Lake Surface Water Temperatures Research Articles

Exploring Trends and Variability of Water Quality over Lake Titicaca Using Global Remote Sensing Products

Understanding the current water quality dynamics is necessary to ensure that ecological and sociocultural services are provided to the population and the natural environment. Water quality monitoring of lakes is usually performed with in situ measurements; however, these are costly, time consuming, laborious, and can have limited spatial coverage. Nowadays, remote sensing offers an alternative source of data to be used in water quality monitoring; by applying appropriate algorithms to satellite imagery, it is possible to retrieve water quality parameters. The use of global remote sensing water quality products increased in the last decade, and there are a multitude of products available from various databases. However, in Latin America, studies on the inter-comparison of the applicability of these products for water quality monitoring is rather scarce. Therefore, in this study, global remote sensing products estimating various water quality parameters were explored on Lake Titicaca and compared with each other and sources of data. Two products, the Copernicus Global Land Service (CGLS) and the European Space Agency Lakes Climate Change Initiative (ESA-CCI), were evaluated through a comparison with in situ measurements and with each other for analysis of the spatiotemporal variability of lake surface water temperature (LSWT), turbidity, and chlorophyll-a. The results of this study showed that the two products had limited accuracy when compared to in situ data; however, remarkable performance was observed in terms of exhibiting spatiotemporal variability of the WQ parameters. The ESA-CCI LSWT product performed better than the CGLS product in estimating LSWT, while the two products were on par with each other in terms of demonstrating the spatiotemporal patterns of the WQ parameters. Overall, these two global remote sensing water quality products can be used to monitor Lake Titicaca, currently with limited accuracy, but they can be improved with precise pixel identification, accurate optical water type definition, and better algorithms for atmospheric correction and retrieval. This highlights the need for the improvement of global WQ products to fit local conditions and make the products more useful for decision-making at the appropriate scale.

Simulation and Prediction of Thermokarst Lake Surface Temperature Changes on the Qinghai–Tibet Plateau

Thermokarst lakes are shallow bodies of freshwater that develop in permafrost regions, and they are an essential focus of international permafrost research. However, research regarding the mechanisms driving temperature fluctuations in thermokarst lakes and the factors that influence these changes is limited. We aimed to analyze seasonal variations in the surface water temperature, clarify historical trends in the phenological characteristics of lake ice, and predict future temperature changes in surface water of the thermokarst lakes using the air2water model. The results indicated that in comparison with air temperature, the thermokarst lake’s surface water temperature showed a certain lag and significantly higher values in the warm season. The warming rate of the thermokarst lake’s average surface water temperature based on historical data from 1957 to 2022 was 0.21 °C per decade, with a notably higher rate in August (0.42 °C per decade) than in other months. Furthermore, the ice-covered period steadily decreased by 2.12 d per decade. Based on the Coupled Model Intercomparison Project 6 projections, by 2100, the surface water temperatures of thermokarst lakes during the warm season are projected to increase by 0.38, 0.46, and 2.82 °C (under scenarios SSP126, SSP245, and SSP585), respectively. Compared with typical tectonic lakes on the Qinghai–Tibet Plateau, thermokarst lakes have higher average surface water temperatures during ice-free periods, and they exhibit a higher warming rate (0.21 °C per decade). These results elucidate the response mechanisms of thermokarst lakes’ surface water temperature and the phenological characteristics of lake ice in response to climate change.

Trends of Summer Lake Surface Water Temperature on the Tibetan Plateau and Their Response to Climate Change

AbstractThe Tibetan Plateau (TP) is covered by numerous lakes, and lake surface water temperature (LSWT) is an essential indicator of climate change, while few observations hinder our understanding of LSWT variation and its causes over TP. This study aims to simulate the summer LSWT long‐term trends of 81 TP lakes during 1980–2018 and quantify the impacts and contributions of atmospheric variables. Results show that TP lakes warmed with 0.32°C decade−1 on average. Northern TP lakes warmed faster than the southern ones (0.44 vs. 0.16°C decade−1) due to stronger trends of atmospheric variables and higher sensitive of colder lakes to atmospheric changes. 55 (67.9%) lakes of the total lakes studied in current work warmed slower than air due to weakened shortwave radiation (SW↓). Attribution analysis suggests that the air warming and wetting over TP dominate lakes' warming. Regarding synthesis contributions, air warming contributed 79.3%, with increased surface air temperature (SAT) and downward longwave radiation (LW↓) accounting for 41.6% and 37.7%, respectively, and air wetting indicated by increased surface specific humidity (SSH) contributed 39.0%, followed by a positive contribution (16.8%) from declined wind speed (WS). The negative contribution (−35.1%) from weakened SW↓ nearly counterbalances the positive effects of increased LW↓. 55.1% of the total synthesis contribution arises from the cross contribution through interactions among atmospheric variables and is mainly reflected in SAT and SSH, accounting for 26.8% and 24.8%, respectively. The findings enhance understanding of climate change impacts on lake systems and offer insights for lake resource management.

