Optimized Split-Window Coefficients for Deriving Surface Temperatures From

Remote Sensing of Environment 115 (2011) 3758–3769

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Remote Sensing of Environment

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Optimized split-window coefﬁcients for deriving surface temperatures from inland water bodies☆

Glynn C. Hulley a,⁎, Simon J. Hook a, Philipp Schneider b a Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA b Norwegian Institute for Air Research, Oslo, Norway article info abstract

Article history: Large inland water bodies constituting lakes, reservoirs and inland-seas are excellent proxy indicators for climate Received 7 March 2011 change. Using thermal infrared satellite data, a recent study found that a global set of inland water bodies showed Received in revised form 9 September 2011 significant warming in seasonal nighttime Lake Surface Water Temperatures (LSWTs) between 1985 and 2009. Accepted 20 September 2011 Split-window land surface temperature (LST) retrievals are typically tuned for a broad range of land surface Available online 18 October 2011 emissivities and global atmospheric conditions, and are not optimized for inland water body surfaces, whereas split-window sea-surface temperatures (SSTs) are only tuned for a single emissivity (water), but over ocean at- Keywords: Surface temperature mospheres. Over inland water bodies, these two approaches can lead to region dependent errors in LSWTs, spu- Lakes rious trends, and inconsistencies between sensors in the long-term temperature record of inland water bodies. Split-window coefficients To address this issue, the primary goal of this paper was to develop a methodology for deriving a set of optimized Warming split-window coefficients, individually tuned for the regional atmospheric conditions of 169 globally distributed, MODIS saline and freshwater inland water bodies from multiple satellite sensors including the Moderate Resolu- ATSR tion Imaging Spectroradiometer (MODIS) on Terra and Aqua; Along Track Scanning Radiometer (ATSR) in- AVHRR cluding ATSR-1, ATSR-2, AATSR; and Advanced Very High Resolution Radiometer (AVHRR-3). The new Inland Water-body Surface Temperature (IWbST) v1.0 algorithm was applied to Terra MODIS and Advanced Along Track Scanning Radiometer (AATSR) data and validated with in situ water temperature data from sites with widely contrasting atmospheric conditions: Lake Tahoe in California/Nevada, a high-elevation cool and dry site, and the Salton Sea in California, a low-elevation warm and humid site. Analysis showed improved accuracy in LSWTs in terms of bias and RMSE when compared to the standard MODIS LST and AATSR SST products. For example, the IWbST RMSE at Salton Sea was reduced by 0.4 K when compared to the operational MODIS product. For the AATSR data, the IWbST RMSE was reduced by 0.36 K at Tahoe and 0.29 K at Salton Sea when compared to results obtained using the operational AATSR split-window coefficients. The IWbST improvements are significant in relation to the current accuracy of water temperature retrievals from space (b0.5 K), and will enable the derivation of long-term, accurate LSWTs consistently across multiple sensors for climate studies. © 2011 Published by Elsevier Inc.

1. Introduction adverse effects. For example global warming could lead to longer summer stratiﬁcation, a phenomenon that reduces a lake's natural circula- Recent limnology studies have shown that the temperatures of tion, and in turn could lead to development of harmful algal blooms, large inland water bodies are a good proxy indicator for climate the introduction of non-native species, and oxygen depleted dead change (Adrian et al., 2009; Austin & Colman, 2007; Livingstone, zones (Blenckner et al., 2010; Livingstone, 2003; Sahoo & Schladow, 2003; Schneider et al., 2009). For example, Schneider and Hook 2008). Small increases in Arctic lake surface temperatures over time (2010) found that seasonal nighttime surface temperatures for a set may also be indicative of thawing permafrost in the local region, of 104 globally distributed inland water bodies have been warming which could result in increased outgassing of greenhouse gases such between 1985 and 2009 with an average rate of 0.045 °C/year. Such as CO2 and CH4— commonly known as a positive climate feedback warming has implications for the health of lacustrine ecosystems (Schuur et al., 2009; Smith et al., 2007). (Arnell et al., 1995; Verburg et al., 2003), since water has a high Until recently in situ temperature were used exclusively to mea- heat capacity and small changes in temperature can introduce sure the impact of climate change on lakes (Coats et al., 2006; Quayle et al., 2002; Verburg et al., 2003). While in situ observations of LSWTs usually have high accuracy, their availability is restricted to a few ☆ (c) 2011 California Institute of Technology. Government sponsorship acknowledged. sites, with even fewer sites having a continuous, reliable, long-term ⁎ Corresponding author at: Jet Propulsion Laboratory, 4800 Oak Grove Dr, Pasadena, CA, 91109, USA. Tel.: +1 818 354 2979. record of observations. More recently scientists have investigated E-mail address: [email protected] (G.C. Hulley). the use of satellite thermal infrared (TIR) data to measure lake surface

