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Techniques for Using MODIS Data to Remotely Sense Lake Water Surface
Temperatures
JOSEPH A. GRIM AND JASON C. KNIEVEL
National Center for Atmospheric Research,* Boulder, Colorado
ERIK T. CROSMAN
University of Utah, Salt Lake City, Utah
(Manuscript received 3 January 2013, in final form 8 April 2013)
ABSTRACT
This study describes a stepwise methodology used to provide daily high-spatial-resolution water surface
temperatures from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data for use nearly
in real time for the Great Salt Lake (GSL). Land surface temperature (LST) is obtained each day and then
goes through the following series of steps: land masking, quality control based on other concurrent datasets,
bias correction, quality control based on LSTs from recent overpasses, temporal compositing, spatial hole
filling, and spatial smoothing. Although the techniques described herein were calibrated for use on the GSL,
they can also be applied to any other inland body of water large enough to be resolved by MODIS, as long as
several months of in situ water temperature observations are available for calibration. For each of the buoy
verification datasets, these techniques resulted in mean absolute errors for the final MODIS product that were
at least 62% more accurate than those from the operational Real-Time Global analysis. The MODIS product
provides realistic cross-lake temperature gradients that are representative of those directly observed from
individual MODIS overpasses and is also able to replicate the temporal oscillations seen in the buoy datasets
over periods of a few days or more. The increased accuracy, representative temperature gradients, and ability
to resolve temperature changes over periods down to a few days can be vital for providing proper surface
boundary conditions for input into numerical weather models.
1. Introduction
Previous studies have shown that large lakes can have
a significant effect on the weather and climate of their
surrounding areas, driving lake-effect snowstorms (e.g.,
Eichenlaub 1970; Steenburgh et al. 2000; Laird et al.
2009), downwind precipitation shadows (e.g., Blanchard
and L
opez 1985), and lake and land breezes (Kopec 1967;
Laird et al. 2001; Zumpfe and Horel 2007). Even more
moderately sized lakes, such as the Great Salt Lake1
1
The surface area was 3090 km2 as of December 2012.
* The National Center for Atmospheric Research is sponsored
by the National Science Foundation.
Corresponding author address: Joseph A. Grim, Research Applications Laboratory, National Center for Atmospheric Research,
3450 Mitchell Lane, Boulder, CO 80301.
E-mail: [email protected]
DOI: 10.1175/JTECH-D-13-00003.1
Ó 2013 American Meteorological Society
(GSL), are known to produce notable lake-effect snowstorms (e.g., Carpenter 1993; Steenburgh et al. 2000;
Steenburgh and Onton 2001; Onton and Steenburgh
2001), as well as lake and land breezes (Rife et al. 2002,
2004; Zumpfe and Horel 2007). Kristovich and Laird
(1998) and Wright et al. (2013) have shown that small
variations in water temperature over the Great Lakes can
have an appreciable effect on the amount of downwind
precipitation and cloudiness. For the GSL in particular,
Onton and Steenburgh (2001) used the fifth-generation
Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) sensitivity
simulations of a major lake-effect snowstorm to show that
varying the lake surface temperature by 628C resulted in
an increase (decrease) of maximum downstream precipitation of 32% (24%). Because of its shallow depth2 and
its midlatitude arid continental climate within the Great
2
The maximum depth was 9.0 m as of December 2012.
OCTOBER 2013
GRIM ET AL.
Basin of the western United States, the response time of
the GSL water surface temperature (WST) to solar and
atmospheric forcing is particularly short for a lake of its
size (McCombie 1959; Lofgren and Zhu 2000; Steenburgh
et al. 2000; Crosman and Horel 2009). In addition, it is
important that strong horizontal gradients in WST be sufficiently resolved, since they drive air–water interactions
that affect patterns of static stability, vertical and horizontal wind shear, and divergence in the planetary
boundary layer (Warner et al. 1990; Chelton et al. 2004).
Numerical weather prediction models have shown that
increasing the resolution of the WST field can improve
simulations in the vicinity of water bodies (e.g., Chelton
2005; Song et al. 2009; Knievel et al. 2010).
Over the past three decades, satellite-derived estimates of lake WST have been sporadically developed
for a number of large lakes around the world (e.g.,
Bolgrien et al. 1995; Li et al. 2001; Bussieres and
Schertzer 2003; Mogilev and Gnatovskiy 2003; Plattner
et al. 2006). These include studies of saline and endorheic lakes, such as the Salton Sea (Cardona et al. 2008),
Dead Sea (Nehorai et al. 2009), Lake Eyre (Barton and
Takashima 1986), and the GSL (Crosman and Horel
2009). Recent understanding of lakes as drivers of regional weather and climate (e.g., Hondzo and Stefan
1991; Austin and Coleman 2007; Liu et al. 2009) and the
availability of multidecadal satellite records have renewed interest in using and improving the quality of
remote sensing retrievals of WSTs to better understand
the temperature trends of inland water bodies (e.g.,
Schneider et al. 2009; Schneider and Hook 2010;
MacCallum and Merchant 2012; Politi et al. 2012).