Lake Cold Spells Are Declining Worldwide

AbstractExtremes in lake surface water temperature can have profound impacts on ecosystems. Although extensive research has been conducted on lake heatwaves, little is known about cold‐water extremes, referred to as lake cold spells. Here, we employ a numerical lake model to investigate global lake cold spell dynamics from 1979 to 2100. From 1979 to 2022, lake cold spells have become shorter‐lasting and weaker. Change rates in the occurrence (i.e., total days) and intensity of lake cold spells are −3.96 days decade−1 and 0.26°C decade−1, respectively. In Europe, the decline in lake cold spells is due to increases in the mean of surface water temperatures, whereas in North America, the reduction is due to decreases in the variance of surface water temperature. In the future, lake cold spells will shorten by 19.3 days and weaken by 1.97°C in the end of the 21st century under the high‐greenhouse‐gas‐emission scenario, underscoring a shift in the surface temperature regime that may have an important influence on lake ecosystems.

Reconstruction of Surface Water Temperature in Lakes as a Source for Long-Term Analysis of Its Changes

Reconstruction of Surface Water Temperature in Lakes as a Source for Long-Term Analysis of Its Changes

Lake surface water temperature in China from 2001 to 2021 based on GEE and HANTS

Warming of lakes' surface water leads to accelerated loss of biodiversity and eco-environmental collapse of aquatic systems. Changes in lack surface water temperature (LSWT) are a crucial indicator of lake warming. LSWT growth potentially leads to a higher greenhouse gas emissions and deterioration of the ecological environment within lake systems. However, the magnitude of these changes remains uncertain due to data limitations, particularly for small lakes (1–5 km2). Small lakes will experience increasing perturbation with accelerating climate change and our methods demonstrate how the impacts of changes in lakes can be accurately measured and monitored. Our study assessed the spatial and temporal patterns of LSWT in China from 2001 to 2021. We utilized Google Earth Engine (GEE) and the Harmonic Analysis of Time Series (HANTS) algorithm to reconstruct LSWT series and detect spatiotemporal dynamics. The innovative connection of GEE and HANTS provides powerful tool for LSWT analysis. Our results show LSWT increased at a rate of 0.24 °C per decade, albeit with notable spatial and temporal variations. The nighttime rate of increase was greater than the daytime rate of increase. However, there was an abrupt change in daytime LSWT in approximately 2010 and this occurred earlier than an abrupt change in nighttime LSWT. Geographically, the lakes in the Eastern Plain zone exhibited the most significant LSWT warming trend. The majority of lakes warmed more rapidly between 2011 and 2021 as compared to 2001 to 2010. We found a concurrent and pronounced increase in the frequency of algal bloom occurrences after 2010. Our results demonstrate how GEE and HANTS can deliver the continued monitoring and assessment of LSWT trends needed to inform management strategies aimed at mitigating potential negative impacts of climate change on lake ecosystems, both locally and globally. Building on this method, future research should explore the underlying mechanisms driving LSWT trends and their long-term impacts on lake health.

How can scorching weathers alter lake surface water temperature? Explore the long-term effects

How can scorching weathers alter lake surface water temperature? Explore the long-term effects

Periodically asymmetric responses of deep chlorophyll maximum to light and thermocline in a clear monomictic lake: Insights from monthly and diel scale observations

