Unbalanced impacts of nature and nurture factors on the phenology, area and intensity of algal blooms in global large lakes: MODIS observations

Mapping satellite-derived chlorophyll-a concentrations from 2013 to 2023 in Western Lake Ontario using Landsat 8 and 9 imagery

Algal blooms are a major environmental issue in many freshwater environments. While traditional in-situ measurements remain indispensable to monitor algal dynamics, they offer only limited spatiotemporal coverage, especially when dealing with large water bodies. Satellite remote sensing can help overcome this limitation. Here, a semi-empirical model for retrieving surface water Chlorophyll-a (Chl-a) concentrations, a proxy of phytoplankton biomass, was developed for the western basin of Lake Ontario, one of the Laurentian Great Lakes. ACOLITE-corrected Landsat 8 and 9 imagery between 2013 and 2023 was calibrated and verified with local in-situ Chl-a measurements. The nearshore areas of Western Lake Ontario, including the semi-enclosed Hamilton Harbour, are prone to algal blooms, while oligotrophic conditions prevail in the offshore areas. Three bloom indicators-intensity, extent, and severity-were used to characterize the variability and seasonality of algal blooms in different areas of the lake. Time-series analyses revealed contrasting temporal trends in Chl-a concentrations of the nearshore and offshore waters over the eleven-year period of observation. Analysis of external factors impacting algal blooms in Western Lake Ontario and Hamilton Harbour revealed temperature, wind speed, and cloud cover as the most influential, with around 80 % of blooms occurring under moderate conditions (temperature 4-26 °C and wind speed 2.5-5. m s-1). Overall, our research underlines the great potential for cost-effective monitoring of algal dynamics in large lakes, utilizing publicly available satellite imagery, in order to support eutrophication management.

Projected response of algal blooms in global lakes to future climatic and land use changes: Machine learning approaches

The eutrophication of lakes and the subsequent algal blooms have become significant environmental issues of global concern in recent years. With ongoing global warming and intensifying human activities, water quality trends in lakes worldwide varied significantly, and the trend of algal blooms in the next few decades is unclear. However, there is a lack of comprehensive quantitative research on the future projection of lake algal blooms globally due to the scarcity of long-term algal blooms observational data and the complex nonlinear relationships between algal blooms and their driving factors. We aimed to develop a global projection model to evaluate the future trend in algal bloom occurrences in large lakes under various socio-economic development scenarios. We focused our research on 161 natural lakes worldwide, each exceeding 500 km2. The results indicated that the Random Forest model performed best (Overall Accuracy: 0.9697, Kappa: 0.8721) among various machine learning models which were applied in this study. The predicted results showed that, by the end of this century, the number of lakes experiencing algal blooms and the intensity of these blooms will worsen under higher forcing scenarios (SSP370 and SSP585) (p < 0.05). In different regions, lakes with increasing algal blooms are mainly distributed in Africa, Asia, and North America, while lakes with decreasing occurrence are primarily found in Europe. Additionally, underdeveloped regions, such as Africa, exhibit greater sensitivity to different SSP scenarios due to high variability in population and economic growth. This study revealed the spatiotemporal distribution of algal blooms in global lakes from 2020 to 2100 and suggested that the intensifying algal blooms due to global warming and human activities may offset the effort of controlling the water quality.

Monitoring, simulation and early warning of cyanobacterial harmful algal blooms: An upgraded framework for eutrophic lakes

