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Lake Dianchi is one of the most eutrophic lakes in China. The decline in water quality and the occurrence of massive algal blooms pose a significant threat to the health and environmental safety of the water ecosystem, making Lake Dianchi a key concern for algal bloom management in China. Obtaining the spatiotemporal dynamics of algal blooms for the longest time possible is crucial to algal bloom management and future prediction. However, it is difficult to acquire a long-term record of algal blooms from a single sensor in order to cover a more extended period of eutrophication in the lake due to the limitation of the spatial and temporal resolution of the sensors. In this study, Landsat and Moderate-Resolution Imaging Spectroradiometer (MODIS) images were combined with the Floating Algae Index (FAI) to reconstruct a unified time series of bloom areas to analyze the algal bloom dynamics in Lake Dianchi in recent decades. Regarding the interannual variation, the bloom area showed an increasing trend from 1987 to 2021, with larger bloom areas in 1991–1992, 2000–2003, 2012–2013, and 2020–2021. In terms of seasonal characteristics, the bloom area was significantly more prominent in the rainy season compared with the dry season during the year. The spatial distribution of the bloom frequency showed a pattern of higher frequencies in the north and lower frequencies in the south. From 2000 to 2021, the initial bloom time and bloom duration showed a trend of delaying and then advancing and decreasing and then increasing, respectively. We analyzed the importance of long-term records of algal blooms and found that the percentage of rainy season images is an essential factor in reconstructing time series based on different sensors. In addition, the relationship between environmental factors and algal blooms was analyzed. The results show that wind speed and air temperature were the main meteorological factors controlling the interannual variation in algal blooms in Lake Dianchi. Water quality factors such as nutrients have less of an influence on the variation in algal blooms because the algal growth demand has been met. Environmental management measures taken by local governments have led to improvements in the lake's trophic state, and continued strengthening of environmental pollution control is expected to curb the algal blooms in Lake Dianchi. This study provides a long-term record of algal blooms in Lake Dianchi, which provides essential reference information for a comprehensive understanding of the development process of algal blooms in Lake Dianchi and its sustainable development. [ABSTRACT FROM AUTHOR]

Eutrophication and warming are changing the functioning of lake ecosystems, and their impacts on lake carbon dioxide (CO2) variability have received increasing attention. However, how eutrophication and warming change lakes' carbon cycle has not been determined. Here, the surface partial pressure of CO2 (pCO2) and CO2 flux in Lake Taihu, a large and eutrophic lake in eastern China, was investigated based on monthly samplings over a 24‐yr period (1992–2015), during which the lake experienced profound anthropogenic and climate changes. The results showed that eutrophication caused by nutrient enrichment plays a role in three aspects: (1) nutrient concentrations controlled the CO2 variability on decadal scales; (2) peak pCO2 and CO2 fluxes occurred in river mouths due to large external nutrient loading inputs; and (3) eutrophication effects on CO2 varied among subzones, which was linked to external inputs and in‐lake primary production. Meanwhile, temperature controls the seasonal variation in CO2 by stimulating primary production, leading to significantly lower pCO2 and CO2 fluxes in warm seasons with algal blooms. Further analysis suggested that temperature effects varied spatially and temporally, high nutrient loading may confound the temperature effects via stimulating CO2 production. To our knowledge, this study presents the longest field measurements (24 yr) of CO2 from such large and ice‐free freshwater lakes with monthly surveys, which may provide a powerful example to demonstrate that eutrophication and warming can shape CO2 variability from a temporal perspective. Future studies should focus on the interactive warming and eutrophication effects to accurately predict future CO2 emission. [ABSTRACT FROM AUTHOR]