0034-4257/$ – see front matter © 2011 Published by Elsevier Inc. doi:10.1016/j.rse.2011.09.014 G.C. Hulley et al. / Remote Sensing of Environment 115 (2011) 3758–3769 3759 temperatures from space (Crosman & Horel, 2009; Hook et al., 2003, algorithm that is valid for a wide range of atmospheric conditions and 2007; Reinart & Reinhold, 2008). Thermal infrared (TIR) satellite surface types (Minnett, 1986, 1990). Typically the largest errors will data from sensors such as the series of Along Track Scanning Radiometers occur for conditions of extreme temperatures, humidities and/or view (ATSR) on European Space Agency (ESA) platforms, the Moderate angles. Consequently, to retrieve the most accurate surface temperatures Resolution Imaging Spectroradiometer (MODIS) on the National for a set of inland water bodies, an optimized set of split-window coeffi- Aeronautical and Space Administration (NASA) Terra and Aqua plat- cients tuned independently for the atmospheric conditions and surface forms, and the Advanced Very High Resolution Radiometer (AVHRR) characteristics of each water body is needed. Furthermore, the method on the US National Oceanic and Atmospheric Administration (NOAA) should be valid across multiple sensor platforms to maximize its utility. platforms, supplemented with in situ data, have been used to moni- In this study we have derived a split-window algorithm to meet tor the temperature of inland water bodies in California/Nevada these requirements referred to as the Inland Water-body Surface (Hook et al., 2007; Schneider et al., 2009), Sweden (Reinart & Reinhold, Temperature (IWbST) v1.0 algorithm. The algorithm is optimized 2008), the Great Salt Lake, Utah (Crosman & Horel, 2009), European for surface elevation, local atmospheric and surface temperature con- Alpine lakes (Oesch et al., 2005), Africa (Wooster et al., 2001), and ditions (day and night) for a set of 169 freshwater and saline (6) in- the North American Great Lakes (Schwab et al., 1999). land water bodies utilized by Schneider and Hook (2010) in their Three techniques currently exist for retrieving the surface tempera- study of global warming in inland water bodies. Note Schneider and ture from multi-band TIR remote sensing measurements; 1) physics- Hook (2010) evaluated data from 169 lakes but only analyzed data based algorithms, 2) single channel algorithms and 3) split-window al- from 104 lakes based on completeness of the satellite record from gorithms. Physics-based algorithms such as the ASTER Temperature ATSR and AVHRR. The IWbST algorithm results are validated with in Emissivity Separation algorithm (TES) (Gillespie et al., 1998), and situ data collected at Lake Tahoe; a large freshwater lake in California/ MODIS day/night algorithm (Wan & Li, 1997), are designed to retrieve Nevada, and the Salton Sea; a large saline lake in southeastern California. both the surface emissivity and temperature dynamically in clear-sky Both of these sites are part of an automated calibration and validation fa- conditions. The single-channel TIR method requires surface emissivity cility where bulk and radiometric temperature measurements are pro- and an accurate radiative transfer model with atmospheric profiles vided from the two sites every two minutes (Hook et al., 2007). The (Jimenez-Munoz & Sobrino, 2010). In terms of processing and data re- two lakes are situated in regions that encompass a wide range of atmo- quirements the simplest and most efficient approach is the split- spheric conditions, elevation, and surface variations. The full set of day window algorithm (Brown & Minnett, 1999; Coll & Caselles, 1997; and nighttime coefficients for each of the 169 lakes and for the following Kilpatrick et al., 2001; Prata, 1994; Price, 1984; Wan & Dozier, sensors: MODIS (Aqua/Terra), ATSR (ATSR 1, 2 and AATSR) and AVHRR- 1996; Yu et al., 2008). The split-window approach typically uses a ra- 3, are available from the author on request. diative transfer simulation model with atmospheric profiles (e.g. temperature, humidity) and surface emissivity to simulate at-sensor 2. Methodology brightness temperatures in two nearby longwave TIR bands for a wide range of different conditions. The simulated data is then A lake simulation model based on radiative transfer calculations regressed to either model-derived or in situ surface temperatures was developed using one year of NCEP data (2004) to account for at- to derive the split-window coefficients in a parameterized equation. mospheric variability at each specific lake's geographical location Atmospheric effects are compensated for by using the differential ab- shown in Fig. 1. The year 2004 was chosen since the maximum num- sorption characteristics of the atmospheric water vapor continuum in ber of datasets was available for this year. Using an alternative year the two thermal bands. A variation on this approach that is employed did not yield any noticeable change in the results. Unlike most split- by AVHRR Pathfinder data for sea surfaces uses a regression between window based simulation models which take into account global at- in situ SST measurements and ‘nearly-coincident’ satellite observations, mospheric conditions and many different land surface emissivity termed matchups (Kilpatrick et al., 2001; Kumar et al., 2000). This pro- types, our lake simulation model is tuned for each individual lake duces an algorithm tuned to bulk SST measurements. The bulk tempera- and only dependent on local atmospheric conditions and surface ele- ture of a water body represents the temperature of the upper few meters vation. The emissivity of water, which is high and spectrally flat, can below the surface. There is a noticeable difference between the bulk and be computed with high accuracy using the view-angle dependent skin temperature of a water body that varies over the diurnal cycle and Masuda emissivity model (Masuda et al., 1988). The remainder of arises due to conductive heat loss from the ocean surface (termed skin this section includes a background on the split-window approach effect). The other physical process responsible for this temperature dif- and algorithm chosen, details of the lake simulation model, and emis- ference is from diurnal heating, especially during the day when the sivity effects. skin layer becomes warmer than the sub-surface layers due to stronger solar heating effects. 2.1. The split-window sea surface temperature (SST) approach The split-window approach applied to land surfaces is widely recog- nized and uses the assumption that a broad range of land cover types The split-window approach is based on the fact that the difference in have stable and fixed emissivities in the 10–12 μm wavelength range, atmospheric absorption of water vapor between two selected longwave where surface emissivities are fixed and assigned using land cover clas- infrared channels is proportional to the measurement difference be- sification maps (Snyder et al., 1998). Land surface split-window algo- tween them expressed as a brightness temperature (Anding & Kauth, rithms are typically stable over densely vegetated areas and water 1970). This difference arises because the longer split-window wave- surfaces (Coll et al., 2009) where the emissivity is well known, but length (typically ~12 μm) has a stronger absorption than the shorter have known problems over semi-arid and arid regions where the emis- split-window wavelength (~11 μm). There exist both linear and non- sivity is highly variable, both spatially and spectrally (Hulley & Hook, linear derivations of split-window algorithms for ocean surfaces. The 2009). The core MODIS, AATSR and AVHRR-3 surface temperature prod- linear algorithm, or commonly termed the multi-channel SST (MCSST) ucts all rely on the split-window approach, and have the ability to re- algorithm is generally based on the following form: trieve surface temperatures for a broad range of different land cover types (including forests, shrubs, croplands, inland water bodies, snow ¼ þ þ − þ − ðÞ− ðÞθ ð Þ Ts ao a1Ti a2 Ti Tj a3 Ti Tj 1 sec 1 and ice, and bare areas) and atmospheric conditions to accuracies of ap- proximately 1 K.

