Lake Surface Temperature

4 LAKE SURFACE TEMPERATURE Philipp Schneider*, Nathan C. Healey†, Glynn C. Hulley†, Simon J. Hook† *NILU—Norwegian Institute for Air Research, Kjeller, Norway. †Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States

CHAPTER OUTLINE 4.1 Introduction 129 4.2 Validation of Lake Surface Water Temperature 130 4.2.1 Category A: Comparison Against In Situ Measurements 131 4.2.2 Category B: Radiance-Based Validation 133 4.2.3 Category C: Intercomparison With Similar LST/LSWT Products 134 4.2.4 Category D: Time-Series Analysis 135 4.3 Satellite Data and Availability 135 4.3.1 Thermal Infrared Satellite Data 136 4.3.2 Availability 137 4.4 Use of Lake Surface Water Temperature for Trend Studies 144 References 147 Further Reading 150

4.1

Introduction

Over the past few decades, studies have shown that lake water surface temperatures have undergone rapid warming, particularly for nighttime temperatures (Austin and Colman, 2007; Livingstone, 2003; O’Reilly et al., 2015; Schneider and Hook, 2010; Schneider et al., 2009; Sharma et al., 2015). Like the oceans, lakes have a high heat capacity that dampens short-term temperature variability and enhances long-term variations making them excellent indicators of climate change (Coats et al., 2006; Quayle et al., 2002; Verburg et al., 2003). For example, a global study of large inland water bodies showed warming between 1985 and Taking the Temperature of the Earth. https://doi.org/10.1016/B978-0-12-814458-9.00004-6 # 2019 Elsevier Inc. All rights reserved.

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2009 with an average rate of 0.45°C/decade (Schneider and Hook, 2010). Warming such as this can have profound implications for the health of lacustrine ecosystems because water has a high heat capacity, and even small and subtle changes can lead to adverse effects. For example, longer summer stratification, a phenomenon that reduces a lake’s natural circulation, and could lead to development of harmful algal blooms and changes in biochemcial compositions of algae species (Flaim et al., 2014), the introduction of nonnative species, and oxygen-depleted dead zones (Blenckner et al., 2010; Sahoo and Schladow, 2008). Changes in the thermal behavior of lakes also has implications for local communities and their economies because lakes provide important services such as drinking water, agricultural irrigation, power generation, water transport corridors, recreation and tourism, and support for freshwater fisheries (Piccolroaz et al., 2018). Antarctic lakes have also been found to exhibit warming rates of 0.06°Cyr 1 between 1980 and 1995 (Quayle et al., 2002). Subtle increases in Arctic lake surface temperatures over time may also be indicative of thawing permafrost in the local region, a positive climate feedback loop that could result in increased outgassing of potent greenhouse gases such as CO2 and CH4 (Schuur et al., 2009; Smith et al., 2007).

4.2

Validation of Lake Surface Water Temperature

Independently assessing the quality of data products derived from measurement systems defines the concept of validation. Validation metrics such as uncertainty, bias, precision, and completeness require attention for robust validation of Earth Observation (EO) products. Therefore, a robust validation is essential for proper assessment of findings derived from any EO product (Loew et al., 2017). Thermal infrared-based observations, in particular, require a thorough and comprehensive validation in order to be exploited in the best possible way. Examples of thermal EO products include sea surface temperature (SST) and land surface temperature (LST), of which lake surface water temperature (LSWT) is a subset. LSWT products have been used in the past to detect long-term trends (e.g., Schneider et al., 2009; Schneider and Hook, 2010; O’Reilly et al., 2015), for which it is particularly crucial to have a robust understanding of the underlying uncertainty in the data. Validation of LSWT products is in principle quite similar to validation of SST and LST because so many of the same principles and best practices apply. There are several ways of validating thermal infrared earth observation data. Schneider et al. (2012) suggested four distinct categories for validating LST, which also apply to

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validation of LSWT: (A) Comparison against in situ measurements; (B) radiance-based validation; (C) Intercomparison with similar LST products; and (D) Time-series analysis. These categories are in many ways complementary, and a comprehensive validation would ideally incorporate elements of all four of them.

