- Email: [email protected]
A daily merged MODIS Aqua–Terra land surface temperature data set for the conterminous United States
Remote Sensing of Environment 119 (2012) 315–324
Contents lists available at SciVerse ScienceDirect
Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse
A daily merged MODIS Aqua–Terra land surface temperature data set for the conterminous United States William L. Crosson a,⁎, Mohammad Z. Al-Hamdan a, Sarah N.J. Hemmings a, Gina M. Wade b a b
Universities Space Research Association, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL 35805, USA Von Braun Center for Science and Innovation, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL 35805, USA
a r t i c l e
i n f o
Article history: Received 21 April 2011 Received in revised form 5 December 2011 Accepted 22 December 2011 Available online 2 February 2012 Keywords: Land surface temperature MODIS Aqua Terra Clouds
a b s t r a c t A major shortcoming of any remotely-sensed land surface temperature (LST) dataset is the lack of observations for cloud-covered areas. A method is presented that uses the Moderate Resolution Imaging Spectroradiometer (MODIS) flying on the Terra platform to fill in spatial gaps in the Aqua MODIS LST dataset over the conterminous United States (CONUS) and limited adjacent areas. Over this domain, data are available for only about 50% of all times and pixels for each of the two MODIS sensors. Coverage is highest in summer and lowest in winter, with major regional variations. The relative close temporal proximity (~3 h) of the Aqua and Terra overpasses provides an opportunity to combine information from the two data sources, which can reduce the data loss, most of which we assume is cloud-related. We applied the approach to create a ‘merged’ data set that supplements existing Aqua and Terra daytime and nighttime LST products. We used Terra LST data to fill gaps in Aqua data, resulting in a data set tied to the ~1:30 AM/PM overpass times, so that the resulting data closely approximate daily minimum and maximum LST values. In order to use Terra LST observations to supplement Aqua data, an adjustment was applied to account for the different overpass times of the two platforms. Terra's 10:30 AM overpass usually senses a cooler surface than does Aqua with its 1:30 PM overpass. Conversely, for nighttime overpasses, Terra typically measures a warmer surface at 10:30 PM than does Aqua at 1:30 AM. Our approach was to determine, by season, mean Aqua and Terra LST values on the CONUS grid, based on data from a multi-year (2003–2008) period. Adding the mean Aqua-Terra LST differences for the respective season and time of day to a daily gridded Terra LST field removes the mean offset related to overpass time, resulting in LST values that can then be used to fill Aqua LST data gaps. Using independent offsets for each grid cell and season provides a first-order accounting for factors such as land cover, elevation, terrain slope and aspect, latitude, season and snow cover, which control the diurnal cycle of LST. For the six-year period, the merged data set increases data coverage by 24% and 30% for daytime and nighttime overpasses, respectively, relative to the Aqua LST product alone. The CONUS data set is a potentially valuable tool for weather and climate studies in which high spatial and temporal coverage are desired. Crown Copyright © 2012 Published by Elsevier Inc. All rights reserved.
1. Introduction 1.1. Description of MODIS LST data The National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) instruments were launched on the Terra and Aqua platforms in December 1999 and May 2002, respectively. Both the Terra and Aqua satellites have a sunsynchronous near-polar orbit; however, they ascend/descend the equator at different times. Terra descends (ascends) the equator around 10:30 AM (10:30 PM) local time (LT). In contrast, Aqua descends ⁎ Corresponding author. Tel.: +1 256 961 7913; fax: +1 256 961 7788. E-mail addresses: [email protected] (W.L. Crosson), [email protected] (M.Z. Al-Hamdan), [email protected] (S.N.J. Hemmings), [email protected] (G.M. Wade).
(ascends) the equator at 1:30 PM (1:30 AM) LT. MODIS has a viewing swath width of 2330 km (http://modis.gsfc.nasa.gov), allowing global coverage every 1 to 2 days. The MODIS Land Surface Temperature (LST) daily products are generated using the split window algorithm (Price, 1984; Wan & Dozier, 1996), which uses bands 31 and 32 of MODIS's 36 spectral bands. The Level 3 product (MOD11A1 for Terra, MYD11A1 for Aqua) is produced in tiles, with each tile containing 1200 rows by 1200 columns at approximately 1-km resolution. The MODIS LST Level 2 pixels are placed onto a gridded sinusoidal projection, and if any pixels overlap during this process, they are averaged with the overlap area used as weight (Wan, 2007), resulting in the Level 3 LST product. An important tool in developing the LST products is the MODIS Cloud Mask algorithm, which determines the confidence of cloudfree pixels, for which LST can be measured. With a 1-km resolution,
0034-4257/$ – see front matter. Crown Copyright © 2012 Published by Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2011.12.019
316
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
Table 1 Annual mean percentages of the CONUS domain for which LST data are available, for Aqua and Terra, for daytime and nighttime overpasses. Year
2003 2004 2005 2006 2007 2008 Total period
Daytime
Nighttime
Aqua (1:30 PM)
Terra (10:30 AM)
Aqua (1:30 AM)
Terra (10:30 PM)
44.7 44.1 46.2 45.9 46.5 46.7 45.7
47.5 47.3 49.4 47.5 48.2 49.9 48.3
46.2 44.1 47.1 47.3 47.2 48.3 46.7
50.2 48.7 51.2 51.4 51.4 52.9 51.0
1.3. Previous research
Table 2 Days of year of missing Aqua and Terra LST data. Year
2003 2004 2005 2006 2007 2008 Total missing days
Daytime
Nighttime
Aqua
Terra
355 1
32, 350–358 49, 359 266 235 347 252, 355–357 19
estimate daily minimum and maximum near-surface air temperature (Mostovoy et al., 2006; Vancutsem et al., 2010). The close temporal proximity (~3 h) of the Aqua and Terra satellite overpasses provides an opportunity to combine information from the two data sources and can reduce areas of missing data, most of which we assume are cloud-related. The objective of this study is to use both data sources to create a ‘merged’ data set that supplements the existing Aqua LST data product. We chose to use Terra LST data to fill gaps in Aqua data, so that the resulting data set, which is tied to the ~1:30 AM/PM overpass times, approximates daily minimum and maximum LST values. Since the method depends on the availability of Terra observations to fill Aqua data gaps, it is most useful under conditions of partial or short-lived cloudiness.
Aqua
Terra 32, 351–358 50 235
336 1
356–358 14
the Cloud Mask algorithm uses a series of visible and infrared threshold tests and tests of consistency to determine a confidence of the satellite's view of the Earth's surface being unobstructed by clouds. If clouds are present, then LST data will not be available for the location (http://modis-atmos.gsfc.nasa.gov/). This paper describes a method to fill in missing MODIS Aqua LST data with Terra observations when the latter are available.
1.2. Objectives Land surface temperature is an important measurement in many modeling and analytical applications, as it represents the integrated effects on the land surface of shortwave and longwave radiation, land cover type, vegetation amount, antecedent precipitation, soil moisture, and near-surface meteorology (Dai et al., 1999). LST is a critical factor in determining the surface energy balance and fluxes of heat into the boundary layer (Atlas et al., 1993; Segal et al., 1989) and thus it influences weather and climate across a wide range of temporal scales. MODIS LST has been used, with some difficulty, to
Some studies in the research literature have shown that the diurnal cycle of land surface temperature can be estimated using observations taken at two different times. In order to estimate the diurnal LST cycle, Jin and Dickinson (1999) developed an algorithm to interpolate time series of surface skin temperature measured from polar orbiting satellites. They combined climate-land coupled model results with satellite and surface observations and interpolated satellite twicedaily observations into the diurnal cycle. They developed and evaluated their algorithm using Advanced Very High Resolution Radiometer (AVHRR) and Geostationary Operational Environmental Satellite (GOES)-8 data. Jin and Treadon (2003) developed an algorithm to correct the orbital drift effect on AVHRR LST measurements, which are collected at different local times during the satellite's lifetime. They also combined coupled model results with satellite observations. Using data collected over Canada, Coops et al. (2007) developed a simpler algorithm to estimate the afternoon MODIS LST based on the morning MODIS overpass accounting for location, land cover, and season. In this paper, we describe a similar kind of algorithm to estimate the afternoon MODIS LST based on the morning MODIS overpass (i.e., Terra data), and then merge those estimates with the LST data from the afternoon MODIS LST overpass (i.e., Aqua data). This process generates a national daily LST data set for the conterminous United States (CONUS) for 2003–2008 that provides the best possible spatial coverage from MODIS data. 1.4. Problem quantification The presence of clouds leads to large temporal and spatial gaps in LST data, particularly over the eastern U.S. The 2003–2008 annual mean percentages over the CONUS domain for which LST data are available are given in Table 1 for Aqua and Terra, for day and night
Fig. 1. Daytime Terra and Aqua LST for 11 July 2008. Areas of missing data are in white. The southeast corner of the CONUS domain is 22.79° N, 74.44° W, and the northwest corner is 48.18° N, 128.83° W.
