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In the context of global warming, the lake surface water temperature (LSWT) exhibits a general upward trend. As an indicator of climate change, the surface water temperature of plateau lakes is particularly sensitive to climate warming. In this study, the machine learning model and physical process model are combined to predict water temperature in lake systems: The Air2water model and the long short term memory (LSTM) model are combined to form a prediction model for the inversion and prediction of 11 typical plateau lakes in the Yunnan–Guizhou Plateau. The modeling results show that the Air2water model performs best, followed by the LSTM model. Overall, the Air2water model and the LSTM model effectively reproduce the monthly, seasonal, and interannual variations of LSWT dynamics in the 11 lakes. The research results show that the LSWT dataset from 2021 to 2025 is constructed based on the forecast result, and it is found that six lakes, including Caohai, Dianchi, Erhai, Qionghai, Xingyun Lake, and Yangzonghai, showed significant warming in the next five years (the maximum warming rate was less than +0.2 °C/year), while the remaining five lakes showed no significant changes, and the size classification of LSWT data in different spaces on the interannual scale was consistent with the classification of altitudes. By conducting extensive exploration and research on the inversion of LSWT using physical process models and machine learning models, this study offers novel solutions for LSWT prediction and inversion. The study also provides a solid theoretical and practical foundation for future LSWT research, thereby holding practical significance and research value.

In the context of global warming, the lake surface water temperature (LSWT) exhibits a general upward trend. As an indicator of climate change, the surface water temperature of plateau lakes is particularly sensitive to climate warming. In this study, the machine learning model and physical process model are combined to predict water temperature in lake systems: The Air2water model and the long short term memory (LSTM) model are combined to form a prediction model for the inversion and prediction of 11 typical plateau lakes in the Yunnan–Guizhou Plateau. The modeling results show that the Air2water model performs best, followed by the LSTM model. Overall, the Air2water model and the LSTM model effectively reproduce the monthly, seasonal, and interannual variations of LSWT dynamics in the 11 lakes. The research results show that the LSWT dataset from 2021 to 2025 is constructed based on the forecast result, and it is found that six lakes, including Caohai, Dianchi, Erhai, Qionghai, Xingyun Lake, and Yangzonghai, showed significant warming in the next five years (the maximum warming rate was less than +0.2 °C/year), while the remaining five lakes showed no significant changes, and the size classification of LSWT data in different spaces on the interannual scale was consistent with the classification of altitudes. By conducting extensive exploration and research on the inversion of LSWT using physical process models and machine learning models, this study offers novel solutions for LSWT prediction and inversion. The study also provides a solid theoretical and practical foundation for future LSWT research, thereby holding practical significance and research value. [ABSTRACT FROM AUTHOR]

Lake surface water temperature (LSWT) is indicative of changes in climate and geomorphology. It has been found that LSWT has shown faster warming than air temperature in recent decades, which has greatly affected the aquatic ecosystem of lakes and even led to ecological collapse. It was mainly caused by climate warming and geomorphological changes caused by human activities. However, lakes with different properties respond differently to these changes, resulting in temporal and spatial heterogeneity in the changes of LSWT. After an extensive literature review, it was found that the research on LSWT is mainly focused on the following aspects: 1) qualitative or quantitative analysis of natural and anthropogenic factors affecting LSWT changes; 2) impacts of LSWT changes on lake ecology; 3) acquisition of LSWT data, such as retrieval based on remote sensing imagery, model-based simulation prediction; and 4) study of temporal and spatial variation characteristics of LSWT long time series. Dynamically monitoring LSWT changes, predicting, and quantifying the response of lakes with different properties to abrupt or gradual climate changes and changes in human activity intensity, can inform modeling of ecosystem response processes and long-term management planning of large freshwater ecosystems. Therefore, this article provides a systematic review of LSWT from the above four aspects to provide empirical insight and methodological reference for conducting LSWT research.