4.2.1

Category A: Comparison Against In Situ Measurements

Validations using in situ comparisons is highlighted here since this is the only “true” validation approach. We will give an example of validating LSWT retrievals by comparison against dedicated in situ observations and discuss some efforts regarding validation of trend values derived from long-term time series. As far as validation of LSWT is concerned, validation against in situ measurements (category A) is used quite frequently. Dedicated in situ observations of LSWT at a suitable site tend to provide the highest-quality validation that can currently be achieved. However, due to the significant complexity and cost of operating the necessary equipment, validation sites providing such measurements are quite rare on a global scale. In situ-based validation of LSWT ideally requires high-frequency observations of the incoming and outgoing thermal-infrared radiance at a location within the lake that is far away enough from the shorelines and islands such that the co-located satellite pixels are entirely covered by water surface. Establishing fixed buoys within the lake is the obvious solution for this. One example of a dedicated validation site for LSWT is the Lake Tahoe validation site, which is located at the border of California and Nevada and operated by the Jet Propulsion Laboratory (JPL). The site features a set of four buoys that have been measuring thermal infrared radiation as well as a wide variety of relevant other variables such as meteorology at a 2-min sampling interval from the year 2000 to present. Fig. 4.1 shows one of the buoys and their spatial distribution throughout Lake Tahoe. The data collected by the buoy-mounted instruments at the Lake Tahoe site has been used in many different studies over the years (e.g., Hook et al., 2003, 2007; Wilson et al., 2013). Fig. 4.2 shows an example of the results validating the LSWTestimates from three different satellite instruments against in situ observations at the Lake Tahoe validation site (Schneider et al., 2009). The root mean squared error (RMSE) in all three cases is <0.3 K, which indicates the high accuracy of satellite-based LSWT estimates under ideal conditions. If data from a dedicated validation site, which monitors in- and outgoing thermal infrared radiation, is not available, it is possible

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Fig. 4.1 The Lake Tahoe (California/Nevada) validation site. The left panel shows a single buoy, whereas the right panel shows a map of Lake Tahoe with the location of the four buoys marked with red triangles labeled TB1 through TB4. For more information about the Lake Tahoe validation site, see https://laketahoe.jpl.nasa.gov/.

Fig. 4.2 Example of validation of satellite-based lake surface water temperature observations against in situ measurements using a radiometer mounted on a buoy at Lake Tahoe. From Schneider, P., Hook, S.J., Radocinski, R.G., Corlett, G.K., Hulley, G.C., Schladow, S.G., Steissberg, T.E., 2009. Satellite observations indicate rapid warming trend for lakes in California and Nevada. Geophys. Res. Lett. 36(22), 1–6. https://doi.org/ 10.1029/2009GL040846.

to carry out in situ validation also with regular bulk temperature measurements obtained at buoys or from ships (Reinart and Reinhold, 2008; Chavula et al., 2009; Crosman and Horel, 2009; MacCallum and Merchant, 2012; Zhang et al., 2014;

Chapter 4 LAKE SURFACE TEMPERATURE

Riffler et al., 2015). In such cases, it is important to take into account the difference in skin temperature as observed by the satellite and the bulk temperature obtained at the in situ site € ssel et al., 1990; Fairall et al., 1996; Wilson et al., 2013). (Schlu An obvious application of space-based LSWT observations is to assemble long times series of satellite observations for many large lakes and other inland water bodies and to use these times series for estimating long-term trends. This allows for the possibility of acquiring information about the change in surface temperature at the large number of worldwide lakes where no or only very sporadic in situ information is available. However, when doing so, it is not always clear if the satellite-observed trends are true geophysical trends in LSWTor if they are caused by reasons other than the actual change in LSWT. This can be the case, for example, when long-term time series are generated by combining the data from multiple satellite instruments without adequate homogenization of the sensor data. It can also take place when other parameters such as cloud cover or atmospheric aerosol load exhibit trends themselves and thus affect the LSWT retrieval and the underlying calibration. One way to investigate whether the trends derived from a LSWT time series actually reflect true geophysical trends is to directly validate them against trends derived from time series of data collected at in situ sites. Thus, it is important to use the same overall time period and the same averaging period as the satellite data. In addition, it is useful to use only the in situ observations that match the overpass time of the satellite instrument. Fig. 4.3 shows an example of direct validation of satellite-based trends in surface temperature against trends calculated from long-time series of in situ observations of bulk temperature measured at a network of buoys located in the North American Great Lakes (Schneider and Hook, 2010). The results indicate overall good correspondence with a root mean squared error of 0.025°Cyr 1 and 0.013°Cyr 1 for the AVHRR time series and a merged AVHRR/ATSR time series, respectively.