However, the availability and quality of satellitederived lake WSTs are generally lower than satellitederived sea surface temperatures (SSTs), owing to the
many unique difficulties involved in remote sensing of
lake WSTs, as compared to oceanic SSTs. These difficulties include shoreline contamination of pixels; variations in levels of reservoirs and endorheic lakes; highly
variable atmospheric profiles of temperature, water
vapor, and aerosols; rapidly changing WSTs of shallow
or small lakes; a lack of available data during cloudy
periods; and the need for more sophisticated cloudmasking algorithms. In addition, there has been a lack of
available in situ measurements over most lakes with
which to tune lake-specific WST algorithms. Consequently, satellite-derived WST estimates for lakes continue only to be sporadically incorporated into most
regional numerical weather prediction models and climate studies (Zhao et al. 2012).
As reviewed by Hulley et al. (2011), there are three
primary techniques used to estimate surface temperature from satellite data by correcting for the effects of
2435
the atmosphere on the upwelling thermal infrared radiation (TIR): physics-based algorithms, single-channel
algorithms, and split-window algorithms. The vast majority of algorithms use the more practical split-window
technique, which uses the difference in radiance between two or more satellite TIR channels as a proxy
for atmospheric absorption of the upwelling TIR by
water vapor and aerosols. These split-window algorithms employ coefficients that are tuned using in situ
observations. Both the level 2 Moderate Resolution
Imaging Spectroradiometer (MODIS) land surface
temperature (LST) and SST products are derived using
a generalized split-window algorithm. Efforts are currently underway to derive improved split-window algorithm coefficients for lakes worldwide (Hulley et al.
2011), as well as to improve estimates of WSTs using
physics-based algorithms (MacCallum and Merchant
2012). However, in addition to improving the lakespecific algorithms, there exists a need to improve the
filtering of WST retrievals from inevitable cloud contamination and other sources of error. In most previous
studies of WST, only general bias corrections based on
in situ and satellite matchups are employed and no
additional efforts are made to further reduce cloud
contamination or other errors. Our specific need was
for a dataset that can be generated in near–real time
and incorporated into operational mesoscale numerical
weather prediction; thus, it is necessary for the dataset
to be both temporally and spatially complete. Therefore,
we also explored methods for spatiotemporal compositing of WSTs in order to achieve a sufficiently accurate, yet
complete, final product.
In this article, we discuss a stepwise methodology to
improve bulk WST estimates through a series of bias
correction, land masking, filtering, and spatial and temporal compositing techniques. Since many bodies of
water, including the GSL, only have occasional in situ
measurements, these techniques were specifically designed not to require near–real-time in situ observations. Rather, only occasional in situ observations are
needed for a calibration period. Last, we compare our
dataset with other available datasets to show how the
presented techniques result in the most accurate nearreal-time GSL WST product that is currently available.
2. Data
a. In situ
Many large lakes have permanently deployed buoys
for obtaining open water WST measurements. This
has been impractical, however, for the GSL because of
the following factors: a harsh hypersaline environment,
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FIG. 1. Topography and bathymetry of the GSL area. Blue shades indicate bathymetry/
topography below the lake’s highest level in recorded history, while brown shades indicate
topography above the high water mark. A star indicates the location of the USGS buoy used for
WST calibration (buoy 1), three triangles indicate the locations of the USGS buoys used for
verification (buoys 2267, 2565, and 3510), and a circle indicates the location of the Salt Lake
City sounding.
wintertime ice flows from the surrounding freshwater
bays, and large interannual variations in surface area
and depth. WST measurements along the shore of the
GSL (such as at the Saltair Boat Harbor) have been
obtained in the past (Steenburgh et al. 2000), but these
measurements in shallow water (,0.5 m) can consistently see WSTs oscillate by more than 58C in a single
day and are often unrepresentative of open water
WSTs. What is of greater meteorological interest is
obtaining data showing the spatial and temporal characteristics of GSL bulk WSTs so that the entire lake’s
influence on atmospheric forcing can be predicted—
something that can only currently be accomplished
through satellite remote sensing.
For calibration purposes, we were able to obtain archived bulk WST data at 0.4 m below surface level
from a seasonal U.S. Geological Survey (USGS) buoy
moored ;10 km west of the northern tip of Antelope
Island3 (see star on Fig. 1 for location of buoy 1). This
buoy was deployed from May to December 2010 and
from May to November 2011, and temperature data
were archived at 10-min intervals. Temperature data
3
The station name is ‘‘Great Salt Lake Station 7 Miles W of
Antelope Is’’ and the number is 410342112223201. For simplicity,
this will be referred to as ‘‘buoy 1’’ or the ‘‘calibration buoy.’’
Station data were not available in real time, are provisional, and are
subject to revision.
OCTOBER 2013
GRIM ET AL.
were from a Precision Measurement Engineering T-chain
thermistor (http://www.pme.com/HTML%20Docs/
TChain_Temperature.html), with an accuracy of 60.18C
over the range of 08–368C. The thermistor is tethered to
the buoy so that its temperature should be negligibly
affected by the temperature of the buoy material itself.
In addition, three other buoy water surface temperature
sensors were sporadically deployed between June 2006
and October 2007 at other locations on the Great Salt
Lake (see triangles in Fig. 1 for locations of buoys 2267,
2565, and 3510). Temperature data of 6–9 months were
obtained from each of these buoys using Onset brand
thermistors with an accuracy of 60.28C over the range
of 08–558C (Beisner et al. 2009). The thermistors were
tethered between an anchor on the lake floor and a buoy
floating at or near the water surface. Since lake surface
elevations varied throughout the periods of deployment,
the depth of the temperature sensors also varied from
0.3 to 1.3 m below the water surface. As a result of their
variable depth and short deployment, data from these
three buoys are only used for verification purposes.
b. MODIS instrument
This study employs near-real-time MODIS observations of Earth’s surface and atmosphere, which come in
36 spectral bands in the visible and infrared spectra (0.4–
14.4 mm), located on board the sun-synchronous and
polar-orbiting Terra and Aqua satellites (Esaias et al.