Past studies have demonstrated the difﬁculty of obtaining optimal where Ti and Tj are brightness temperature measurements in bands i split-window performance for a speciﬁc region using a globally tuned and j, θ is the sensor view angle and ai are constants derived typically 3760 G.C. Hulley et al. / Remote Sensing of Environment 115 (2011) 3758–3769

Fig. 1. Locations of the 169 inland water bodies used in this study.

numerically from model or satellite observations. The third term intro- 2.2. Lake radiative transfer simulation model duces a view angle adjustment suggested by Llewellyn-Jones et al. (1984), and scaled by the brightness temperature difference in order 2.2.1. Radiative transfer to minimize errors for cases of large view angles that arise due to the in- A simulation model was developed based on radiative transfer cal- herent non-linearity of the Planck function, surface emissivity view culations using a full year of atmospheric data chosen nominally angle dependencies, and greater uncertainty in modeling the attenua- (2004) at each lake's location. The most recent version of MODTRAN, tion of surface emitted radiance for longer atmospheric path lengths. version 5.2 was used, which includes an improved molecular This approach reduces errors at large scan angles in moist atmospheres band model, termed the Spectrally Enhanced Resolution MODTRAN by more than 1 K (Brown & Minnett, 1999), and validation studies over (SERTRAN), resulting in more accurate modeling of band absorption the North American Great Lakes have shown biases of 0.5 K and RMSEs features in the longwave TIR window regions (Berk et al., 2005). of less than 2 K (Li et al., 2001; Schwab et al., 1999). The location of typical split-window bands fall in very clear regions The non-linear SST (NLSST) version of the split-window algorithm of the atmosphere (see Table 1), where water vapor is the primary was introduced by Walton (1988) and is based on the fact that the a2 absorbing gas. CO2 and O3 absorptions in this region can be consid- and a3 terms in Eq. (1) are inherently nonlinear in terms of both total ered negligible and standard MODTRAN profiles for these gases water vapor content and brightness temperature, with the degree of were used in the simulation model. Aerosol transmission and scatter- nonlinearity increasing with higher temperatures and view angles. ing in these bands is also negligible with typical transmissions of The NLSST algorithm is expressed as: N0.95. Therefore, average aerosol distributions (rural extinction) de- fined in MODTRAN were used. This assumption would be violated if volcanic eruptions, for example, changed the aerosol amount. ¼ þ þ − þ − ðÞ− ðÞθ ðÞ Ts ao a1Ti a2 Ti Tj Tguess a3 Ti Tj 1 sec 2 2.2.2. Atmospheric profiles Atmospheric profiles of temperature, humidity and geopotential

Where Tguess is a first-guess SST value which can be obtained by height were extracted from the Global Data Assimilation System using the MCSST result in Eq. (1) as a first guess, or from an SST clima- tology. This formulation is currently the operational NOAA algorithm for the AVHRR pathfinder SST product (Kilpatrick et al., 2001) and for the standard MODIS SST product (Brown & Minnett, 1999). To further Table 1 fi A summary of AVHRR, MODIS and ATSR product characteristics and thermal infrared improve accuracy, the MODIS product provides two sets of coef - bands used for the split-window approach. cients which distinguish between two water vapor regimes using a brightness temperature difference threshold (greater or less than AVHRR-3 MODIS AATSR 0.7 K). Satellite NOAA-15 Terra/Aqua ENVISAT The IWbST v1.0 algorithm developed in this study is based on the Launch May 1998 Dec 1999/May 2002 Feb 2002 MCSST algorithm given by Eq. (1). Using the NLSST equation for the Equator crossing 4:36 am/pm 10:30 /1:30 am/pm 10:00 am/pm time two lakes in this study (Lake Tahoe, Salton Sea) did not result in Swath width 2700 km 2330 km 512 km any noticeable improved accuracy, a similar result found in European Product 1.5 K 1 K 2.5 K (day), 1 K Alpine lakes (Oesch et al., 2005) and the Great Lakes (Li et al., 2001). accuracy (night) To reduce nonlinear effects at high view angles, the IWbST coeffi- Spatial 1100 m 1000 m 1000 m resolution cients are limited to view angles less than 45°, which was found to Scan angle ±55.4° ±56° Nadir and forward 55° be a good compromise between accuracy and data yield. This restric- Split-window 4 (10.3– 31 (10.78– 7 (10.08–11.71 μm) tion further reduces uncertainties that arise from difficult cloud (and 11.3 μm) 11.28 μm) shadow) conditions, aerosol effects for larger air masses, and shore- thermal bands 5 (11.5- 32 (11.77- 8 (11.04-13.1 μm) μ μ line pixels. 12.5 m) 12.27 m) G.C. Hulley et al. / Remote Sensing of Environment 115 (2011) 3758–3769 3761