4.2.2

Category B: Radiance-Based Validation

Radiance-based validation offers a methodology to utilize if in situ observations are not available. This approach simulates top-of-atmosphere brightness temperatures (BTs) that a satellite would theoretically observe using radiative transfer models that include surface skin temperature, surface emissivity, and atmospheric profiles of air temperature, water vapor, and aerosol content (if available). This essentially inverts the surface temperature as observed by a satellite by adding perturbations of the input skin temperature until simulated BTs match satellite-retrieved BTs. This method has been applied to various satellite temperature products

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Fig. 4.3 Direct validation of 1985–2009 LSWT trends derived from time series of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) against bulk surface water temperatures measured by a network of buoys in the Great Lakes. The labels identify the individual buoys. From Schneider, P., Hook, S.J., 2010. Space observations of inland water bodies show rapid surface warming since 1985. Geophys. Res. Lett. 37(22), 1–5. https://doi. org/10.1029/2010GL045059 (web archive link).

including validation of MODIS LST (Wan and Li, 2008), AATSR LST (Coll et al., 2012), and ASTER and MODIS at sensor radiance at Lake Tahoe (Hook et al., 2007). The radiance-based approach cannot completely replace the use of validation using in situ observations, although it provides a viable alternative for long-term product evaluation on a global scale (Coll et al., 2012).

4.2.3

Category C: Intercomparison With Similar LST/LSWT Products

Example sources of LSWT products suitable for intercomparisons include products derived from instruments onboard airborne and/or other satellite platforms and proxy data such as reanalysis products like ECMWF (Uppala et al., 2005; Dee et al., 2011) or NCEP (Kalnay et al., 1996). Utilizing the intercomparison of one LSWT product with another is important when numerous products are available and/or when attempting to combine LSWT observations into a “best available product.” However, this approach cannot provide the same fidelity of validation as comparisons with in situ observations because products may be consistent but inherently biased. Numerous issues must be addressed in this

Chapter 4 LAKE SURFACE TEMPERATURE

approach including: data quality control, spatial re-gridding, and temporal matching. Using a comparison with different sensors on the same platform or instruments flying in similar orbit will provide the most accurate result. Overall, the key requirements for the intercomparison approach are (1) observation times must be within a specific threshold (recommended maximum 10 min), and (2) reference data must be operationally available.

4.2.4

Category D: Time-Series Analysis

Time-series analysis can be used to screen LSWT products to identify problems associated with issues such as calibration drift, offsets between different sensors within a family/series, or unrealistic outliers. The most effective use of this approach is examining time series at small spatial scales as opposed to global scales. This can provide more specific details about geophysical signals or instrument issues that are difficult to differentiate in validations at a global scale. For instance, if insufficient time series are available or if multiple in situ data sets (with varying gaps in data records, different sampling protocols, etc.) must be implemented for all regions, uncertainty can become problematic. Identifying homogeneous surfaces that exhibit relatively stable emissivity is vital for this approach because temporal variability in surfaces such as those covered in vegetation can vary widely over time. Thus, metrics must be developed (e.g., examining the temporal profile of a surface’s properties, or implementing geostatistical analysis) to quantify validation accuracy using time-series data.