Deep chlorophyll maximum (DCM), a chlorophyll peak in the water column, has important implications for biogeochemical cycles, energy flow and water surface algal blooms in deep lakes. However, how an observed periodically asymmetric DCM response to environmental variables remains unclear, limiting our in-depth understanding and effective eco-environmental management of deep lakes. Based on both monthly field investigations in 2021 and diel continuous observations in 2021–2023 in clear, monomictic Lake Fuxian, Southwest China, the temporal dynamics and drivers of DCM were examined and periodic features of DCM were found, with a formation period (FP, February–July) and a weakening period (WP, August–December). On the monthly scale, although DCM dynamics were partly attributed to thermocline structures, the role of light penetration depths varied with period. In the FP, the influence of light on DCM was direct, i.e., increased depth and thickness but decreased magnitude. Differently, the influence of light mainly occurred by affecting thermocline structures in the WP, where water quality was another important driver. On the diel scale, light was a major reason for a thicker and lower (magnitude) DCM during day than at night, and the response of DCM to environmental factors between the FP and WP differed also more during day. This periodically asymmetric response of daytime DCM not only being caused by light but possibly also related to other physical factors such as lake surface water temperature, wind speed and precipitation. Bayesian network modelling suggested that water darkening and stratification intensification may promote a shallower, thinner and larger (magnitude) DCM in both FP and WP, but achieving such changes in DCM requires different light and thermocline thresholds. Our findings provide new information valuable for modelling DCM and for predicting the related surface algal blooms in deep lakes under climate change and eutrophication.

Machine learning-based modeling of surface water temperature dynamics in arctic lakes.

Lake surface-water temperature (LSWT) regulates physical and biochemical processes in lakes. Therefore, understanding the LSWT dynamics is important, especially in Arctic zone since the region is experiencing a warming rate that is greater than the Earth's average. However, regular measurements of LSWT in the remote Arctic lakes always face difficulties or cannot be done by satellites accurately due to the cloud cover and their limited spatiotemporal resolution. Here, we used a historically rich data (1960-2023) to develop four machine learning-based algorithms for the daily LSWT modeling in Lake Inari, situated in Arctic zone, using the air-temperature data. Our results showed that both air-temperature (0.030 °C/yr) and LSWT (0.023m °C/yr) were warming with a rate faster than those in the globe. The long-short-term memory model, with the coefficients of determination varied from 0.96 to 0.98, outperformed other algorithms in modeling of the daily LSWT dynamics in Lake Inari, followed by both support vector regression and neural network tools, and random forest model. As the air-temperature data are widely accessible through synoptic stations and remote sensing techniques, our suggested models can be simply adopted for other Arctic lakes, where the local water-temperature data are often lacking or contain large windows of missing data due to harsh atmospheric conditions and equipment failure.

Disproportionate impact of atmospheric heat events on lake surface water temperature increases

Disproportionate impact of atmospheric heat events on lake surface water temperature increases

Dynamics of the risk of algal blooms induced by surface water temperature in an alpine eutrophic lake under climate warming: Insights from Lake Dianchi

Climate change-induced higher water temperature is increasing the risk of algal blooms (ABs) in eutrophic water bodies. However, it is not well known how and to what extent warming affects ABs risk. In this work, with simple data input (satellite product, lake surface observation, and climate) and method structure (ensemble learning, statistical downscaling, and hierarchical clustering), a systematic lake surface water temperature (LSWT) and ABs risk evaluation system are constructed and applied in a typical eutrophic freshwater lake. The main results are as follows: 1. The warming rate of LSWT and air temperature from 2000 to 2023 reached 0.21 °C/10a and 0.30 °C/10a respectively, resulting in an extended ABs risk in the historical period. 2. The Nash–Sutcliffe efficiency between climate and LSWT reached 0.89, and LSWT showed a significant (p < 0.05) positive correlation with algal density. 3. The escalation of ABs risks mainly spread to spring and autumn in the forthcoming future, the adverse effect of high emission scenarios will evidently emerge in the mid-21st century. Policymakers and catchment managers should be cautious of lake warming and develop countermeasures in advance.

Spatiotemporal variability of lake surface water temperature and water quality parameters and its interrelationship with water hyacinth biomass in Lake Tana, Ethiopia.