Cyanobacterial Harmful Algal Bloom (CyanoHAB) is a global aquatic environmental issue, posing considerable eco-environmental challenges in freshwater lakes. Comprehensive monitoring and accurate prediction of CyanoHABs are essential for their scientific management. Nevertheless, traditional satellite-based monitoring and process-oriented prediction methods of CyanoHABs failed to satisfy this demand due to the limited spatiotemporal resolutions of both monitoring data and prediction results. To address this issue, this paper proposes an upgraded framework for comprehensive monitoring and accurate prediction of CyanoHABs. A collaborative CyanoHAB monitoring network was firstly constructed by integrating space, aerial, and ground-based monitoring means. As a result, CyanoHAB conditions were assessed frequently covering the entire lake, its key areas, and core positions. Furthermore, by overcoming technical limitations associated with high-precision simulation of the growth-drift-accumulation process of CyanoHABs, such as the unclear drifting process of CyanoHABs and the mechanism of its coastal accumulation, the multi-scale CyanoHAB prediction was realized interconnecting the entire lake and its nearshore areas. The implemented framework has been applied in Lake Chaohu for over three years. It provided high-frequency and high-spatial-resolution CyanoHAB monitoring, as well as its multi-scale and accurate simulation. The application of this framework in Lake Chaohu had significantly improved the accuracies of CyanoHAB monitoring, simulation, and early warning. This advancement holds significant scientific value and offers potential for CyanoHAB prevention and control in eutrophic lakes.

Re-frying oil emulsion as buoy-bead for microalgae harvesting: A promising approach for blooms of microalgae management

Blooms of microalgae can pose a major threat to ecological balance and human health. Therefore, a novel method of harvesting microalgae was investigated, using re-frying oil to make buoy-bead for the harvesting process. The effectiveness of the method was evaluated by water samples from the Huaihe River basin and Chaohu Lake. The results showed that the buoy-bead flotation method achieved a harvesting efficiency of 97.21 %.The climate change emissions for harvesting 1 m3 of microalgae are 0.33 kg CO2 eq, and with a positive NPV, the economic feasibility of the microalgae harvesting plan is preliminarily assessed.

Long-term spatiotemporal mapping in lacustrine environment by remote sensing:Review with case study, challenges, and future directions

Satellite remote sensing, unlike traditional ship-based sampling, possess the advantage of revisit capabilities and provides over 40 years of data support for observing lake environments at local, regional, and global scales. In recent years, global freshwater and coastal waters have faced adverse environmental issues, including harmful phytoplankton blooms, eutrophication, and extreme temperatures. To comprehensively address the goal of 'reviewing the past, assessing the present, and predicting the future', research increasingly focuses on developing and producing algorithms and products for long-term and large-scale mapping. This paper provides a comprehensive review of related research, evaluating the current status, shortcomings, and future trends of remote sensing datasets, monitoring targets, technical methods, and data processing platforms. The analysis demonstrated that the long-term spatiotemporal dynamic lake monitoring transition is thriving: (i) evolving from single data sources to satellite collaborative observations to keep a trade-off between temporal and spatial resolutions, (ii) shifting from single research targets to diversified and multidimensional objectives, (iii) progressing from empirical/mechanism models to machine/deep/transfer learning algorithms, (iv) moving from local processing to cloud-based platforms and parallel computing. Future directions include, but are not limited to: (i) establishing a global sampling data-sharing platform, (ii) developing precise atmospheric correction algorithms, (iii) building next-generation ocean color sensors and virtual constellation networks, (iv) introducing Interpretable Machine Learning (IML) and Explainable Artificial Intelligence (XAI) models, (v) integrating cloud computing, big data/model/computer, and Internet of Things (IoT) technologies, (vi) crossing disciplines with earth sciences, hydrology, computer science, and human geography, etc. In summary, this work offers valuable references and insights for academic research and government decision-making, which are crucial for enhancing the long-term tracking of aquatic ecological environment and achieving the Sustainable Development Goals (SDGs).

Harmful algal blooms in inland waters

Harmful algal blooms can produce toxins that pose threats to aquatic ecosystems and human health. In this Review, we outline the global trends in harmful algal bloom occurrence and explore the drivers, future trajectories and potential mitigation strategies. Globally, harmful algal bloom occurrence has risen since the 1980s, including a 44% increase from the 2000s to 2010s, especially in Asia and Africa. Enhanced nutrient pollution owing to urbanization, wastewater discharge and agricultural expansion are key drivers of these increases. In contrast, changes have been less substantial in high-income regions such as North America, Europe and Oceania, where policies to mitigate nutrient pollution have stabilized bloom occurrences since the 1970s. However, since the 1990s, climate warming and legacy nutrient pollution have driven a resurgence in toxic algal blooms in some US and European lakes, highlighting the inherent challenges in mitigating harmful blooms in a warming climate. Indeed, advancing research on harmful algal bloom dynamics and projections largely depends on effectively using data from multiple sources to understand environmental interactions and enhance modelling techniques. Integrated monitoring networks across various spatiotemporal scales and data-sharing frameworks are essential for improving harmful algal bloom forecasting and mitigation.