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Satellite observation can significantly reduce the uncertainties in CO 2 emission estimations compared to insufficient field data. However, big challenges remain in developing remote sensing-based models for mapping concentrations of dissolved carbon dioxide (c CO 2) in lakes at regional or global scales. We developed a c CO 2 estimation model using Sentinel-3-derived lake environmental variables and near-synchronous field c CO 2 data from 16 lakes in the middle and lower reaches of the Yangtze and Huai River (ML_YHR) basins in Eastern China (N = 248). Stepwise quadratic polynomial regressions of several combinations of chlorophyll- a (Chl- a), water temperature (Tw), Secchi disk depth (Z SD), and photosynthetic active radiation (PAR)-related variables were tested and validated to select the best approach. The final model showed high performance in calibration and validation (R 2 > 0.72, RMSE < 6.35 μmol L−1, MAPE < 30.31%). The model sensitivity analysis, based on Monte Carlo simulations, showed the model's estimated bias as <25% based on uncertainties of all input variables. Spatial and temporal dynamics of dissolved CO 2 concentrations in 113 lakes (≥ 10 km2) in the ML_YHR basins were mapped from 2016 to 2021 using the Sentinel-3 data. The result showed that CO 2 concentrations were low in the summer and autumn but high in the winter and spring with dramatic variations (e.g., mean coefficient of variation: 52.59%). The annual mean CO 2 concentrations of lakes revealed that about 28% of the lakes acted as weak atmospheric CO 2 sinks (14.96 ± 1.13 μmol L−1) while the rest were sources (19.22 ± 2.02 μmol L−1), compared with a mean concentration of CO 2 atmospheric equilibrium (16.29 μmol L−1). CO 2 concentrations decreased with increasing eutrophication and decreasing lake size (p < 0.05). This study advances current knowledge about CO 2 emissions from emerging meso-eutrophic lakes and shows how satellite remote sensing can expand the spatiotemporal coverage of lake CO 2. • A dissolved CO 2 concentration (c CO 2) model was developed for meso-eutrophic lakes. • Time-related input variables helped improve the model performance. • Long-term c CO 2 in hundreds of lakes were mapped for the first time. • c CO 2 decreased with increasing eutrophication and decreasing lake size. • About 72% of lakes acted as sources of atmospheric CO 2 , while the rest were sinks. [ABSTRACT FROM AUTHOR]

Inland waters emit large amounts of carbon dioxide (CO 2) to the atmosphere, but emissions from urban lakes are poorly understood. This study investigated seasonal and interannual variations in the partial pressure of CO 2 (p CO 2) and CO 2 flux from Lake Wuli, a small eutrophic urban lake in the heart of the Yangtze River Delta, China, based on a long-term (2000–2015) dataset. The results showed that the annual mean p CO 2 was 1030 ± 281 μatm (mean ± standard deviation) with a mean CO 2 flux of 1.1 ± 0.6 g m−2 d−1 during 2000–2015, suggesting that compared with other lakes globally, Lake Wuli was a significant source of atmospheric CO 2. Substantial interannual variability was observed, and the annual p CO 2 exhibited a decreasing trend due to improvements in water quality driven by environmental investment. Changes in ammonia nitrogen and total phosphorus concentrations together explained 90% of the observed interannual variability in p CO 2 (R 2 = 0.90, p < 0.01). The lake was dominated by cyanobacterial blooms and showed nonseasonal variation in p CO 2. This finding was different from those of other eutrophic lakes with seasonal variation in p CO 2 , mostly because the uptake of CO 2 by algal-derived primary production was counterbalanced by the production of CO 2 by algal-derived organic carbon decomposition. Our results suggested that anthropogenic activities strongly affect lake CO 2 dynamics and that environmental investments, such as ecological restoration and reducing nutrient discharge, can significantly reduce CO 2 emissions from inland lakes. This study provides valuable information on the reduction in carbon emissions from artificially controlled eutrophic lakes and an assessment of the impact of inland water on the global carbon cycle. Image 1 • The lake was a source of atmospheric CO 2 with mean flux of 1.1 g m−2 d−1. • CO 2 production and emission from the urban lake were driven by nutrient loadings. • A decreasing trend in annual CO 2 flux was observed due to environmental investment. A eutrophic urban lake was a significant source of atmospheric CO 2 , but environmental investments significantly reduced CO 2 emissions. [ABSTRACT FROM AUTHOR]

