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Article

Landsat-5 TM Imagery for Retrieving Historical Water Temperature Records in Small Inland Water Bodies and Coastal Waters of Lithuania (Northern Europe)

by
Toma Dabulevičienė
* and
Diana Vaičiūtė
Marine Research Institute, Klaipeda University, Universiteto Ave. 17, 92294 Klaipėda, Lithuania
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(9), 1715; https://doi.org/10.3390/jmse13091715
Submission received: 30 July 2025 / Revised: 30 August 2025 / Accepted: 3 September 2025 / Published: 5 September 2025
(This article belongs to the Section Marine Environmental Science)
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Abstract

Water surface temperature (WST) is an important environmental variable, and its monitoring is essential for understanding and mitigating the impacts of climate change and human activities. For this, satellite remote sensing is particularly useful in providing WST data, especially in cases when in situ monitoring is limited or absent, as is often the case in small inland water bodies. In this study, the approach of retrieving the historical WST data from Landsat-5 Thematic Mapper (TM) was tested by analysing different cases across various water bodies in Lithuania, including two small inland lakes, an artificial reservoir, the Curonian Lagoon, and the coastal waters of the southeastern Baltic Sea. Our results demonstrate that WST can be accurately estimated from single-band Landsat-5 TM images, achieving an R2 of around 0.9 in comparison with both in situ (with RMSE of 1.35–1.73 °C) and with MODIS satellite (RMSE of 1.11–1.23 °C) water temperature data, thus enabling analysis of water temperature variations in small-sized lakes and other water bodies, and contributing to the reliable monitoring of WST trends.