1998). The Terra and Aqua orbital paths are designed to
cross over the equator at 1.5 h either side of noon local
solar time (LT) (1030 LT Terra, 1330 LT Aqua) on the
sunward-facing side of the earth, and vice versa with
respect to midnight local solar time (2230 LT Terra, 0130
LT Aqua). With a swath width of 2330 km, MODIS is
able to view most locations on the globe 4 times each
day, with both satellites repeating nearly the exact same
orbital paths every 16 days.
The National Aeronautics and Space Administration
(NASA) SST product (MOD28) provides cloud-masked
and quality controlled (QCed) daytime and nighttime
water skin temperature (the top few tens of micrometers
of the water surface) datasets. This product is also
intended to be provided for inland bodies of water large
enough to be resolved by the satellite instruments
(1/24th of a degree for the NASA SST product) and has
comparable accuracy to this study’s final product (not
shown); however, the SST product was not used for this
study, since its processing algorithm incorrectly rejected
most clear-sky images during the summer and winter
months (Fig. 2; Crosman and Horel 2009). Instead, LST
data from the level 3 MOD11A1 and the MYD11A1
version 5 dataset will be used, since they are much more
frequently available (Fig. 2). It should be noted that
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FIG. 2. Availability by month for 2010 and 2011 of at least a single
valid pixel over the GSL from the MODIS SST product for daytime
(SST day) and nighttime (SST ngt), as well as LST for daytime
(LST day) and nighttime (LST ngt).
‘‘land’’ surface temperatures provide an estimate of the
surface temperature observed by the satellite instrument,
whether this surface be land, water, or clouds.
c. RTG data
The Real-Time Global (RTG) dataset incorporates
satellite-derived SSTs and lake WSTs, bias-adjusted by
in situ data from buoys and ships (Thi
ebaux et al. 2003).
The RTG has global coverage, providing surface temperature estimates for oceans as well as lakes. These
data are available in near–real time on time-averaging
scales of 1 and 7 days and spatial resolutions of 0.0838
and 18. An advantage of this dataset is that it benefits
from both the extensive spatial coverage of the satellitederived SSTs and lake WSTs, as well as the high temporal
resolution of the in situ measurements. Unfortunately, as
we will later show, the RTG dataset frequently suffers
from irregular temporal adjustments to its WSTs over the
GSL, so that WSTs are sometimes biased by 38C or more
for periods of weeks. It is possible this is a result of infrequent or no adjustments from in situ observations, as
well as the improperly applied land mask from the satellite datasets. Knievel et al. (2010), in a study of Atlantic
Ocean SSTs off the eastern coast of the United States,
determined that near the coast, where water temperature
can be more heterogeneous and quickly changing, their
MODIS-based product agreed more with buoy observations than did the RTG product. Therefore, we base
some of our techniques on those of Knievel et al. (2010),
while applying several additional steps to obtain increased accuracy.
d. Climatographies
Great Salt Lake WST climatographies have been derived from bimonthly manual bucket measurements
(Steenburgh et al. 2000), and from MODIS data
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(Crosman and Horel 2009; Alcott et al. 2012). Alcott
et al. (2012) provide the latest WST climatography,
employing a Fourier fit between the day of the year and
MODIS-observed WSTs between 2000 and 2007, given
by the following equation:
TCLIMO 5 13:8 2 11:9 cos(0:0172j) 2 4:09 sin(0:017j)
2 0:93 cos(0:0344j) 1 0:677 sin(0:0344j)
2 0:482 cos(0:0516j) 2 0:600 sin(0:0516j) ,
where j is the day of the year. Climatographies can
provide a good first guess for GSL water surface temperatures, although periods of unseasonably cool or warm
weather can result in large and persistent errors.
3. Methodology
In addition to the cloud masking that has already been
applied to the LST field, we use a series of steps to further eliminate potentially erroneous data, correct for
biases, and fill in data gaps, in order to create a complete,
reliable, and sufficiently accurate final dataset. The steps
we employ are depicted in the flowchart in Fig. 3 and are
described in this section.
a. Obtaining data and applying time-dependent
land mask
The MOD11A1 and MYD11A1 datasets are obtained
from the National Aeronautics and Space Administration Land Processes Distributed Active Archive
Center (Wan 2008). Each MODIS file includes the following scientific datasets (SDSs): LST, sky cover, satellite viewing angle, quality control parameter, and
channels 31 and 32 (10.780–11.280 and 11.770–12.270 mm,
respectively) emissivity. These fields, originally on a
;1-km-resolution grid, are then subsetted over the
GSL and reinterpolated onto a 0.018 3 0.018 grid using
a simple inverse-distance-squared weighting method of
the four nearest points (see Fig. 4 for examples of each
SDS).
Next, the nonlake pixels are masked from the MODIS
datasets. The GSL surface area has varied widely during
the past 50 years (http://ut.water.usgs.gov/greatsaltlake/
elevations/) from 2460 to 8500 km2, with the magnitude
of intra-annual variations as much as 600 km2. As a result, it is important to use timely estimates of the GSL
area. This is accomplished by first obtaining the median
lake surface elevation for the preceding 15 days from
the USGS station at Saltair Boat Harbor on the GSL.
