Introduction

Lakes are considered worldwide as sentinels of climate change1,2. Any change in the surrounding catchment due to climate forcings will reflect on the physical, chemical and biological processes in lakes2. The surface temperature as being direct and sensitive to long-term changes in thermal structure of the lakes is a good indicator to understand the changes in the lake characteristics1,3. Thermal variations on the lake surface and epilimnion are crucial for key responses like vertical mixing and stratification, nutrient and oxygen dynamics, as well as spread and geographical expansion of biota1,2,4. Recent studies on a large number of lakes worldwide indicated a global trend of rapid warming related to climate change5,6,7,8. Global warming has a direct impact on thermal characteristics of lakes, influencing variations in the physical as well as biological characteristics9,10. Geographical expansion of toxic cyanobacteria is reported owing to the warming of lakes11,12,13,14. The expansion of tropical bloom-forming cyanobacterium Cylindrospermopsis raciborskii (Woloszynska) to the temperate lakes in the mid latitudes is triggered by global warming15. The study “Blooms like it hot”12 explain climate change as a potent catalyst for the expansion of toxic blooms. In Sweden, annual phytoplankton bloom in larger lakes was found to be anticipated by a month due to the early warming in spring16.

Lake Surface Water Temperature (LSWT) exhibits a rapid and direct response to climate forcing prominently induced by changes in air temperature, cloud cover, short wave radiation, latitude and the lakes morphometry5. Detailed synthesis of in-situ and satellite derived LSWT over lakes globally, indicated rapid warming during summer. Moreover, LSWT and air temperature trends during summer are found to diverge over many lakes globally which signifies the need of regional studies in understanding the change dynamics5. Investigations carried out on the large lakes south of the Alps using non-parametric tests on volume weighted spring mean temperature derived from long-term in-situ data series from 1970 to 2009 showed a significant warming rate of 0.012–0.028 °C yr−1, at a rate comparable with that of other European and North American lakes17,18. The year-to-year fluctuations in the thermal structure were shown to be strictly controlled by large scale atmospheric dynamics between the N-Atlantic and the Mediterranean regions19,20. Results of limnological studies carried out in the largest and deepest lakes south of the Alps Lake Garda, Lake Iseo, Lake Como, Lake Lugano and Lake Maggiore 17, pointed out the need of integrating interdisciplinary approaches to the scientific based long-term monitoring. Lack of long-term in-situ data at a high temporal frequency is the main obstacle in identifying long-term trends. In this context, data from remote sensing as a substitute to in-situ data could play a key role in limnological studies.

Surface temperature is one of the accurate and reliable measurement using remote sensing10. Remote sensing of the temperature is based on recording the emitted radiation from earth surface in the spectral domain of 8–14 μm21. Inverse Planck’s law is applied to convert the emitted radiance recorded in the thermal infrared region to Top Of Atmosphere (TOA) Brightness Temperatures (BT). There are multiple approaches to estimate land/water surface temperature from the brightness temperatures22. In the case of inland water bodies like large lakes, the most common approach is the split-window technique where the difference between the two adjacent thermal channels (10.5–11.5 μm, 11.5–12.5 μm) is taken as a measure of atmospheric attenuation to derive the Surface Temperature (ST)23. The accuracy of the derived ST depends on the split-window coefficients, which in turn is based upon multiple parameters like spectral response function and emissivity of the channels, column water vapour in the atmosphere and View Zenith Angle (VZA) of the sensor. These coefficients are derived by regressing simulated BT’s from radiative transfer models like MODTRAN against the atmospheric profiles. The coefficients are generally derived on a regional or global level for the estimation of Land Surface Temperature (LST) and Sea Surface Temperature (SST)24,25,26. For lakes, both LST and SST based algorithms are used interchangeably and with higher accuracy of RMSE between 0.5–1.5 °C6,18,27. Furthermore, lake and sensor specific constants are published for an exhaustive list of global lakes by31.