Influence of climate extremes on long-term changes in cyanobacterial blooms in a eutrophic and shallow lake

Climate change and human activities have crucial effects on the variations in phytoplankton blooms in lakes worldwide. A record-breaking heatwave and drought event was reported in the middle and lower reaches of the Yangtze River during the summer of 2022, but only little is known about how cyanobacterial blooms in lakes respond to such climate extremes. Here, we utilized MODIS images to generate the area, occurrence, and initial blooming date (IBD) of cyanobacterial blooms in Lake Chaohu from 2000 to 2022. We found that the area and occurrence of cyanobacterial blooms were largely reduced. At the same time, the IBD was delayed in 2022 compared with the previous 20 years. The annual occurrence and mean area of cyanobacterial blooms in 2022 were 17 % and 23.1 km2, respectively, which were the lowest reported levels since the 21st century. The IBD in 2022 was four months late compared with the IBD in 2020. The high wind speed in spring delayed the spring blooms in 2022. The record-breaking heatwaves and drought from June to August reduced the blooms by influencing the growth of cyanobacteria and reducing the flow of nutrients from the watershed into the lake. This study highlights the compound impact of heatwave and drought climate events on reducing cyanobacterial blooms in a long-term period, enhancing additional understanding of the changes in phytoplankton blooms in lakes.

Algal extracellular polymeric substance compositions drive the binding characteristics, affinity, and phytotoxicity of graphene oxide in water

Graphene oxide (GO, a popular 2D nanomaterial) poses great potential in water treatment arousing considerable attention regarding its fate and risk in aquatic environments. Extracellular polymeric substances (EPS) exist widely in water and play critical roles in biogeochemical processes. However, the influences of complex EPS fractions on the fate and risk of GO remain unknown in water. This study integrates fluorescence excitation-emission matrix-parallel factor, two-dimensional correlation spectroscopy, and biolayer interferometry studies on the binding characteristics and affinity between EPS fractions and GO. The results revealed the preferential binding of fluorescent aromatic protein-like component, fulvic-like component, and non-fluorescent polysaccharide in soluble EPS (S-EPS) and bound EPS (B-EPS) on GO via π-π stacking and electrostatic interaction that contributed to a higher adsorption capacity of S-EPS on GO and weaker affinity than of B-EPS. Moreover, the EPS fractions drive the morphological and structural alterations, and the attenuated colloid stability of GO in water. Notably, GO-EPS induced stronger phytotoxicity (e.g., photosynthetic damage, and membrane lipid remodeling) compared to pristine GO. Metabolic and functional lipid analysis further elucidated the regulation of amino acid, carbohydrate, and lipid metabolism contributed to the persistent phytotoxicity. This work provides insights into the roles and mechanisms of EPS fractions composition in regulating the environmental fate and risk of GO in natural water.

A dataset of trophic state index for nation-scale lakes in China from 40-year Landsat observations

Trophic state index (TSI) serves as a key indicator for quantifying and understanding the lake eutrophication, which has not been fully explored for long-term water quality monitoring, especially for small and medium inland waters. Landsat satellites offer an effective complement to facilitate the temporal and spatial monitoring of multi-scale lakes. Landsat surface reflectance products were utilized to retrieve the annual average TSI for 2693 lakes over 1 km2 in China from 1984 to 2023. Our method first distinguishes lake types by pixels with a decision tree and then derives relationships between trophic state and algal biomass index. Validation with public reports and existing datasets confirmed the good consistency and reliability. The dataset provides reliable annual TSI results and credible trends for lakes under different area scales, which can serve as a reference for further research and provide convenience for lake sustainable management.

