Editorial Feature

Analyzing Surface Water Temperature with Remote Sensing

Lake temperature can act as an indicator of climate change, with surface temperature analysis at different temporal scales at the core of understanding variability. A study has analyzed the monthly, seasonal, and annual surface temperature trends of 14 lakes in south-central Chile, contributing to ongoing efforts to monitor the effects and progression of global warming. 

Chile lake, river, global warming, lake temperature

Image Credit: Christina Fink/Shutterstock.com

Inland water ecosystems offer numerous ecosystem services useful for irrigation, human consumption, transportation, sanitation, recreation, culture, and industry. However, in recent years these ecosystems have experienced high stress due to anthropogenic activities and climate change.

Many scientists have examined lake surface water temperature (LSWT) worldwide to explore the effects of global warming on these ecosystems, identifying variable increases in water temperatures. 

Water temperature is a significant component in aquatic ecosystems, directly or indirectly controlling many physicochemical mechanisms and reactions that occur within them.

Previous research has shown that global warming has affected air temperature patterns, and as a result, LSWT. 

Conventional in situ monitoring is typically used to obtain LSWT data, which is often impeded by geographically complex locations, and limited human and economic resources. Traditional methods also possess spatial and temporal limitations, further complicating their use. 

Recent satellite Earth Observation imagery has provided a complementary and alternative method for the monitoring of LSWT at a higher spatial and temporal resolution. Thus, the Moderate Resolution Imaging Spectroradiometer (MODIS) has been identified as a valuable satellite product for predicting LSWT, thanks to its temporal, spatial, spectral, and radiometric resolution.

Here, we discuss research reviewing satellite imagery products for regional, local, and national scale development and growth. The study analyzes the spatial and temporal trends and behavior of LSWT in 14 south-central Chilean lakes, between 2000 and 2016, using MODIS satellite imagery.


The area for research was distributed across four regions: Maule, Bío-Bío, Araucanía, and Los Ríos. Around 18% comprises the inland water bodies selected for the research (Figure 1). 

The Maule, Bío-Bío, Araucanía, and Los Ríos regions, located in south-central Chile and the lakes analyzed in this study.

Figure 1. The Maule, Bío-Bío, Araucanía, and Los Ríos regions, located in south-central Chile and the lakes analyzed in this study. Image Credit: Aranda, et al., 2021

The study selected lakes with a surface area ≥10 kmand the 14 selected lakes present a temperate monomictic circulation pattern, with thermal stratification during summer. Eleven of the lakes are oligotrophic, Villarrica is meso-oligotrophic, while Vichuquén and Lanalhue are eutrophic. Table 1 shows the geographical and morphometric characteristics that impact LSWT.

Table 1. Morphometric parameters that influence LSWT: location, elevation, surface area, perimeter, volume, mean depth, and maximum depth for the 14 inland lakes selected in this study. Source: Aranda, et al., 2021.

Latitude Longitude Altitude Surface
Perimeter Volume Mean
(°S) (°W) m a.s.l. km2 km km3 m m  
Vichuquén 34°49′ 72°04′ 5 12.68 35.12 0.21 2.5 6.3 Eutrophic
Maule 36°05′ 70°50′ 2166 58.28 78.98 170 NR NR Oligotrophic
Lanalhue 37°55′ 73°19′ 12 32.60 64.76 0.42 13.1 26 Eutrophic
Laja 37°19′ 71°18′ 1360 77.90 142.9 5.59 75 120 Oligotrophic
Lleulleu 38°09′ 73°19′ 5 38.96 98.51 0.93 23.5 46.9 Oligotrophic
Budi 37°19′ 71°19′ 2 73.29 328.8 0.22 4.4 15 Oligotrophic
Galletué 38°41′ 71°17′ 1350 13.08 20.61 0.40 NR 50 Oligotrophic
Colico 39°05′ 71°58′ 500 54.96 52.28 NR 416 NR Oligotrophic
Huilipilún 39°08′ 72°10′ 343 11.33 18.74 NR NR 212 Oligotrophic
Villarrica 39°18′ 72°05′ 230 176.0 71.20 21 120 165 Meso-oligotrophic
Caburga 39°07′ 71°45′ 505 52.27 51.73 8.88 117 327 Oligotrophic
Calafquén 39°32′ 72°09′ 203 114.9 122.38 NR 115 212 Oligotrophic
Riñihue 39°50′ 72°20′ 117 77.50 77.00 12.8 162 323 Oligotrophic
Panquipulli 39°43′ 71°13′ 140 117 124.05 NR 126 268 Oligotrophic

NR = not reported.

The lake's surface temperature was obtained through a free web-based hydrometeorological service. Measurements were taken between 09:00 and 15:00 at a depth of ~50 cm.

The researchers processed around 774 MODIS images from 18 February 2000 (Julian day 49) to 26 December 2016 (Julian day 361). A thermal infrared imagery database with high spatial resolution downloaded from the NASA Earth Observing System Data and Information System (EOSDIS) was also utilized.

Daytime images were used for pre-processing and the original scenes in HDF format were converted to raster GeoTIFF format. Pixels contaminated by cloud cover were reviewed and modified to remove cloud-contaminated images.

Validation was carried out using a least-squares linear fit to determine the relationship between the surface water temperature acquired by processing MODIS images and in situ data.

The Mann–Kendall non-parametric test, a statistical test commonly used for the analysis of the trend in climatology and in hydrologic time series, was employed to evaluate LSWT trends in the time series (2000–2016). 


The MODIS-derived one-meter below surface temperature was necessary to validate the MODIS-derived skin temperature against the one-meter below surface temperature from the in situ measurements.