4.3

Satellite Data and Availability

Spaceborne platforms (i.e., satellites) now provide some of the most widely used data in Earth observation (EO) science. With a variety of sensors now deployed on hundreds of different satellites that are operated by a variety of different space programs, data for global-scale EO analyses are now widely available. Spaceborne platforms with thermal infrared sensors onboard, however, are far more limited when compared to sensors examining other regions of the electromagnetic spectrum. Determining the most appropriate thermal data for research objectives focused on temperature analysis has to be carefully evaluated because, although a wide variety of data products now exist, some are proprietary while others are freely available. Sea surface temperatures (SST) are the most common use of thermal satellite data over aquatic targets. Although when focusing on inland water bodies (i.e., lakes and reservoirs), several factors must be

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considered. In particular, spatial and temporal resolutions of different datasets vary, so evaluating the most suitable data for lakes in each analysis is a critical first step. This chapter is intended to provide an overview of satellite data availability, sources to access data, and availability of commonly used satellite temperature data products. With a specific focus on lake surface water temperature (LSWT), careful consideration is required in order to properly examine temperature phenomena, trends over time, etc. Lake morphometry varies widely, so factors like size, shape, and depth can pose challenges to the use of satellite temperature retrievals with different spatial resolutions. For example, ensuring that land and man-made features (shoreline, islands, marinas, etc.) are sufficiently excluded from LSWTanalysis is fundamentally important in any analysis investigating LSWT. Thus, determining which satellite dataset to utilize is ultimately determined by lake morphometric characteristics and temporal resolution. Thermal inertia of water slows the response of LSWT to synoptic meteorological conditions and within-lake temperature gradients are common. Thus, a variety of approaches to analyze LWSTare possible, depending on specific research objectives. Many studies have investigated LSWT using satellite temperature retrievals where some use a window of pixels (e.g., 3  3) that represent lake-wide average LSWT (e.g., O’Reilly et al., 2015; Schneider and Hook, 2010; Schneider et al., 2009), while others attempt to use all available water surface pixels (e.g., Pareeth et al., 2016; MacCallum and Merchant, 2012). Therefore, the objective of this chapter is to provide an overview of current thermal infrared satellite data products and their availability, so end users can determine how best to use them.

4.3.1

Thermal Infrared Satellite Data

In general, the term “thermal infrared radiation” (TIR) refers to the wavelength range of 3–14 μm in the electromagnetic (EM) radiation spectrum. There are two main windows of the EM spectrum where Earth’s atmosphere allows for transmittance of TIR: 3–5 and 8–14 μm. Earth’s atmosphere absorbs TIR in narrow bands due to the presence of water vapor, carbon dioxide, and ozone, so most spaceborne sensors omit these regions of the EM spectrum. Thus, manufacturers of TIR sensors intended to be launched onboard spaceborne platforms have engineered a variety of different sensor configurations that impact the spatial and temporal resolution of data products produced. It is important to acknowledge that TIR observations by spaceborne sensors used to analyze LSWT essentially provide an indication of the temperature of an extremely thin

Chapter 4 LAKE SURFACE TEMPERATURE

layer of the water surface (500 μm) where turbulent transfer of energy between the water surface and atmosphere occurs.

4.3.2

Availability

4.3.2.1

Satellite Temperature Data Options

Thermal infrared radiation (TIR) observations from satellites are available in various formats ranging from Level 0 to Level 4. First, “Level 0” (L0) data are unprocessed instrument data. “Level 1” (L1) data represent unprocessed instrument and payload data at full resolution with artifacts from communications (synchronization frames, communications headers, and duplicate data) removed. Then, “level 1A” (L1A) data are similar to L1 but ancillary information like radiometric and geometric calibration coefficients and georeferencing parameters are computed, but not applied to the L0 data. “Level 1B” (L1B) data are similar to L1A and have been processed to sensor units (e.g., brightness temperatures). “Level 2” (L2) data have been processed with radiometric and geometric parameters applied and represent geophysical variables, such as temperature, soil moisture, sea ice concentration, etc. “Level 3” (L3) data are products that have been mapped on uniform spacetime grid scales, and “Level 4” (L4) data are typically model outputs or results from analyses of lower-level data like a combination of multiple measurements. Fig. 4.4 depicts a timeline of satellites that carry TIR sensors onboard, and Table 4.1 provides specific details of a few example datasets that are currently freely available to the scientific community. As mission durations vary, some satellites have since been decommissioned, so data are not available from all satellites listed in this figure. A list of web portals where land surface temperature can be accessed is presented in Table 4.2.