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
317
Fig. 2. Daytime Terra and Aqua LST for 1 January 2008, with areas of missing data in white.
overpass times. These percentages were computed excluding days for which no data were available for the CONUS region (Table 2), since the objective here was to estimate the effect of cloud cover on data coverage. Overall, slightly less than 50% of the spatio-temporal domain is cloud-free. Data coverage is slightly higher for the nighttime periods and for Terra relative to Aqua. The spatial extent of missing Terra and Aqua LST data is illustrated in Figs. 1–2 for 11 July 2008 and 1 January 2008, respectively. These scenes are representative of winter and summer days in terms of mean cloudiness for the CONUS domain. The Terra and Aqua LST images for 11 July (Fig. 1) show similar patterns of LST and of cloudiness,
with a large cloudy area in the Southwest and somewhat smaller regions in the Southeast and Northeast. LST data are available from Terra for 51.3% of the CONUS and for 49.0% from Aqua. Large data gaps due to cloudiness in the 1 January image (Fig. 2) exist from the Midwest to the Northeast, as well as in the Pacific Northwest. On this date, there are also regions of missing Aqua LST in Tennessee and in Arizona/New Mexico for which Terra observations are available. Domain-wide, spatial coverage for this day is 50.3% and 42.8% from Terra and Aqua, respectively. The prevalence of missing data over a year varies greatly across the CONUS domain, as shown in Fig. 3 for summer and in Fig. 4 for
Fig. 3. Percentages of all summer days during 2003–2008 for which daytime Terra and Aqua LST data are available.
Fig. 4. Percentages of all winter days during 2003–2008 for which daytime Terra and Aqua LST data are available.
318
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
Table 3 Seasonal mean percentages of the CONUS domain for which LST data are available, for Aqua and Terra, for daytime and nighttime overpasses. Season
Spring (Mar–May) Summer (Jun–Aug) Fall (Sep–Nov) Winter (Dec–Feb)
Daytime
Nighttime
Aqua (1:30 PM)
Terra (10:30 AM)
Aqua (1:30 AM)
Terra (10:30 PM)
45.4 53.8 49.8 33.7
48.1 56.9 52.2 35.5
47.2 53.1 49.6 36.6
50.9 58.0 54.3 40.3
winter. In this analysis, ‘winter’ is defined as December–February and ‘summer’ is defined as June–August. These figures show the percentage of days for the given season during 2003–2008 for which LST values are available for each 1-km pixel. For summer (Fig. 3), the far western states exhibit excellent data coverage, with both Terra and Aqua LST values being available for over 90% of days at some points in California. Conversely, LST data are available for areas around the Gulf of Mexico on fewer than 10% of summer days due to daytime convective cloud cover. For winter (Fig. 4), data coverage is considerably lower on average, ranging from about 10% of days in the Northeast and Great Lakes region to about 75% in the Southwest. Data coverage near the Gulf of Mexico is higher in winter than in summer. Domain-wide, coverage statistics for Terra and Aqua and for daytime and nighttime overpasses are similar, as shown in Table 1. Data availability percentages for Aqua and Terra are presented by season in Table 3. Data coverage is best in summer, when more than 53% of pixels are cloud-free from both Aqua and Terra for day and night overpasses. The amount of available LST data is lowest in winter, averaging about 36% for the platform/overpass time combinations. Availability is slightly above 50% in fall and slightly below 50% in spring. One interesting feature of the daytime summer LST ‘availability’ map (Fig. 3) is that large cities can clearly be seen to have fewer days with LST observations for both Terra and Aqua MODIS sensors. This is not seen in the winter maps (Fig. 4) or at night (not shown) and seems to be the result of occasional loss of LST retrievals over cities in summer, as illustrated in Fig. 5 for the Philadelphia area on 28 August 2003. There were no clouds in the area at this time, as verified by National Weather Service observations and MODIS visible reflectance imagery,
but there are missing LST observations in the urban core, surrounded by high LST values (305–311 K). This phenomenon occurs on other clear summer days and is at least partially responsible for the reduced LST data frequency in summer for urban areas. Incorrectly labeling hot and bright urban pixels as cloudy is a known issue with the current MODIS Collection 5, particularly in the presence of high aerosol loadings; this will be improved in Collection 6 (R. Frey, personal communication, 13 April 2011). The impact of missing LST observations over urban areas on seasonal mean LST for Aqua and Terra is difficult to determine. The potential effect would be a summertime cool bias in urban cores due to the systematic elimination of very hot observations. However, the actual impact does not appear to be large, as MODIS mean daytime summer LSTs for cities are in fact considerably warmer than surrounding areas, as discussed in Section 2. Fig. 6 shows an example of the false cloudy problem for the Philadelphia–New York area on 17 July, 2006. The top panel shows the Aqua Quality Control fields, indicating the areas masked as cloud (indigo) and other pixels for which there are ‘quality issues’, i.e. the estimated LST and emissivity errors are substantial. Green dots indicate city centroids as defined by the U.S. Census. The bottom panel, the MODIS Aqua reflectance on this day, indicates clear skies for the region. The large errors in emissivity and LST relate to hot, bright urban pixels, with the result that many pixels are incorrectly masked as cloud. 2. Methodology Daily Aqua and Terra MODIS LST (Level 3, Collection 5) data sets for the CONUS and surrounding areas were downloaded for the sixyear period spanning 1 January 2003–31 December 2008 from NASA's Land Processes Distributed Active Archive Center (DAAC) using the Warehouse Inventory Search Tool. The MODIS Reprojection Tool (Land Processes DAAC, 2008) was used to extract daily LST data from multiple grid tiles and to mosaic the data onto a 1-km CONUS spatial grid that consists of 3280 rows and 5158 columns. In order to use Terra LST observations to supplement Aqua data, an adjustment is needed to account for the different overpass times of the two platforms. During daytime, Terra's 10:30 AM overpass usually senses a cooler surface than does Aqua with its 1:30 PM overpass. Conversely, for nighttime overpasses, Terra typically measures a warmer surface at 10:30 PM than Aqua does at 1:30 AM. Our approach was to determine, by season, mean Aqua and Terra LST values
Fig. 5. Aqua daytime LST showing missing LST retrievals in Philadelphia's urban core, 28 August 2003.