(GDAS) product provided by the National Centers for Environmental using random numbers for each specific sensor (0.1 K for Terra Prediction (NCEP) (Kalnay et al., 1990) which is produced at 1° spatial MODIS and ATS, and 0.5 K for AVHRR-3). A full schematic of the sim- resolution. The NCEP model uses current weather conditions ob- ulation model highlighting all components and inputs is shown in served from various sources including radiosondes, surface observa- Fig. 2. tions, and weather satellites as input to dynamic mathematical models of the atmosphere to predict the weather, which is typically 2.2.3. Lake simulation results output in 6-hourly increments (00, 06, 12, 18 UTC). In this study we Fig. 3 shows a scatterplot of simulated MODIS band 31 minus band assume that using a full year of NCEP model data would statistically 32 brightness temperature versus NCEP surface temperature for Lake represent a full range of atmospheric conditions at each site location. Tahoe using NCEP nighttime data and the Masuda pure water emis- Atmospheric profiles were extracted from an NCEP pixel node closest sivity model. The spread in the differences increase above 280 K in space to each lake's location, and an observation time selected to (~15 °C) and become more positive since MODIS band 31 (11 μm) is represent either daytime or nighttime data (e.g. 06 UTC was used less variable than band 32 (12 μm) that is situated in a stronger for night, and 18 UTC for daytime data at Lake Tahoe). water vapor absorption region. This can be seen in Fig. 4 which In the lake simulation model, surface temperature was varied shows the simulated brightness temperature difference between from Tair -10 K to Tair +10 K in 5 K increments, and eight view angles bands 31 and 32 versus view angle. The largest differences can be were chosen between 0° and 60°. Typical surface temperature varia- seen above a view angle of 45° due to longer atmospheric path tions used over land surfaces are larger, (−16 to 16 K) (Wan, lengths and nonlinearity of the radiative transfer process. Real data 1999), while over ocean surface are smaller (−0.5 to 1.5 K) (Brown typically also show large temperature differences between bands 31 & Minnett, 1999). Using different surface-air variations did not and 32 due to thin cirrus cloud and other types of low reflectance change the results significantly, but it was found that overestimating cloud. A cloud threshold test in the JPL MODIS cloud mask optimized rather than underestimating the surface-air difference led to more ac- for inland water bodies (Hulley, 2009) is used to mask these pixels curate results. Similarly using eight instead of five view angles im- using a method described by (Saunders & Kriebel, 1988), however proved the accuracy of the results. At-sensor brightness some uncertainty may exist due to sub-pixel cloud contamination at temperatures were calculated in the 769–1000 cm− 1 (10–13 μm) the MODIS 1 km scale. spectral range at 1 cm− 1 resolution using MODTRAN 5.2 for a total The IWbST regression coefficients using Eq. (1) were computed in of 14,600 conditions (365 profiles×5 surface temperatures×8 view two steps. First, a standard least squares regression was used to re- angles). Each profile was adjusted for the lake's surface elevation, move outliers with a 1.5 σ threshold, and second, a robust regression which were extracted from the Global Lakes and Wetlands Database was used to compute the final coefficients with an iteratively re- (Lehner & Doll, 2004), satellite altitude was set to 705 km at nadir, weighted least squares solution using a bi-square weighting function. and noise-equivalent differential temperature (NEΔT) was applied The daytime and nighttime coefficients along with RMSEs of the

Initialization: Radiative Transfer Module:

Lake location (lat/lon) i = 1:12 Loop months Observation time (day/night) Extract daily NCEP data: Tair, RH, gph, Ts Emissivity (salt/fresh water) Year (e.g. 2004) Ts = Ts-10 K : Ts+10 K Sensor (e.g. MODIS, AATSR) View angle = [0 15 30 40 45 50 55 60º]

MODTRAN5.2

Brightness Temperature i = i+1 Module: N = 14,600 At-sensor Radiances (W/m2/µm/sr) Convert Radiances to 769-1000 cm-1 Brightness Temperature Add noise Convolve to sensor spectral Sensor SRF response function (SRF) Split-window bands

Split-Window Coefficients Module:

Setup least squares using eq. (1) Table 5 Apply robust least squares Coefficients regression Remove 1 residuals

Fig. 2. Schematic diagram summarizing the steps of the lake simulation radiative transfer model. Input NCEP data include air temperature (Tair), relative humidity (RH) and geopotential height (gph) proﬁles, and surface temperature (Ts). 3762 G.C. Hulley et al. / Remote Sensing of Environment 115 (2011) 3758–3769

Table 2 MODIS: Summary of lake surface water temperature (LSWT) validation results with in situ data from Lake Tahoe and Salton Sea for the standard MOD11 product (MOD11_L2), and the Inland Water-body Surface Temperature (IWbST) algorithm developed in this study.