Satellite mapping reveals phytoplankton biomass's spatio-temporal dynamics and responses to environmental factors in a eutrophic inland lake

Chlorophyll a (Chla) concentration can be used as an indicator of algal biomass, and the accumulation of algal biomass in water column is essential for the emergence of surface blooms. By using Moderate Resolution Imaging Spectrometer (MODIS) data, a machine learning algorithm was previously developed to assess algal biomass within the euphotic depth (Beu). Here, a long-term Beu dataset of Lake Taihu from 2003 to 2020 was generated to examine its spatio-temporal dynamics, sensitivity to environmental factors, and variations in comparison to the surface algal bloom area. During this period, the daily Beu (total Beu within the whole lake) exhibited temporal fluctuations between 40 and 90 t Chla, with an annual average of 63.32 ± 5.23 t Chla. Notably, it reached its highest levels in 2007 (72.34 t Chla) and 2017 (73.57 t Chla). Moreover, it demonstrated a clear increasing trend of 0.197 t Chla/y from 2003 to 2007, followed by a slight decrease of 0.247 t Chla/y after 2017. Seasonal variation showed a bimodal annual cycle, characterized by a minor peak in March ∼ April and a major peak in July ∼ September. Spatially, the average pixel-based Beu (total Beu of a unit water column) ranged from 21.17 to 49.85 mg Chla, with high values predominantly distributed in the northwest region and low values in the central region. The sensitivity of Beu to environmental factors varies depending on regions and time scales. Temperature has a significant impact on monthly variation (65.73%), while the level of nutrient concentrations influences annual variation (55.06%). Wind speed, temperature, and hydrodynamic conditions collectively influence the spatial distribution of Beu throughout the entire lake. Algal bloom biomass can capture trend changes in two mutant years as well as bimodal phenological changes compared to surface algal bloom area. This study can provide a basis for scientific evaluation of water environment and a reference for monitoring algal biomass in other similar eutrophic lakes.

Single-atom Mn-embedded carbon nitride as highly efficient peroxymonosulfate catalyst for the harmful algal blooms control

In recent years, water quality deterioration caused by harmful algal blooms (HABs) has become one of the global drinking water safety issues, and sulfate radical driven heterogeneous advanced oxidation technology has been widely used for algae removal. However, the shortages of low active site exposure, metal leaching, and secondary contamination limit its further application. Therefore, the single-atom Mn anchored on inorganic carbon nitride was constructed to enhance the oxidation and coagulation of algal cells while maintaining cell integrity in this study. The removal efficiency of Microcystis aeruginosa was as high as 100 % within 30 min under the optimal conditions of 400 mg/L single-atom Mn-embedded g-C3N4 (SA-MCN) and 0.32 mM peroxymonosulfate (PMS). Importantly, the K+ release, malondialdehyde concentration, floccules morphology and variation of algal organic matters further showed that the algal cells still maintained high integrity without severe rupture during the catalytic reaction. Furthermore, the catalytic mechanisms of algae removal by moderate oxidation and simultaneous coagulation in this system were explored by quenching experiments, EPR analysis, theoretical calculation, and Zeta potential. In brief, this study highlighted the single-atom heterogeneous catalyst with high-efficiency and environmental-friendliness in harmful algal blooms control.

Study on the spatiotemporal distribution of algal blooms and its influencing factors in young reservoirs based on remote sensing interpretation