There are a multitude of LST and SST datasets derived from multiple sensors publicly available such as Pathfinder from AVHRR data32, EuroLST33, GlobTemperature from ATSR series34 and the MODIS temperature products. ArcLakes35 and the LSWT data by30 are two lake specific products derived exclusively from satellite data for a set of selected lakes. A global database of lake summer surface water temperatures for 291 lakes combining both in-situ and satellite data was recently published by36. Nevertheless, the products are often based on selected sensors aboard multiple instruments with different specifications and acquisition times. The algorithm behind surface temperature retrieval also differ among the products. In addition, many temperature products from moderate resolution (~1 km) sensors are resampled to coarser resolution (~5 km) which results in missing spatial coverage or even complete data loss at lake locations. Since multiple datasets cover different time frames, we need to combine them in order to generate a time series appropriate for trend analysis in the context of climate impact studies. But combining these data from multiple sources without applying acquisition time correction and homogenisation would result in spurious trends.

Our aim was to develop a new methodology to derive daily homogenised LSWT data from multiple moderate resolution sensors. As a result, we developed a new homogenised LSWT time series for Lake Garda using the dual thermal channels from the sensors - AVHRR/2 (NOAA-9/11/12/14), AVHRR/3 (NOAA-16/17/18/19), ATSR1 (ERS-1), ATSR2 (ERS-2), AATSR (Envisat) and MODIS (Aqua/Terra). The new time series offers 30 years of daily LSWT from 1986 to 2015 developed using split-window algorithm with satellite specific coefficients published by26, acquisition time/orbital drift corrected, spatially sampled at 1 km resolution and gap filled using harmonic analysis. To our knowledge, this is the first time a homogenised LSWT was developed merging data from 13 satellites. The new method is reproducible and extensible to other lakes globally, provided that satellite data is available. The entire methodology is implemented using open source software packages. Hereafter, in the following sections “AVHRR” represents both AVHRR/2 and AVHRR/3 sensors and “ATSR series” represents ATSR1, ATSR2 and AATSR unless explicitly specified.

Study area

Lake Garda is located east of the southern sub-Alpine region between the coordinates - Longitude: 45.44°E–45.92°E; Latitude: 10.44°N–10.91°N. With a surface of 368 km2 and a volume of 49 km3, it is the largest lake in Italy. Along with the lakes Iseo, Como, Maggiore and Lugano it forms part of the group of deep lakes located south of the Alpine chain. These lakes are an important economic resource in Europe. Besides representing a major tourist destination, their waters are also used for drinking water purposes. Lake Garda is divided into two basins, northwestern and southeastern, which are divided by an underwater ridge connecting the Sirmione peninsula with Punta S. Vigilio (Fig. 1). The northwestern basin is large and deep, the shores descend at sharp slopes and the bottom spreads over 20 km at depths from 300 to 350 m (maximum depth). The maximum depth in the southeastern basin is around 80 m, whereas the shape is nearly conical. From a practical point of view, the lake can, however, be divided into two major zones separating the deeper northern area from the shallower southern area (mostly <150 m). This distinction is consistent with the observations in ref. 37 where, using temporal multi-spectral satellite images, found significant spatial and temporal variability in the concentrations of chlorophyll-a in the northern and southern areas of Lake Garda. The low ratio between the surface of the catchment area and the surface of the lake (6.1) explains the long theoretical water renewal time of about 27 years in comparison with other deep lakes in the area.

Figure 1
figure 1

Study area map of Lake Garda; dark blue area depicts the deep basin and light blue area depicts the shallow basin.

Push pins represent locations of in-situ water temperature monitoring. The maps were generated using the software GRASS GIS 7.048 (URL - https://grass.osgeo.org/grass7/).