Lake Tahoe Salton Sea (2000–2010) (2008–2010)

Bias [K] RMSE [K] Bias [K] RMSE [K]

Night MOD11 0.064 0.438 0.866 1.138 IWbST −0.041 0.411 0.504 0.814 Day MOD11 −0.212 0.574 0.722 1.001 IWbST −0.326 0.609 0.236 0.616

the Masuda model by including effects from reflected emissions, Fig. 3. Simulated Terra MODIS band 31 minus 32 brightness temperatures versus NCEP fi fl surface temperature for Lake Tahoe using nighttime NCEP profiles and MODTRAN 5.2. while Nalli et al. (2008) developed a simpli ed quasi-specular re ec- tance model taking into account reflected downwelling atmospheric radiance. Masuda (2006) recently updated their model by taking into account the enhanced reflected emission from the sea surface predicted fit for Lake Tahoe and Salton Sea are reported in Tables 4 in a more physically consistent way than from previous studies. Cur- and 5. RMSEs for the Salton Sea data were between 0.1 and 0.2 K for rently the Masuda (2006) and Wu and Smith (1997) are the most all sensors, while Lake Tahoe RMSEs were typically less than 0.1 K. popular models used within the broad scientific community. Emissiv- ity estimates from these models are used in radiative transfer calcula- 2.2.4. Emissivity modeling tions for NASA's Atmospheric Infrared Sounder (AIRS), NCEP GDAS One of the largest sources of uncertainty for land surface split- products, NOAA atmospheric products, and the Joint Center for Satel- window algorithms is the knowledge of band emissivities in the lite Data Assimilation (JCSDA). two split-window bands. However, for the purposes of this study In this study, we adopted the Masuda (2006) model that uses the re- only the emissivity of water is required, which is typically high and fractive index of water from Hale and Querry (1973), extinction coeffi- spectrally flat in the two split-window bands. As a result, variations cient from Segelstein (1981) and salinity corrections from Friedman in surface emissivity should not constitute a large error source in (1969). Normal ocean deviations from the Friedman salinity correction the simulation results except for drier atmospheres and higher view have been found to be negligible (Nalli et al., 2001; Newman et al., angles where emissivity effects may introduce larger uncertainties. 2005). The wind correction reported in the models is not taken into ac- Given the importance of oceans in climate studies over the past count for the IWbST simulation since we found the effects to be negligi- few decades and combined with their large spatial extent, there has ble, a similar result found by Brown and Minnett (1999).Theemissivity been extensive work in the past on modeling the TIR emissivity of view angle dependency, however, is treated explicitly in the simulation ocean surfaces affected by wind and view angle (Masuda, 2006; model. Fig. 5(a) shows water emissivity spectra in the split-window Masuda et al., 1988; Nalli et al., 2001; Wu & Smith, 1997; Yoshimori wavelength region from 10 to 13 μm for the Masuda model (pure and et al., 1995). The first set of model calculations were published by ocean), and from the ASTER spectral library (ASTlib) (Baldridge et al., Masuda et al. (1988) with the emissivity dependent on a given set 2009). The ASTlib emissivity values are at most 0.3% lower than the of optical constants, emission angle, and wind speed. Further work Masuda model in this wavelength range, and are view angle indepen- by Watts et al. (1996) and by Wu and Smith (1997) improved on dent which introduces error at higher view angles. The Masuda ocean emissivities are shifted to longer wavelengths by ~0.1% when compared to the Masuda pure water emissivity results. For completeness, the Masuda ocean model is applied to 6 saline lakes out of a total of 169 used in this study. Fig. 5(b) shows emissivity variations for four viewing angles from 0° to 60° using the Masuda pure water model. The

Table 3 AATSR: Summary of lake surface water temperature (LSWT) validation results with in situ data from Lake Tahoe and Salton Sea for the operational AATSR SST product, and the Inland Water-body Surface Temperature (IWbST) algorithm developed in this study.

Lake Tahoe Salton Sea (2002–2010) (2008–2010)

Bias [K] RMSE [K] Bias [K] RMSE [K]

Night AATSR −0.411 0.561 - - IWbST −0.015 0.299 - - Day AATSR −0.776 0.883 −0.412 0.753 Fig. 4. Simulated Terra MODIS band 31 and 32 brightness temperature differences versus IWbST −0.337 0.526 0.175 0.463 satellite view angle for Lake Tahoe using nighttime NCEP proﬁles and MODTRAN 5.2. G.C. Hulley et al. / Remote Sensing of Environment 115 (2011) 3758–3769 3763

Table 4 emissivities. A physics-based day/night algorithm produces the Lake Tahoe daytime and nighttime IWbST split window coefficients for the MODIS, MOD11B1 and MYD11B1 products (Daily L-3 ISIN Grid at 5 km) (Wan AVHRR and ATSR sensors. &Li,1997), which consists of surface temperature and emissivity for Lake Tahoe seven MODIS bands in the midwave infrared (MWIR; bands 20, 22, 23) and TIR (bands 29, 31–33) at 5 km spatial resolution. Cloud screen- ao a1 a2 a3 RMSE [K] ing was accomplished by using a modified MODIS cloud mask algorithm Daytime MODIS-Terra −1.4454 1.0010 2.6353 −0.8883 0.086 developed at JPL and optimized for inland water bodies (Hulley, 2009). MODIS-Aqua −1.4451 1.0060 2.5843 −0.8400 0.087 Undetected thin cirrus and low reflectance cloud, including possible jet AVHRR-3 −1.5159 1.0071 1.7187 −1.2361 0.078 contrails and valley fog at Tahoe, were removed using a spatial homoge- ATSR-1 −0.2555 1.0021 1.8755 −1.0605 0.076 neity test in which surface temperature standard deviations of greater ATSR-2 1.2082 0.9962 1.4854 −0.5973 0.081 than 0.2 K on a 3×3 pixel array were removed. AATSR 0.7770 0.9979 1.3952 −0.6209 0.079 In this study we used ten years of Terra MODIS data (2000–2010) Nighttime from twice-daily MODIS scenes covering the Lake Tahoe and the MODIS-Terra −1.9495 1.0080 2.5350 −0.9760 0.087 Salton Sea validation sites to evaluate the IWbST algorithm coeffi- − − MODIS-Aqua 1.9705 1.0080 2.4804 0.9320 0.088 cients. LSWT comparisons are made between the IWbST algorithm AVHRR-3 −1.6784 1.0077 1.6928 −1.2910 0.079 ATSR-1 −0.4288 1.0027 1.8444 −1.1033 0.075 and the standard MOD11_L2 product in terms of biases and RMSEs ATSR-2 0.9898 0.9970 1.4544 −0.6238 0.078 with the in situ data skin temperatures. AATSR 0.5872 0.9986 1.3690 −0.6454 0.077 3.2. AATSR data

