and spatial transferability of both models. Given the relationships among TSM and various indicators (e.g. nutrients, water clarity, chlorophyll-a) of the trophic state of aquatic ecosystems, these models would greatly facilitate TSM monitoring in lakes, and provide water resources managers with additional tools to assess the impact of water protection measures. [ABSTRACT FROM AUTHOR]

Lakes and reservoirs provide important ecosystem services that support human welfare and socio-economic activities. However, in many world regions, the ecological integrity of lakes and reservoirs is threatened by human perturbations and climate change. The Secchi disk depth (SDD) is a widely-used proxy representing the trophic status of lakes and reservoirs and can be retrieved from remote sensing data. Despite their potential for large-scale (regional, global) and long-term lake and reservoir water clarity assessment, the transferability of remote sensing-based models has been a major limitation. In this study, we assembled in situ SDD in lakes and reservoirs across North America (NA) from five different sources. We identified a subset of 3235 samples collected within ±7 days of a Landsat satellite overpass. Relationships between various spectral index models calculated from Landsat top-of-atmosphere reflectance and in situ SDD were analyzed. A model based on Landsat blue/green plus blue/red ratios (denoted as RGRB) was selected to retrieve the SDD of all NA lakes and reservoirs. The RGRB model performed well during calibration (R2 = 0.81) and validation (R2 = 0.78, MAPE = 30.85 %). This model also exhibited stable and reliable performances regardless of the Landsat sensors (TM, ETM+, and OLI), despite spectral configuration differences among these sensors. RGRB was implemented to generate SDD maps for all lakes and reservoirs (water surface area ≥1 ha) across NA in 2019. More than 2.9 million lakes and reservoirs were mapped with Landsat OLI images, resulting in an average SDD of 3.84 ± 1.77 m. A strong positive relationship between average SDD and log-transformed water surface area (R2 = 0.80, p < 0.001) indicated that large lakes and reservoirs tend to be more transparent than small ones. Latitudinal variations were found in the water clarity gradient, with maximum SDD recorded at the 35°N–60°N latitude and lower SDD at the 10°N–30°N latitude. This model can be implemented using the Google Earth Engine platform to derive SDD for NA lakes and reservoirs at annual or even seasonal time steps to assess water eutrophication variation in both time and space at the continental scale. [ABSTRACT FROM AUTHOR]

As a major fraction of carbon in inland waters, dissolved organic carbon (DOC) plays a crucial role in carbon cycling on a global scale. However, the quantity of DOC stored in lakes and reservoirs was not clear to date. In an attempt to examine the factors that determine the DOC storage in lakes and reservoirs across China, we assembled a large database (measured 367 lakes, and meta-analyzed 102 lakes from five limnetic regions; measured 144 reservoirs, and meta-analyzed 272 reservoirs from 31 provincial units) of DOC concentrations and water storages for lakes and reservoirs that are used to determine DOC storage in static inland waters. We found that DOC concentrations in saline waters (Mean/median ± S.D: 50.5/30.0 ± 55.97 mg/L) are much higher than those in fresh waters (8.1/5.9 ± 6.8 mg/L), while lake DOC concentrations (25.9/11.5 ± 42.04 mg/L) are much higher than those in reservoirs (5.0/3.8 ± 4.5 mg/L). In terms of lake water volume and DOC storage, the Tibet-Qinghai lake region has the largest water volume (552.8 km 3 ), 92% of which is saline waters, thus the largest DOC (13.39 Tg) is stored in these alpine lake region; followed by the Mengxin lake region, having a water volume of 99.4 km 3 in which 1.75 Tg DOC was stored. Compared to Mengxin lake region, almost the same amount of water was stored in East China lake region (91.9 km 3 ), however, much less DOC was stored in this region (0.43 Tg) due to the lower DOC concentration (Ave: 3.45 ± 2.68 mg/L). According to our investigation, Yungui and Northeast lake regions had water storages of 32.14 km 3 and 19.44 km 3 respectively, but relatively less DOC was stored in Yungui (0.13 Tg) than in Northeast lake region (0.19 Tg). Due to low DOC concentration in reservoirs, especially these large reservoirs having lower DOC concentration (V > 1.0 km 3 : 2.31 ± 1.48 mg/L), only 1.54 Tg was stored in a 485.1 km 3 volume of water contained in reservoirs across the entire country. [ABSTRACT FROM AUTHOR]