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
319
are different in the two pairs of figures. These seasonal mean LST values are not intended to be an LST climatology. Instead, since LST observations are available only for clear pixels, the means shown here should be interpreted as mean clear-sky LST and were created solely to enable calculation of Aqua–Terra LST differences for each season, which are needed in the merging process described below. The seasonal means for each sensor provide the information needed to offset Terra MODIS LST observations to the Aqua observation time. For each season, we first computed mean LST differences (Aqua minus Terra) on the CONUS grid. Adding these differences for the respective season and time of day to a daily gridded Terra LST field removes the mean offset related to overpass time, resulting in LST values that can then be used to fill Aqua LST data gaps. By calculating independent offsets for each grid cell and season, this approach provides a first-order accounting for factors that control the diurnal cycle of LST, such as land cover, elevation, terrain slope and aspect, latitude, season and snow cover. These land surface characteristics were considered in a spatially aggregated sense by Jin and Dickinson (1999) in using polar orbiting satellite data to construct diurnal LST cycles. The Aqua–Terra merged LST CONUS data set was created using the described approach for the period 2003–2008. Two gridded fields were created for each day, corresponding to the 1:30 PM and 1:30 AM Aqua overpasses. The Aqua LST grid was the starting point for the merged data. Where the Aqua LST value was missing, it was replaced by the Terra LST value after adding the Aqua–Terra LST difference for the correct season and overpass time. Calculations were performed independently for each grid cell over the CONUS domain.
3. Results 3.1. LST spatial patterns
Fig. 6. MODIS Aqua QC flags (top) and visible band reflectance (bottom) for 17 July 2006.
on the CONUS grid, based on data from 2003 to 2008. As examples, mean summer and winter Aqua LST for daytime and nighttime overpasses are shown in Figs. 7 and 8, respectively. Note that the color scales
In the seasonal mean temperature images (Figs. 7–8), the typical temperature patterns associated with latitude and elevation are easily seen. Bodies of water are also evident, being usually cooler than land areas in summer and warmer in winter. Urban areas can also be identified by their higher temperatures, especially in summer (Fig. 7). As shown by Aqua daytime LST for the northeastern U.S. on 25 July 2003 in Fig. 9, the urban areas of Philadelphia and Greater New York City are clearly several degrees hotter than nearby urban regions, matching the mean summer LST pattern evident in Fig. 7. Mean Aqua–Terra LST difference images are shown in Fig. 10 for summer and in Fig. 11 for winter. For the daytime overpasses, Aqua LST typically exceeds Terra LST by 1–5 K. The main exception occurs in winter in the Great Plains in the northern U.S. and southern Canada, where the Aqua–Terra LST difference is predominantly negative (Fig. 11, left panel). The reason for this negative difference is under investigation. This region is characterized in winter by snowcovered grasslands and fallow cereal crops such as wheat, and it differs from southeastern Canada (where Aqua LST is higher than Terra LST), which is mainly forested. Complex patterns in the daytime LST difference are visible in scattered areas in the Rocky Mountains, where Terra LST is higher than Aqua LST in both summer and winter. This seems to be associated with east-facing slopes, which reach daily maximum temperature earlier. In summer during the daytime (Fig. 10, left panel), cities have a larger positive Aqua–Terra LST difference compared to rural areas. This pattern is not evident at night or during the winter. Bodies of water can be identified in the LST difference maps due to the different rates of diurnal warming and cooling relative to land areas. For nighttime overpasses, the Aqua–Terra LST differences are mainly between −1 and −5 K during summer (Fig. 10, right panel). In winter, nighttime differences range primarily from 0 to −4 K, but scattered areas of small positive values are observed (Fig. 11, right panel).
320
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
Fig. 7. Summer mean LST (K) for daytime and nighttime Aqua overpasses during 2003–2008.
Fig. 8. Winter mean LST (K) for daytime and nighttime Aqua overpasses during 2003–2008.
3.2. Merged LST data Examples of the daytime merged LST data set are shown in Figs. 12 and 13 for 11 July 2008 and 1 January 2008, respectively, the same
dates as the Aqua and Terra LST images shown in Figs. 1–2. For 11 July, areas where Terra data were used to fill gaps are scattered, but they can easily be observed in the Northern Plains by comparing Figs. 1 and 12. For this date, merging increased Aqua's LST coverage
Fig. 9. Aqua daytime LST for the Philadelphia–New York City region, 25 July 2003.
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
321
Fig. 10. Difference in summer mean LST (K), Aqua minus Terra, for daytime and nighttime overpasses during 2003–2008.
Fig. 11. Difference in winter mean LST (K), Aqua minus Terra, for daytime and nighttime overpasses during 2003–2008.
from about 49% to 62% of the CONUS land area. For the winter case of 1 January 2008, comparison of Figs. 2 and 13 reveals large areas of gap-filling in the Southwest and in Tennessee. Data coverage is increased from about 43% to 58%. The percentages of the CONUS land area for which the merged LST data are available, i.e. either Aqua or Terra LST is available, are given in Table 4. Days for which all data from a given sensor are missing were not included in this analysis. Also given in Table 4 are the
increases in coverage due to merging, relative to Aqua LST alone, i.e. (Merged LST coverage − Aqua LST coverage) / Aqua LST coverage. Nighttime data coverage is slightly greater, showing the effect of slightly lower cloud amounts. For daytime periods, using Terra MODIS LST to fill gaps in Aqua LST observations increases the data coverage by almost 25%, and the increase is 30% for the nighttime overpasses. Inter-annual differences in coverage, or in increases relative to Aqua coverage alone, are small.
Fig. 12. Merged Aqua–Terra LST (K), daytime, 11 July 2008.
322
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
Fig. 13. Merged Aqua–Terra LST (K), daytime, 1 January 2008.
Table 4 Annual mean percentages of the CONUS domain for which LST data are available, for the merged data set, for daytime and nighttime overpasses. Also shown are the percent increases in coverage after merging, with respect to the Aqua data alone. Year
Daytime
2003 2004 2005 2006 2007 2008 Total period
Nighttime
Merged
% increase
Merged
% increase
55.5 55.6 57.6 57.0 57.7 58.1 56.9
24.1 26.0 24.5 24.1 24.3 24.4 24.5
59.5 58.4 61.0 61.4 61.5 62.5 60.7
28.7 32.4 29.5 30.0 30.2 29.5 30.0
3.3. Validation of results The Aqua–Terra ‘merged’ LST data set generated by the procedure described above is intended to supplement the Aqua daytime and nighttime LST products. While it adds value to the existing data sets, it is important to note a potential limitation of this approach. When and where Aqua LST values are missing, it is very likely due to cloud cover. These gaps are filled with Terra data from 3 h earlier if they are available, i.e. if the Terra pixel is cloud-free. The adjustment applied to the Terra LST value is based on seasonal Aqua and Terra mean LSTs, which are only available for cloud-free pixels. Thus, a value filled by the adjusted Terra observation is not, strictly speaking, an estimate of the LST for the missing, i.e. cloud-obscured, pixel at the
time of the Aqua overpass. Instead, the filled value is an approximation of the theoretical LST that would have been observed without cloud cover at the Aqua overpass time. However, the difference between the theoretical cloud-free and actual cloud-covered LST is not likely to be large at the places and times that the gap-filling algorithm is implemented, since the availability of a Terra LST observation 3 h prior to the Aqua overpass time implies conditions of partial or intermittent cloudiness. Under these conditions, LST values are not likely to be reduced substantially by cloud cover. We have performed two types of analysis to validate that the resulting product is useful. The first consists of a comparison of adjusted Terra LST and Aqua LST observations for days/pixels where both Terra and Aqua LSTs are available. Of course, this is not a situation where the gap filling procedure would be used, but is a first-order check on the performance of the seasonal, grid-based adjustment process. We selected 12 days during 2008 – one in the early, middle and later parts of each season – to evaluate how well the seasonal adjustment works within each season. For each day, we added the appropriate seasonal offset to the Terra LST grid and computed the differences between this adjusted Terra grid and Aqua LST. Fig. 14 shows one example, 28 August 2008, with LST differences shown only for grid cells for which Aqua and Terra LSTs are both available. The histogram of differences over the entire grid (Fig. 15), which shows an apparently normal distribution centered near zero, indicates no significant bias in estimating Aqua LST. The spatial mean difference for this case is −0.62 K. For the 12 days examined, the mean differences ranged from −0.62 K to 1.05 K. There was no apparent trend in these differences within a
Fig. 14. Difference between adjusted Terra LST and Aqua LST, for 28 August 2008. White areas are grid cells for which either Terra or Aqua LST was not available.