The lake surface elevation is then used in conjunction
with a bathymetric and topographic dataset (Fig. 1) to
FIG. 3. Flowchart of steps employed in creating the final
WST dataset.
determine the current area covered by the GSL. Next,
all lake pixels within 4 km of shoreline are masked; this
is because mean diurnal WST oscillations of 58–108C
are commonly indicated at these locations (not shown).
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GRIM ET AL.
FIG. 4. Preland mask examples from 31 Oct 2011 of MODIS Terra daytime SDS fields used in
this study: (a) raw LST; (b) LST after land masking, QC step, and bias-correction step; (c) QC
parameter; (d) sky cover; (e) channel 31 (10.780–11.280 mm) emissivity; (f) channel 32 (11.770–
12.270 mm) emissivity; (g) satellite viewing angle; and (h) visible composite.
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TABLE 1. List of parameters used in processing, QC, and bias correcting the MODIS WST dataset.
Symbol
c0(Te-dy)
c0(Te-nt)
c0(Aq-dy)
c0(Aq-nt)
c1
dTday-max
dTday-min
dTnight-max
dTnight-min
Value
Parameter description
8
0.01
4
0.01
0.98
0.70
20.36
1.32
20.14
21.18
0.4
21.2
1.9
20.6
5
0.05
No. of days to use in the temporal composite
Lat/lon increment to use for GSL subgridded MODIS data
Distance from shoreline (km) to use for retrieving valid LSTs
QC number must be less than this to be used
Sky cover must be greater than this to be used
Terra daytime bias compared with USGS buoy
Terra nighttime bias compared with USGS buoy
Aqua daytime bias compared with USGS buoy
Aqua nighttime bias compared with USGS buoy
WST correction coefficient for secant of viewing angle
Daytime max T change threshold of Terra–Aqua overpasses
Daytime min T change threshold of Terra–Aqua overpasses
Nighttime max T change threshold of Terra–Aqua overpasses
Nighttime min T change threshold of Terra–Aqua overpasses
No. of nearest pixels to use in filling holes in the temporal composite
Min threshold for the ratio of valid pixels for a given day to the total
possible pixels over the lake
No. of five-point spatial smoothes to perform on final day/night WST fields
10
Although such extreme skin temperature oscillations
may be possible for these locations, they are likely the
result of either very shallow water (a few tenths of 1 m or
less; Fig. 1) or land contamination of adjacent water
pixels near the shoreline, and are therefore not representative of bulk water temperatures at the depths of
the buoy temperature sensors. Deeper lakes might allow valid pixels for distances closer than 4 km from
shoreline, so this distance should be determined based
on what is optimal for the body of water of interest.
To determine the appropriate thresholds for masking
cloud cover, we visually inspected 120 days (30 from
each season) of visible satellite composites from 2010
(an example from 2011 is shown in Fig. 4h) and compared them with the other MODIS datasets. Inspecting
only overlake pixels, threshold values were then chosen
to eliminate as many of the cloud-contaminated pixels as
possible. For the sky cover field, a minimum threshold of
0.98 is used, while a maximum threshold of 0.01 is used
for the QC parameter (Table 1). For the channels 31 and
b. Quality control using concurrent fields
The LSTs, as well as each of these SDSs, have already
gone through a QC process. This QC process resulted in
valid LST retrievals of pixels over the open water of the
GSL 58.0% of the time during 2006–07. For the next
step, a series of threshold tests were applied using the
MODIS SDSs to filter the LST retrievals for cloud
contamination. According to the MODIS Land Surface
Temperature Products Users’ Guide (http://www.icess.
ucsb.edu/modis/LstUsrGuide/MODIS_LST_products_
Users_guide_C5.pdf), the sky cover SDS is derived from
the gross MODIS cloud-masking algorithm, and has
values ranging from 0, where there is a high likelihood of
cloud contamination, to 1, where there is greater than
a 66% chance of clear skies (Fig. 4d). The QC SDS is
similar to the sky cover field, except that its values range
from 0 to 255, where smaller values are more likely to be
unaffected by clouds (Fig. 4c). Channels 31 and 32 emissivity are only available for daytime overpasses and are
useful in eliminating clouds that are missed by the sky
cover and QC parameters (Figs. 4e,f). The viewing angle is
the angle from nadir at which each pixel is viewed (Fig. 4g).
FIG. 5. Threshold levels for channels 31 and 32, as a function of
viewing angle from nadir.
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GRIM ET AL.
FIG. 6. Scatterplots of MODIS–buoy temperature differences vs viewing angle: (a)–(d)
unadjusted and (e)–(h) adjusted for viewing angle. Thick solid lines in (a)–(d) indicate the
best-fit lines for the secant of the viewing angle, while thick dashed lines indicate the bestfit lines for the oblique column water vapor content. A thin solid line indicates the 08C
temperature difference level, which is by definition the best-fit line in (e)–(h).
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FIG. 7. Scatterplot of MODIS–buoy temperature differences vs
the secant of the viewing angle. Thick black line indicates the bestfit line for all four daily overpasses, while thinner colored lines
indicate the best-fit line for each overpass.