1. Introduction

The ongoing climate change and various human activities emphasise the need for precise and long-term water temperature measurements that are essential for understanding the dynamics and functioning of different aquatic environments. Water temperature plays a vital role in the physical, chemical, and biological processes within these systems [1], and is also an important climatic and environmental variable influencing the structure and functioning of aquatic ecosystems [2]. Being directly impacted by climate change with sea surface temperatures (SST) rising globally in oceans [3], including coastal regions [4], as well as regional seas, e.g., the Baltic [5,6], Black Sea [7], Mediterranean Sea [8], and others, SST has been extensively studied here using various techniques, from in situ data to remote sensing [9] or numerical modelling [10].
Nevertheless, rising water surface temperatures (WST) are also observed globally in rivers [11,12] and lakes [13,14], with an estimated increase of lake temperature by 1–3 °C over the last century [15]. Such an increase in water temperature may, for example, lead to shorter periods of ice cover in lakes, cause alterations in lake mixing, decline dissolved oxygen levels, and result in a range of other impacts on aquatic ecosystems [16,17]. Therefore, growing evidence of rising lake WST over recent decades worldwide [18] makes it crucial to understand the changes in lake surface temperatures [19], which is also essential for mitigating the impacts of climate change and human activities.
The monitoring of larger water bodies is typically included in state monitoring programmes, however, the monitoring of the smaller ones is not [20], as in many cases of inland waters in Lithuania, historical water surface temperature data are lacking. Nevertheless, the absence of in situ measurements here can be to some extent compensated for by remote sensing platforms, which can indirectly measure water temperature [21]. In turn, satellite remote sensing, particularly nowadays, is widely used for water surface temperature studies not only in oceans and seas but also in inland waters [22]. Due to their technical characteristics, satellites can provide greater spatial and/or temporal coverage in regions where in situ point measurements are sparsely distributed, thus allowing better characterisation of local, seasonal, and other WST changes [9,23,24,25]. This makes satellite data very useful for smaller inland water bodies in particular (e.g., [18,26,27,28]).
Satellite WST data can be retrieved from various satellites having different coverage, as well as spatial and temporal resolutions. For example, microwave radiometers can provide higher spatial coverage but lower resolution, typically around 25 km. In contrast, the thermal infrared radiometers have lower spatial coverage but higher resolution (about 1 km) [29,30]. Infrared sensors like the Sea and Land Surface Temperature Radiometer (SLSTR) aboard Sentinel-3, the Moderate-resolution Imaging Spectroradiometer (MODIS) aboard NASA’s Terra and Aqua satellites, and the Advanced Very High Resolution Radiometer (AVHRR) on board NOAA’s satellite are extensively used for WST analysis in various seas and coastal lagoons (e.g., [31,32,33,34]). Recently, land surface temperature (LST) datasets from MODIS Terra and Aqua have been evaluated for monitoring thermal conditions in some of Poland’s largest lakes, showing good agreement with in situ measurements there [28]. However, their application for smaller spatial scales is still subject to some inaccuracies [35] and limitations in temporal or spatial scales [24]. WST mapping in small water bodies requires both high spatial and temporal resolutions, which can be challenging even today, as sensors like MODIS and SLSTR, despite their high temporal resolution, are characterised by relatively coarse spatial resolutions, rendering them unsuitable for remote sensing applications over most lakes and reservoirs [36]. Additionally, they fail to depict spatial WST variations at a smaller scale in coastal areas. Meanwhile, Landsat Program (missions 5, 7–9) satellites provide data for calculating water surface temperature with relatively high spatial resolutions (~30–100 m) compared to others. The Landsat Program has collected Earth observation data for over five decades and continues to ensure the continuity of high-quality measurements for scientific and operational investigations [37], enabling measurements with significantly enhanced detail (e.g., [38,39]). Recently, the retrieval of WST from the latest Landsat missions, 8 and 9, has become the main focus of many studies analysing water temperature (e.g., [27,40,41,42,43]). However, the potential of retrieving historical WST data from discontinued Landsat satellites remains insufficiently explored, although some studies have successfully used Landsat-5 TM to derive and analyse water temperatures across various water bodies (e.g., [24,38,39,44,45]). To our knowledge, no previous studies have analysed its applicability for retrieving archival WST data in our study area. Therefore, this study focused on examining the approach of WST retrieval from satellite data that could be applied to small inland water bodies, thus providing the opportunity to reconstruct water temperature time series over several decades. To achieve this, the possibility to utilise the historical Landsat-5 TM satellite data, which provides a long time series (1984–2012), was explored by analysing selected cases and validated against satellite and in situ data for mapping water surface temperatures in various small (~1000–6000 ha) inland water bodies in Lithuania. The capability of Landsat-5 TM to depict temperature variations on a finer scale in coastal regions was also examined.

2. Materials and Methods

2.1. Study Site

The three inland water bodies are situated in Lithuania (northern Europe), exhibiting temperate climate patterns (Figure 1). Plateliai Lake (area 1200 ha, average depth 10.5 m) is a mesotrophic lake of natural thermal regime in the western part of Lithuania [46]. The mean surface water temperature of the lake during ice-free periods reaches around 19 °C in July and August, dropping to around 5 °C in early April and November [26]. Druksiai Lake is a transboundary lake crossing the state border between Lithuania and Belarus, and it is also Lithuania’s largest lake (area 4900 ha, maximum depth 33.3 m, average depth 7.6 m) [47]. However, the hydrothermal regime of the lake was altered when it became a cooler for the Ignalina Nuclear Power Plant (INPP) in 1984, with discharged effluents raising the average monthly surface temperature of the lake by 3–4 °C [48]. As the water discharged from the cooling system was warmer than the lake’s natural water temperature, a great part of the aquatic area became ice-free in winter [49]. The decision to shut down Unit 1 of INPP on 31 December 2004, was later taken, followed by the closure of the remaining Unit 2 on 31 December 2009. Another water body, Kaunas Reservoir, is the largest artificial waterbody in Lithuania, created during the construction of the Kaunas Hydroelectric Plant (Kaunas HP) on the Nemunas River in 1959 [50]. It belongs to a group of shallow waterbodies with an average depth of 7.3 m, a maximum depth of 24.6 m, and a reservoir surface area of 6350 ha. It serves as a water resource for two big power plants: Kaunas HP and the Kruonis Pumped Storage Plant [51].
Additionally, the Curonian Lagoon and part of the SE Baltic Sea were included in the analysis to validate Landsat-5 TM WST against MODIS SST measurements. The water temperature of the SE Baltic Sea has a strong seasonality, with the lowest temperatures of around 2 °C observed in winter months, while in summer months, such as July and August, the average water temperature is around 18–19 °C [31]. The Curonian Lagoon is a shallow, highly eutrophied, and mainly freshwater basin connected to the sea via the narrow (0.4–1.1 km) Klaipeda Strait at the northern end of the Lagoon. During winter months and early spring (December–March), an irregular ice cover may be observed in the lagoon, depending on air temperature [52], with water temperatures in summer reaching 20 °C or higher (see, e.g., [31]).