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
323
differences, mostly less than 1 K, are indicated. The overall mean difference is −0.89 K. To interpret this result, we first note that the difference at any grid cell is between the annual mean merged LST, computed over all days for which either Terra or Aqua LST is available, and the annual mean Aqua LST, computed over the smaller number of days for which Aqua LST is available. The LST difference is due to gap-filling on days when Terra LST is available and Aqua LST is missing. Analysis (not shown here) indicates that Terra LST on these days is on average lower than Terra LST on days for which both Terra and Aqua LSTs are available, since these days are characterized by partial or intermittent cloudiness at a given location. It follows that the differences presented here will, on average, be negative. To address the issue of temporal variability and to further validate the merged data product, we selected eight grid cells distributed across the CONUS domain. Table 5 provides for each location the number of days for which Aqua and merged LST are available, mean Aqua and merged LST, the mean merged–Aqua differences by season, and the standard deviations of Aqua and merged LST. Due to different numbers of days of available data across seasons, the annual mean differences are not simply the averages of the seasonal means. For this reason, the differences in annual means shown in Fig. 16 and Table 5 are not the best indicators of the overall impact of gap filling; seasonal means serve this purpose better if the sample size is adequate. Note that the large positive difference for Northern NY in winter is based on only 2 days of available Aqua LST. As for temporal variability, the standard deviations for these grid cells show no systematic differences.
Fig. 15. Frequency histogram of difference between adjusted Terra LST and Aqua LST for 28 August 2008.
season, i.e. the procedure seems to work similarly in the early and late parts of a season. The second type of analysis was to compare the difference between Aqua and merged LST means for calendar year 2008. As shown in Fig. 16, for most of the country, differences (merged− Aqua) are negative, ranging to about −6 K. Over the southeastern US, positive
Fig. 16. Difference between mean merged LST and mean Aqua LST for all daytime overpasses in 2008.
Table 5 Validation statistics for eight selected grid cells for 2008. Annual mean LST (K) (no. of days with LST data)
Annual standard deviation (K)
Seasonal mean merged–Aqua LST differences (K) (no. of days with Aqua LST data)
Location (U.S. state)
Aqua
Merged
Difference
Aqua
Merged
Winter
Spring
Summer
Fall
Northwest ND
292.9 (143) 290.2 (213) 296.9 (175) 299.0 (154) 290.8 (100) 315.4 (297) 293.1 (151) 298.6 (136)
292.3 (181) 292.7 (275) 297.3 (207) 300.3 (198) 287.6 (139) 315.5 (326) 293.1 (181) 295.3 (178)
−0.6
16.7
16.4
2.5
19.0
19.1
0.4
8.7
8.6
1.3
9.2
9.2
−3.2
8.9
11.3
0.1
11.3
11.5
0.0
9.9
9.7
−3.3
14.3
15.7
0.30 (22) 0.38 (58) 0.19 (35) 0.07 (38) 6.29 (2) −0.33 (65) −0.20 (24) 1.66 (13)
−0.14 (29) −0.19 (55) −0.50 (46) 0.46 (40) −2.70 (30) −0.01 (87) −0.66 (25) 1.00 (26)
−0.36 (55) 0.71 (45) −0.25 (43) −0.30 (26) 0.01 (39) −0.38 (64) 0.02 (55) −0.52 (62)
−1.74 (37) 1.54 (55) 0.53 (51) 0.27 (50) −0.83 (29) 0.27 (81) −0.67 (47) −1.93 (35)
Central CO North-central AL West-central GA Northern NY Southwest AZ Central IN Southeast WA
324
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
4. Summary and conclusions A procedure has been developed to use Terra MODIS LST observations to fill spatial gaps, which we believe to occur predominantly due to cloud cover, in Aqua MODIS LST data. The merged LST data set is created by filling in the missing Aqua data with Terra observations, adjusted to account for the different overpass times of the two platforms. The temperature adjustment is determined for each grid cell on a 1-km CONUS grid. The adjustment is also specific to season and time of day (day or night overpass). For the period 2003–2008, the merged data set increases data coverage by 25% and 30% for daytime and nighttime overpasses, respectively, relative to the Aqua LST product alone. The merged LST data set is intended as a supplement to the Aqua day/night data. It is important to recognize that gaps in the Aqua data, which occur primarily due to cloudiness, are replaced in this algorithm with Terra cloud-free LST observations. Due to analyses discussed herein, we believe that this does not introduce significant inconsistencies. The 6-year CONUS data set is a potentially valuable tool for weather and climate studies in which high spatial and temporal coverage are desired. Role of the funding source This research was funded by NASA's Applied Science Program and CDC through Cooperative Agreement NNM08AA04A to Universities Space Research Association. Neither NASA nor CDC played a role in the study design, the analysis and interpretation of the data, or the writing of the manuscript. Acknowledgments The authors would like to thank Ashutosh Limaye at NASA Marshall Space Flight Center for a thorough and insightful review of the manuscript. Communication with Zhengming Wan at the University of California, Santa Barbara, and Richard Frey at the University of Wisconsin helped greatly to clarify urban LST issues. Leslie McClure
and Shia Kent at the University of Alabama — Birmingham, Maury Estes and Sue Estes with Universities Space Research Association and Helen Flowers, Ambarish Vaidyanathan and Judith Qualters from the Centers for Disease Control and Prevention (CDC) provided valuable feedback on the development of the data product. References Atlas, R., Wolfson, N., & Terry, J. (1993). The effect of SST and soil moisture anomalies on GLA model simulations of the 1988 U.S. summer drought. Journal of Climate, 6, 2034–2048. Coops, N. C., Duro, D. C., Wulder, M. A., & Han, T. (2007). Estimating afternoon MODIS land surface temperatures (LST) based on morning MODIS overpass, location, and elevation information. International Journal of Remote Sensing, 28, 2391–2396. Dai, A., Trenberth, K. E., & Karl, T. R. (1999). Effects of clouds, soil moisture, precipitation, and water vapor on diurnal temperature range. Journal of Climate, 12, 2451–2473. Jin, M., & Dickinson, R. E. (1999). Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle. Part 1: Without clouds. Journal of Geophysical Research, 104, 2105–2116. Jin, M., & Treadon, R. E. (2003). Correcting the orbit drift effect on AVHRR land surface skin temperature measurements. International Journal of Remote Sensing, 24, 4543–4558. Land Processes DAAC (2008). USGS Earth Resources Observation and Science Center, & South Dakota School of Mines and Technology. MODIS reprojection tool user's manual, release 4.0. Mostovoy, G. V., King, R. L., Reddy, K. R., Kakani, V. G., & Filippova, M. G. (2006). Statistical estimation of daily maximum and minimum air temperatures from MODIS LST data over the state of Mississippi. Geoscience and Remote Sensing, 43, 78–110. Price, J. (1984). Land surface temperature measurements from the split window channels of the NOAA 7 advanced very high resolution radiometer. Journal of Geophysical Research, 89(D5), 7231–7237. Segal, M., Garratt, J. R., Kallos, G., & Pielke, R. A. (1989). The impact of wet soil and canopy temperatures on daytime boundary-layer growth. Journal of Atmospheric Science, 46, 3673–3684. Vancutsem, C., Ceccato, P., Dinku, T., & Connor, S. J. (2010). Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment, 114, 449–465. Wan, Z. (2007). Collection-5 MODIS land-surface temperature products users' guide. Available online. http://g.icess.ucsb.edu/modis/LstUsrGuide/MODIS_LST_products_ Users_guide_C5.pdf Wan, Z., & Dozier, J. (1996). A generalized split-window algorithm for retrieving landsurface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34, 892–905.
