High latitudes in the Northern Hemisphere have experienced significant recent warming, with Yukon and parts of Alaska experiencing the greatest warming of sub-arctic environments over the last 50 years [1
]. This warming trend is expected to continue throughout the Arctic [3
]. Temperature changes will affect many aspects of northern alpine ecosystems and the associated cryosphere, including snow extent, tundra land cover composition and distribution, permafrost, net ecosystem productivity, and population dynamics of animals and plants [4
Mean surface temperature provides a fundamental measure for understanding change occurring in Arctic, sub-Arctic and alpine land surface processes. However, the absence of fine scale, continuous temperature monitoring over large geographical areas makes identifying climate induced changes difficult. Air temperature is commonly measured hourly at ground-based monitoring stations which are usually sparsely located in valley bottoms, and thus the lower end of the elevation gradient in alpine regions. For example, in Yukon, Canada, which has an area of 483,450 km2
, there are only eleven meteorological stations maintained by Environment Canada ( http://www.climate.weatheroffice.gc.ca
). Furthermore, in the mountainous southwest Yukon the highest elevation station is 807 m above sea level. Spatial interpolation of air temperature data can lead to considerable uncertainties in the resulting temperature fields [5
], especially at higher elevation.
An increasingly common method for tracking surface temperature trends in the Arctic involves the use of infrared satellite measurements of surface temperature [6
]. Polar and near polar orbiting satellites exhibit progressively overlapping swaths, thus at high latitudes many ground surface observations are available each day. However, the swath overlap of near polar orbiting satellites over sub-polar regions is often insufficient to construct diurnal temperature curves because of cloud cover, thus necessitating a robust gap filling method to convert single daily observations to daily averages.
Land surface temperature (or skin temperature) is likely a better descriptor than air temperature for processes that are strongly linked to the ground surface such as low stature arctic vegetation growth, permafrost dynamics, and gas fluxes. Terrestrial ecosystems are often described in terms of their characteristic annual and seasonal temperature and precipitation patterns [7
]. The distribution and abundance of arctic vegetation is dictated to a large extent by summer temperature, which can be characterised by satellite derived land surface temperature metrics [9
MODIS Terra and Aqua satellites are in sun-synchronous near polar orbits, with Aqua in an ascending orbit and Terra in a descending orbit with equatorial crossings at 10:30 AM for Terra and 1:30 PM for Aqua (local solar time). Both satellites require approximately 90 min to complete an Earth orbit. MODIS Terra data became available in February 2000 and Aqua data became available in July 2002. At the latitude of our study site (60°N), Aqua coverage coincided with dawn and predawn. MODIS is in a low altitude orbit (705 km), and has 36 spectral bands thus improving the cloud detection ability of MODIS over previous satellites.
The daily record of LST observations is limited to those from the MODIS Terra platform between 2000 and 2002, which halves the amount of daily observations provided by the Terra and Aqua ensemble. Validation of MODIS land surface temperature products has emphasised the night-time product [11
], which is easier to validate. The effects of relative humidity, wind speed, soil moisture, air temperature and sensor view zenith angle on night time land surface temperatures have been investigated, of which only sensor view zenith angle showed a weak influence on LST error propagation [11
Analysis of thermal images and up to 18–20 daily MODIS LST returns collected over wet polygonal tundra in Siberia [13
] and high Arctic tundra in Svalbard [14
] indicated that several improvements regarding the performance of MODIS LST over tundra should be considered: (i) improved cloud cover masking and gap filling techniques; (ii) accounting for water bodies; (iii) accounting for snow cover and soil properties. The acquisition of daily satellite observations is complicated by extensive cloud cover commonly experienced in the arctic, and summer cloud cover has been shown to be increasing in the circumpolar arctic [15
]. Cloud contamination, due to a failure of the cloud detection algorithm, is a known cause of LST error in the split window temperature extraction method [16
]. However cloud contamination continues to be an issue with MODIS LST data where approximately 15% of data contain unidentified cloud contamination [17
Due to the spatial and temporal autocorrelation of thermal infrared satellite data, spatial distances of 100–300 km and temporal intervals of up to two days were sufficient to represent missing data [19
]. Furthermore, the diurnal cycle of LST could be modeled with solar geometry and two daily LST data points [20
]. Maximum air temperature can be modelled with the diurnal cycle, cloud fraction and minimum air temperature derived from night-time LST [21
]. The use of a generic modelled diurnal cycle in the interpolation of surface temperature to a diurnal cycle has also been proposed [22
], where maximum daily LST, under clear sky conditions will correspond to peak solar insolation, with a small adjustment for phase lag [22
] and minimum LST will correspond to time of sunrise [23
]. It is important when aggregating LST observations in high latitudes to pay close attention to daytime length, satellite tile area and over pass time. These factors will contribute to the magnitude of within scene and between scene measurements, especially for observations on the continental scale.