4.3.2.2

Published Satellite Temperature Data Products

Satellite temperature data are provided in L1B, L2, and L3 formats. A multitude of land surface temperature (LST) and sea surface temperature (SST) datasets are publically available (e.g., Kilpatrick et al., 2001 (AVHRR); Ghent, 2012 (ATSR); NASA LP DAAC, 2018 (MODIS, ASTER, VIIRS, etc.)). However, to utilize L1B data, conversion from radiance to temperature requires the use of radiative transfer modeling to account for atmospheric effects of the signal acquired by the satellite’s sensor at the top of Earth’s atmosphere. Aside from radiometric calibration, there are three other factors that are critical to proper temperature derivation: (1) emissivity, (2) atmospheric properties at image time, and (3) topography. Currently, there are three categories of algorithms that have been devel and Vidal-Madjar, 1992; Jime nezoped: (1) single-channel (Ottle

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Fig. 4.4 Examples of major Earth observation satellites with thermal sensors onboard (platform—sensor) and the flag(s) indicating nationalities associated with each mission. Modified from Kuenzer, C., Ottinger, M., Wegmann, M., Wikelski, M., 2014. Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks. Int. J. Remote Sens. 35(18), 6599–6647. https://doi.org/10.1080/01431161.2014.964349 (web archive link).

Mun˜oz and Sobrino, 2003), (2) split-window (McMillin, 1975; Becker and Li, 1990), and (3) triple-window algorithms (Sun and Pinker, 2003). Single-channel methodology is a simple inversion of the radiative transfer equation (Planck function), but emissivity and atmospheric profile information must be known. The triple-window approach combines two thermal and one middle infrared channel to retrieve surface temperature for nighttime observations. The split-window approach retrieves surface temperature based on

Table 4.1 Examples of Major Earth Observation Satellites With Thermal Sensors Onboard Launch Satellite Sensor Date

Decommission Date

Temporal Orbit Resolution (km) Spatial Resolution (km) (days) Thermal Band(s) (mm)

Landsat 5 TM Landsat 7 ETM + Landsat 8 TIRS

01/Mar/1984 15/Apr/1999 11/Feb/2013

05/Apr/2013 – –

750 705 705

0.12 0.06 0.10

16 16 16

Terra

MODIS

18/Oct/1999

–

705

1.0

Twice daily

Aqua

MODIS

04/May/2002 –

705

1.0

Twice daily

Terra

ASTER

18/Oct/1999

–

705

0.90

Twice daily

ERS-1 ERS-2 Envisat Sentinel-3 TIROS-N NOAA-6 NOAA-7 NOAA-8 NOAA-9 NOAA-10 NOAA-11 NOAA-12 NOAA-14

ATSR-1 ATSR-2 AATSR SLSTR AVHRR/1 AVHRR/1 AVHRR/2 AVHRR/1 AVHRR/2 AVHRR/1 AVHRR/2 AVHRR/2 AVHRR/2

17/Jul/1991 21/Apr/1995 01/Mar/2002 16/Feb/2016 13/Oct/1978 27/Jun/1979 23/Jun/1981 28/Mar/1983 12/Dec/1984 17/Sep/1986 24/Sep/1988 14/May/1991 30/Dec/1994

10/Mar/2000 07/Apr/2011

785 785 790 814 833 833 843 833 848 833 848 833 853

1.0 1.0 1.0 1.0 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

3 3 3 2 Twice Twice Twice Twice Twice Twice Twice Twice Twice

– 03/Jan/1980 04/Mar/1983 01/Feb/1985 14/Oct/1985 07/Nov/1988 16/Sep/1991 31/Dec/1994 20/Dec/1998 15/Oct/2002