Contents lists available at SciVerse ScienceDirect
Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse
A daily merged MODIS Aqua–Terra land surface temperature data set for the conterminous United States William L. Crosson a,⁎, Mohammad Z. Al-Hamdan a, Sarah N.J. Hemmings a, Gina M. Wade b a b
Universities Space Research Association, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL 35805, USA Von Braun Center for Science and Innovation, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL 35805, USA
a r t i c l e
i n f o
Article history: Received 21 April 2011 Received in revised form 5 December 2011 Accepted 22 December 2011 Available online 2 February 2012 Keywords: Land surface temperature MODIS Aqua Terra Clouds
a b s t r a c t A major shortcoming of any remotely-sensed land surface temperature (LST) dataset is the lack of observations for cloud-covered areas. A method is presented that uses the Moderate Resolution Imaging Spectroradiometer (MODIS) flying on the Terra platform to fill in spatial gaps in the Aqua MODIS LST dataset over the conterminous United States (CONUS) and limited adjacent areas. Over this domain, data are available for only about 50% of all times and pixels for each of the two MODIS sensors. Coverage is highest in summer and lowest in winter, with major regional variations. The relative close temporal proximity (~3 h) of the Aqua and Terra overpasses provides an opportunity to combine information from the two data sources, which can reduce the data loss, most of which we assume is cloud-related. We applied the approach to create a ‘merged’ data set that supplements existing Aqua and Terra daytime and nighttime LST products. We used Terra LST data to fill gaps in Aqua data, resulting in a data set tied to the ~1:30 AM/PM overpass times, so that the resulting data closely approximate daily minimum and maximum LST values. In order to use Terra LST observations to supplement Aqua data, an adjustment was applied to account for the different overpass times of the two platforms. Terra's 10:30 AM overpass usually senses a cooler surface than does Aqua with its 1:30 PM overpass. Conversely, for nighttime overpasses, Terra typically measures a warmer surface at 10:30 PM than does Aqua at 1:30 AM. Our approach was to determine, by season, mean Aqua and Terra LST values on the CONUS grid, based on data from a multi-year (2003–2008) period. Adding the mean Aqua-Terra LST differences for the respective season and time of day to a daily gridded Terra LST field removes the mean offset related to overpass time, resulting in LST values that can then be used to fill Aqua LST data gaps. Using independent offsets for each grid cell and season provides a first-order accounting for factors such as land cover, elevation, terrain slope and aspect, latitude, season and snow cover, which control the diurnal cycle of LST. For the six-year period, the merged data set increases data coverage by 24% and 30% for daytime and nighttime overpasses, respectively, relative to the Aqua LST product alone. The CONUS data set is a potentially valuable tool for weather and climate studies in which high spatial and temporal coverage are desired. Crown Copyright © 2012 Published by Elsevier Inc. All rights reserved.
1. Introduction 1.1. Description of MODIS LST data The National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) instruments were launched on the Terra and Aqua platforms in December 1999 and May 2002, respectively. Both the Terra and Aqua satellites have a sunsynchronous near-polar orbit; however, they ascend/descend the equator at different times. Terra descends (ascends) the equator around 10:30 AM (10:30 PM) local time (LT). In contrast, Aqua descends ⁎ Corresponding author. Tel.: +1 256 961 7913; fax: +1 256 961 7788. E-mail addresses: [email protected] (W.L. Crosson), [email protected] (M.Z. Al-Hamdan), [email protected] (S.N.J. Hemmings), [email protected] (G.M. Wade).
(ascends) the equator at 1:30 PM (1:30 AM) LT. MODIS has a viewing swath width of 2330 km (http://modis.gsfc.nasa.gov), allowing global coverage every 1 to 2 days. The MODIS Land Surface Temperature (LST) daily products are generated using the split window algorithm (Price, 1984; Wan & Dozier, 1996), which uses bands 31 and 32 of MODIS's 36 spectral bands. The Level 3 product (MOD11A1 for Terra, MYD11A1 for Aqua) is produced in tiles, with each tile containing 1200 rows by 1200 columns at approximately 1-km resolution. The MODIS LST Level 2 pixels are placed onto a gridded sinusoidal projection, and if any pixels overlap during this process, they are averaged with the overlap area used as weight (Wan, 2007), resulting in the Level 3 LST product. An important tool in developing the LST products is the MODIS Cloud Mask algorithm, which determines the confidence of cloudfree pixels, for which LST can be measured. With a 1-km resolution,
0034-4257/$ – see front matter. Crown Copyright © 2012 Published by Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2011.12.019
316
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
Table 1 Annual mean percentages of the CONUS domain for which LST data are available, for Aqua and Terra, for daytime and nighttime overpasses. Year
2003 2004 2005 2006 2007 2008 Total period
Daytime
Nighttime
Aqua (1:30 PM)
Terra (10:30 AM)
Aqua (1:30 AM)
Terra (10:30 PM)
44.7 44.1 46.2 45.9 46.5 46.7 45.7
47.5 47.3 49.4 47.5 48.2 49.9 48.3
46.2 44.1 47.1 47.3 47.2 48.3 46.7
50.2 48.7 51.2 51.4 51.4 52.9 51.0
1.3. Previous research
Table 2 Days of year of missing Aqua and Terra LST data. Year
2003 2004 2005 2006 2007 2008 Total missing days
Daytime
Nighttime
Aqua
Terra
355 1
32, 350–358 49, 359 266 235 347 252, 355–357 19
estimate daily minimum and maximum near-surface air temperature (Mostovoy et al., 2006; Vancutsem et al., 2010). The close temporal proximity (~3 h) of the Aqua and Terra satellite overpasses provides an opportunity to combine information from the two data sources and can reduce areas of missing data, most of which we assume are cloud-related. The objective of this study is to use both data sources to create a ‘merged’ data set that supplements the existing Aqua LST data product. We chose to use Terra LST data to fill gaps in Aqua data, so that the resulting data set, which is tied to the ~1:30 AM/PM overpass times, approximates daily minimum and maximum LST values. Since the method depends on the availability of Terra observations to fill Aqua data gaps, it is most useful under conditions of partial or short-lived cloudiness.
Aqua
Terra 32, 351–358 50 235
336 1
356–358 14
the Cloud Mask algorithm uses a series of visible and infrared threshold tests and tests of consistency to determine a confidence of the satellite's view of the Earth's surface being unobstructed by clouds. If clouds are present, then LST data will not be available for the location (http://modis-atmos.gsfc.nasa.gov/). This paper describes a method to fill in missing MODIS Aqua LST data with Terra observations when the latter are available.
1.2. Objectives Land surface temperature is an important measurement in many modeling and analytical applications, as it represents the integrated effects on the land surface of shortwave and longwave radiation, land cover type, vegetation amount, antecedent precipitation, soil moisture, and near-surface meteorology (Dai et al., 1999). LST is a critical factor in determining the surface energy balance and fluxes of heat into the boundary layer (Atlas et al., 1993; Segal et al., 1989) and thus it influences weather and climate across a wide range of temporal scales. MODIS LST has been used, with some difficulty, to
Some studies in the research literature have shown that the diurnal cycle of land surface temperature can be estimated using observations taken at two different times. In order to estimate the diurnal LST cycle, Jin and Dickinson (1999) developed an algorithm to interpolate time series of surface skin temperature measured from polar orbiting satellites. They combined climate-land coupled model results with satellite and surface observations and interpolated satellite twicedaily observations into the diurnal cycle. They developed and evaluated their algorithm using Advanced Very High Resolution Radiometer (AVHRR) and Geostationary Operational Environmental Satellite (GOES)-8 data. Jin and Treadon (2003) developed an algorithm to correct the orbital drift effect on AVHRR LST measurements, which are collected at different local times during the satellite's lifetime. They also combined coupled model results with satellite observations. Using data collected over Canada, Coops et al. (2007) developed a simpler algorithm to estimate the afternoon MODIS LST based on the morning MODIS overpass accounting for location, land cover, and season. In this paper, we describe a similar kind of algorithm to estimate the afternoon MODIS LST based on the morning MODIS overpass (i.e., Terra data), and then merge those estimates with the LST data from the afternoon MODIS LST overpass (i.e., Aqua data). This process generates a national daily LST data set for the conterminous United States (CONUS) for 2003–2008 that provides the best possible spatial coverage from MODIS data. 1.4. Problem quantification The presence of clouds leads to large temporal and spatial gaps in LST data, particularly over the eastern U.S. The 2003–2008 annual mean percentages over the CONUS domain for which LST data are available are given in Table 1 for Aqua and Terra, for day and night
Fig. 1. Daytime Terra and Aqua LST for 11 July 2008. Areas of missing data are in white. The southeast corner of the CONUS domain is 22.79° N, 74.44° W, and the northwest corner is 48.18° N, 128.83° W.