• A novel blended algorithm was proposed to remotely observe lake POC across China. • POC concentrations in 450 large lakes were first mapped using OLCI/Sentinel-3 data. • Lake POC concentration across China was low in the west and high in the east. Remote sensing monitoring of particulate organic carbon (POC) concentration is essential for understanding phytoplankton productivity, carbon storage, and water quality in global lakes. Some algorithms have been proposed, but only for regional eutrophic lakes. Based on in-situ data (N = 1269) in 49 lakes across China, we developed a blended POC algorithm by distinguishing Type-I and Type-II waters. Compared to Type-I, Type-II waters had higher reflectance peak around 560 nm (>0.0125 sr−1) and mean POC (4.65 ± 4.11 vs. 2.66 ± 3.37 mg/L). Furthermore, because POC was highly related to algal production (r = 0.85), a three-band index (R 2 = 0.65) and the phytoplankton fluorescence peak height (R 2 = 0.63) were adopted to estimate POC in Type-I and Type-II waters, respectively. The novel algorithm got a mean absolute percent difference (MAPD) of 35.93 % and outperformed three state-of-the-art formulas with MAPD values of 40.56–76.42 %. Then, the novel algorithm was applied to OLCI/Sentinel-3 imagery, and we first obtained a national map of POC in 450 Chinese lakes (> 20 km2), which presented an apparent spatial pattern of "low in the west and high in the east". In brief, water classification should be considered when remotely monitoring lake POC concentration over a large area. Moreover, a process-oriented method is required when calculating water column POC storage from satellite-derived POC concentrations in type-II waters. Our results contribute substantially to advancing the dynamic observation of the lake carbon cycle using satellite data. [Display omitted] [ABSTRACT FROM AUTHOR]

• A robust phycocyanin pigments model (ERFR-PC) for freshwater lakes was developed. • A microcystins human health risk scheme was developed from OLCI observations. • Microcystins risk was first evaluated for eastern China lakes using OLCI images. • 80% lakes showed high risk potential, but low risk frequency in 70.93% water areas. Cyanobacterial blooms release a large number of algal toxins (e.g., Microcystins, MCs) and seriously threaten the safety of drinking water sources what the SDG 6.1 pursues (to provide universal access to safe drinking water by 2030, United Nations Sustainable Development Goal). Nevertheless, algal toxins in lake water have not been routinely monitored and evaluated well and frequently so far. In this study, a total of 100 large lakes (>25 km2) in densely populated eastern China were studied, and a remote sensing scheme of human health risks from MCs based on Sentinel-3 OLCI data was developed. The spatial and temporal dynamics of MCs risk in eastern China lakes since OLCI satellite observation data (2016–2021) were first mapped. The results showed that most of the large lakes in eastern China (80 out of 100) were detected with the occurrence of a high risk of more than 1 pixel (300×300 m) at least once. Fortunately, in terms of lake areas, the frequency of high human health risks in most waters (70.93% of total lake areas) was as less as 1%. This indicates that drinking water intakes can be set in most waters from the perspective of MCs, yet the management departments are required to reduce cyanobacterial blooms. This study highlights the potential of satellite in monitoring and assessing the risk of algal toxins and ensuring drinking water safety. It is also an important reference for SDG 6.1 reporting for lakes that lack routine monitoring. [Display omitted] [ABSTRACT FROM AUTHOR]

Lakes are recognized as important sources of carbon dioxide (CO 2) emissions, which vary greatly across land use type. However, CO 2 emissions from lakes in urban landscapes are generally overlooked despite their daily connections to human activity. Furthermore, the role of management actions in CO 2 emissions remained unclear mostly because of the lack of long-term observations. Here, the CO 2 partial pressure (p CO 2) from two urban lakes (Lake Wuli and Lake Donghu) in eastern China were investigated based on 16-year (2002–2017) field measurements. This long-term measurements showed the annual mean p CO 2 were 1150 ± 612 μatm for Lake Wuli and 1143 ± 887 μatm for Lake Donghu, with corresponding estimated flux of 21.12 ± 19.60 mmol m−2 d−1 and 16.42 ± 20.39 mmol m−2 d−1, respectively. This indicates significant CO 2 evasion into the atmosphere. Strong links between CO 2 and human-derived nutrients (e.g., ammonium) and dissolved organic carbon, dissolved oxygen, and trophic state index were found. Although p CO 2 was relatively uniform across sites and seasons in each lake, substantial inter-annual variability with significant decreasing trends were found. The decrease in annual CO 2 can be partly explained by the reduction of pollutant loadings with management actions, which held the hypotheses that management actions mitigated the CO 2 emission risks. Overall, management actions (e.g., ecological restoration and municipal engineering) should be considered for better understanding the roles of anthropogenic aquatic ecosystems in carbon cycle. • CO 2 dynamics of two urban lakes were reported based on 16-year measurements. • Two eutrophic urban lakes acted as significant CO 2 sources. • Anthropogenic input and organic carbon mineralization contributed to the emissions. • Management actions can significantly reduce the CO 2 emissions from urban lakes. [ABSTRACT FROM AUTHOR]