2.2. Data Validation

Landsat-5 TM (L-5) is an advanced multispectral scanning sensor with seven spectral bands, out of which, Band 6 senses thermal infrared radiation emitted within the wavelengths of 10.40–12.50 µm, with a spatial resolution of 120 m (resampled to 30 m to match optical bands) and a temporal resolution of approximately 16 days (https://landsat.gsfc.nasa.gov/thematic-mapper/ (accessed on 7 July 2025)). Its high spatial resolution and extensive thermal data time series, spanning from 1984 to 2012, led to the selection of this satellite for testing the retrieval of historical WST across our study region. The methodology developed by the authors in [38] proved effective for mapping L-5 WST and showing the overall pattern of water temperatures in a small coastal embayment on Australia’s southeast coast. Therefore, the linear regression equation (Equation (1)) for WST calculations, originally developed in the aforementioned study [38], was tested and further used here to retrieve L-5 WST in our study region as well.
WSTL5 = 0.53DN − 44.2
where WSTL5 is the predicted water surface temperature and DN is the Band 6 digital number.
For this, Level 1 data from USGS Landsat 4–5 TM Collection 2 were downloaded using the USGS Earth Explorer Service (https://earthexplorer.usgs.gov/ (accessed on 7 July 2025)), which is freely available to registered users. For WST calculations, Band 6 was utilised.
In addition to Landsat-5 TM, water temperature data from the NASA MODIS satellite were also used in this study. The 1 km resolution Level-2 WST data were extracted from both MODIS Terra and Aqua daytime products obtained from the NASA OceanColor website (http://oceancolor.gsfc.nasa.gov/ (accessed on 7 July 2025)). Validation of the MODIS product with in situ observations in the SE Baltic Sea and Curonian Lagoon, carried out by authors of [31], showed a very good agreement between spaceborne and traditional water temperature measurements (R2 not less than 0.78), indicating that MODIS-based temperature retrievals can be further used to analyse WST. Consequently, MODIS WST was initially chosen to validate the method developed by the authors of [38] in the Baltic Sea and the Curonian Lagoon. For this, only cloud-free L-5 and MODIS data from the same date and closest in time were used. Water temperature data were extracted at the same coordinates, resulting in 49 match-ups in the Curonian Lagoon and 80 match-ups in the Baltic Sea.
For WST validation in relatively small-sized inland water bodies such as Plateliai Lake, Kaunas Reservoir, and Druksiai Lake, available in situ data collected by the Environmental Protection Agency and Marine Research Institute were further used. In situ WST here refers to the temperature measured in the upper (~0–0.5 m) layer during the daytime. Although the measurement depth differs, validation against in situ data is recognised as an essential way to prove the quality of satellite WST data. However, the in situ measurements in these lakes were relatively rare (1–10 times annually); additionally, some of the Landsat-5 satellite images, especially during cold periods, were hindered by cloud cover. Therefore, only relatively cloud-free Landsat scenes within the same date or ±3 days of coincident in situ measurements were selected for L-5 WST validation across these water bodies. This resulted in 11 match-ups in Plateliai Lake, 13 in Kaunas Reservoir, and 9 in Druksiai Lake (Table 1).
The statistical metrics used to compare the two datasets were the coefficient of determination (R2) and the root mean square error (RMSE) values.