This study aims to extract water surface area and lake surface water temperature (LSWT), and to present long-term spatio-temporal analysis of these variables together with meteorological parameters. Three lakes in Türkiye's Lakes Region, namely, Lake Burdur, Egirdir, and Beysehir, were considered as test sites. The normalized difference water index (NDWI) was applied to Landsat 5 and 8 data from 2000 to 2021 to extract the water extent in the Google Earth Engine (GEE) cloud-based platform. In addition to the lake surface area, Landsat thermal images were used to examine the LSWT. The findings indicated that water pixels could be extracted rather accurately using NDWI, with an overall accuracy of 98%. Between 2000 and 2021, the water surface area value of Lake Burdur decreased by more than 22%, while Lake Egirdir has dropped by less than 4%, and Lake Beysehir has not changed noticeably. LSWT of Burdur and Egirdir Lakes increased by more than 2.13 °C and 0.32 °C, respectively, while it decreased about 1.5 °C for Beysehir Lake. The obtained results were evaluated with meteorological parameters and our findings indicated that human-induced activities were more dominant than climate effects over Lake Burdur, unlike the others. [ABSTRACT FROM AUTHOR]

Lake surface water temperature (LSWT) is an important factor of water ecological environment. As global warming, LSWT is also on the rise. Research on the main reasons of LSWT rising is the basis for controlling and improving the regional ecological environment. However, it is difficult for the existing remote sensing images to take into account the temporal and spatial resolution. Low-resolution images have a serious impact on data accuracy and even produce incorrect results. Therefore, obtaining high temporal and spatial resolution images by downscaling is of great significance to more accurately analyze the temporal and spatial characteristics of LSWT. In this article, Dianchi Lake is selected as research area, and the high spatial resolution image (Landsat) and high temporal resolution image (MODIS) are taken as data. Based on the downscaling algorithm of statistics and learning, DisTrad– super-resolution convolutional neural network (SRCNN) downscaling model is proposed, and the monthly average dataset of LSWT with 50 m resolution is constructed. The results showed 1) DisTrad–SRCNN can reflect the most distribution characteristics of LSWT (SSIMday = 0.96, PSNRday = 23.97; SSIMnight = 0.95, PSNRnight = 24.99). 2) LSWT had an overall upward trend (CRday = 0.22 °C/10a, CRnight = 0.21 °C/10a), showing a cyclical change of cold–warm–cold about 4 years. 3) The northern and lakeshore area were basically in the high temperature, and the whole lake presents a 4–5-year warm–cold–warm periodic change; the LSWT closer to the urban and residential areas and its change rate were relatively high, which indirectly reflected the serious impact of human activities on LSWT.

This study aims to extract water surface area and lake surface water temperature (LSWT), and to present long-term spatio-temporal analysis of these variables together with meteorological parameters. Three lakes in Türkiye’s Lakes Region, namely, Lake Burdur, Egirdir, and Beysehir, were considered as test sites. The normalized difference water index (NDWI) was applied to Landsat 5 and 8 data from 2000 to 2021 to extract the water extent in the Google Earth Engine (GEE) cloud-based platform. In addition to the lake surface area, Landsat thermal images were used to examine the LSWT. The findings indicated that water pixels could be extracted rather accurately using NDWI, with an overall accuracy of 98%. Between 2000 and 2021, the water surface area value of Lake Burdur decreased by more than 22%, while Lake Egirdir has dropped by less than 4%, and Lake Beysehir has not changed noticeably. LSWT of Burdur and Egirdir Lakes increased by more than 2.13 °C and 0.32 °C, respectively, while it decreased about 1.5 °C for Beysehir Lake. The obtained results were evaluated with meteorological parameters and our findings indicated that human-induced activities were more dominant than climate effects over Lake Burdur, unlike the others.