• Red/Red edge and Blue/Red combinations can qualify trophic state index. • The > 77% of investigated eastern lakes (>100 km2) are eutrophic. • The eutrophication would not be improved before- and after- COVID-19 lockdown. Lake eutrophication has become a critical environmental issue due to the global effects of anthropogenic activities and climate change, and has been comprehensively studied for many years. A series of models and indicators have been proposed to assess the trophic state of lakes. The trophic state index (TSI) is a synthetic index that integrates chlorophyll-a, water clarity, and total phosphorus and is widely used to evaluate the trophic state of aquatic environments. In this study, we collected in situ lake samples (N = 431) from typical lakes to match Sentinel-2 MultiSpectral Instrument (MSI) imagery data using the Case 2 Regional Coast Color processor. Then we developed a new empirical model, TSI = –34.04 × (band 4/band 5) – 1.114 × (band 1/band 4) + 97.376). This model is valid for all of China, with good performance and few errors (RMSE = 7.36; MAE = 6.25) for the validation dataset. Recognizing that over 94% of the Chinese population located along eastern watersheds and large lakes have competing water uses, and given the TSI model on the seasonal scales, we further estimated the mean TSI and trophic state in eastern Chinese lakes (>100 km2) from 2019 to 2020. The results revealed that more lakes were eutrophic in autumn (94.28%) than in spring (>77.14%), indicating a serious eutrophication of eastern lakes. Although the eastern lakes have been studied in more detail, this study found that eutrophication still has markedly negative impacts on lake ecosystems. In addition, no significant improvement was observed in spring, most likely due to the months of curfew/lockdown from January 2020 onwards due to COVID-19. This may be due to the enrichment of nutrients deposited in sediment or watershed soil, which can be characterized as "autochthonous sources" of lake eutrophication, over decades with high rates of economic development. This study demonstrates the applicability of Sentinel-2 MSI data to monitor lake eutrophication as well as the feasibility of blue/red and red/red edge combinations. The framework and TSI model used bands available on MSI sensors to develop a novel approach for generating historical eutrophication data for large-scale evaluation of and decision-making related aquatic environmental changes, even in poorly studied areas. [ABSTRACT FROM AUTHOR]

• A K d (490) estimation algorithm was developed based on MODIS/Aqua image in Lake Hongze. • The spatiotemporal patterns of K d (490) were mapped from 2002 to 2016 in Lake Hongze. • The affection of the optical properties, the concentration of water constituents and their PSD characteristics on K d (490) were discussed. • The response of K d (490) spatiotemporal patterns on precipitation and wind speed were discussed from the annual and inter-annual scales. The diffuse attenuation coefficient K d (λ) is an important optical parameter that can be determined remotely. It plays a meaningful role in inland and coastal biogeochemical processes and aids in determining water quality. Four data-gathering expeditions were conducted in Lake Hongze (HZL), China, between 2015 and 2017. Based on the in situ optical and biological data, we constructed and evaluated a band ratio model for retrieving K d (490) of HZL from MODIS/Aqua images between 2002 and 2016. Our results are as follows: (1) The developed algorithm, using the visible bands (R rs (red)/ R rs (green)), performs robustly for K d (490) estimation, with R 2 of 0.82 and p < 0.0001 for the modeling data. By validating with the in situ observed data, the mean absolute percentage error is determined to be 17.82%, and the root means square error is 1.52 m−1. (2) The spatial distribution of K d (490) strong relates with the input river that formed a cone-shaped distribution with the vertex at Huai River Estuary, decreasing from the southeast (RC) to the northern (NE) and western bays in HZL. The largest K d (490) value is in RC, with 7.10 ± 3.03 m−1, followed by NE and WL (wetland) with values of 6.10 ± 2.58 m−1 and 5.32 ± 2.09 m−1, respectively. (3) K d (490) reaches high values in winter and low values in the summer although the variance is quite different in different lake areas. The seasonal distribution of K d (490) in HZL is driven by the runoff produced by precipitation. (4) K d (490) decreased systematically as the East Asian monsoon weakened from 2002 to 2012 that means wind has great influence on K d (490) variation from year scale. However, human activity like sand-mining disrupted this trend, leading to the rise of K d (490) during 2012 and 2016. [ABSTRACT FROM AUTHOR]