3. Results and Discussion

3.1. Validation

The accuracy of the Landsat-5-generated water surface temperature was first examined by evaluating the L-5 WST against corresponding MODIS data in the SE Baltic Sea and Curonian Lagoon. A good correlation was observed between L-5 and MODIS WST data, with R2 values of 0.897 (RMSE 1.11 °C) for the sea and 0.871 (RMSE 1.23 °C) for the lagoon, indicating that L-5 WST data is well corresponding with MODIS WST measurements (Figure 2).
Furthermore, satellite imagery revealed that the high spatial resolution of L-5 WST data enables the acquisition of a highly detailed picture of water dynamics. Satellite data of different resolutions taken the same day with ~1 h difference are shown in Figure 3, demonstrating that high (30 m) resolution L-5 data can much better reflect the spatial variations in water surface temperature compared to 1 km spatial resolution MODIS data. For instance, the L-5 data provides a clearer representation of the interaction between the sea and lagoon, depicting a narrow band of warmer lagoon waters flowing into the sea—an aspect not captured by MODIS data. Additionally, L-5 WST also more accurately depicts the zones of elevated water temperatures in the southern part of the Curonian Lagoon, as well as in very small embayments where MODIS data may be inaccurate or distorted due to shoreline effects or dense aquatic vegetation.
After receiving a good agreement for the Baltic Sea and the Curonian Lagoon WST measurements, the same algorithm was also adapted for smaller freshwater inland water bodies in Lithuania: Plateliai Lake, Kaunas Reservoir, and Druksiai Lake. Figure 4 shows the validation results of L-5 WST against in situ measurements, indicating that a good correlation was achieved for inland waters as well, with R2 around 0.9 across all water bodies. This demonstrates even better validation results in our region than those achieved in the original study (a coefficient of determination R2 of 0.74, [38]). However, the RMSE values achieved with L-5 scenes taken ±3 days from in situ measurement date (Figure 4A) were relatively higher (~1.5–1.7 °C), than those observed during the L-5 validation with MODIS data of the same dates as L-5, which were approximately ~1.1–1.2 °C. Validation of L-5 data having ±1 day difference from in situ measurements (Figure 4B) showed slightly improved results with a lower RMSE of 1.35 °C, indicating a difference in accuracy related to the temporal proximity of the measurements. Nevertheless, considering that the analysis included Landsat scenes taken within several days of the concurrent in situ measurements, and that the satellites only measure the temperature of the uppermost layer, the agreement between the Landsat-5 and in situ observations was strong.

3.2. Mapping Historical WST with L-5 Satellite Imagery

In Figure 5 and Figure 6, L-5 examples are presented, demonstrating its capabilities to retrieve historical water temperatures and to depict WST variations even in small inland water bodies.
The analysed data demonstrate the ability of L-5 satellite imagery to distinguish even minor temperature variations in inland water bodies, showing that the shallower coastal areas of Plateliai Lake and Kaunas Reservoir exhibit approximately 3–4 °C higher water temperatures than the central areas in the selected cases. Meanwhile, the satellite image of Druksiai Lake serves as an example of anthropogenic impact on water temperature, with the lake’s area affected by warm water discharges from cooling operations at the Ignalina Nuclear Power Plant (INPP), which was clearly distinguishable from the ambient waters by being nearly 8–10 °C warmer in this case.
The L-5 WST imagery presented in Figure 6 demonstrates the water temperatures of Druksiai Lake during INPP’s operational years (1984–2009) and after the closure of its Units (Unit 1 in 2004, Unit 2 in 2009) in more detail.
It can be seen that on April 29, 1990 (A), the discharged water from the INPP reached temperatures as high as 21 °C. In contrast, the unaffected regions of the lake maintained a temperature of around 12 °C, with only some coastal areas being slightly warmer at about 14–15 °C. Another WST map from the same season (B), taken after the complete closure of both units (23 April 2011), shows that the temperatures in the former water discharge area were between 12–13 °C now, which is typical for that time of year. An example from 9 August 2007 (C) demonstrates that although the thermal loading of the lake decreased following the decision to shut down Unit 1 of the INPP on 31 December 2004 [44], resulting in a slightly smaller area of warmer water discharge, the temperature difference between the INPP-affected and unaffected parts of the lake remained significantly high, up to 6–8 °C. Within a year following the closure of INPP, the water temperatures had already returned to seasonal norms, as shown by the L-5 WST example from 1 August 2010.