Satellite-derived lake surface water temperature (LSWT) measurements can be used for monitoring purposes. However, analyses based on the LSWT of Lake Ontario and the surrounding land surface temperature (LST) are scarce in the current literature. First, we provide an evaluation of the commonly used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSWT/LST (MOD11A1 and MYD11A1) using in situ measurements near the area of where Lake Ontario, the St. Lawrence River and the Rideau Canal meet. The MODIS datasets agreed well with ground sites measurements from 2015–2017, with an R2 consistently over 0.90. Among the different ground measurement sites, the best results were achieved for Hill Island, with a correlation of 0.99 and centered root mean square difference (RMSD) of 0.73 K for Aqua/MYD nighttime. The validated MODIS datasets were used to analyze the temperature trend over the study area from 2001 to 2018, through a linear regression method with a Mann–Kendall test. A slight warming trend was found, with 95% confidence over the ground sites from 2003 to 2012 for the MYD11A1-Night datasets. The warming trend for the whole region, including both the lake and the land, was about 0.17 K year−1 for the MYD11A1 datasets during 2003–2012, whereas it was about 0.06 K year−1 during 2003–2018. There was also a spatial pattern of warming, but the trend for the lake region was not obviously different from that of the land region. For the monthly trends, the warming trends for September and October from 2013 to 2018 are much more apparent than those of other months.

Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.

Lake surface water temperature (LSWT) is an important parameter with which to assess aquatic ecosystems and to study the lake’s response to climate change. The AVHRR archive of the University of Bern offers great potential to derive consistent LSWT data suited for the study of climate change and lake dynamics. To derive such a dataset, challenges such as orbit drift correction, non-water pixel detection, and homogenization had to be solved. The result is a dataset covering over 3.5 decades of spatial LSWT data for 26 European lakes. The validation against in-situ temperature data at 19 locations showed an uncertainty between ±0.8 K and ±2.0 K (standard deviation), depending on locations of the lakes. The long-term robustness of the dataset was confirmed by comparing in-situ and satellite derived temperature trends, which showed no significant difference. The final trend analysis showed significant LSWT warming trends at all locations (0.2 K/decade to 0.8 K/decade). A gradient of increasing trends from south-west to north-east of Europe was revealed. The strong intra-annual variability of trends indicates that single seasonal trends do not well represent the response of a lake to climate change, e.g., autumn trends are dominant in the north of Europe, whereas winter trends are dominant in the south. Intra-lake variability of trends indicates that trends at single in-situ stations do not necessarily represent the lake’s response. The LSWT dataset generated for this study gives some new and interesting insights into the response of European lakes to climate change during the last 36 years (1981–2016).

Data from four in situ high-frequency monitoring sites in Lake Taihu, China were used to 1) assess the accuracy of MODIS land surface temperature (LST) products; 2) characterize the spatial and temporal variability in lake surface water temperature (LSWT) with Aqua MODIS thermal-infrared imagery; and 3) explore the causes of these variations. The validation showed that MODIS-derived LSWTs and in situ water temperatures were significantly correlated, with a coefficient of determination higher than 0.96 and a root mean square error between 1.2°C and 1.8°C. These results indicate MODIS LST products can be used to assess the spatial and temporal thermal behavior of Lake Taihu. A spatial analysis of daytime LSWT showed different thermal zones along the lake due to the differential heat storage capacity between the deep and shallow regions of the lake, such as the littoral and East Lake Taihu. In contrast, the nighttime LSWT showed little variation, and there was a uniform surface water temperature. Following a change in solar radiation, the annual cycle of the average LSWT (with a nearly 24°C range) started to increase in January and peaked in July. In Lake Taihu, the mean nighttime LSWT from July to September rapidly warmed over the period of 2002–2013 at an average rate of 0.059 \pm 0.053^\circ \rmC/year. Our study promotes the use of MODIS LST products in limnology through a validation using a large amount of real-time synchronous water temperature data based on high-frequency observations, which were seldom used in previous studies. [ABSTRACT FROM PUBLISHER]