Algal blooms caused by excessive proliferation of phytoplankton in young reservoirs have been frequently reported, seriously threatening the unstable aquatic ecosystem, water quality safety and public health. Thus, there is an urgent need to investigate the dynamics of phytoplankton in these young reservoirs, and many current studies on phytoplankton in young reservoirs are based on point monitoring information. This study used remote sensing interpretation to invert the chlorophyll-a concentration in 131 images of Zipingpu Reservoir from 2013 to 2021, and analyzed the spatiotemporal characteristics of algal blooms. Partial least squares-structural equation modeling was used to identify the environmental influencing factors of algal blooms. The results showed that the average chlorophyll-a concentration in the reservoir was 4.49 mg/m3, and the frequency of algal blooms was 28%. The maximum area of algal blooms shows a significant increase trend in the interannual (increase by 0.05%/yr in the proportion of water surface area), and the average blooms area shows a weaker increase trend (0.01%/yr). The prone period of algal bloom is from April to August every year. The solar duration and wind speed had significant direct positive effects on the maximum and average algal bloom area, which was the similar effects in different years and months (path coefficient exceeds 0.44). TP also has a significant direct positive effect on the average algal bloom area between different years (path coefficient of 0.30). The suitable meteorological factors level making the bloom-prone period from April to August, the prevailing westerly and southerly winds provide transport for the aggregation of phytoplankton and algal blooms outbreak in the northeastern waters. This study expand the monitoring frequency and spatial information of algal blooms, which provided a reference for young reservoir management and prevention of blooms.

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

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

Analyzing MC-LR distribution characteristics in natural lakes by a novel fluorescence technology

The death of aquatic and terrestrial organisms caused by cyanobacterial blooms has been a topic of considerable concern since the 19th century. Microcystin-LR (MC-LR) produced by cyanobacterial blooms threaten natural ecosystems and human health. Therefore, establishing an effective monitoring and early warning system to detect MC-LR in water bodies is crucial. However, rapidly and intuitively assessing the distribution traits of MC-LR in lakes is a challenging task due to the complexities and expenses associated with conventional detection methods. To overcome these technical limitations, we introduce a novel and effective method for evaluating the distribution of MC-LR in lakes. This method is achieved by using a fluorescence probe (BAD) technology, marking the first application of this technology in evaluating the distribution of MC-LR in natural lake environments. The probe BAD is endowed with unique functions through clever functionalization modification. Experimental results exhibit that BAD has different fluorescence signals at various lake sampling points. The correlation analysis of fluorescence data and physicochemical indicators determines that the fluorescence data of the probe exhibit good correlation with MC-LR, implying that BAD is capable of detecting MC-LR in lakes. Moreover, the introduction of fluorescence technology to achieve the intuitive distribution of MC-LR in the entire plateau lake. This study provides a new method for evaluating the distribution of MC-LR in plateau lakes. It opens a new avenue for exploring the relationship between cyanobacterial blooms and MC-LR in natural waters.

A Digital Twin Lake Framework for Monitoring and Management of Harmful Algal Blooms

Harmful algal blooms (HABs) caused by lake eutrophication and climate change have become one of the most serious problems for the global water environment. Timely and comprehensive data on HABs are essential for their scientific management, a need unmet by traditional methods. This study constructed a novel digital twin lake framework (DTLF) aiming to integrate, represent and analyze multi-source monitoring data on HABs and water quality, so as to support the prevention and control of HABs. In this framework, different from traditional research, browser-based front ends were used to execute the video-based HAB monitoring process, and real-time monitoring in the real sense was realized. On this basis, multi-source monitored results of HABs and water quality were integrated and displayed in the constructed DTLF, and information on HABs and water quality can be grasped comprehensively, visualized realistically and analyzed precisely. Experimental results demonstrate the satisfying frequency of video-based HAB monitoring (once per second) and the valuable results of multi-source data integration and analysis for HAB management. This study demonstrated the high value of the constructed DTLF in accurate monitoring and scientific management of HABs in lakes.