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
317
Fig. 2. Daytime Terra and Aqua LST for 1 January 2008, with areas of missing data in white.
overpass times. These percentages were computed excluding days for which no data were available for the CONUS region (Table 2), since the objective here was to estimate the effect of cloud cover on data coverage. Overall, slightly less than 50% of the spatio-temporal domain is cloud-free. Data coverage is slightly higher for the nighttime periods and for Terra relative to Aqua. The spatial extent of missing Terra and Aqua LST data is illustrated in Figs. 1–2 for 11 July 2008 and 1 January 2008, respectively. These scenes are representative of winter and summer days in terms of mean cloudiness for the CONUS domain. The Terra and Aqua LST images for 11 July (Fig. 1) show similar patterns of LST and of cloudiness,
with a large cloudy area in the Southwest and somewhat smaller regions in the Southeast and Northeast. LST data are available from Terra for 51.3% of the CONUS and for 49.0% from Aqua. Large data gaps due to cloudiness in the 1 January image (Fig. 2) exist from the Midwest to the Northeast, as well as in the Pacific Northwest. On this date, there are also regions of missing Aqua LST in Tennessee and in Arizona/New Mexico for which Terra observations are available. Domain-wide, spatial coverage for this day is 50.3% and 42.8% from Terra and Aqua, respectively. The prevalence of missing data over a year varies greatly across the CONUS domain, as shown in Fig. 3 for summer and in Fig. 4 for
Fig. 3. Percentages of all summer days during 2003–2008 for which daytime Terra and Aqua LST data are available.
Fig. 4. Percentages of all winter days during 2003–2008 for which daytime Terra and Aqua LST data are available.
318
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
Table 3 Seasonal mean percentages of the CONUS domain for which LST data are available, for Aqua and Terra, for daytime and nighttime overpasses. Season
Spring (Mar–May) Summer (Jun–Aug) Fall (Sep–Nov) Winter (Dec–Feb)
Daytime
Nighttime
Aqua (1:30 PM)
Terra (10:30 AM)
Aqua (1:30 AM)
Terra (10:30 PM)
45.4 53.8 49.8 33.7
48.1 56.9 52.2 35.5
47.2 53.1 49.6 36.6
50.9 58.0 54.3 40.3
winter. In this analysis, ‘winter’ is defined as December–February and ‘summer’ is defined as June–August. These figures show the percentage of days for the given season during 2003–2008 for which LST values are available for each 1-km pixel. For summer (Fig. 3), the far western states exhibit excellent data coverage, with both Terra and Aqua LST values being available for over 90% of days at some points in California. Conversely, LST data are available for areas around the Gulf of Mexico on fewer than 10% of summer days due to daytime convective cloud cover. For winter (Fig. 4), data coverage is considerably lower on average, ranging from about 10% of days in the Northeast and Great Lakes region to about 75% in the Southwest. Data coverage near the Gulf of Mexico is higher in winter than in summer. Domain-wide, coverage statistics for Terra and Aqua and for daytime and nighttime overpasses are similar, as shown in Table 1. Data availability percentages for Aqua and Terra are presented by season in Table 3. Data coverage is best in summer, when more than 53% of pixels are cloud-free from both Aqua and Terra for day and night overpasses. The amount of available LST data is lowest in winter, averaging about 36% for the platform/overpass time combinations. Availability is slightly above 50% in fall and slightly below 50% in spring. One interesting feature of the daytime summer LST ‘availability’ map (Fig. 3) is that large cities can clearly be seen to have fewer days with LST observations for both Terra and Aqua MODIS sensors. This is not seen in the winter maps (Fig. 4) or at night (not shown) and seems to be the result of occasional loss of LST retrievals over cities in summer, as illustrated in Fig. 5 for the Philadelphia area on 28 August 2003. There were no clouds in the area at this time, as verified by National Weather Service observations and MODIS visible reflectance imagery,
but there are missing LST observations in the urban core, surrounded by high LST values (305–311 K). This phenomenon occurs on other clear summer days and is at least partially responsible for the reduced LST data frequency in summer for urban areas. Incorrectly labeling hot and bright urban pixels as cloudy is a known issue with the current MODIS Collection 5, particularly in the presence of high aerosol loadings; this will be improved in Collection 6 (R. Frey, personal communication, 13 April 2011). The impact of missing LST observations over urban areas on seasonal mean LST for Aqua and Terra is difficult to determine. The potential effect would be a summertime cool bias in urban cores due to the systematic elimination of very hot observations. However, the actual impact does not appear to be large, as MODIS mean daytime summer LSTs for cities are in fact considerably warmer than surrounding areas, as discussed in Section 2. Fig. 6 shows an example of the false cloudy problem for the Philadelphia–New York area on 17 July, 2006. The top panel shows the Aqua Quality Control fields, indicating the areas masked as cloud (indigo) and other pixels for which there are ‘quality issues’, i.e. the estimated LST and emissivity errors are substantial. Green dots indicate city centroids as defined by the U.S. Census. The bottom panel, the MODIS Aqua reflectance on this day, indicates clear skies for the region. The large errors in emissivity and LST relate to hot, bright urban pixels, with the result that many pixels are incorrectly masked as cloud. 2. Methodology Daily Aqua and Terra MODIS LST (Level 3, Collection 5) data sets for the CONUS and surrounding areas were downloaded for the sixyear period spanning 1 January 2003–31 December 2008 from NASA's Land Processes Distributed Active Archive Center (DAAC) using the Warehouse Inventory Search Tool. The MODIS Reprojection Tool (Land Processes DAAC, 2008) was used to extract daily LST data from multiple grid tiles and to mosaic the data onto a 1-km CONUS spatial grid that consists of 3280 rows and 5158 columns. In order to use Terra LST observations to supplement Aqua data, an adjustment is needed to account for the different overpass times of the two platforms. During daytime, Terra's 10:30 AM overpass usually senses a cooler surface than does Aqua with its 1:30 PM overpass. Conversely, for nighttime overpasses, Terra typically measures a warmer surface at 10:30 PM than Aqua does at 1:30 AM. Our approach was to determine, by season, mean Aqua and Terra LST values
Fig. 5. Aqua daytime LST showing missing LST retrievals in Philadelphia's urban core, 28 August 2003.