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Results

We processed a total of 62,799 level-1B input images in order to derive daily LSWT’s. A single LSWT map has 363 pixels of 1 km resolution representing the entire Lake Garda. After applying the inner mask to remove the edge pixels, a total of 223 pixels were considered for deriving long-term climatologies (Fig. 2). The aggregated seasonal and annual maps were developed by averaging the daily data. To demonstrate the spatial variability of the new LSWT maps, Fig. 2 shows the aggregated summer mean temperature maps for the years 1992 and 2003, along with a temperature difference map. The year 1992 had a relatively cool summer, while the year 2003 had a record warm summer due to a heatwave all across Europe. Harmonic analysis filled data gaps by repetitively applying a least square fit and removing the outliers until the remaining data was within the valid range. The amplitude of the first harmonic in all the years was larger than the other harmonics depicting a strong unimodal pattern of LSWT. To demonstrate the data reconstruction using HANTS, Fig. 3 shows the smoothed LSWT plotted over the daily averaged LSWT after homogenisation for the year 2003.

Figure 2
figure 2

(a) Summer mean LSWT of year 1992 (b) summer mean LSWT of year 2003 (c) difference map between summer means of 2003 and 1992. The boundary layer shown in black over Lake Garda is the inner buffer used to mask out the edge pixels. The maps were generated using the software GRASS GIS 7.048 (URL - https://grass.osgeo.org/grass7/).

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Figure 3
figure 3

Plots showing gap-filled reconstructed LSWT using HANTS (red line) over the averaged homogenised LSWT (grey bars) from multiple observations over a particular day of the year.

The daily LSWT time series from the deep and shallow basins for the year 2003 is shown as an example.

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Validation

To validate the new LSWT data, we performed a cross platform comparison of same day observations between the satellite pairs, followed by regression analysis between final homogenised LSWT and the in-situ data. The RMSE of the various cross-platform pairs for both deep and shallow basins varied between 0.38 °C and 1.28 °C (Table 1). The minimum RMSE of 0.38 °C was reported for the satellite pair NOAA18/NOAA19 for both the deep and shallow basins. The highest RMSE of 1.2 °C was reported for the pair NOAA17/Terra in the deep basin and of 1.28 °C for NOAA14/Terra in the shallow basin. An average RMSE of 0.88 °C was reported for all the satellite pairs taking into consideration both the basins. Furthermore, we validated LSWT derived from individual satellites with the corresponding in-situ data. We were only able to use data from 11 satellites for the validation due to the non-availability of matching in-situ data against NOAA9 and NOAA12 (Table 2). An average RMSE of 0.86 °C and 0.94 °C was obtained for the deep and shallow basins respectively. For the deep basin, LSWT derived from NOAA16 had the lowest RMSE against in-situ data (0.42 °C), while NOAA18 had the highest RMSE (1.29 °C). In the shallow basin, the lowest RMSE was reported for ERS-2 (0.67 °C), whereas the highest RMSE was reported for Aqua against in-situ data (1.18 °C). An average RMSE of 0.92 °C was estimated between satellite derived LSWT and in-situ observations. The regression analysis between the final homogenised LSWT and the in-situ data revealed a coefficient of determination (R2) of 0.98 for both basins. The mean RMSE estimations were 0.83 °C and 0.75 °C for deep and shallow basins respectively (Fig. 4).

Table 1 RMSE in °C reported between same day observations from different pair of satellites for the deep and shallow lake basin.
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Table 2 RMSE in °C reported at deep basin and shallow basin from absolute difference between in-situ data and satellite derived LSWT.
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Figure 4
figure 4

Boxplots of lake mean LSWT difference between final homogenised LSWT and corresponding in-situ data.

The values shown in black over the lower whisker represents N and the values in red is the reported RMSE in °C.