Coastal aquaculture ponds (CAPs) are of great importance for food security, ecosystem health, and greenhouse emissions. However, existing data often lacks geographic information on both national and international scale spatial data. Satellite remote sensing makes large-scale monitoring of CAPs more efficient. This is especially true when using synthetic aperture radar (SAR) data, which can provide all-day acquisition as a high-quality data source for large-scale CAP mapping. In this study, focusing on CAPs in China and Vietnam, a novel method of large-scale CAPs mapping using Sentinel-1 images was developed. Moreover, high-resolution (10 m) CAP datasets in 2015 and 2020 were produced, and spatiotemporal changes and drivers were analyzed. The key results are as follows: (1) the overall accuracy of the mapping method proposed was >90%. (2) In 2020, CAP area in China was 8774.93 km2 (69.43%) and it was 3864.10 km2 (30.57%) in Vietnam. (3) CAPs showed an opposite trend in China and Vietnam. The CAP area decreased by 891.29 km2 with a change rate of about −9.22% in China, while the CAP area increased by 920.35 km2 with a change rate of about 31.26% in Vietnam. China had made unprecedented ecofriendly efforts to restrict expansion for improving the ecological environment. Vietnam focused more on increasing production than on environmental protection, leading to significant expansion. Our results offer some new insights on satellite remote sensing and its use in coastal eco-environmental management and contribute to Sustainable Development Goals 2, 6 and 14. • An approach for large-scale mapping coastal aquaculture ponds using Sentinel-1. • High-resolution (10 m) distribution maps of CAPs were obtained. • Change trend of CAPs from 2015 to 2020 in China and Vietnam was opposite. • Different policies were the key factor driving for CAPs changes. [ABSTRACT FROM AUTHOR]

With the increasing problem of eutrophication, lacustrine ecosystem can undergo complex changes, often resulting in a shift from a clear macrophyte-dominated state to a turbid phytoplankton-dominated state. However, it's not clear how lake transitions occur at regional and global scales. This is due to a lack of long-term monitoring and an effective algorithm that can distinguish floating/emergent aquatic vegetation (FEAV), submerged aquatic vegetation (SAV) and algal bloom (AB). This paper presents a novel three-step classification algorithm based on Landsat imagery, (ie., vegetation and bloom indices (VBI) algorithm): (1) to distinguish between aquatic vegetation (AV) and non-aquatic vegetation (non-AV) extents by an aquatic vegetation index (AVI) derived from tasseled cap transformation; (2) to identify FEAV and SAV within AV extent by using the normalized difference vegetation index (NDVI), and (3) to extract AB from non-AV extent with floating algae index (FAI). The performance of the VBI algorithm was validated using 1307 field samples collected across 22 lakes in the middle and lower reaches of the Yangtze River (MLY) in China, with a pooled overall accuracy of 84.49%. Finally, the VBI algorithm was applied to Landsat data from 1985 to 2021 to quantify the temporal changes of FEAV, SAV and AB in large lakes (i.e., > 50 km2) along the MLY. Within these lakes, AV showed a significant decrease over the past 37 years, mainly due to the decrease of SAV; while AB occurred with higher frequency and in more lakes. The transition from a macrophyte-dominated state to a phytoplankton-dominated aquatic systems is still ongoing in the MLY area. This study shows outstanding innovation in the construction of a new classification index (i.e., AVI), and the method of calculating the adaptive threshold, which makes VBI algorithm have great potential in mapping long-term changes and capturing transformation between lake communities on a global scale. • A novel Landsat-based algorithm was developed for quantifying aquatic communities. • An index for aquatic vegetation (AV) was proposed to classify AV and non-AV. • Long-term algal blooms and AV (floating/emergent/submerged) in MLY lakes were mapped. • AV decreased while algal blooms increased in most MLY lakes from 1985 to 2021. • Shift from macrophyte- to phytoplankton-dominated state is ongoing in MLY lakes. [ABSTRACT FROM AUTHOR]