32 emissivity, visual inspection revealed that the
threshold over the saline water of the GSL varies by
viewing angle, ranging from 0.458 to 0.480 (Fig. 5).
Threshold values over freshwater or ocean water
might be slightly different. In addition, on dates when
clouds covered most of the lake, inspection of the
visible composite images indicated that many of the
few remaining ‘‘valid’’ LST pixels were likely contaminated by cloud edges. Therefore, all pixels were
eliminated on dates when less than 5% of the lake had
valid pixels. The SDS QC steps resulted in a reduction
in the number of valid pixels for the entire 2010–11
dataset from 58.0% to 40.4% of the total possible.
c. View-dependent bias correction
MODIS LSTs over water are typically around 18 cooler
than bulk WSTs from the buoy temperature sensors for
the following reasons: 1) satellite-retrieved LSTs are an
estimate of the skin temperature (the top few tens of mm
of the water surface), which are on average 0.18–0.58C
(though occasionally several degrees) cooler than the
‘‘bulk’’ water immediately below the surface (Crosman
and Horel 2009); 2) the generalized MODIS split-window
algorithm undercorrects for atmospheric attenuation in
the GSL region (Crosman and Horel 2009, their Fig. 4);
and 3) there is a slight cool bias due to the hypersalinity of
the GSL (Friedman 1969; Crosman 2005). Therefore, the
MODIS LST measurements must be corrected for these
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and any other biases. Since the calibration buoy 1 is located
relatively close to, but not on, the water surface (0.4-m
depth), the in situ measurements are likely more representative of the bulk lake temperature, and so the MODIS
LSTs are bias corrected to this temperature.4 For endorheic
saline lakes such as the GSL, salinity varies as the lake
volume changes. For example, the GSL salinity has varied
between 6% and 29% in the past 50 years, as its volume has
changed a comparable amount. This change in salinity results in a slight change in the bias of the MODIS LSTs. This
calibration process should be repeated if the salinity of the
water body has significantly changed in recent years.
Nicl
os et al. (2007) developed an equation relating
WSTs to channels 31 and 32 brightness temperatures
and the oblique column water vapor content W, where
W is defined as the product of the vertical column water
vapor Wo, and the secant of the viewing angle u: W 5
Wosec(u). The downloaded MODIS LST fields have
already been adjusted according to the channels 31 and
32 brightness temperature through the split-window algorithm; therefore, further adjustment is only needed
os et al.’s equation requires
for u and Wo. Since Nicl
channels 31 and 32 brightness temperature estimates,
which are not available in near–real time as a SDS, it was
simplest to develop an equation to correct for u and Wo. To
do this, a 2-yr calibration dataset from 2010 and 2011 was
employed using WST data from MODIS and buoy 1. To
calculate the MODIS–buoy temperature difference DT,
buoy 1 WSTs were extracted at the time nearest to each
MODIS overpass, allowing for 1017 MODIS–buoy coincident data pairs in the calibration dataset. Scatterplots in
Figs. 6a–d show how DT varies with u; Wo was calculated
from the Salt Lake City sounding5 (marked with a circle on
Fig. 1) that was nearest in time to the MODIS overpass of
interest. Next, linear least squares fits between DT and
W, DT 5 c1 3 Wosecu 1 c0, were calculated for each daily
overpass. These best-fit lines were overlaind on Figs. 6a–d,
showing that they provided inadequate fits at angles .508.
The mean RMSE about these best-fit lines was 0.868C. To
determine if it might be better to only use the relationship
between DT and u, another set of linear least squares
fits were calculated between DT and only secu, DT 5 c1 3
secu 1 c0. Since there is no apparent physical reason for
c1 to vary with overpass time, the viewing angle and
4
Note that this temperature might be a couple tenths of a degree
different than what is most optimal for assessing the lake’s effect on
the atmosphere, but this is well within the margin of error of the in
situ sensor and the MODIS retrievals, and correcting for this small
difference is beyond the scope or intent of this study.
5
Note that this sounding location is on the southeast side of the
lake and may not necessarily be representative of the atmospheric
column above the lake.
OCTOBER 2013
GRIM ET AL.
2443
FIG. 8. Histograms of daytime and nighttime temperature differences between the Terra and Aqua overpasses, for
(top) the buoy at the same time as the MODIS overpasses and (bottom) the MODIS overpasses at the location of the
buoy. Thresholds are indicated with dashed vertical lines. Histograms are created using data from 2010 to 2011 only
when the buoy was present.
temperature difference arrays from all four overpasses
were combined into single arrays (Fig. 7). Next, c1 and
c0 were calculated using a single best-fit line and found
to be 21.18 and 0.38, respectively. Then, c1 was fixed at
21.18 and c0 (effectively the bias) was calculated for
each of the four individual overpasses. Values of c0
ranged from 20.36 to 1.32 (Table 1). These best-fit
lines are overlaid on Figs. 6a–d, providing improved
fits at steep angles and slightly decreasing the mean
RMSE about these best-fit lines to 0.848C. Therefore,
the LST is adjusted using only secu and the coefficients for these best-fit lines. Figures 6e–h show the
scatterplots of DT and u with the biases removed.