The AATSR, launched in 2002, is flown onboard the ESA environmental satellite (ENVISAT) (Llewellyn-Jones et al., 2001), and derives emissivity varies by less than 1% up to 45° in the split-window wave- its heritage from the ATSR (1991–1996) and ATSR-2 (1996–2003) length region, but the differences at 60° range are much higher and sensors. The AATSR instrument characteristics are summarized in range from 3 to 6%, and increase with increasing wavelength. Table 1.The ATSR-series of instruments have a special conical scanning mechanism that gives two views of the Earth's surface at a for- 3. Data ward view of 55°, and a nadir view typically between 0° and 21.7°. Spatial resolutions are typically 1×1 km and 1.5×2 km for the nadir 3.1. MODIS data and forward views respectively, with a repeat cycle of three days. The primary goal of the ATSR-series of instruments is the retrieval MODIS is a multi-spectral imager onboard NASA's Terra and Aqua of accurate sea surface temperatures (SSTs). The operational ATSR fi satellites launched in 1999 and 2002 respectively, and has been the flag- Standard SST coef cients are derived from radiative transfer simula- ship instrument for NASA's Earth Observing System (EOS). The MODIS tions (Zavody et al., 1995) and include dependencies on sensor instrument characteristics are summarized in Table 1.MODISscans type, sensor temperature, scan angle, latitude, and aerosol content ±55° from nadir and provides daytime and nighttime imaging of any (Merchant et al., 1999). Since the operational cloud mask for ATSR fi point on the surface of the Earth every 1–2 days with a spatial resolu- is optimized for oceans, a modi ed cloud test was needed for inland tion of ~1 km at nadir and 5 km at higher viewing angles at the scan water bodies in this study. A spatial homogeneity test was employed, fi edge (Wolfe et al., 1998). MODIS standard surface temperature prod- and a cloud identi ed if a 3×3 pixel window had a standard deviation fi ucts, termed MOD11 from Terra, and MYD11 from Aqua, are generated larger than 0.2 °C (Schneider et al., 2009). This test was veri ed with by two different algorithms; a generalized split -window (GSW) algo- a visual inspection for all AATSR images between April 2002 and Sep- rithm which produces the MOD11_L2 and MYD11_L2 products (Daily tember 2003. L-2 swath at 1 km), and the MOD11A1 and MYD11A1 products (Daily For the analysis all AATSR matchups between 2002 and 2009 at L-3 ISIN Grid at 1 km) (Wan & Dozier, 1996). This algorithm generates Lake Tahoe and from 2007 to 2010 at Salton Sea were used to make surface temperature and two TIR longwave land classification comparisons between the IWbST algorithm and the SST's derived using the operational AATSR coefficients, and in situ data matchups.

3.3. In situ measurements

Table 5 Lake Tahoe is large clear freshwater lake situated on the California/ Salton Sea daytime and nighttime IWbST split window coefficients for the MODIS, AVHRR and ATSR sensors. Nevada border at 1,996 m elevation making it the largest Alpine lake in North America, and USA's second deepest. The Lake Tahoe automated Salton Sea calibration/validation site, was established in 1999 with four buoys, re- ao a1 a2 a3 RMSE [K] ferred to as TB1, TB2, TB3 and TB4, which provide simultaneous mea- Daytime surements of skin and bulk temperatures in addition to meteorological MODIS-Terra −1.2807 1.0062 2.7367 −1.4419 0.153 data (air temperature, relative humidity, wind speed and direction) MODIS -Aqua −1.1168 1.0055 2.6906 −1.3542 0.153 every 2 min (Hook et al., 2007). Each buoy includes a custom-built radi- AVHRR-3 −2.7735 1.0121 1.7542 −1.5112 0.180 ometer developed by JPL that has accuracies below the 0.1 K level. Cali- ATSR-1 −0.2193 1.0024 1.9513 −1.2417 0.171 ATSR-2 2.8605 0.9906 1.6047 −0.6378 0.179 bration results have shown good agreement with other well-calibrated AATSR 1.5423 0.9955 1.4804 −0.6956 0.177 radiometers to within ±0.05 K (Barton et al., 2004). The radiometric measurements are converted to skin temperatures by accounting for Nighttime the effects of emissivity and reflected downwelling sky radiation. For MODIS-Terra −1.4760 1.0068 2.7386 −1.5070 0.127 MODIS-Aqua −1.3389 1.0062 2.6896 −1.4273 0.127 emissivity, an emissivity spectrum of water from the ASTER spectral li- AVHRR-3 −3.1397 1.0132 1.7478 −1.5731 0.164 brary is used (http://speclib.jpl.nasa.gov)(Baldridge et al., 2009), and ATSR-1 −0.7626 1.0043 1.9121 −1.3358 0.152 the reflected downwelling irradiance is computed using radiative trans- ATSR-2 1.6818 0.9949 1.5111 −0.7228 0.150 fer simulations with atmospheric profiles input from NCEP data (Hook et AATSR 0.6718 0.9986 1.4197 −0.7641 0.152 al., 2003). 3764 G.C. Hulley et al. / Remote Sensing of Environment 115 (2011) 3758–3769

Fig. 5. Plots of water emissivity spectra showing (a) the Masuda model (pure and ocean) and ASTER spectral library, and (b) the emissivity view angle dependence of the Masuda model.