(HRPT), (HRPT), (HRPT), (HRPT), (HRPT), (HRPT), (HRPT), (HRPT), (HRPT),

1.1  4.0 1.1  4.0 1.1  4.0 1.1  4.0 1.1  4.0 1.1  4.0 1.1  4.0 1.1  4.0 1.1  4.0

(GAC) (GAC) (GAC) (GAC) (GAC) (GAC) (GAC) (GAC) (GAC)

daily daily daily daily daily daily daily daily daily

B6 (10.40–12.50) B6 (10.40–12.50) B10 (10.60–11.19) B11 (11.50–12.51) B29 (8.40–8.70) B31 (10.78–11.28) B32 (11.77–12.27) B29 (8.40–8.70) B31 (10.78–11.28) B32 (11.77–12.27) B10 (8.13–8.48) B11 (8.48–8.83) B12 (8.93–9.28) B13 (10.25–10.95) B14 (10.95–11.65) B2 (3.70) B3 (10.80) B4 (12.00) B5 (3.70) B6 (10.80) B7 (12.00) B5 (3.70) B6 (10.80) B7 (12.00) S7 (3.74) S8 (10.85) S9 (12.00) B4 (10.30–11.30) B4 (10.30–11.30) B4 (10.30–11.30) B5 (11.50–12.50) B4 (10.30–11.30) B4 (10.30–11.30) B5 (11.50–12.50) B4 (10.30–11.30) B4 (10.30–11.30) B5 (11.50–12.50) B4 (10.30–11.30) B5 (11.50–12.50) B4 (10.30–11.30) B5 (11.50–12.50) Continued

Table 4.1 Examples of Major Earth Observation Satellites With Thermal Sensors Onboard—Cont’d Launch Satellite Sensor Date

Decommission Date

Temporal Orbit Resolution (km) Spatial Resolution (km) (days) Thermal Band(s) (mm)

NOAA-15 AVHRR/3 13/May/1998 –

833

1.1 (HRPT), 1.1  4.0 (GAC)

Twice daily

NOAA-16 AVHRR/3 21/Sep/2000

–

833

1.1 (HRPT), 1.1  4.0 (GAC)

Twice daily

NOAA-17 AVHRR/3 24/Jun/2002

–

812

1.1 (HRPT), 1.1  4.0 (GAC)

Twice daily

NOAA-18 AVHRR/3 20/May/2005 –

854

1.1 (HRPT), 1.1  4.0 (GAC)

Twice daily

NOAA-19 AVHRR/3 06/Feb/2009

–

856

1.1 (HRPT), 1.1  4.0 (GAC)

Twice daily

MetOp-A

AVHRR/3 19/Oct/2006

–

837

1.1

Twice daily

MetOp-B

AVHRR/3 17/Sep/2012

–

837

11

Twice daily

Suomi NPP

VIIRS

–

830

0.75

1

28/Oct/2011

B3B (3.55–9.93) B4 (10.30–11.30) B5 (11.50–12.50) B3B (3.55–9.93) B4 (10.30–11.30) B5 (11.50–12.50) B3B (3.55–9.93) B4 (10.30–11.30) B5 (11.50–12.50) B3B (3.55–9.93) B4 (10.30–11.30) B5 (11.50–12.50) B3B (3.55–9.93) B4 (10.30–11.30) B5 (11.50–12.50) B3B (3.55–9.93) B4 (10.30–11.30) B5 (11.50–12.50) B3B (3.55–9.93) B4 (10.30–11.30) B5 (11.50–12.50) M14 (8.40–8.70) M15 (10.26–11.26) M16 (11.54–12.49)

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Table 4.2 Examples of Web Portals Where Satellite Temperature Data can be Accessed Website

URL

NASA LP DAAC NASA Earth Observations NASA Land, Atmosphere Near Real-time Data and Imagery USGS EarthExplorer USGS Global Visualization Viewer NOAA Comprehensive Large Array-Data Stewardship System ESA Copernicus Open Access Hub ESA Earth Observation Link ESA GlobTemperature ATSR Exploitation Board EUMETSAT INPE Image Catalogue World Data Center for Remote Sensing of the Atmosphere Japan Aerospace Exploration Agency, JASMES China Meteorological Data Center/Fengyun Satellite Data Center