Urbanization, agriculture, and climate change affect water quality and water hyacinth growth in lakes. This study examines the spatiotemporal variability of lake surface water temperature, turbidity, and chlorophyll-a (Chl-a) and their association with water hyacinth biomass in Lake Tana. MODIS Land/ Lake surface water temperature (LSWT), Sentinel 2 MSI Imagery, and in-situ water quality data were used. Validation results revealed strong positive correlations between MODIS LSWT and on-site measured water temperature (R = 0.90), in-situ turbidity and normalized difference turbidity index (NDTI) (R = 0.92), and in-situ Chl-a and normalized difference chlorophyll index (NDCI) (R = 0.84). LSWT trends varied across the lake, with increasing trends in the northeastern, northwestern, and southwestern regions and decreasing trends in the western, southern, and central areas (2001-2022). The spatial average LSWT trend decreased significantly in pre-rainy (0.01 ℃/year), rainy (0.02 ℃/year), and post-rainy seasons (0.01℃/year) but increased non-significantly in the dry season (0.00 ℃/year) (2001-2022, P < 0.05). Spatial average turbidity decreased significantly in all seasons, except in the pre-rainy season (2016-2022). Likewise, spatial average Chl-a decreased significantly in pre-rainy and rainy seasons, whereas it showed a non-significant increasing trend in the dry and post-rainy seasons (2016-2022). Water hyacinth biomass was positively correlated with LSWT (R = 0.18) but negatively with turbidity (R = -0.33) and Chl-a (R = -0.35). High spatiotemporal variability was observed in LSWT, turbidity, and Chl-a, along with overall decreasing trends. The findings suggest integrated management strategies to balance water hyacinth eradication and its role in water purification. The results will be vital in decision support systems and preparing strategic plans for sustainable water resource management, environmental protection, and pollution prevention.

Lake and Atmospheric Heatwaves Caused by Extreme Dust Intrusion in Freshwater Lake Kinneret in the Eastern Mediterranean

The role of dust intrusions in the formation of lake heatwaves has not yet been discussed in previous publications. We investigated a lake heatwave (LHW) and an atmospheric heatwave (AHW) in the freshwater Lake Kinneret in the Eastern Mediterranean: these were caused by an extreme dust intrusion that lasted for a 10-day period (7–17 September 2015). The AHW and LHW were defined as periods of abnormally high air temperature (Tair) and lake surface water temperature (SWT) compared to their 90th percentile thresholds in September. In the daytime, the maximal intensities of AHW and LHW reached 3 °C and 2 °C, respectively. This was despite the pronounced drop in solar radiation due to the dust radiative effect. The satellite SWT retrievals were incapable of representing the abnormally high SWT in the presence of the extreme dust intrusion. Both METEOSAT and MODIS-Terra showed a sharp decrease in the SWT compared to the actual SWT: up to 10 °C in the daytime and up to 15 °C in the nighttime. Such a significant underestimation of the actual SWT in the presence of a dust intrusion should be considered when using satellite data to analyze heatwaves. In the absence of moisture advection, the AHW and LHW were accompanied by an increase of up to 30% in absolute humidity (ρv) over the lake. Being a powerful greenhouse gas, water vapor (characterized by an increased ρv) absorbed most of both the upwelling and downwelling longwave thermal radiation, heating the near-ground atmospheric layer (which is in direct contact with the lake water surface), in the daytime and nighttime. In the nighttime, the maximal intensity of the AHW and LHW reached 4 °C and 3 °C, respectively. Because of the observed steadily increasing dust pollution over the Eastern Mediterranean during the past several decades, we anticipate that dust-related lake heatwaves will intensify adverse effects on aquatic ecosystems such as reducing fishery resources and increasing harmful cyanobacteria blooms.

Impact of climate change on the Vrana Lake surface water temperature in Croatia using support vector regression

Study regionThe case study presents Vrana Lake on Cres island in Croatia, which is the largest freshwater resource on the Mediterranean islands. It is used for the public water supply of the residents and tourists who inhabit this island during the summer months. Study focusUsing the Support Vector Regression (SVR), the influence of future climate changes on the lake surface water temperature (LSWT) was analysed. Input data were monthly air temperatures (AT). Model training and validation were based on measured LSWT and AT in the period 1981–2022. Expected LSWTs for the period 2023–2070 under the RCP8.5 emission scenario were forecast based on climate modelling data of monthly AT for the period 1971–2070. New hydrological insights for the regionThe results showed that the applied SVR model can effectively forecast monthly LSWTs, which was confirmed by a correlation coefficient of approximately 0.99 between the measured and simulated LSWTs. Root mean square errors were lower than 1 °C. The LSWT warming trend in 2023–2070 is expected to be lower than that observed in 1981–2022, and will vary from 0.2 °C dec-1–0.3 °C dec-1. The largest increase in LSWT can be expected in spring while the increase in LSWT will be the smallest in the summer months. In approximately 50 years, the expected LSWT could be higher by an average of 1.2 °C.