• Nine state-of-the-art Chla algorithms for inland waters were evaluated. • Sensitivity of Chla algorithms to AC was quantified for the first time. • An RFR algorithm insensitive to AC uncertainty was developed for turbid lakes. • OLCI Chla products showed most lakes in eastern China are eutrophic. A robust and reliable chlorophyll-a (Chla) concentration algorithm is still lacking for optically complex waters due to the lack of understanding of the bio-optical process. Machine learning approaches, which excel at detecting potential complex nonlinear relationships, provide opportunities to estimate Chla accurately for optically complex waters. However, the uncertainties in atmospheric correction (AC) may be amplified in different Chla algorithms. Here, we aim to select one state-of-the-art algorithm or establish a new algorithm based on machine learning approaches that less sensitive to AC uncertainties. Firstly, nine state-of-the-art empirical, semianalytical, and optical water types (OWT) classification-based Chla algorithms were implemented. These existing algorithms showed good performance by using in situ database, however, failed in actual OLCI applications due to their sensitivity to AC uncertainties. Thus, four popular machine learning approaches (random forest regression (RFR), extreme gradient boosting (XGBoost), deep neural network (DNN), and support vector regression (SVR)) were then employed. Among them, the "RFR-Chla" model performed the best and showed less sensitivity to AC uncertainties. Finally, the Chla spatiotemporal variations in 163 major lakes across eastern China were mapped from OLCI between May 2016 and April 2020 using the proposed RFR-Chla model. Generally, the lakes in eastern China are severely eutrophic, with an average Chla concentration of 33.39 ± 6.95 μg/L. Spatially, Chla in the south of eastern China was significantly higher than those in northern lakes. Seasonally, Chla was high in the summer and autumn and low in the spring and winter. This study provides a reference for water quality monitoring in turbid inland waters suffering certain AC uncertainties and supports aquatic management and SDG 6 reporting. [ABSTRACT FROM AUTHOR]