4. Summary and Conclusions

Many inland water bodies in our study region are too small for accurate water surface temperature assessments using coarse-resolution satellite WST data and often lack historical in situ measurements. To address this, we sought an alternative solution—higher-resolution satellite imagery. In this study, we aimed to retrieve archival WST data for small inland water bodies and coastal areas of the Baltic Sea from the Landsat-5 TM satellite, utilising the methodology proposed by the authors of [38]. Our validation results demonstrate strong agreement between L-5 and in situ measurements in inland water bodies, with R2 values of 0.92–0.93 and RMSEs of 1.35–1.61 °C, as well as good correlation with MODIS data in the Curonian Lagoon and southeastern Baltic Sea coastal waters (R2 of 0.87–0.90; RMSEs of 1.11–1.23 °C), with higher accuracy achieved using data with closer temporal proximity. The findings also highlight the capabilities of L-5 satellite imagery to detect even minor spatial temperature variations. These results serve as an initial validation of relatively high-resolution WST measurements derived from the L-5 satellite across various inland water bodies in Lithuania, demonstrating that water surface temperature can be accurately retrieved from single-band Landsat-5 TM imagery in our region as well, enabling the reconstruction of water temperature time series spanning several decades.
Although some temporal data gaps remain due to less frequent revisit times, L-5 WST provides invaluable opportunities to analyse historical water temperatures. Moreover, the possibility of incorporating data from Landsat missions 7–9, extending the L-5 WST time series to recent observations, significantly enhances the potential for comprehensive water temperature assessments across diverse aquatic ecosystems.

Author Contributions

Conceptualization, T.D. and D.V.; methodology, T.D. and D.V.; validation, T.D.; formal analysis, T.D.; investigation, T.D., data curation, T.D. and D.V.; writing—original draft preparation, T.D.; writing—review and editing, D.V.; visualization, T.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The satellite data used in this work are publicly available online through the NASA OceanColor website (http://oceancolor.gsfc.nasa.gov/ (accessed on 7 July 2005)) and USGS Earth Explorer Service (https://earthexplorer.usgs.gov/ (accessed on 7 July 2005)). In situ data are available on request from the Lithuanian Hydrometeorological Service and Marine Environmental Assessment Division of the Lithuanian Environmental Protection Agency.