Figures 4a and 4b show LST from an example case
before and after employing the land mask, QC, and
bias-correction steps.
d. Eliminating errors using LSTs from nearby times
To further quality control the WST data for cloud
contamination, we developed threshold tests based on
comparisons of WST data with those from other recent
MODIS overpasses. First, the temperature difference
between the times of the Terra and Aqua overpasses
during the same diurnal period (dTday and dTnight) was
calculated for both the MODIS and buoy 1 data. Figure 8
shows histograms of dTday and dTnight, and for both the
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FIG. 9. Histograms of (left) buoy and (right) MODIS day-to-day temperature differences from each overpass time for
1–10 days apart. Histograms are created using data from 2010 to 2011 only when the buoy was present.
MODIS and buoy 1 datasets. Since the daytime Terra
overpass is a couple hours earlier than the Aqua overpass during the daily heating period, most of its histogram is negative. Likewise, since the nighttime Terra
overpass is a few hours earlier during the nighttime
cooling period, most of its histogram is positive. Comparing the MODIS and buoy 1 histograms with each
other shows the MODIS histograms are much wider,
indicating that a significant minority of the retrieved
MODIS WSTs are suspect. Many of these suspect outliers are likely due to cloud contamination. In addition,
some of the large positive and negative temperature
changes in the MODIS-derived WSTs observed during
the day and night, respectively, likely result from rapid
solar heating of the daytime surface layer or nocturnal
cooling of the water surface during light winds (Hook
et al. 2003). However, despite the fact some of the outliers may be accurate, they are generally very shallow
and short-lived, and would introduce errors into multiday temporal composites; thus, they are deemed unrepresentative for the purpose of this operational
dataset. Therefore, the buoy dT histograms were used to
limit the MODIS data comparisons. For the calibration
buoy comparison at the times of the MODIS overpasses,
only 1% of the dTs were less than 21.28C, while only 1%
of the dTs were greater than 0.48C. Therefore, as part of
the operational WST retrieval processing, these subjective thresholds are used to eliminate both Terra and
Aqua pixel pairs if they are not within these thresholds.
The same was done for the nighttime overpass pairs,
using thresholds determined to be 20.68 and 1.98C. This
threshold step resulted in a removal of 17.0% of the
WST data from the 2010 to 2011 calibration dataset
(Fig. 8, bottom).
Since not all WST retrievals have valid pairs from its
corresponding daytime or nighttime overpass pair, it is
also useful to compare WST changes from preceding
days. Therefore, histograms were created from 2010 and
2011 MODIS and calibration buoy data at the time of
FIG. 10. Mean buoy diurnal temperature oscillation by month,
normalized about zero. Solid lines are from the 2010 and 2011
calibration buoy data (see star on Fig. 1), while dashed lines are
from buoy 3510 from the winter of 2006–07. There were no buoy
data available during the month of April.
OCTOBER 2013
GRIM ET AL.
2445
FIG. 11. Terra daytime WST plots (8C) for 23 Oct–3 Nov 2011.
the MODIS overpasses. This was done to assess the
range of typical values for multiday temperature
changes. Figure 9 shows that with increasing number of
days between comparisons, the histograms widen to
greater ranges of dT. The calibration buoy data were
again used to determine thresholds for the MODIS
data, using the 1% upper and lower limits and comparisons for up to eight days apart. (The reason we
chose 8 days apart will be given shortly in the discussion of temporal compositing.) In the operational
process, if any MODIS dT data pairs are outside of
these thresholds, then both the data points in the pair
are eliminated.
e. Creating temporal composites
We now turn our attention to the spatial and temporal
compositing of LST data, which requires careful consideration given the shallow, rapidly changing nature of
the GSL WSTs and the propensity for multiday cloudy
periods when no satellite imagery is available. Our original intent was to create separate daytime and nighttime
WST products, since the daytime overpasses are on either side of maximum solar heating, while the nighttime
overpasses are on either side of local midnight. To do this,
it was important to determine the timing of the overpasses within the diurnal cycle of WSTs. Therefore, the
buoy 1 WSTs were averaged for each hour of the day
and stratified by month of the year (except April, when
no buoy data were available), and then normalized
about zero. Figure 10 shows that the averages of diurnal
temperature oscillations over the open water of the lake
have trough-to-peak amplitudes as large as 1.68C in July
and as small as 0.48C in January. (Solid lines in Fig. 10
are from the 2010 and 2011 calibration buoy data, while
dashed lines are from buoy 3510 over open water from
the winter of 2006/07.) The time of day of maximum
WST ranged from 1400 mountain standard time (MST)
in January to 2000 MST in June, while the time of minimum WST ranged from 0700 MST in June to 1000 MST
in October. (Note that exact magnitudes and timing of
these patterns are likely highly influenced by lake location and interannual variability within these small
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JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
FIG. 12. MODIS vs buoy MAE and availability for temporal
composites of 1–20 days. Dashed line indicates MAE without including a lakewide trend. Thick solid line indicates MAE with
a lakewide trend. Dotted line indicates availability. Thin vertical
solid line marks values for the 8-day composite that was the chosen
option.
monthly subsets.) Most noteworthy of all though is
that the overpass times are near the inflection points of
the diurnal cycles, which makes it very difficult to use
the MODIS WST data to infer the magnitude of individual diurnal cycles. Therefore, for this study, only
a single daily mean WST estimate is calculated from all
four overpasses.
For the 2010–11 calibration dataset, the QC steps resulted in valid WST retrievals up to 46% of the time for
individual pixels over the middle of the lake, decreasing
toward zero along the shoreline (not shown). Figure 11
shows the Terra daytime WST retrievals during an example 12-day period in late 2011. Eight of these days had
less than half of the lake with valid WST retrievals.