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Long term warming trends

We found a significant (*P < 0.05) warming trend for lake mean LSWT at an annual rate of 0.020 °C yr−1 (Fig. 5). For the deep basin, we found a similar warming trend (0.014 °C yr−1; *P < 0.05) for annual mean LSWT, but for shallow basin there was no significant annual trend. For summer months, we report a significant warming trend at the rate of 0.036 °C yr−1 (***P < 0.001) for the lake mean LSWT (Fig. 5). We report a similar increasing rate for the mean LSWT over the deep basin (0.039 °C yr−1; ***P < 0.001), while no significant warming was found for the shallow basin. We did not observed any significant trends during the other seasons. Furthermore, standard deviation of 0.32 °C and 0.53 °C were estimated from the time series of annual and summer mean LSWT respectively. For comparison, we performed summer and annual trend analysis on the lake mean temperature computed from in-situ data and recently published satellite derived LSWT by30. We obtained a significant warming trend at the rate of 0.039 °C yr−1 (*P < 0.05) and 0.027 °C yr−1 (*P < 0.05) for both summer and annual time series from the data by30, while no significant warming was found from the in-situ data (Fig. 6).

Figure 5
figure 5

Annual (top) and summer (bottom) trends of lake mean LSWT derived from the new homogenised daily LSWT between 1986 to 2015.

Data were smoothed using the loess interpolation (blue line). The gray area is the 95% confidence interval.

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Figure 6
figure 6

Annual trends of lake mean LSWT computed from homogenised LSWT (deep basin), in-situ data and satellite derived LSWT30.

Data is smoothed using the loess interpolation (blue line). The area shown around the smoothed line is the 95% confidence interval.

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Discussion

Remotely sensed satellite data offer a great alternative to in-situ data. We demonstrated the usability of satellite derived LSWT in estimating long-term annual and seasonal trends of lake mean LSWT. The inevitable breaks in historical satellite data due to different life periods and the overpass time of multiple satellites is the main challenge in developing a continuous time series (Fig. 7). In order to make a long-term time series of any bio-geophysical parameter from satellite data, it is necessary to proceed with a combination of different time series, which demand homogenisation to correct for the different acquisition times. Hence, we developed a workflow to derive daily LSWT maps of Lake Garda for the last 30 years (1986–2015) at 1 km spatial resolution recorded by six moderate resolution sensors on board of 13 different satellites. For AVHRR data acquired from multiple NOAA instruments, we used the calibration techniques which take into account the inter-satellite calibration38. For the ATSR series, the calibration coefficients provided with the data were of superior quality ensuring continuity of data with transition of the instrument6. Moreover, the split-window technique with satellite specific coefficients used in this study could derive LSWT’s at an average RMSE of 0.88 °C on a single day.

Figure 7
figure 7

Plot of variation in observation times of all the satellites used in this study.

Note the large orbital drifts of the earlier NOAA-9/11/12/14 instruments.

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We found similar RMSE (<1 °C) before and after applying time correction procedures between satellite derived LSWT and corresponding in-situ data. This could be due to the fact that lake surface temperature of a deep sub-Alpine lake is a slow changing parameter during the day. Precisely, the temperature measured at different times during the day was close enough to exhibit any noticeable change in accuracy indicators before and after the time correction procedure. Nevertheless, for the older satellites, like earlier NOAA instruments which underwent considerable orbital shifts during their course in orbit, this step was crucial. We argue that the acquisition time correction is necessary while combining data from different satellites due to at least two reasons: i) to merge the data obtained from multiple satellites which have different quality, acquisition times and orbital decays; ii) for more dynamic surfaces where temperature varies at short temporal scales, this step may prove to be crucial. Moreover, identical slopes from different linear models between individual satellites and in-situ data confirmed the comparability of different satellite derived LSWT to each other.