The Salton Sea validation site is situated on a platform located in In situ measurements at these two lakes provide the most com- the southwest corner of the lake and was established more recently prehensive and largest data record of water skin temperatures avail- at the end of 2007. In contrast to Lake Tahoe, the Salton Sea is a able. The high quality and frequency of the measurements over long large saline lake situated in southeastern California at an elevation time periods and for a wide range of surface temperatures (~4 to of 75 m below sea-level. 35 °C) and atmospheric conditions make this an excellent in situ

Fig. 6. Lake Tahoe: Scatterplots of MODIS (top) and AATSR (bottom) standard and IWbST nighttime surface temperature product versus in situ surface temperature; and MODIS/ AATSR minus in situ surface temperature versus in situ surface temperature. G.C. Hulley et al. / Remote Sensing of Environment 115 (2011) 3758–3769 3765

Fig. 7. Salton Sea: Scatterplots of MODIS nighttime (top) and AATSR daytime (bottom) standard and IWbST nighttime surface temperature product versus in situ surface temperature; and MODIS/AATSR minus in situ surface temperature versus in situ surface temperature.

dataset for validation and calibration of multiple sensors with differ- and at-sensor brightness temperatures were filtered for clouds ent overpass times (Hook et al., 2003, 2004, 2007; Tonooka et al., using the techniques described earlier. Any remaining outliers due 2005). to undetected cloud or bad quality data were removed using the inter-quartile range (IQR). The IQR represents the distance between 4. Results and discussion the 25th and 75th percentiles in a dataset (middle 50%), and observations were removed that fell beyond a 2*IQR threshold. All cloud-free The IWbST lake coefficients were used to retrieve LSWTs for a set of day and nighttime Terra MODIS and AATSR overpasses over Lake Tahoe for the periods 2000–2010 and 2002–2010 respectively, and over the Salton Sea for the period 2007–2010. Due to the daily repeat cycle of MODIS, a large number of matchups were obtained with the in situ data which are collected automatically every 2 min. For the AATSR dataset, even though all available years were used (2002– 2010) for the matchups, a longer repeat cycle (3 days at equator) resulted in far fewer matchups. Validation data over Salton Sea were only available from the end of 2007 and onwards for both datasets. The LSWT's computed from the IWbST algorithm were compared to the standard MODIS GSW product (MOD11_L2), the AATSR SSTs, and validated with in situ data skin temperatures acquired from both sites. For the validation matchups, average MODIS and AATSR at-sensor brightness temperatures and surface temperatures for 3×3, ~1 km2 pixels were extracted directly over TB1 at Tahoe (39.153° N, 120° W) and the validation platform at Salton Sea (33.225° N, 115.824° W) for all available scenes during the validation periods chosen. Comparisons with the standard MODIS and AATSR products were used as a quality metric to assess the performance of Fig. 8. Tahoe: MODIS minus in situ surface temperatures using the IWbST and MOD11 the IWbST algorithm against current operational sensor accuracies. algorithms versus satellite view angle for nighttime data. Solid lines represent a two The resulting time series of MODIS and AATSR surface temperatures degree polynomial fit to the data. 3766 G.C. Hulley et al. / Remote Sensing of Environment 115 (2011) 3758–3769

RMSEs were reduced by ~0.35 K. This result was to be expected since the AATSR SST coefficients are tuned for oceans, while IWbST is tuned for conditions at Lake Tahoe. At the Salton Sea, surface and atmospheric conditions are more extreme and variable which poses more of a challenge in the retrieval for several reasons: the site is situated below sea-level (75 m) resulting in longer atmospheric path lengths from space; water salinity is higher than ocean water which increases emissivity uncertainty; and the North American monsoon brings in large amounts of lower and upper level moisture from the Gulf of Mexico during the summertime combined with maximum air temperatures ranging from 40 to 45 °C. For this site, the MOD11 results underestimated LSWTs with respect to in situ data with positive biases (RMSE in parentheses) of 0.72 (1.0) K during the daytime, and 0.87 (1.14) K during the night (Table 2). This is most likely due to the MODIS coefficients not accounting for large variability in water vapor distribution in this area. The optimized IWbST on the other hand performed better with biases and RMSEs reduced down to 0.24 (0.6) K and 0.5 (0.81) K with respect to in situ data during day Fig. 9. Salton Sea: MODIS minus in situ surface temperatures using the IWbST and and night respectively. The IWbST AATSR results also showed improve- MOD11 algorithms versus satellite view angle for nighttime data. Solid lines represent ments with regard to the SSTs from the operational coefficients for day- fi a two degree polynomial t to the data. time data, with biases and RMSEs reduced by 0.24 K and 0.29 K respectively (Table 3). There were insufficient AATSR cloud-free processed data were then matched with the in-situ data for observa- matchups for nighttime data over the Salton Sea. This was most likely tion time differences of less than 5 min. due to lack of in situ data during the AATSR overpasses, or this location For Lake Tahoe validation data, the MOD11 and IWbST algorithms did not fall within the AATSR nighttime overpass swath. performed similarly in terms of bias and RMSE (Table 2), with IWbST Fig. 6 shows a scatterplot of MODIS (top) and AATSR (bottom) doing slightly better at nighttime, and slightly worse at daytime but LSWTs versus in situ nighttime skin temperatures at Lake Tahoe de- only by ~0.1 K RMSE in both instances. This was to be expected rived from the operational and IWbST algorithms. For the MODIS since the lake's high elevation at near 2000 m minimizes errors data, both the MOD11 and IWbST algorithms show agreement with from atmospheric contamination. For the AATSR results (Table 3), in situ data with high R2 values above 0.99. The right panel shows IWbST performed better than the AATSR SSTs with biases reduced the temperature difference versus in situ temperature. The differ- by ~0.4 K during both day and nighttime data. Corresponding ences generally range from −1 K to 1.5 K and are uncorrelated with