Therefore, WST retrievals from the other overpasses
and preceding days must be utilized to fill in these
missing pixels. Knievel et al. (2010) used a 12-day
composite for their study over a portion of the Atlantic
Ocean along the eastern coast of the United States, averaging each MODIS SST pixel at a given location
during the preceding 12 days. Then, to adjust the average to account for seasonal and intraseasonal changes,
half of the 12-day trend was calculated from the coincident RTG dataset. Since our study is focused over
a region with a lower frequency of cloud cover than the
northwest Atlantic Ocean, we must determine the optimal number of days to use in this temporal composite.
To create the temporal composite, the most recent Nd
days are compiled for all four daily overpasses. Then,
VOLUME 30
the WSTs from each overpass are adjusted by a small
amount to account for its median departure from the
daily mean of all four overpasses; values ranged from
20.228C for Terra night to 0.218C for Terra day. Next,
the 4 3 Nd temperature values are averaged. The extra
step was added in this study so that the WSTs were not
skewed by more frequent valid data from overpasses at
a certain time of day. Figure 12 shows the availability
percentage for at least a single valid pixel over the lake,
and the MODIS–buoy mean absolute error (MAE), for
Nd-day temporal composites ranging from 1 to 20 days.
The dotted availability line shows that with increasing
Nd, the availability increases rapidly, becoming nearly
asymptotic as it nears 100%, so that very little benefit is
gained in availability after Nd 5 8 days. The dashed line
in Fig. 12 shows that the MAE increase is relatively
gradual at ;0.058C day21 between 1 and 20 days. Therefore, an operational dataset such as this one suggests
that a temporal composite of around eight days should
be used to first maximize availability, while secondarily
maximizing accuracy. The choice for the number of days
used in the temporal composite is somewhat subjective.
For example, a value ranging from 5 to 9 days could be
argued for this dataset. For locations with more frequent
cloudiness and/or deeper water, a larger number would
likely be best.
During periods in which the water and air temperatures are quite different, especially during spring and
fall, it may be important to also include a trend when
creating the composite. The trend is calculated over the
entire lake in order to minimize spurious oscillations
from single pixels. The lakewide trend is also scaled by
the number of valid retrievals divided by the total possible, as well as by the number of days since the most
recent retrieval; this was done to avoid large spurious
trends following periods of very few valid retrievals.
Adding half of the Nd-day lakewide trend to each individual pixel reduces the MAE for composites of 6 days
or more (Fig. 12). Since there is a slight improvement for
our 8-day composite, the lakewide trend is employed.
The temporal composite step fills in nearly all missing pixels. All remaining pixels are then filled in with
the mean of the nearest five pixels. Last, 10 passes of
a five-point equal-area-weighted smoother are applied
to smooth out any unrealistic temperature discontinuities resulting from recent partial lake retrievals. This
resulted in valid WST retrievals on all but one day in the
2010–11 calibration dataset. In the rare instances this
would occur operationally, the WSTs from the most
recent valid day would be used. Figure 13 shows the
MODIS WSTs for the entire lake for the same 12-day
period as Fig. 11. When compared with the QC-adjusted
WST product in Fig. 11, the final MODIS dataset has
OCTOBER 2013
GRIM ET AL.
2447
FIG. 13. Final MODIS WST plots (8C) for 23 Oct–3 Nov 2011.
replicated the major cross-lake gradients, albeit muted
somewhat by the compositing and smoothing process.
Resolving these cross-lake gradients is important for
numerical weather prediction, since they drive air–water
interactions that affect patterns of static stability, vertical and horizontal wind shear, and divergence in the
planetary boundary layer (Warner et al. 1990; Chelton
et al. 2004; Chelton 2005; Song et al. 2009; Knievel et al.
2010).
4. Comparison of WSTs from MODIS with those
from other sources
Figure 14 presents a time series of WST retrievals at
the location of calibration buoy 1 from the final MODIS
product, RTG analysis, climatography, and the buoy.
Oscillations in the buoy 1 temperature are closely
matched by the final MODIS product, demonstrating
its ability to represent oscillations in WST for periods
of a few days or more. This is particularly important,
since the response time of the GSL WST to solar and
atmospheric forcing is particularly short for a lake of its
size (McCombie 1959; Lofgren and Zhu 2000; Steenburgh
et al. 2000; Crosman and Horel 2009). The RTG dataset
does a good job at times of matching the observed buoy
WST oscillations, but it greatly suffers from long periods
of very little adjustment. The climatography provides
estimates within 38C a majority of the time, but it can
also be erroneous by as much as 78C during periods of
rapid cooling in the fall and early winter. It is important
to recall that buoy 1 was present mainly during the warm
season each year; also, all statistics given in this section
are calculated from dates when all four datasets were
available. The MODIS product, as would be expected
when compared against the buoy 1 data used for calibration, has a very small bias of just 0.018C. The RTG
dataset has a bias of 21.478C, while the climatography
has a bias of 20.278C. The MAE of the MODIS product
compared with the buoy 1 temperature is just 0.668C—
by far the best of the three products. (Bias and MAE
values are also shown in Table 2). To remove any inherent differences between the datasets, such as that the
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JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
VOLUME 30
TABLE 2. Bias and bias-adjusted MAE of MODIS, RTG. and climatological (climo) WST compared with those from the buoys.
