Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake

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.

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Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽

10.3390/w13081109 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1109

Author(s):

Nobuaki Kimura ◽

Kei Ishida ◽

Daichi Baba

Keyword(s):

Climate Change ◽

Surface Water ◽

Water Temperature ◽

Transfer Learning ◽

Air Temperature ◽

Climate Models ◽

Temporal Variations ◽

Training Data ◽

Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

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Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European Lakes

Remote Sensing ◽

10.3390/rs10070990 ◽

2018 ◽

Vol 10 (7) ◽

pp. 990 ◽

Cited By ~ 9

Author(s):

Gian Lieberherr ◽

Stefan Wunderle

Keyword(s):

Surface Water ◽

Water Temperature ◽

Sensor Data ◽

Surface Water Temperature ◽

Lake Surface

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Parameterization of surface water temperature and long-term trends in Europe’s fourth largest lake shows recent and rapid warming in winter

Limnologica ◽

10.1016/j.limno.2020.125777 ◽

2020 ◽

Vol 82 ◽

pp. 125777 ◽

Cited By ~ 1

Author(s):

Burak Öğlü ◽

Tõnu Möls ◽

Tanel Kaart ◽

Fabien Cremona ◽

Külli Kangur

Keyword(s):

Surface Water ◽

Water Temperature ◽

Surface Water Temperature ◽

Long Term Trends

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Comparison of Methods to Estimate Lake-Surface-Water Temperature Using Landsat 7 ETM+ and MODIS Imagery: Case Study of a Large Shallow Subtropical Lake in Southern Brazil

Water ◽

10.3390/w11010168 ◽

2019 ◽

Vol 11 (1) ◽

pp. 168 ◽

Cited By ~ 5

Author(s):

Matheus Tavares ◽

Augusto Cunha ◽

David Motta-Marques ◽

Anderson Ruhoff ◽

J. Cavalcanti ◽

...

Keyword(s):

Surface Water ◽

Water Temperature ◽

Single Channel ◽

Atmospheric Correction ◽

Ecological Status ◽

Surface Water Temperature ◽

Lake Surface ◽

Subtropical Lake ◽

Landsat 7 ◽

The Impact

Water temperature regulates many processes in lakes; therefore, evaluating it is essential to understand its ecological status and functioning, and to comprehend the impact of climate change. Although few studies assessed the accuracy of individual sensors in estimating lake-surface-water temperature (LSWT), comparative analysis considering different sensors is still needed. This study evaluated the performance of two thermal sensors, MODIS and Landsat 7 ETM+, and used Landsat methods to estimate the SWT of a large subtropical lake. MODIS products MOD11 LST and MOD28 SST were used for comparison. For the Landsat images, the radiative transfer equation (RTE), using NASA’s Atmospheric Correction Parameter Calculator (AtmCorr) parameters, was compared with the single-channel algorithm in different approaches. Our results showed that MOD11 obtained the highest accuracy (RMSE of 1.05 ° C), and is the recommended product for LSWT studies. For Landsat-derived SWT, AtmCorr obtained the highest accuracy (RMSE of 1.07 ° C) and is the recommended method for small lakes. Sensitivity analysis showed that Landsat-derived LSWT using the RTE is very sensitive to atmospheric parameters and emissivity. A discussion of the main error sources was conducted. We recommend that similar tests be applied for Landsat imagery on different lakes, further studies on algorithms to correct the cool-skin effect in inland waters, and tests of different emissivity values to verify if it can compensate for this effect, in an effort to improve the accuracy of these estimates.

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