https://lpdaac.usgs.gov/ https://neo.sci.gsfc.nasa.gov/ https://earthdata.nasa.gov/earth-observation-data/nearreal-time https://earthexplorer.usgs.gov/ https://glovis.usgs.gov/ https://www.class.ngdc.noaa.gov/saa/products/catSearch https://scihub.copernicus.eu/ https://earth.esa.int/web/guest/home http://data.globtemperature.info/ https://atsrsensors.org/ATSRData.htm https://www.eumetsat.int/website/home/Data/index.html http://www.dgi.inpe.br/CDSR/ https://wdc.dlr.de/ http://www.eorc.jaxa.ip/JASMES/index_map.html https://data.cma.cn/en

the differential water vapor absorption in two adjacent infrared channels (typically 11 and 12 μm). Since it was first proposed by McMillin (1975) a variety of split-window algorithms have been developed (Vidal, 1991; Becker and Li, 1995; Wan and Dozier, 1996; Coll and Caselles, 1997; Sobrino et al., 1993, 1994; Sobrino and Romaguera, 2004; among others). One split-window algorithm that has been specifically designed to retrieve LSWT has been developed by Hulley et al. (2011) and termed the Inland Waterbody Surface Temperature (IWbST) algorithm. This algorithm provides optimized split-window coefficients that are individually derived for each lake and each satellite sensor. Another similar approach has been developed by Pareeth et al. (2016). Various L1B products can be acquired from NASA’s Level-1 and Atmosphere Archive &

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Distribution System (LAADS) Distributed Active Archive Center (DAAC) (https://ladsweb.modaps.eosdis.nasa.gov/), and Sentinel3A SLSTR L1B data can be accessed via the EUMETSAT website (https://www.eumetsat.int/). Various L2 temperature products such as daily MODIS temperatures include MOD11 and MYD11, MOD21 and MYD21 for terra and aqua satellites, respectively. Also, derived temperatures from the VIIRS instrument onboard the Suomi NPP satellite are provided as VLST (VIIRS Land Surface Temperature). Various L2 and L3 products can be acquired from NASA’s Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC) (https://ladsweb.modaps.eosdis.nasa. gov/). When referring to temperature retrievals, Level 3 data commonly represent temporally averaged (e.g., 8-day) composites of temperature. Example datasets are the MODIS 8-day, 16-day, and 32-day temperature composites, which are also available via the LAADS DAAC. Some comprehensive LSWT data are available for a variety of the world’s largest lakes that represent a combination of TIR observations from multiple satellite platforms. One example of a global dataset of processed summertime average (July-September: JAS in the northern hemisphere, and January-March: JFM in the southern hemisphere) satellite temperature retrievals (AVHRR/1, AVHRR/2, AVHRR/3, ATSR-1, ATSR-2, AATSR, MODIS terra, MODIS aqua) that is freely available covering 291 lakes over the years 1985–2009 is provided by the Global Lake Temperature Collaboration (GLTC—http://www.laketemperature.org/index.html) (Sharma et al., 2015) (Fig. 4.5). Another example (ATSR-1, ATSR-2,

Fig. 4.5 Lakes included in the Global Lake Temperature Collaboration (GLTC) dataset.

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AATSR) that covers 1628 lakes for the years 1992–2011 is provided by MacCallum and Merchant (2012) called ARC-Lake (http://www. laketemp.net/home/index.php) (Fig. 4.6). Lastly, NASA’s Jet Propulsion Laboratory (NASA JPL) provides data for 169 of the world’s largest lakes, and 268 of North/Central America’s largest lakes (https://largelakes.jpl.nasa.gov/) (Fig. 4.7).

Fig. 4.6 Lakes included in the ARC-Lake dataset.

Fig. 4.7 Lakes included in the NASA Jet Propulsion Laboratory’s large lakes dataset.