Harmonic analysis was used to remove the gaps in the daily data due to undetected clouds and unresolved spurious calibration. We found that the gap filled daily data was over smoothed which could inadvertently remove some of the actual temporal dynamic change. In spite of this smoothing, the final homogenised LSWT data was able to pick up the long-term thermal dynamics over the lake surface. From the long-term summer trend of LSWT (Fig. 5), it was evident that the new LSWT data was able to pick up the low and high peaks in the last 30 years. Moreover, the spatial coverage of 1 km is another advantage of this dataset in comparing the thermal dynamics of different locations in the lake. The temperature difference map in Fig. 2 clearly illustrates how LSWT is distributed over the surface of Lake Garda, whereby the shallow basin is more warm than the deep basin. Figure 2 also shows that in the deep basin, the western shores are warmer than at the eastern side. This is explained by the high mountain chain of Mt. Baldo (2218 m) in the east, which blocks the sun for a long time during the day. The amplitude and phase maps obtained for each harmonic per year provide additional information on lake’s thermal variation over time. The amplitude of the first harmonic is the highest for all the years, showing the influence of annual cycle of LSWT on the lake characteristics.

We performed validation at different stages of the work flow before and after homogenisation. We found that the RMSE obtained between individual satellite derived LSWT and in-situ data varied significantly between the deep and the shallow basins (Table 2). The higher RMSE in the shallow basin may be due to the varying skin effects in Lake Garda. The difference between skin and bulk temperature often termed as skin effect could be a deciding factor in varying accuracy over the deep and the shallow basins6,27,41. It must be noted that, the satellite measured LSWT represents temperature of a sub-micron layer between the lake surface and the air and is highly variable according to the meteorological conditions. The in-situ data on the other hand represents bulk temperature, often measured between 0 to 0.5 m. The RMSE values were closer in the cross-platform comparison analysis in both the deep and shallow basins (Table 1), which demonstrates the superior quality of split-window coefficients. With respect to the sensor performance in deriving LSWT, we found that the ATSR series exhibited lower RMSE of 0.75 °C and 0.74 °C, followed by AVHRR with RMSE of 0.88 °C and 0.96 °C, MODIS with RMSE of 0.92 °C and 1.18 °C for deep and shallow basins respectively. A similar study by30 also reported RMSE between 1 to 1.6 °C for AVHRR derived LSWT against in-situ data from multiple European lakes. For a better comparison it is important to have high frequency in-situ data matching the exact acquisition time of the satellites6,29. The majority of the LSWT difference in both basins were below 1.0 °C. This suggests that the homogenisation procedure follwed by HANTS retained the same pattern of accuracy and the RMSE in a similar range. Moreover, the comparison between summer lake mean trends obtained from our new LSWT data series and a similar satellite derived LSWT by30 showed that both the data series are comparable and exhibits similar trends (Fig. 6).

To understand the long-term dynamics of lake surface temperature, it is crucial to have high frequency in-situ observations. In this study, we demonstrate that the satellite derived data can complement the missing in-situ observations of surface water temperature, with high spatial and temporal resolution. Global coverage of the satellite data is an added advantage which enables us to use the same data set for analyzing multiple lakes subsequently for comparative analyses. The correct evaluation of the long-term trends of water temperature should take into account the advantages and limits of measurements made by both remote sensing techniques and in-situ recordings. Remote sensing technologies are limited to the detection of long-term trends circumscribed to the surface of water bodies. While this limit can be compensated by measurements performed at high temporal (daily) and spatial (whole lake) scales, the direct measurement of surface data in the field at lower temporal frequency can seriously impair the evaluation of long-term trends. Conversely, the lower temporal and spatial coverage of in-situ data can be partly balanced by a complete availability of data collected in the water column. The thermal structure of the mixolimnetic layer is less vulnerable to transient (daily to weekly scales) fluctuations of surface water temperatures originating from changes in local meteorological conditions. Therefore, most of the studies which have focused on the long-term trends of lake water temperatures have been based on measurements recorded in the water column. Lake Garda being a deep sub-Alpine lake, undergo complex mixing processes during winter and spring which have a strong influence on surface water temperatures20. The inter-annual change of LSWT could be helpful in identifying impacts on the associated physical and biological processes in the lake.