MODIS Terra and Aqua images bring non-negligible effects to phytoplankton blooms derived from satellites in eutrophic lakes

Phytoplankton-induced lake eutrophication has drawn ongoing interest on a global scale. One of the most popular remote sensing satellite data for observing long-term dynamic changes in phytoplankton is Moderate-resolution Imaging Spectroradiometer (MODIS). However, it is worth noting that MODIS provides two images with different transit times: Terra (local time, about 10:30 am) and Aqua (local time, about 1:30 pm), which may result in a considerable bias in monitoring phytoplankton bloom areas due to the rapid migration of phytoplankton under wind or hydrodynamic conditions. To analyze this quantitatively, we selected MODIS Terra and Aqua images to generate datasets of phytoplankton bloom areas in Lake Taihu from 2003 to 2022. The results showed that Terra more frequently detected larger ranges of phytoplankton blooms than Aqua, whether on daily, monthly, or annual scales. In addition, long-term trend changes, seasonal characteristics, and abrupt years also varied with different transit times. Terra detected mutation years earlier, while Aqua displayed more pronounced seasonal characteristics. There were also differences in sensitivity to climate factors, with Terra being more responsive to temperature and wind speed on monthly and annual scales, while Aqua was more sensitive to nutrient and meteorological factors. These conclusions have also been further confirmed in Lake Chaohu, Lake Dianchi, and Lake Hulun. In conclusion, our findings strongly advocate for a linear relationship to fit Terra to Aqua results to mitigate long-term monitoring errors of phytoplankton blooms in inland lakes (R2 = 0.70, RMSE = 101.56). It is advised to utilize satellite data with transit times between 10 am and 1 pm to track phytoplankton bloom changes and to consider the diverse applications resulting from the transit times of Terra and Aqua.

Feasibility and mechanism of recycling carbon resources from waste cyanobacteria and reducing microcystin toxicity by dielectric barrier discharge plasma

Recycling carbon resources from discarded cyanobacteria is a worthwhile research topic. This study focuses on the use of dielectric barrier discharge (DBD) plasma technology as a pretreatment for anaerobic fermentation of cyanobacteria. The DBD group (58.5 W, 45 min) accumulated the most short chain fatty acids (SCFAs) along with acetate, which were 3.0 and 3.3 times higher than the control. The DBD oxidation system can effectively collapse cyanobacteria extracellular polymer substances and cellular structure, improve the biodegradability of dissolved organic matter, enrich microorganisms produced by hydrolysis and SCFAs, reduce the abundance of SCFAs consumers, thereby promoting the accumulation of SCFAs and accelerating the fermentation process. The microcystin-LR removal rate of 39.8% was obtained in DBD group (58.5 W, 45 min) on day 6 of anaerobic fermentation. The toxicity analysis using the ECOSAR program showed that compared to microcystin-LR, the toxicity of degradation intermediates was reduced. The contribution order of functional active substances to cyanobacteria cracking was obtained as eaq- > •OH > 1O2 > •O2- > ONOO-, while the contribution order to microcystin-LR degradation was eaq- > •OH > •O2- > 1O2 > ONOO-. DBD has the potential to be a revolutionary pretreatment method for cyanobacteria anaerobic fermentation.

Microcystins risk assessment in lakes from space: Implications for SDG 6.1 evaluation

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.

Quantification of Diffusive Methane Emissions from a Large Eutrophic Lake with Satellite Imagery

Lakes are major emitters of methane (CH4); however, a longstanding challenge with quantifying the magnitude of emissions remains as a result of large spatial and temporal variability. This study was designed to address the issue using satellite remote sensing with the advantages of spatial coverage and temporal resolution. Using Aqua/MODIS imagery (2003-2020) and in situ measured data (2011-2017) in eutrophic Lake Taihu, we compared the performance of eight machine learning models to predict diffusive CH4 emissions and found that the random forest (RF) model achieved the best fitting accuracy (R2 = 0.65 and mean relative error = 21%). On the basis of input satellite variables (chlorophyll a, water surface temperature, diffuse attenuation coefficient, and photosynthetically active radiation), we assessed how and why they help predict the CH4 emissions with the RF model. Overall, these variables mechanistically controlled the emissions, leading to the model capturing well the variability of diffusive CH4 emissions from the lake. Additionally, we found climate warming and associated algal blooms boosted the long-term increase in the emissions via reconstructing historical (2003-2020) daily time series of CH4 emissions. This study demonstrates the great potential of satellites to map lake CH4 emissions by providing spatiotemporal continuous data, with new and timely insights into accurately understanding the magnitude of aquatic greenhouse gas emissions.