Analysis of the temporal and spatial evolution of turbidity in Tonle Sap Lake and its influencing factors

Turbidity is a crucial indicator of water quality. The European Commission's Copernicus Land Monitoring Service Platform provides free turbidity data for large lakes to monitor the global water quality of lakes. However, the data were missing from April 2012 to April 2016, severely limiting long-term analysis. Based on MODIS and turbidity data, Random Forest and XGBoost models are used to invert Tonle Sap Lake's turbidity. Random Forest outperformed the XGBoost model. Based on Random Forest model, missing data were filled in to construct long-term series data of Tonle Sap Lake turbidity (2004–2021). Trend, persistence and correlation analyses were conducted to reveal spatiotemporal characteristics and driving mechanism of turbidity. The results showed that: (1) spatially, the average annual, monthly, and seasonal turbidity was higher in the north but lower in the south, with regions of higher turbidity exhibiting more significant changes; (2) temporally, the annual turbidity mean was 53.99 NTU and showed an increasing trend. Monthly, turbidity values were higher from March to August and lower from September to February, with the highest and lowest recorded in June and November at 110.06 and 5.82 NTU, respectively. Seasonally, turbidity was higher in spring and summer compared to autumn and winter, with mean turbidity values of 84.16, 93.47, 15.33 and 23.21 NTU, respectively; (3) In terms of sustainability, the Hurst exponent for annual turbidity was 0.23, indicating a reverse trend in the near future; (4) Dam construction's impact on turbidity was not significant. Compared with natural factors (permanent wetlands, grasslands, lake surface water temperature, and remote sensing ecological index), human activities (barren, urban and built-up lands, croplands and population density) had a more significant impact on turbidity. Turbidity was highly correlated with croplands (r = 0.76), followed by population density (r = 0.71), and urban and built-up lands (r = 0.69).

Investigating long-term changes in surface water temperature of Dongting Lake using Landsat imagery, China.

Lake surface water temperature (LSWT) plays a crucial role in assessing the health of aquatic ecosystems. Variations in LSWT can significantly impact the physical, chemical, and biological processes within lakes. This study investigates the long-term changes in surface water temperature of the Dongting Lake, China. The LSWT is retrieved using Landsat thermal infrared imageries from 1988 to 2022 and validated with in situ observations, and the change characteristics of LSWT and near-surface air temperature (NSAT) as well as the spatial distribution characteristics of LSWT are analyzed. Additionally, the contribution rates of different meteorological factors to LSWT are quantified. The results show that the accuracy assessment of satellite-derived temperatures indicates a Nash-Sutcliffe efficiency coefficient (NSE) of 0.961, suggesting an accurate retrieval of water temperature. From 1988 to 2022, both the annual average LSWT and NSAT of Dongting Lake exhibit an increasing trend, with similar rates of warming. They both undergo a mutation in 1997 and have the main periods on the 11-year and 4-year time scales. The changes in NSAT emerge as one of the important factors contributing to variations in LSWT. Among the multiple meteorological factors, NSAT exhibits a significant correlation with LSWT (R = 0.822, α < 0.01). Furthermore, NSAT accounts for the highest contribution rate to LSWT, amounting to 67.5%. The distribution of LSWT within Dongting Lake exhibits spatial variations, with higher LSWT observed on the west part compared to the east part during summer, while lower LSWT occurs on the west part during winter. The findings of this study can provide a scientific understanding for the long-term thermal regimes of lakes and help advance sustainable lake management.

Lake Surface Water Temperature in high altitude lakes in the Pyrenees: Combining satellite with monitoring data to assess recent trends

Lake Surface Water Temperature (LSWT) influences critical bio-geological processes in lake ecosystems, and there is growing evidence of rising LSWT over recent decades worldwide and future shifts in thermal patterns are expected to be a major consequence of global warming.At a regional scale, assessing recent trends and anticipating impacts requires data from a number of lakes, but long term in situ monitoring programs are scarce, particularly in mountain areas. In this work, we propose the combined use of satellite-derived temperature with in situ data for a five-year period (2017–2022) from 5 small (<0.5km2) high altitude (1880–2680 masl) Pyrenean lakes. The comparison of in situ and satellite-derived data in a common period (2017–2022) during the summer season showed a notably high (r = 0.94, p < 0.01) correlation coefficient, indicative of a robust relationship between the two data sources. The root mean square errors ranged from 1.8 °C to 3.9 °C, while the mean absolute errors ranged from 1.6 °C to 3.6 °C.We applied the obtained in situ-satellite eq. (2017–2022) to Landsat 5, 7 and 8/9 data since 1985 to reconstruct the summer surface temperature of the five studied lakes with in situ data and to four additional lakes with no in situ monitoring data. Reconstructed LSWT for the 1985–2022 showed an upward trend in all lakes. Moreover, paleolimnological reconstructions based on sediment cores studies demonstrate large changes in the last decades in organic carbon accumulation, sediment fluxes and bioproductivity in the Pyrenean lakes.Our research represents the first comprehensive investigation conducted on high mountain lakes in the Pyrenees that compares field monitoring data with satellite-derived temperature records. The results demonstrate the reliability of satellite-derived LSWT for surface temperatures in small lakes, and provide a tool to improve the LSWT in lakes with no monitoring surveys.