Recent studies by18 and5 have confirmed global warming of lakes due to climate change between 1985 and 2009 at the rate of 0.03–0.04 °C yr−1. The main difference between the two studies is that18, used exclusively the satellite derived LSWT, while5 used a combination of in-situ and satellite data to derive the long-term trends. There are regional studies showing rapid warming of North American large lakes and European lakes which uses satellite derived LSWT6,30. With a different approach7,17, and42 reported warming at similar rates using in-situ volume weighted mean bulk temperature for the large lakes in Europe and North America. In contrast to our approach, many earlier studies used LST and SST data for lake studies which reported similar results. More recently, studies by30 and31 developed lake specific surface temperature data using optimized split window coefficients. We used the data by30 for comparing the trends and obtained similar increasing temperature trends during summer. Study by1 reported July warming since 1970 at the rate of 0.02–0.05 °C yr−1 computed from in-situ data for multiple northern hemisphere lakes. Although we did our analysis separately for the deep and shallow basins of Lake Garda, we could not find any significant trend in the shallower, southern basin. Moreover, the thermal variation over the deep basin was similar to the entire lake basin, depicting that the lake is more influenced by the characteristics of the deep basin.

One of the main advantages of the developed method is that it gives the opportunity to study the long-term thermal dynamics of other lakes given the availability of the satellite data over study area. The spatial and temporal coverage obtained from this method is unique and cannot be achieved by any other data source. The outliers due to undetected clouds and other unsuitable data, are often difficult to remove completely. Though the method is robust, manually checking the accuracy of cloud masks for thousands of images is not practical. The automated cloud masking may leave undetected cloud pixels as clear sky ones. A future enhancement for refining cloud masks would be to use seasonal thresholds in SPARC algorithm (see Methods) instead of global ones. Nevertheless in this study, HANTS were able to remove the outliers as shown by the validation results at the expense of losing some of the short term temporal variability. The spatial resolution of 1 km is the highest possible when considering moderate resolution satellite data, but it is not high enough to study the smaller lakes. Moreover at this resolution, the issue with mixed pixels along the shore were solved by using an inner lake buffer while computing long-term climatologies.

Methods

Data

In this study we used multiple moderate resolution sensor data to estimate long-term LSWT’s over the past 30 years. We used data acquired by six sensors - AVHRR/2, AVHRR/3, ATSR1, ATSR2, A(A)TSR and MODIS which were on board 13 satellites (Fig. 8a). These sensors offer dual thermal channels at 10.5–11.5 μm and 11.5–12.5 μm with a spatial resolution of approximately 1 km. This was ideal for deriving surface water temperature using a split-window algorithm. The sensors AVHRR and MODIS provide daily images, while ATSR series provide data every three days. We downloaded and processed level-1B data of AVHRR, ATSR series and MODIS from Comprehensive Large Array-data Stewardship System (CLASS - http://www.class.ncdc.noaa.gov/), Merci data archive (http://ats-merci-ds.eo.esa.int/merci/welcome.do) and Level 1 and Atmosphere Archive and Distribution System (LAADS - https://ladsweb.nascom.nasa.gov/) respectively for the study area. For AVHRR, we used Local Area Coverage (LAC) data available at its original spatial resolution of 1.1 km. For ATSR series, we downloaded and processed nadir viewing TOA data. The level-1B swath products MYD021KM and MOD021KM for Aqua and Terra satellites respectively were used to extract dual thermal channels from MODIS sensor. Figure 8b shows the year wise distribution of data from multiple satellites. We processed the AVHRR and MODIS data using Pytroll libraries (http://www.pytroll.org/). Furthermore, we used BEAM software provided by European Space Agency to process the ATSR series of data (http://www.brockmann-consult.de/cms/web/beam/). For validation of the satellite derived water temperatures, long-term monthly in-situ data between 1991–2015 collected from two locations in Lake Garda were used. We used bulk temperature (0–0.5 m) measured at two locations - Brenzone in the deep basin and Bardolino in the shallow basin (see Fig. 1).

Figure 8
figure 8

Availability of moderate resolution satellite data between 1986 and 2015.