Fig. 10. Model simulated and in situ surface temperature distribution plots for Salton Sea (2008–2010) and Lake Tahoe (2000–2010) during day and nighttime. G.C. Hulley et al. / Remote Sensing of Environment 115 (2011) 3758–3769 3767

Fig. 11. Scatterplot of meteorological station measurements of (a) wind speed and (b) relative humidity from Lake Tahoe buoy 1 and MODIS minus in situ derived LSWTs using the IWbST algorithm for the period from 2004–2006. temperature. The bottom plot shows that the AATSR SST product situ surface temperatures from 2004 to 2006. The broad distribution overestimates the LSWTs at Tahoe, and the errors become larger of points verifies that the temperature errors of the IWbST algo- with higher temperatures. This is expected because the SST coeffi- rithm when compared to in situ data are invariant with respect to cients are optimized for oceans. The IWbST algorithm on the other emissivity dependence on wind speed, and atmospheric correction hand shows almost zero bias with differences less than ±1 K. effects. Fig. 7 shows Salton Sea LSWT scatterplots for MODIS (night) and AATSR (day). The IWbST performs significantly better than MOD11 in this case, with improvements of up to 0.4 K at higher temperatures. 5. Conclusions Both algorithms, however, have a cooler bias with respect to in situ skin temperature measurements of more than 0.5 K. For AATSR Recent studies have indicated that a large number of globally dis- data, the SST algorithm once again overestimates the LSWTs by up tributed inland water bodies have shown warming trends since 1985. to 1.5 K, while the IWbST again has almost zero bias and maximum Because water has a high heat capacity, even small changes in tem- differences less than ±1 K. perature could have drastic serious implications for the health of Figs. 8 and 9 show MODIS minus in situ LSWTs as a function of sat- lake ecosystems in terms of algal blooms, invasive species and oxy- ellite view angle for the MOD11 and IWbST algorithms at Lake Tahoe gen-depleted dead zones. and Salton Sea respectively. For the Tahoe data, IWbST errors are less Current split-window based temperature retrievals developed for than 1 K below ~45° but increase to larger values as high as 2.5 K at land surfaces are sensor-specific, and are typically derived to encom- higher view angles. The MOD11 errors are relatively invariant with pass a broad range of land surface emissivities and global atmospheric view angle, but do show a trend for larger errors at high view angles. conditions. This can lead to region dependent errors in lake water At Salton Sea, the reverse is true and IWbST is more invariant than surface temperatures (LSWTs), spurious trends, and inconsistencies MOD11 with regard to view angle. In this case the MOD11 LSWTs between sensors in the long term temperature record of inland have larger positive biases with increasing view angle. The different water bodies. view-angle dependencies most likely occur because the IWbST and To address these issues we have derived a new split-window algo- MOD11 are based on different split-window formulations, and emis- rithm, termed the Inland Water body Surface Temperature (IWbST) sivity estimates. For example, at Tahoe, the LSWT accuracies will be v1.0 algorithm, with optimized split-window coefficients for deter- more sensitive to emissivity errors than at Salton Sea due to a drier mining the most accurate LSWTs consistently across multiple sensors atmosphere. In any case, since achieving higher accuracy is a higher for a global set of 169 large inland water bodies including lakes and priority than global coverage in this study, the IWbST coefficients large inland seas which can be readily observed with moderate reso- are restricted to data below view angles of 45° to minimize these lution (1 km) satellite data. The coefficients are valid for day and view angle effects. nighttime, and account for local atmospheric conditions, emissivity Fig. 10 shows the temperature distribution histograms at each effects, view-angle, and lake elevation. The coefficients are available lake, day and night, and for both simulated (black) and in situ (dotted upon request for the MODIS (Terra, Aqua), ATSR series (ATSR 1, 2 gray) surface temperatures. The model temperatures are Gaussian in and AATSR) and AVHRR-3 sensors from the lead author. Validation shape and capture the full natural variability at each lake. Model dis- of the IWbST v1.0 algorithm with a large matchup dataset of Terra tribution peaks do not match the in situ data because of the damping MODIS and AATSR data using in-situ skin temperatures from Lake effect of model data at 1° resolution. Increasing the simulated model Tahoe and Salton Sea showed a significant improvement in accuracy surface-air temperature difference from +10 K to +15 K had negligi- in terms of reduced biases and RMSEs (0.3–0.4 K) when compared ble effect on the validation results, as did decreasing the minimum to the operational MODIS split-window product ( MOD11_L2) and difference to −15 K. However, larger surface-air perturbations may AATSR SSTs at both sites. be needed to account for higher surface variability, or larger temperature inversions at other lake locations. Low lying lakes at humid sites should have smaller diurnal variability than higher elevation drier Acknowledgements sites, but many other factors play a part including wind (intensity, direction), and average temperature. The research described in this paper was carried out at the Jet Pro- Fig. 11 shows plots of wind speed and relative humidity mea- pulsion Laboratory, California Institute of Technology, under the con- sured at the Tahoe buoy 1 location versus MODIS IWbST minus in tract with the National Aeronautics and Space Administration. 3768 G.C. Hulley et al. / Remote Sensing of Environment 115 (2011) 3758–3769

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