Acknowledgments

The authors are grateful to the Lithuanian Environmental Protection Agency for providing the in situ monitoring data. T.D. acknowledges the support of Klaipeda University Post-Doctoral Fellowship Program.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map of study site showing locations of selected inland water bodies.
Figure 1. Map of study site showing locations of selected inland water bodies.
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Figure 2. Scatter plot of Landsat-5 WST versus MODIS WST in the Baltic Sea (A) and in the Curonian Lagoon (B).
Figure 2. Scatter plot of Landsat-5 WST versus MODIS WST in the Baltic Sea (A) and in the Curonian Lagoon (B).
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Figure 3. Example of WST retrievals from MODIS (10:45 UTC) and Landsat-5 TM (09:23 UTC) measurements acquired on 3 July 2006 over the Curonian Lagoon and SE part of the Baltic Sea.
Figure 3. Example of WST retrievals from MODIS (10:45 UTC) and Landsat-5 TM (09:23 UTC) measurements acquired on 3 July 2006 over the Curonian Lagoon and SE part of the Baltic Sea.
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Figure 4. (A,B) Validation of Landsat-5 WST with combined in situ measurements from inland water bodies, including L-5 images taken within ±3 days (A) and ±1 day (B) from in situ measurement day; (C) scatter plot of Landsat-5 WST versus in situ measurements from Plateliai Lake; (D) scatter plot of Landsat-5 WST versus in situ measurements from Kaunas Reservoir; (E) scatter plot of Landsat-5 WST vs. in situ measurements from Druksiai Lake.
Figure 4. (A,B) Validation of Landsat-5 WST with combined in situ measurements from inland water bodies, including L-5 images taken within ±3 days (A) and ±1 day (B) from in situ measurement day; (C) scatter plot of Landsat-5 WST versus in situ measurements from Plateliai Lake; (D) scatter plot of Landsat-5 WST versus in situ measurements from Kaunas Reservoir; (E) scatter plot of Landsat-5 WST vs. in situ measurements from Druksiai Lake.
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Figure 5. Examples of Landsat-5 WST in different inland water bodies.
Figure 5. Examples of Landsat-5 WST in different inland water bodies.
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Figure 6. L-5 WST of Druksiai Lake before (A,C) and after (B,D) closure of the Ignalina Nuclear Power Plant (INPP).
Figure 6. L-5 WST of Druksiai Lake before (A,C) and after (B,D) closure of the Ignalina Nuclear Power Plant (INPP).
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Table 1. Dates (dd/mm/yy) of in situ sampling and Landsat-5 WST measurements.
Table 1. Dates (dd/mm/yy) of in situ sampling and Landsat-5 WST measurements.
Druksiai LakePlateliai LakeKaunas Reservoir
In SituL-5In SituL-5In SituL-5
18 October 200017 October 200014 June 199414 June 199426 April 200623 April 2006
27 June 200130 June 20019 September 19986 September 199827 June 200626 June 2006
9 September 20038 September 200315 June 199918 June 199929 June 200626 June 2006
15 October 200317 October 20034 May 19994 May 199912 September 200614 September 2006
8 September 200410 September 200424 May 200125 May 200127 September 200630 September 2006
11 July 200614 July 200614 October 200315 October 200329 August 200729 August 2007
23 May 200721 May 20075 September 20065 September 200629 July 200929 July 2009
6 August 20079 August 200730 June 20063 July 200631 August 200930 August 2009
15 April 200814 April 200829 August 200730 August 200727 September 201025 September 2010
27 April 201025 April 201011 October 201011 October 2010
27 September 201126 September 201120 April 201121 April 2011
2 June 20111 June 2011
12 July 201110 July 2011
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Dabulevičienė, T.; Vaičiūtė, D. Landsat-5 TM Imagery for Retrieving Historical Water Temperature Records in Small Inland Water Bodies and Coastal Waters of Lithuania (Northern Europe). J. Mar. Sci. Eng. 2025, 13, 1715. https://doi.org/10.3390/jmse13091715

AMA Style

Dabulevičienė T, Vaičiūtė D. Landsat-5 TM Imagery for Retrieving Historical Water Temperature Records in Small Inland Water Bodies and Coastal Waters of Lithuania (Northern Europe). Journal of Marine Science and Engineering. 2025; 13(9):1715. https://doi.org/10.3390/jmse13091715

Chicago/Turabian Style

Dabulevičienė, Toma, and Diana Vaičiūtė. 2025. "Landsat-5 TM Imagery for Retrieving Historical Water Temperature Records in Small Inland Water Bodies and Coastal Waters of Lithuania (Northern Europe)" Journal of Marine Science and Engineering 13, no. 9: 1715. https://doi.org/10.3390/jmse13091715

APA Style

Dabulevičienė, T., & Vaičiūtė, D. (2025). Landsat-5 TM Imagery for Retrieving Historical Water Temperature Records in Small Inland Water Bodies and Coastal Waters of Lithuania (Northern Europe). Journal of Marine Science and Engineering, 13(9), 1715. https://doi.org/10.3390/jmse13091715

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