Slow response of surface water temperature to fast atmospheric variability reveals mixing heterogeneity in a deep lake

Slow and long-term variations of sea surface temperature anomalies have been interpreted as a red-noise response of the ocean surface mixed layer to fast and random atmospheric perturbations. How fast the atmospheric noise is damped depends on the mixed layer depth. In this work we apply this theory to determine the relevant spatial and temporal scales of surface layer thermal inertia in lakes. We fit a first order auto-regressive model to the satellite-derived Lake Surface Water Temperature (LSWT) anomalies in Lake Garda, Italy. The fit provides a time scale, from which we determine the mixed layer depth. The obtained result shows a clear spatial pattern resembling the morphological features of the lake, with larger values (7.18± 0.3 m) in the deeper northwestern basin, and smaller values (3.18 ± 0.24 m) in the southern shallower basin. Such variations are confirmed by in-situ measurements in three monitoring points in the lake and connect to the first Empirical Orthogonal Function of satellite-derived LSWT and chlorophyll-a concentration. Evidence from our case study open a new perspective for interpreting lake-atmosphere interactions and confirm that remotely sensed variables, typically associated with properties of the surface layers, also carry information on the relevant spatial and temporal scales of mixed-layer processes.

Functional principal component analysis for incomplete space–time data

Environmental signals, acquired, e.g., by remote sensing, often present large gaps of missing observations in space and time. In this work, we present an innovative approach to identify the main variability patterns, in space–time data, when data may be affected by complex missing data structures. We formalize the problem in the framework of functional data analysis, proposing an innovative method of functional principal component analysis (fPCA) for incomplete space–time data. The functional nature of the proposed method permits to borrow information from measurements observed at nearby spatio-temporal locations. The resulting functional principal components are smooth fields over the considered spatio-temporal domain, and can lead to interesting insights in the spatio-temporal dynamic of the phenomenon under study. Moreover, they can be used to provide a reconstruction of the missing entries, also under severe missing data patterns. The proposed model combines a weighted rank-one approximation of the data matrix with a roughness penalty. We show that the estimation problem can be solved using a majorize–minimization approach, and provide a numerically efficient algorithm for its solution. Thanks to a discretization based on finite elements in space and B-splines in time, the proposed method can handle multidimensional spatial domains with complex shapes, such as water bodies with complicated shorelines, or curved spatial regions with complex orography. As shown by simulation studies, the proposed space–time fPCA is superior to alternative techniques for Principal Component Analysis with missing data. We further highlight the potentiality of the proposed method for environmental problems, by applying space–time fPCA to the study of the lake water surface temperature (LWST) of Lake Victoria, in Central Africa, starting from satellite measurements with large gaps. LWST is considered one of the fundamental indicators of how climate change is affecting the environment, and is recognized as an essential climate variable.

An analysis of the Lake Surface Water Temperature evolution of the world’s largest lakes during the years 2003-2020 using MODIS data

The Lake Surface Water Temperature (LSWT) evolution is analysed in ten of the largest lakes in the world: Caspian Sea, Superior, Victoria, Huron, Michigan, Tanganyika, Baikal, Great Slave Lake, Erie and Ontario. The time span selected is 2003-2020 and the satellite product, MODIS Level 3 SST Thermal IR 8 Day 4km V2019.0. Results show warming trends ranging from 0.012⁰C/yr. in the Victoria Lake to 0.083 ⁰C/yr in the Baikal Lake. Results have been validated with the product MOD11L2 LSWT estimations for the years 2003-2014 in the Laurentian Great Lakes, obtaining correlations between 0.962 and a 0.998. The validation has been enlarged by considering Sentinel 3 observations from the Issyk-Kul lake, with a 0.99 correlation. The validation shows that the MODIS SST product is capable of estimating the LSWT parameter with a high precision.