Dissolved organic carbon (DOC) in lakes serves as a substrate for heterotrophic bacterial growth, a regulator of the global carbon cycle, and a light absorption agent. DOC in eutrophic lakes is greatly influenced by phytoplankton phenology and terrigenous input by rivers. Therefore, it is necessary and significant to dynamically monitor the concentration, storage, and riverine exchange flux of DOC. By using in-situ DOC measurements from 2004 until 2018 (N = 2019), a machine learning algorithm, namely, a multilayer back-propagation neural network (MBPNN) model, was developed in this work to improve the remote sensing estimation of DOC concentrations in eutrophic Lake Taihu. The model yielded a mean estimation error of 15.14% for the testing dataset. The monthly mean DOC concentration significantly increased from 2003 to 2018 (N = 192, p < 0.01). High DOCs were observed in lake bays with high chlorophyll a (Chl-a) levels, and phytoplankton growth explained more than 50% of the monthly DOC variations. Then, given the evenly mixed DOC in the water column on the monthly and annual scales, we further estimated the monthly mean DOC storage in the lake from 2003 until 2018 and DOC fluxes (input and output) due to rivers during 2008–2018. Although the mean net riverine DOC input (50.56 ± 32.22 × 103 t C) was approximately 5.2 times the average DOC storage (9.73 ± 1.23 × 103 t C), phytoplankton growth controlled DOC variations, which indicated that much terrigenous DOC was transformed into other carbon forms after entering Lake Taihu. This study verified the feasibility of remote sensing of DOC (surface concentration, storage, and riverine exchange flux) in eutrophic lakes. • Machine learning improved the remote sensing of DOC in eutrophic Lake Taihu. • DOC variations in the Lake Taihu were controlled by phytoplankton growth. • Net DOC input from rivers was ~5.2 times the DOC storage during 2008–2018. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goal (SDG) 6.3.2 of the United Nations (UN) focuses on ambient water quality, while water clarity simplistically and visually reflect water quality and can potentially support SDG 6.3.2 reporting. In this study, based on extensive field data and Sentinel-3 Ocean and Land Color Instrument (OLCI) imagery, a random forest regression (RFR) Secchi depth (Z sd) model suitable for turbid and eutrophic waters was established. With this model, the Z sd of 86 large (> 30 km2) lakes in Eastern China was obtained from May 2016 to April 2018. Additionally, the potential for applying OLCI-derived Z sd data in the SDG 6.3.2 evaluation was assessed. Of six common atmospheric correction (AC) processors (i.e., BAC, C2RCC, POLYMER, BP, MUMM, and 6SV), 6SV often exhibited the best performance except for at 754 nm (root mean square error (RMSE) ≤ 0.0094 sr−1, mean absolute percentage error (MAPE) ≤ 36.27%, and mean normalized bias (MNB) ≤ 15.89%). The RFR model had higher accuracy (R2 = 0.70, RMSE = 0.13 m, MAPE = 33.43%, and MNB = 14.55%) and was more suitable for eutrophic and turbid inland lakes across Eastern China than the other available Z sd algorithms. The average OLCI-derived Z sd of the lakes in Eastern China was 0.44 ± 0.13 m, suggesting that these lakes are extremely turbid. The average Z sd of lakes in the Eastern Plain Lake (EPL) zone (0.45 ± 0.12 m) was higher than that of the lakes in the Northeastern Plain and Mountain Lake (NPML) zone (0.40 ± 0.17 m). The majority of lakes showed higher Z sd values in summer (rainy season) than in fall and winter (dry season). A simple SDG 6.3.2 evaluation scheme was developed based on the Z sd product, and only 54.65% of the lakes (N = 47) reached the "good" level during the monitoring period as a result of the eutrophication of the lakes in Eastern China. This study provides water clarity information for large lakes in Eastern China and facilitates the understanding of ambient water quality under the 2030 UN SDG framework as well as data and technical support for future SDG 6.3.2 evaluations. • A random forest regression water clarity algorithm was developed for turbid lakes. • Performances of six atmospheric correction processors were examined on OLCI data. • OLCI showed most lakes were turbid with low Z sd in Eastern China. • A novel Z sd -based scheme was developed for evaluating SDG 6.3.2. [ABSTRACT FROM AUTHOR]

Under the influence of climate warming and human activities, many large lakes have experienced an increase in eutrophication and algal blooms. Although these trends have been identified using low temporal resolution (~16 days) satellites such as those of the Landsat missions, the opportunity to compare high-frequency spatiotemporal variations of algal bloom characteristics between lakes has not been explored. In the present study, we explore daily satellite observations by developing a universal, practical, and robust algorithm to identify the spatiotemporal distribution of algal bloom dynamics in large lakes (>500 km 2 ) across the globe. Data from 161 lakes, taken from 2000 to 2020 showed an average accuracy of 79.9 %. Algal blooms were detected in 44 % of all lakes, with a higher incidence in temperate lakes (67 % of all temperate lakes), followed by tropical lakes (59 %) compared to lakes in arid climates (23 %). We found positive trends in bloom area and frequency (p < 0.05), as well as an earlier bloom time (p < 0.05). Climate factors were found to be linked to changes in annual initial bloom time (44 %); while an increase in human activities was associated to bloom duration (49 %), area (max percent: 53 %, mean percent: 45 %), and frequency (46 %). The study shows the evolution of daily algal blooms and their phenology in global large lakes for the first time. Such information enhances our understanding of algal bloom dynamics and their drivers, with important considerations to improve the management of large lake ecosystems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Lakes play an important role in the global carbon cycle; however, there are still large uncertainties in the estimation of global lake carbon emission due to the limitations in conducting field surveys at large geographic scales. Using long-term Moderate-Resolution Imaging Spectroradiometer (MODIS) imagery and field observation data in eutrophic Lake Taihu, we developed a novel approach to estimate the concentration of dissolved carbon dioxide ( c CO 2 ) in lakes. Based on the MODIS-derived chlorophyll- a concentration, lake surface temperature, diffuse attenuation coefficient of photosynthetically active radiation, and photosynthetically active radiation, a spatially explicit c CO 2 model was developed using multivariate quadratic polynomial regression (coefficient of determination ( R 2 ) = 0.84, root-mean-square error (RMSE) = 11.81 μmol L -1 , unbiased percent difference (UPD) = 22.46%). Monte Carlo simulations indicated that the model is stable with relatively small deviations in c CO 2 estimates caused by input variables (UPD = 26.14%). MODIS data from 2003 to 2018 showed a significant declining trend (0.42 μmol L -1 yr -1 , p < 0.05) in the annual mean c CO 2 . This was associated with a complex balance between the increasing algae biomass and decreasing external inputs of inorganic carbon, nutrients, and organic matter. The high spatiotemporal variabilities in c CO 2 were attributed to river inputs and seasonal changes in temperature and algae biomass. The study shows that satellite remote sensing can play an important role in the field of inland water carbon cycling, providing timely much-needed insights into the drivers of the spatial and temporal changes in dissolved CO 2 concentrations in inland waters.