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4.4

Use of Lake Surface Water Temperature for Trend Studies

One common scientific use of satellite-derived lake surface temperatures is trend analysis over long time periods. Given that the record of thermal infrared satellite imagery started already in the late 1970s and thus now spans multiple decades, long time series of lake surface temperatures can be compiled and subsequently analyzed for trends. Such datasets provide an independent proxy for assessing the impact of global and regional climate change and provide valuable information for assessing the potential impacts of climate change on the lake ecosystem even in regions where no regular in situ observations are made. As any long-term (20 + years) record of thermal infrared data over the decades is generally compiled from multiple instruments with widely differing characteristics, it is important to (1) use appropriate custom retrieval techniques (Hulley et al., 2011; MacCallum and Merchant, 2012; Riffler et al., 2015), (2) properly homogenize the individual datasets (Pareeth et al., 2016), and (3) to meticulously validate both individual retrievals (Hook et al., 2007; Schneider et al., 2009) as well as derived trends (Schneider and Hook, 2010) against suitable in situ datasets to ensure that the trend analysis reveals true geophysical changes. A first trend analysis of lake surface temperature was for example carried out for a small set of lakes in California and Nevada (Schneider et al., 2009), finding rapid increases in temperature consistent with in situ observations and actually exceeding the rate of surface air temperature increase in some cases. An extension to a global trend analysis of lake surface temperatures was subsequently carried for the majority of large lakes worldwide (Schneider and Hook, 2010, 2011) and found that the summer nighttime surface temperatures of the vast majority of large lakes worldwide has been warming for the period 1985–2009 with an average rate of 0.45°C/decade (Fig. 4.8). Warming rates as high as 1°C/decade were observed for some lakes in Northern Europe. Using the first worldwide synthesis of satellite-derived data and in situ observations of lake temperature, O’Reilly et al. (2015) subsequently studied spatial patterns in lake temperature trends worldwide and found that surface warming rates are a function of climate as well as local characteristics (Fig. 4.9). In addition to the mentioned global-scale trend studies, many authors have investigated regional trends in satellite-derived lake surface water temperatures, e.g., for Northern Italy and the Tibetan Plateau (Pareeth et al., 2017; Zhang et al., 2014).

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Fig. 4.8 (A) Worldwide trends in nighttime lake surface temperature derived from satellite data. JAS trends were computed for all sites located north of 23.5°N and between 0° and 23.5°S, while JFM trends were computed for all sites located south of 23.5°S and between 0° and 23.5°N. (B) Corresponding map of worldwide JAS trends in surface air temperature from GISTEMP.

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(A)

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Fig. 4.9 Classification of lake surface water temperature trends based on key variables. Regression tree (A) and global map showing the class of each studied lake (B). From O’Reilly, C.M., Sharma, S., Gray, D.K., Hampton, S.E., Read, J.S., Rowley, R.J., Schneider, P., Lenters, J.D., McIntyre, P.B., Kraemer, B.M., Weyhenmeyer, G.A., Straile, D., Dong, B., Adrian, R., Allan, M.G., Anneville, O., Arvola, L., Austin, J., Bailey, J.L., Baron, J.S., Brookes, J.D., de Eyto, E., Dokulil, M.T., Hamilton, D.P., Havens, K., Hetherington, A.L., Higgins, S.N., Hook, S., Izmest’eva, L.R., Joehnk, K.D., Kangur, K., Kasprzak, P., Kumagai, M., Kuusisto, E., Leshkevich, G., Livingstone, D.M., MacIntyre, S., May, L., Melack, J.M., Mueller-Navarra, D.C., Naumenko, M., Noges, P., Noges, T., North, R.P., Plisnier, P.D., Rigosi, A., Rimmer, A., Rogora, M., Rudstam, L.G., Rusak, J.A., Salmaso, N., Samal, N.R., Schindler, D.E., Schladow, S.G., Schmid, M., Schmidt, S.R., Silow, E., Soylu, M.E., Teubner, K., Verburg, P., Voutilainen, A., Watkinson, A., Williamson, C.E., Zhang, G.Q., 2015. Rapid and highly variable warming of lake surface waters around the globe. Geophys. Res. Lett. 42(24), 10773–10781. https://doi.org/10.1002/2015GL066235.

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Further Reading Kuenzer, C., Ottinger, M., Wegmann, M., Wikelski, M., 2014. Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks. Int. J. Remote Sens. 35 (18), 6599–6647. https://doi.org/10.1080/01431161. 2014.964349. Metz, M., Rocchini, D., Neteler, M., 2014. Surface temperatures at the continental scale: tracking changes with remote sensing at unprecedented detail. Remote Sens. 6, 3822–3840. https://doi.org/10.3390/rs6053822.