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
319
are different in the two pairs of figures. These seasonal mean LST values are not intended to be an LST climatology. Instead, since LST observations are available only for clear pixels, the means shown here should be interpreted as mean clear-sky LST and were created solely to enable calculation of Aqua–Terra LST differences for each season, which are needed in the merging process described below. The seasonal means for each sensor provide the information needed to offset Terra MODIS LST observations to the Aqua observation time. For each season, we first computed mean LST differences (Aqua minus Terra) on the CONUS grid. Adding these differences for the respective season and time of day to a daily gridded Terra LST field removes the mean offset related to overpass time, resulting in LST values that can then be used to fill Aqua LST data gaps. By calculating independent offsets for each grid cell and season, this approach provides a first-order accounting for factors that control the diurnal cycle of LST, such as land cover, elevation, terrain slope and aspect, latitude, season and snow cover. These land surface characteristics were considered in a spatially aggregated sense by Jin and Dickinson (1999) in using polar orbiting satellite data to construct diurnal LST cycles. The Aqua–Terra merged LST CONUS data set was created using the described approach for the period 2003–2008. Two gridded fields were created for each day, corresponding to the 1:30 PM and 1:30 AM Aqua overpasses. The Aqua LST grid was the starting point for the merged data. Where the Aqua LST value was missing, it was replaced by the Terra LST value after adding the Aqua–Terra LST difference for the correct season and overpass time. Calculations were performed independently for each grid cell over the CONUS domain.
3. Results 3.1. LST spatial patterns
Fig. 6. MODIS Aqua QC flags (top) and visible band reflectance (bottom) for 17 July 2006.
on the CONUS grid, based on data from 2003 to 2008. As examples, mean summer and winter Aqua LST for daytime and nighttime overpasses are shown in Figs. 7 and 8, respectively. Note that the color scales
In the seasonal mean temperature images (Figs. 7–8), the typical temperature patterns associated with latitude and elevation are easily seen. Bodies of water are also evident, being usually cooler than land areas in summer and warmer in winter. Urban areas can also be identified by their higher temperatures, especially in summer (Fig. 7). As shown by Aqua daytime LST for the northeastern U.S. on 25 July 2003 in Fig. 9, the urban areas of Philadelphia and Greater New York City are clearly several degrees hotter than nearby urban regions, matching the mean summer LST pattern evident in Fig. 7. Mean Aqua–Terra LST difference images are shown in Fig. 10 for summer and in Fig. 11 for winter. For the daytime overpasses, Aqua LST typically exceeds Terra LST by 1–5 K. The main exception occurs in winter in the Great Plains in the northern U.S. and southern Canada, where the Aqua–Terra LST difference is predominantly negative (Fig. 11, left panel). The reason for this negative difference is under investigation. This region is characterized in winter by snowcovered grasslands and fallow cereal crops such as wheat, and it differs from southeastern Canada (where Aqua LST is higher than Terra LST), which is mainly forested. Complex patterns in the daytime LST difference are visible in scattered areas in the Rocky Mountains, where Terra LST is higher than Aqua LST in both summer and winter. This seems to be associated with east-facing slopes, which reach daily maximum temperature earlier. In summer during the daytime (Fig. 10, left panel), cities have a larger positive Aqua–Terra LST difference compared to rural areas. This pattern is not evident at night or during the winter. Bodies of water can be identified in the LST difference maps due to the different rates of diurnal warming and cooling relative to land areas. For nighttime overpasses, the Aqua–Terra LST differences are mainly between −1 and −5 K during summer (Fig. 10, right panel). In winter, nighttime differences range primarily from 0 to −4 K, but scattered areas of small positive values are observed (Fig. 11, right panel).
320
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
Fig. 7. Summer mean LST (K) for daytime and nighttime Aqua overpasses during 2003–2008.
Fig. 8. Winter mean LST (K) for daytime and nighttime Aqua overpasses during 2003–2008.
3.2. Merged LST data Examples of the daytime merged LST data set are shown in Figs. 12 and 13 for 11 July 2008 and 1 January 2008, respectively, the same
dates as the Aqua and Terra LST images shown in Figs. 1–2. For 11 July, areas where Terra data were used to fill gaps are scattered, but they can easily be observed in the Northern Plains by comparing Figs. 1 and 12. For this date, merging increased Aqua's LST coverage
Fig. 9. Aqua daytime LST for the Philadelphia–New York City region, 25 July 2003.
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
321
Fig. 10. Difference in summer mean LST (K), Aqua minus Terra, for daytime and nighttime overpasses during 2003–2008.
Fig. 11. Difference in winter mean LST (K), Aqua minus Terra, for daytime and nighttime overpasses during 2003–2008.
from about 49% to 62% of the CONUS land area. For the winter case of 1 January 2008, comparison of Figs. 2 and 13 reveals large areas of gap-filling in the Southwest and in Tennessee. Data coverage is increased from about 43% to 58%. The percentages of the CONUS land area for which the merged LST data are available, i.e. either Aqua or Terra LST is available, are given in Table 4. Days for which all data from a given sensor are missing were not included in this analysis. Also given in Table 4 are the
increases in coverage due to merging, relative to Aqua LST alone, i.e. (Merged LST coverage − Aqua LST coverage) / Aqua LST coverage. Nighttime data coverage is slightly greater, showing the effect of slightly lower cloud amounts. For daytime periods, using Terra MODIS LST to fill gaps in Aqua LST observations increases the data coverage by almost 25%, and the increase is 30% for the nighttime overpasses. Inter-annual differences in coverage, or in increases relative to Aqua coverage alone, are small.
Fig. 12. Merged Aqua–Terra LST (K), daytime, 11 July 2008.
322
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
Fig. 13. Merged Aqua–Terra LST (K), daytime, 1 January 2008.
Table 4 Annual mean percentages of the CONUS domain for which LST data are available, for the merged data set, for daytime and nighttime overpasses. Also shown are the percent increases in coverage after merging, with respect to the Aqua data alone. Year
Daytime
2003 2004 2005 2006 2007 2008 Total period
Nighttime
Merged
% increase
Merged
% increase
55.5 55.6 57.6 57.0 57.7 58.1 56.9
24.1 26.0 24.5 24.1 24.3 24.4 24.5
59.5 58.4 61.0 61.4 61.5 62.5 60.7
28.7 32.4 29.5 30.0 30.2 29.5 30.0
3.3. Validation of results The Aqua–Terra ‘merged’ LST data set generated by the procedure described above is intended to supplement the Aqua daytime and nighttime LST products. While it adds value to the existing data sets, it is important to note a potential limitation of this approach. When and where Aqua LST values are missing, it is very likely due to cloud cover. These gaps are filled with Terra data from 3 h earlier if they are available, i.e. if the Terra pixel is cloud-free. The adjustment applied to the Terra LST value is based on seasonal Aqua and Terra mean LSTs, which are only available for cloud-free pixels. Thus, a value filled by the adjusted Terra observation is not, strictly speaking, an estimate of the LST for the missing, i.e. cloud-obscured, pixel at the
time of the Aqua overpass. Instead, the filled value is an approximation of the theoretical LST that would have been observed without cloud cover at the Aqua overpass time. However, the difference between the theoretical cloud-free and actual cloud-covered LST is not likely to be large at the places and times that the gap-filling algorithm is implemented, since the availability of a Terra LST observation 3 h prior to the Aqua overpass time implies conditions of partial or intermittent cloudiness. Under these conditions, LST values are not likely to be reduced substantially by cloud cover. We have performed two types of analysis to validate that the resulting product is useful. The first consists of a comparison of adjusted Terra LST and Aqua LST observations for days/pixels where both Terra and Aqua LSTs are available. Of course, this is not a situation where the gap filling procedure would be used, but is a first-order check on the performance of the seasonal, grid-based adjustment process. We selected 12 days during 2008 – one in the early, middle and later parts of each season – to evaluate how well the seasonal adjustment works within each season. For each day, we added the appropriate seasonal offset to the Terra LST grid and computed the differences between this adjusted Terra grid and Aqua LST. Fig. 14 shows one example, 28 August 2008, with LST differences shown only for grid cells for which Aqua and Terra LSTs are both available. The histogram of differences over the entire grid (Fig. 15), which shows an apparently normal distribution centered near zero, indicates no significant bias in estimating Aqua LST. The spatial mean difference for this case is −0.62 K. For the 12 days examined, the mean differences ranged from −0.62 K to 1.05 K. There was no apparent trend in these differences within a
Fig. 14. Difference between adjusted Terra LST and Aqua LST, for 28 August 2008. White areas are grid cells for which either Terra or Aqua LST was not available.