A significant correlation between MODIS LSWT and in situ can be seen in Table 2, which also displays the highest correlation data which was obtained from Villarrica lake. The results from the MODIS LSWT data expressed in root mean square error (RMSE) were between 1.07 and 1.88 °C.

Table 2. Validated results for the comparison between MODIS LSWT and in situ LSWT for the six lakes that presented p-values ≤ 0.05. Source: Aranda, et al., 2021

Study Lakes R2 RMSE MAE Slope n
(°C) (°C)
Caburga 0.85 1.88 1.50 0.87 34
Villarrica 0.94 1.07 0.83 0.94 31
Lanalhue 0.94 1.04 0.77 0.87 34
Calafquén 0.85 1.79 1.24 0.85 29
Panguipulli 0.86 1.61 1.20 0.80 43
Riñihue 0.88 1.34 1.01 0.81 40

Figure 2 depicts that the trend analysis of the annual MODIS LSWT time-series found that only six of the 14 lakes present a substantial increase. Researchers noted that their results are reflected in previous research, including data exclusively obtained from the Northern Hemisphere. 

Temporal behavior and trends of the annual MODIS LSWT series

Figure 2. Temporal behavior and trends of the annual MODIS LSWT series. Image Credit: Aranda, et al., 2021.

Significant warming trends in the majority of the lakes were seen in January as shown in Table 3. As well as this, increased LSWT was found to contribute to the potentially toxic cyanobacterial blooms in Villarrica Lake. Further investigations are required to explore this relationship. 

Table 3. Test results of Mann–Kendall and Pettitt tests for the month of January of the MODIS LSWT series (2000–2016). Source: Aranda, et al., 2021.

Mann-Kendall Pettitt
S ZMK P Sen Trend
Interval (95%)
Kt P Change
Vichuquén 78 3.31 0.001 0.15 0.009 [−0.467; 0.895] 60 0.005 2007
Maule 48 2.02 0.048 0.27 0.016 [−1.509; 1.896] 58 0.008 2011
Laja 54 2.28 0.026 0.27 0.016 [−1.342; 1.929] 58 0.009 2011
Lleulleu 56 2.36 0.021 0.16 0.010 [−0.766; 1.314] 48 0.053 2007
Galletehué 48 2.02 0.048 0.19 0.011 [−1.301; 1.910] 50 0.035 2011
Budi 50 2.11 0.039 0.17 0.010 [−1.712; 1.155] 38 0.020 2007
Colico 52 2.19 0.032 0.17 0.010 [−0.972; 1.278] 44 0.094 2011
Caburga 54 2.28 0.026 0.11 0.007 [−0.741; 1.220] 46 0.075 2007
Villarrica 60 2.53 0.014 0.16 0.010 [−0.705; 1.194] 46 0.069 2011
Calafquén 66 2.79 0.007 0.19 0.011 [−0.652; 1.591] 46 0.070 2011
Panguipulli 66 2.79 0.007 0.18 0.011 [−0.712; 1.350] 44 0.096 2007
Riñihue 62 2.62 0.011 0.19 0.011 [−0.843; 1.248] 46 0.071 2010

S: S–statistic of Mann–Kendall; ZMK: Z-statistic of Mann–Kendall; P: p-value; Sen: Sen’s slope; Kt: statistic of Pettitt’s test.

A lack of significant trends at the seasonal scale was reported; however, differing temperature data as seasons changed was observed across the lakes involved in this study (Figure 3).

Temporal behavior and trends of the seasonal MODIS LSWT series.

Figure 3. Temporal behavior and trends of the seasonal MODIS LSWT series. Image Credit: Aranda et al., 2021.

It is important to track the warming of lakes in all seasons. In winter, a higher temperature can favor the emergence of cyanobacteria but negatively affect habitat availability for aquatic species as stenothermal species migrate towards warmer areas. Table 4 shows the results of the Mann–Kendall and Pettitt tests (2000–2016).


Researchers investigated the monthly, seasonal, and annual surface temperature trends in 14 south-central Chilean lakes during the 2000–2016 period using MODIS satellite imagery. in situ LSWT was shown to be a decent methodology, suggesting its potential as an alternative for future observations of lakes >10 km2.

Overall, a significant increase in surface water temperatures was found in lakes at higher altitudes. Furthermore, the Pettitt test results show direct links between LSWT, dissolved oxygen at the surface of the lake, and land use/change.

Continue reading: Continuing NASA's Earth Observation Legacy with Landsat 9.

Journal Reference:

Aranda, A. C., Rivera-Ruiz, D., Rodríguez-López, L., Pedreros, P., Arumí-Ribera, J. L., Luis Morales-Salinas, L., Fuentes-Jaque, G., Urrutia, R. (2021) Evidence of Climate Change Based on Lake Surface Temperature Trends in South Central Chile. Remote Sensing, 13(22), p. 4535. Available at: https://www.mdpi.com/2072-4292/13/22/4535/htm.

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Megan Craig

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Megan Craig

Megan graduated from The University of Manchester with a B.Sc. in Genetics, and decided to pursue an M.Sc. in Science and Health Communication due to her passion for combining science with content creation. As part of her studies, Megan partnered with Jodrell Bank Discovery Centre as a Digital Marketing Assistant, producing content and updating sections of their website. In her spare time, she loves to travel, exploring each location's culture and history - including the local cuisine. Her other interests include embroidery, reading fiction, and practicing her Japanese language skills.


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    Craig, Megan. "Analyzing Surface Water Temperature with Remote Sensing". AZoSensors. https://www.azosensors.com/article.aspx?ArticleID=2363. (accessed December 02, 2021).

  • Harvard

    Craig, Megan. 2021. Analyzing Surface Water Temperature with Remote Sensing. AZoSensors, viewed 02 December 2021, https://www.azosensors.com/article.aspx?ArticleID=2363.

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