(a) Time line of the satellites in orbit; (b) Distribution of level-1B data downloaded for the study area from 1986 to 2015 obtained from respective archives - CLASS (AVHRR), LAADS (MODIS) and Merci (ATSR series) separated by different instruments.

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Pre-processing

The pre-processing of thermal channels at level-1B include thermal calibration to convert from radiances to brightness temperatures using inverse Planck’s law followed by removal of cloud pixels. The data from MODIS and ATSR series were calibrated using the coefficients provided in the header. For AVHRR, the calibration procedure was much more complicated due to (well documented) navigational errors with the older NOAA satellites43. Despite on board thermal calibration in NOAA instruments, the calibration data were prone to solar contamination and atmospheric attenuation of the signal and satellite decays40. In this study, we used the geometrically corrected time series of brightness temperatures derived from dual thermal channels of AVHRR LAC data from 1986 to 2015 developed by44. Furthermore, we considered only those pixels acquired at a zenith angle less than 45 degrees. All the further data processing was restricted to lake pixels masked using a Lake Garda boundary layer. For the cloud masks of level-1B AVHRR LAC and MODIS data, we used the Separation of Pixels Using Aggregated Rating over Canada (SPARC) algorithm developed by45, originally implemented for creating cloud mask over Canada. We used two relevant tests to remove thick clouds and thin cirrus above the lakes, i) brightness temperature test (T-test) using channel Ti and ii) thin cirrus test (C-test) which uses the difference between channel Ti and Tj. The T-test uses channel Ti brightness temperature and compares it with a dynamic threshold which is the surface skin temperature data of the corresponding day and time derived from climatic models. In this study, we replaced the North American regional reanalysis data with European centre for medium-range weather forecasts46 following the successful implementation over Europe by30. The SPARC algorithm is implemented using raster processing tools in GRASS GIS 7.047,48. For ATSR series, we used the quality layer provided with the data to remove the cloud pixels.

Deriving LSWT using split-window algorithm

From the brightness temperatures derived by thermal calibration, the next step was to estimate LSWT’s using split-window algorithm. We used a non-linear split-window Equation 1 to estimate the surface temperature as proposed by26:

where c0–c6 are split-window coefficients, Ti and Tj are at-sensor brightness temperatures derived from the dual thermal channels, ε is mean emissivity, Δε is emissivity difference and W is the total atmospheric water vapour column (g/cm2). For water surface, the mean emissivity is close to unity. As our study is dealing with lakes, the equation 1 was modified by assuming ε as one and Δε to be zero. Hence the modified equation 2 was used in this study to retrieve LSWT from dual thermal channels:

We used satellite specific split-window coefficients published by26. These coefficients were derived by statistical minimization from a simulated database of brightness temperatures and atmospheric profiles obtained from MODTRAN radiative transfer model, emissivity spectra extracted from spectral libraries and the spectral response functions26.

After estimating the LSWT, we applied two levels of filters to remove outliers due to undetected clouds and spurious calibrations. First, we applied a global filter based on a minimum and maximum thresholds derived from the long-term in-situ data. Thus we discarded any LSWT values beyond the range 6.5–29 °C. We further applied an advanced filter based on inter-quartile range of LSWT’s derived every 16 days33,49. The lower and upper thresholds for this filter was computed using the equations 3 and 4 respectively:

where the 1stquartile and 3rd quartile were derived climatologically from the LSWT data every 16 days over the years 1986 to 2015. This filter was then applied using the derived thresholds to those LSWT data of corresponding time frames.