• Lake surface water temperature is simulated by means of machine learning models. • A physically based model is used to have full control of variables. • Nine machine learning algorithms are compared, and none of them clearly prevails. • Best predictors are air temperature and day of the year; others have minor effects. • Using forcing from previous days allows for considering the history of the system. Models based on Machine Learning (ML) are pervading all fields of science and practical applications, including the problem of forecasting water temperature in lakes, a crucial variable for ecosystems and a proxy of climate change. Here, we review the most used ML algorithms in this field and highlight some physical constraints that should be carefully considered when adopting a black-box approach. To illustrate them, we refer to an artificial case study representing a temperate lake simulated by means of a physically based model, for which we take full control of input and output variables, and restrict the analysis to lake surface water temperature (LSWT). Three main factors are relevant for a successful prediction of LSWT by means of ML models: the choice of the predictors (mostly, meteorological variables), their pre-processing (we tested three approaches), and the specific ML algorithm (nine different algorithms). We show that selecting the suitable physical inputs plays the most important role. In our case study, which is the product of a numerical model and not a real lake, the minimum amount of information that is needed to obtain acceptable results is to consider air temperature (AT) and day of the year. The use of additional predictors does not substantially improve the performances (the relative improvement of RMSE was 7.75% for the test data set). We also demonstrate that better results than the normal case are obtained by either pre-processing air temperature data averaging them over a time window or including values from previous days as inputs in the model. Considering the recent history of the forcing (AT) allows one to comply with the physical fact that the large water mass makes lakes acting as "filters" in their thermal response (thus, influenced also by AT from previous days), which changes depending on the lake's depth. Eventually, we did not find a definite answer about a single optimal ML algorithm when using the same inputs (although artificial neural network had slightly better results), suggesting that the insight into the physical dynamics is still the most important factor for a successful exploitation of ML. [ABSTRACT FROM AUTHOR]

Satellite-derived lake surface water temperature (LSWT) measurements can be used for monitoring purposes. However, analyses based on the LSWT of Lake Ontario and the surrounding land surface temperature (LST) are scarce in the current literature. First, we provide an evaluation of the commonly used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSWT/LST (MOD11A1 and MYD11A1) using in situ measurements near the area of where Lake Ontario, the St. Lawrence River and the Rideau Canal meet. The MODIS datasets agreed well with ground sites measurements from 2015–2017, with an R2 consistently over 0.90. Among the different ground measurement sites, the best results were achieved for Hill Island, with a correlation of 0.99 and centered root mean square difference (RMSD) of 0.73 K for Aqua/MYD nighttime. The validated MODIS datasets were used to analyze the temperature trend over the study area from 2001 to 2018, through a linear regression method with a Mann–Kendall test. A slight warming trend was found, with 95% confidence over the ground sites from 2003 to 2012 for the MYD11A1-Night datasets. The warming trend for the whole region, including both the lake and the land, was about 0.17 K year−1 for the MYD11A1 datasets during 2003–2012, whereas it was about 0.06 K year−1 during 2003–2018. There was also a spatial pattern of warming, but the trend for the lake region was not obviously different from that of the land region. For the monthly trends, the warming trends for September and October from 2013 to 2018 are much more apparent than those of other months. [ABSTRACT FROM AUTHOR]

Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product. [ABSTRACT FROM AUTHOR]

Lake surface water temperature (LSWT) is an important parameter with which to assess aquatic ecosystems and to study the lake's response to climate change. The AVHRR archive of the University of Bern offers great potential to derive consistent LSWT data suited for the study of climate change and lake dynamics. To derive such a dataset, challenges such as orbit drift correction, non-water pixel detection, and homogenization had to be solved. The result is a dataset covering over 3.5 decades of spatial LSWT data for 26 European lakes. The validation against in-situ temperature data at 19 locations showed an uncertainty between ±0.8 K and ±2.0 K (standard deviation), depending on locations of the lakes. The long-term robustness of the dataset was confirmed by comparing in-situ and satellite derived temperature trends, which showed no significant difference. The final trend analysis showed significant LSWT warming trends at all locations (0.2 K/decade to 0.8 K/decade). A gradient of increasing trends from south-west to north-east of Europe was revealed. The strong intra-annual variability of trends indicates that single seasonal trends do not well represent the response of a lake to climate change, e.g., autumn trends are dominant in the north of Europe, whereas winter trends are dominant in the south. Intra-lake variability of trends indicates that trends at single in-situ stations do not necessarily represent the lake's response. The LSWT dataset generated for this study gives some new and interesting insights into the response of European lakes to climate change during the last 36 years (1981–2016). [ABSTRACT FROM AUTHOR]