Inland waters emit large amounts of carbon dioxide (CO 2 ) to the atmosphere, but emissions from urban lakes are poorly understood. This study investigated seasonal and interannual variations in the partial pressure of CO 2 (pCO 2 ) and CO 2 flux from Lake Wuli, a small eutrophic urban lake in the heart of the Yangtze River Delta, China, based on a long-term (2000-2015) dataset. The results showed that the annual mean pCO 2 was 1030 ± 281 μatm (mean ± standard deviation) with a mean CO 2 flux of 1.1 ± 0.6 g m -2 d -1 during 2000-2015, suggesting that compared with other lakes globally, Lake Wuli was a significant source of atmospheric CO 2 . Substantial interannual variability was observed, and the annual pCO 2 exhibited a decreasing trend due to improvements in water quality driven by environmental investment. Changes in ammonia nitrogen and total phosphorus concentrations together explained 90% of the observed interannual variability in pCO 2 (R 2  = 0.90, p < 0.01). The lake was dominated by cyanobacterial blooms and showed nonseasonal variation in pCO 2 . This finding was different from those of other eutrophic lakes with seasonal variation in pCO 2 , mostly because the uptake of CO 2 by algal-derived primary production was counterbalanced by the production of CO 2 by algal-derived organic carbon decomposition. Our results suggested that anthropogenic activities strongly affect lake CO 2 dynamics and that environmental investments, such as ecological restoration and reducing nutrient discharge, can significantly reduce CO 2 emissions from inland lakes. This study provides valuable information on the reduction in carbon emissions from artificially controlled eutrophic lakes and an assessment of the impact of inland water on the global carbon cycle., 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.)

Inland lakes receive growing attentions on eutrophication and their roles in global carbon cycle. However, understanding how inland lakes contribute to global carbon cycle is seriously hampered due to a shortage of long-term records. This study investigated the carbon dioxide (CO 2 ) flux from the Lake Taihu, a large (2400 km 2 ) and shallow (mean depth 1.9 m) eutrophic lake in subtropical region, based on a long-term (2000-2015) measurement of the partial pressure of carbon dioxide (pCO 2 ) at high spatiotemporal resolution. We found that the Lake Taihu was a significant source of atmospheric CO 2 with an average CO 2 emission flux at 18.2 ± 8.4 mmol m -2 d -1 (mean±1standard deviation) and a mean annual pCO 2 value of 778 ± 169 μatm. The highest pCO 2 and CO 2 flux were observed in eutrophic zone with a high external input of carbon and nutrient, and the lowest in non-eutrophic zones with no direct external input of nutrient and carbon. A substantial seasonal pattern in pCO 2 was observed, particularly in eutrophic pelagic area, and was significantly negatively correlated with chlorophyll a. Long-term measurement showed the interannual variation in annual lake CO 2 dynamics, which was highly sensitive to human-induced nutrient input. Watershed input of carbon and nutrient leads to the high CO 2 level, counterbalancing the in-lake primary production. All lines of evidence suggest that human activities may have predominate contribution to CO 2 source in the Lake Taihu, and this mechanism might be widespread in global freshwater lakes., (Copyright © 2019 Elsevier Ltd. All rights reserved.)