Homogenising LSWT from multiple satellites

We considered all the satellite observations for a single day over a wide window of time; from 08:00 to 17:00 UTC. Figure 7 clearly shows the orbital drift of earlier satellites - NOAA-9/11/12/14/16. We applied a physical model based on typical pattern technique proposed by50 to standardize the acquisition time to 12:00 UTC. In this approach, monthly diurnal cycles were used as a reference pattern to correct for the varying observation times to produce a standard observation time. The monthly diurnal cycles computed from hourly averages were derived from the existing satellite observations of LSWT and were limited to day time (Fig. 9). We followed the Diurnal Temperature Cycle (DTC) model to derive the diurnal variations of LSWT based on51. The model used was described by the Equation 6:

Figure 9
figure 9

Monthly diurnal cycles during day time estimated using long-term hourly climatologies from satellite observations.

The points in plot are the long-term hourly averages for a particular month and the red line is DTC model fit.

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with

where T0 is the residual temperature at sun rise; Ta and Tb are temperature amplitudes; Ts(t) is surface temperature at time t; tm is the time at which temperature is maximum; tsr is the time of sun rise; ω is calculated using the Equation 6. T0, Ta and Tb are obtained by statistically fitting the model given in the Equation 5. Finally, to apply the time correction to all the LSWT data, for each image with an observation time t, Ts(t) and Ts(12) were calculated using the model as given in Equation 5. The correction factor was then computed using Equation 7 to correct the LSWT to a standard time at 12:00 UTC. A condition based approach is used for the correction as given in Equation 8 and 9:

where cf is the correction factor, Tc is the corrected LSWT to a standard time 12:00 UTC and Tori is the actual LSWT.

In order to derive one homogenised LSWT per day, the subsequent step was to merge by averaging the single day observations from multiple satellites. For this, we first performed a satellite specific linear regression analysis between LSWT and in-situ data to compare the slope of regression lines between different satellites. We performed the analysis on the deep and the shallow basins separately. The slopes varied between 0.85 and 1.1, depicting that the data used for the models were similar to each other and exhibited similar residual patterns. We used the r.mapcalc module in GRASS GIS 7.0 software to apply the time correction model to the LSWT data.

Gap filling using harmonic analysis

The daily LSWT data after time correction and averaging over the same day still suffered from gaps due to cloud cover. We used Harmonic ANalysis of Time Series (HANTS) to reconstruct the gaps in the LSWT data. The harmonic analysis decomposes a time series into several sinusoidal functions with unique amplitude and phase values52. It applies a least square fitting procedure based on the derived harmonics53. The fitting procedure is repeated until the LSWT values with large deviations are removed from the fitted curve54. Hence, the role of HANTS is twofold in this process; i) to remove the outliers and ii) fill the gaps with fitted values. The fitting procedure based on HANTS is explained well by53 and54. We implemented HANTS based reconstruction on LSWT images using the r.hants addon in GRASS GIS 7.0 software55.

Temporal database and trend analysis

The gap filled daily LSWT maps from 1986 to 2015 were then imported into a spatio-temporal database using TGRASS modules in GRASS GIS 7.0. TGRASS is an advanced set of modules which perform time series analysis on spatio-temporal data56. We developed annual and seasonal climatologies from the daily LSWT data to analyse the long-term trends. The seasonal climatologies were developed by aggregating the LSWT over four seasons; winter (December/January/February), spring (March/April/May/), summer (June/July/August) and fall (September/October/November). We applied non-parametric tests on annual and seasonal mean LSWT separately for deep and shallow basins to detect the long-term trends. In particular, we used the Mann-Kendall test to identify the presence of monotonic upward or downward trends. We computed the Theil-Sen slope for the quantitative estimation of significant trends57. We used the Durbin-Watson test to detect potential serial correlation issues in the time series and checked for its statistical significance. To avoid mixed pixels with land along the shore, we used an inner lake buffer considering only water pixels while extracting the annual and seasonal climatologies (Fig. 2). Furthermore, for comparative analysis, we computed the same statistics on long-term in-situ data and satellite derived LSWT from AVHRR sensor by30 for Lake Garda.

Additional Information

How to cite this article: Pareeth, S. et al. Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake. Sci. Rep. 6, 31251; doi: 10.1038/srep31251 (2016).