MODIS–buoy 1
RTG–buoy 1
Climo–buoy 1
MODIS–buoy 2267
RTG–buoy 2267
Climo–buoy 2267
MODIS–buoy 2565
RTG–buoy 2565
Climo–buoy 2565
MODIS–buoy 3510
RTG–buoy 3510
Climo–buoy 3510
FIG. 14. (a) Time series of the GSL water surface temperature
from different products for 2010 and 2011, at the location of the
USGS calibration buoy. Shaded time periods indicate when buoy
data were present. (b) Temperature differences between all products and the buoy. (c) Histograms of temperature differences between each product and the buoy.
Bias (8C)
Bias-adjusted MAE (8C)
0.01
21.47
20.27
0.15
1.04
21.49
0.26
23.23
20.57
0.09
3.97
20.93
0.66
1.75
1.7
1.07
5.09
1.97
0.82
4.38
1.61
0.78
3.53
1.83
tethered thermistors below the water surface varied
between 0.3 and 1.3 m because of fluctuating lake
surface levels. Two buoys were located over deeper
open water, one just south of central Gilbert Bay
(buoy 3510, Fig. 1) and the other in southern Gunnison
Bay (buoy 2565). A third was located in shallower nearshore water between Fremont Island and Promontory
Point (2267). Figure 15 shows that the MODIS product
does not suffer from any significant bias using these
separate buoy datasets, with biases of only between 0.098
and 0.268C for the three buoys. The older RTG dataset
provides results that have not benefitted from more recent improvements in its skill, having a bias of 1.048,
23.238, and 3.978C, while the climatography’s biases were
between 21.498 and 20.578C. Bias-adjusted MAE scores
for the MODIS product are between 0.788 and 1.078C.
However, the RTG bias-adjusted MAE scores are
substantially worse between 3.538 and 5.098C. The climatography bias-adjusted MAE scores are between
1.618 and 1.978C.
5. Conclusions
RTG product represents a skin temperature instead of
a bulk temperature, the bias is subtracted from each
platform’s value. The RTG bias-adjusted MAE is 1.758C;
the MAE would certainly be worse if buoy measurements
had been obtained for the entire period, as the RTG
analysis underrepresented the wintertime cold. The climatography’s MAE is 1.708C.
Since the MODIS product was calibrated against the
2010–11 buoy 1 data, using the same data for verification
does not give a fair measure of its accuracy; therefore,
the MODIS datasets are also compared against three
other buoys during an earlier period from 2006 to
2007. Temperatures from these other buoys are only
used for verification purposes, since the depth of the
This study describes techniques used to provide
daily high-spatial-resolution water surface temperatures
from MODIS satellite data for use nearly in real time
for the Great Salt Lake (GSL). Although the techniques
described herein were calibrated for use on the GSL,
they can also be applied to any other inland body of
water large enough to be resolved by MODIS, as long
as occasional in situ measurements of water temperature are also available for calibration.
A summary of the major steps used in creating the
MODIS bulk lake water surface temperature (WST)
product is given below. Values for parameters and
thresholds will vary by study area and are therefore not
repeated here.
OCTOBER 2013
GRIM ET AL.
2449
FIG. 15. As in Fig. 14, but for the three verification buoys from 2006 to 2007.
1) Apply the land mask to the land surface temperature
(LST) array.
2) Quality control (QC) each LST pixel using these
concurrent scientific datasets from NASA: sky cover,
QC parameter, viewing angle, and channels 31 and
32 (10.780–11.280 and 11.770–12.270 mm) emissivity
(daytime only).
3) Adjust for MODIS bulk WST bias, as well as viewing
angle bias.
4) Threshold the MODIS WST pixels using temperature differences between daytime and nighttime
overpass pairs, based on limits derived from the in
situ buoy calibration dataset.
5) Threshold the MODIS WST pixels using temperature differences between the current and previous
days, based on limits derived from the in situ buoy
calibration dataset.
6) Create a temporal composite that averages all four
daily overpasses over a certain number of days, and
that also adjusts for the recent lakewide temperature
trend.
7) Fill in all remaining missing pixels using the mean of
a certain number of nearby pixels.
8) Smooth the final dataset to remove unrealistically
sharp WST gradients.
For each of the buoy verification datasets, these
techniques resulted in MAEs for the MODIS product
that were at least 62% lower than those obtained using
the operational RTG analysis. The final MODIS product provides realistic cross-lake temperature gradients
that are representative of those from individual MODIS
overpasses, and that can be vital in driving air–water
interactions. Last, we have shown that this product is
able to replicate the temporal oscillations seen in the
buoy dataset over periods of a few days or more, which
is particularly useful for the GSL since it has such a relatively short response time for a lake of its size. The
increased accuracy, and the spatial and temporal consistency achieved with the final MODIS product,
make this product appropriate for use in many scientific applications. When used as input into numerical
weather prediction models, this product should theoretically result in better weather forecasts and climate
reanalyses.
Acknowledgments. The authors thank Dave Naftz,
Ryan Rowland, William Johnson, and Kimberly Beisner
for providing in situ temperature data for validation of
satellite retrievals. We would also like to thank James
Pinto and Matthias Steiner for their helpful reviews, and
John Pace for his support. This work was funded by
the U.S. Army Test and Evaluation Command through
an interagency agreement with the National Science
Foundation.
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JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
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