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
323
differences, mostly less than 1 K, are indicated. The overall mean difference is −0.89 K. To interpret this result, we first note that the difference at any grid cell is between the annual mean merged LST, computed over all days for which either Terra or Aqua LST is available, and the annual mean Aqua LST, computed over the smaller number of days for which Aqua LST is available. The LST difference is due to gap-filling on days when Terra LST is available and Aqua LST is missing. Analysis (not shown here) indicates that Terra LST on these days is on average lower than Terra LST on days for which both Terra and Aqua LSTs are available, since these days are characterized by partial or intermittent cloudiness at a given location. It follows that the differences presented here will, on average, be negative. To address the issue of temporal variability and to further validate the merged data product, we selected eight grid cells distributed across the CONUS domain. Table 5 provides for each location the number of days for which Aqua and merged LST are available, mean Aqua and merged LST, the mean merged–Aqua differences by season, and the standard deviations of Aqua and merged LST. Due to different numbers of days of available data across seasons, the annual mean differences are not simply the averages of the seasonal means. For this reason, the differences in annual means shown in Fig. 16 and Table 5 are not the best indicators of the overall impact of gap filling; seasonal means serve this purpose better if the sample size is adequate. Note that the large positive difference for Northern NY in winter is based on only 2 days of available Aqua LST. As for temporal variability, the standard deviations for these grid cells show no systematic differences.
Fig. 15. Frequency histogram of difference between adjusted Terra LST and Aqua LST for 28 August 2008.
season, i.e. the procedure seems to work similarly in the early and late parts of a season. The second type of analysis was to compare the difference between Aqua and merged LST means for calendar year 2008. As shown in Fig. 16, for most of the country, differences (merged− Aqua) are negative, ranging to about −6 K. Over the southeastern US, positive
Fig. 16. Difference between mean merged LST and mean Aqua LST for all daytime overpasses in 2008.
Table 5 Validation statistics for eight selected grid cells for 2008. Annual mean LST (K) (no. of days with LST data)
Annual standard deviation (K)
Seasonal mean merged–Aqua LST differences (K) (no. of days with Aqua LST data)
Location (U.S. state)
Aqua
Merged
Difference
Aqua
Merged
Winter
Spring
Summer
Fall
Northwest ND
292.9 (143) 290.2 (213) 296.9 (175) 299.0 (154) 290.8 (100) 315.4 (297) 293.1 (151) 298.6 (136)
292.3 (181) 292.7 (275) 297.3 (207) 300.3 (198) 287.6 (139) 315.5 (326) 293.1 (181) 295.3 (178)
−0.6
16.7
16.4
2.5
19.0
19.1
0.4
8.7
8.6
1.3
9.2
9.2
−3.2
8.9
11.3
0.1
11.3
11.5
0.0
9.9
9.7
−3.3
14.3
15.7
0.30 (22) 0.38 (58) 0.19 (35) 0.07 (38) 6.29 (2) −0.33 (65) −0.20 (24) 1.66 (13)
−0.14 (29) −0.19 (55) −0.50 (46) 0.46 (40) −2.70 (30) −0.01 (87) −0.66 (25) 1.00 (26)
−0.36 (55) 0.71 (45) −0.25 (43) −0.30 (26) 0.01 (39) −0.38 (64) 0.02 (55) −0.52 (62)
−1.74 (37) 1.54 (55) 0.53 (51) 0.27 (50) −0.83 (29) 0.27 (81) −0.67 (47) −1.93 (35)
Central CO North-central AL West-central GA Northern NY Southwest AZ Central IN Southeast WA
324
W.L. Crosson et al. / Remote Sensing of Environment 119 (2012) 315–324
4. Summary and conclusions A procedure has been developed to use Terra MODIS LST observations to fill spatial gaps, which we believe to occur predominantly due to cloud cover, in Aqua MODIS LST data. The merged LST data set is created by filling in the missing Aqua data with Terra observations, adjusted to account for the different overpass times of the two platforms. The temperature adjustment is determined for each grid cell on a 1-km CONUS grid. The adjustment is also specific to season and time of day (day or night overpass). For the period 2003–2008, the merged data set increases data coverage by 25% and 30% for daytime and nighttime overpasses, respectively, relative to the Aqua LST product alone. The merged LST data set is intended as a supplement to the Aqua day/night data. It is important to recognize that gaps in the Aqua data, which occur primarily due to cloudiness, are replaced in this algorithm with Terra cloud-free LST observations. Due to analyses discussed herein, we believe that this does not introduce significant inconsistencies. The 6-year CONUS data set is a potentially valuable tool for weather and climate studies in which high spatial and temporal coverage are desired. Role of the funding source This research was funded by NASA's Applied Science Program and CDC through Cooperative Agreement NNM08AA04A to Universities Space Research Association. Neither NASA nor CDC played a role in the study design, the analysis and interpretation of the data, or the writing of the manuscript. Acknowledgments The authors would like to thank Ashutosh Limaye at NASA Marshall Space Flight Center for a thorough and insightful review of the manuscript. Communication with Zhengming Wan at the University of California, Santa Barbara, and Richard Frey at the University of Wisconsin helped greatly to clarify urban LST issues. Leslie McClure
and Shia Kent at the University of Alabama — Birmingham, Maury Estes and Sue Estes with Universities Space Research Association and Helen Flowers, Ambarish Vaidyanathan and Judith Qualters from the Centers for Disease Control and Prevention (CDC) provided valuable feedback on the development of the data product. References Atlas, R., Wolfson, N., & Terry, J. (1993). The effect of SST and soil moisture anomalies on GLA model simulations of the 1988 U.S. summer drought. Journal of Climate, 6, 2034–2048. Coops, N. C., Duro, D. C., Wulder, M. A., & Han, T. (2007). Estimating afternoon MODIS land surface temperatures (LST) based on morning MODIS overpass, location, and elevation information. International Journal of Remote Sensing, 28, 2391–2396. Dai, A., Trenberth, K. E., & Karl, T. R. (1999). Effects of clouds, soil moisture, precipitation, and water vapor on diurnal temperature range. Journal of Climate, 12, 2451–2473. Jin, M., & Dickinson, R. E. (1999). Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle. Part 1: Without clouds. Journal of Geophysical Research, 104, 2105–2116. Jin, M., & Treadon, R. E. (2003). Correcting the orbit drift effect on AVHRR land surface skin temperature measurements. International Journal of Remote Sensing, 24, 4543–4558. Land Processes DAAC (2008). USGS Earth Resources Observation and Science Center, & South Dakota School of Mines and Technology. MODIS reprojection tool user's manual, release 4.0. Mostovoy, G. V., King, R. L., Reddy, K. R., Kakani, V. G., & Filippova, M. G. (2006). Statistical estimation of daily maximum and minimum air temperatures from MODIS LST data over the state of Mississippi. Geoscience and Remote Sensing, 43, 78–110. Price, J. (1984). Land surface temperature measurements from the split window channels of the NOAA 7 advanced very high resolution radiometer. Journal of Geophysical Research, 89(D5), 7231–7237. Segal, M., Garratt, J. R., Kallos, G., & Pielke, R. A. (1989). The impact of wet soil and canopy temperatures on daytime boundary-layer growth. Journal of Atmospheric Science, 46, 3673–3684. Vancutsem, C., Ceccato, P., Dinku, T., & Connor, S. J. (2010). Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment, 114, 449–465. Wan, Z. (2007). Collection-5 MODIS land-surface temperature products users' guide. Available online. http://g.icess.ucsb.edu/modis/LstUsrGuide/MODIS_LST_products_ Users_guide_C5.pdf Wan, Z., & Dozier, J. (1996). A generalized split-window algorithm for retrieving landsurface temperature from space. IEEE Transactions on Geoscience and Remote Sensing, 34, 892–905.