A strong linear correlation between AVHRR LST and air temperature has been reported for temperatures < 0 °C [6
]. Many recent studies have found strong linear correlations between MODIS LST (maximum, minimum and average) and air temperatures for many land cover types in Africa [21
], in Portugal [24
], on the Tibetan Plateau [25
] and over the conterminous United States [26
]. Steps have been taken to compare MODIS LST with much coarser spatial resolution passive microwave surface temperatures [27
] and climate reanalysis products [28
]. The typical range of errors when correlating LST to air temperature is approximately 2–3 °C [29
] irrespective of the methodology, spatial or temporal resolutions.
This study was conducted to assess the viability of using an interpolated air temperature curve and a single day time LST value for the purpose of extending the daily average LST in a mountainous sub-Arctic region when (1) persistent cloud cover often reduces LST acquisition to a single daily value and (2) for early years of Terra data acquisition, when MODIS Aqua was not operational. We produced a Interpolated Curve Mean Daily Surface Temperature (ICM) product by combining single daily tiled day-time MODIS LST observations (regardless of acquisition time during the day) with the daily average air temperature and daily air temperature curve, using data from 2008. We then compared daily average temperatures from these seven sites to daily average LST created from minimum and maximum LST values (MMM) produced from both MODIS Aqua and Terra swath data. We compared both models to daily average air temperature from seven independent meteorology stations located on glacier, barren and tundra land covers. Lastly, we aggregated the MMM model to 8-days and compared the result to similarly aggregated air temperature to assess the consequences of aggregating LST data. The methods outline above identify limitations in previous approaches by (i) incorporating air temperature observations with LST, to bolster limited LST observations, instead of trying to convert LST to air temperature; and (ii) requiring the use of information about the diurnal curve and data availability for improving LST aggregation.
ICM validation showed statistically significant strong linear correlations (0.72 ≥ R2
≥ 0.85) with mean daily air temperature (Figure 5
; Table 2
). RMS errors ranged between 4.09 and 4.90 K (Table 2
), which were approximately 1 to 2 K larger than typical when compared to air temperatures [29
]. Variations in R2
values did not correlate with distance of the interpolated diurnal curve form (Table 1
) indicating factors other than spatial interpolation were playing a dominant role in correlation variability. The strength of the R2
values correlated to land surface structure, with the largest R2
values corresponding with glacier and sparsely vegetated surfaces, and the lowest R2
values corresponding with sites with large relief in topography, tall shrubs and complex vegetation canopies (Tables 1
). The steepest slopes and largest y-intercepts most consistently occurred on glaciers, and to a lesser degree on exposed rock (barren) that displayed high topographic relief. All of the validation data sets were contained in the Burwash Landing Thiessen polygon (Figure 1
) for the ICM method and all are above tree-line.
The MMM show statistically significant strong linear correlations (R2
= 0.90, RSME = 2.67 K) with mean daily air temperature (Figure 6
; Table 2
). However, only 44 out of a possible 1,575 LST observations intersecting the seven air temperature monitoring stations, had maximum and minimum values originating on the same day (Table 3
). The majority of the observations were from the predawn over the icefields, whereas the number of observations were roughly equal further to the east over the lower elevation tundra. Aggregating to 8-day averages had little effect on the number of LST values correlated to air temperature (Table 4
). However, the aggregation did have a noticeable effect on the linear regression (R2
= 0.84, RSME = 1.54 K), where the slope become flatter than the 1:1 line and the y-intercept became positive and increased by 1.6 °C compared to the daily MMM result. The RMS error reported here for daily and aggregated MMM method is similar to that reported elsewhere [29
The simple process of temperature curve shape interpolation applied across large geographic areas defined by Thiessen polygon allocation produced Mean Daily Temperatures. The interpolation of diurnal air temperature curve at distances of 45–62 km from Burwash (A) station, is supported by spatial auto-correlation of LST values across distances of 100–300 km [19
]. The reduced requirement of LST data (e.g., one point instead of two or more daily points) in the ICM is important for regions where extensive cloud cover is a perennial concern and where the number of daily overpasses is reduced compared to high latitude locations. Although the average LST calculated from minimum and maximum LST values (MMM method) provided better agreement with daily air temperature averages, the very small number of daily averages indicated that direct calculation of average LST was not always feasible for the study area. The ICM model should find application in many instances of land surface monitoring where available daily LST observations are insufficient represent measurements at dawn and solar noon. Regardless of which of the two above methods are considered, both methods require independent observations of air temperature and surface thermal infrared temperature, which cannot replace each other, even though they are strongly correlated.
The LST diurnal temperature curve is mostly, but not wholly, comprised of absorbed radiation and atmospheric interactions [22
]. The high measurement accuracy of the air temperature and MODIS LST is very small compared to RMSE values presented here, which indicates that the variability in the results is a function of a time lag difference in processes that affect LST and air temperature differently and not measurement error. The difference between ICM and daily average air temperature was likely the combination of several factors, including:
Cloud contamination or surface cloud shadow likely contributed to variation in the ICM values because this method uses a single daily input compared to two inputs required for the MMM method, which has been shown to increase correlation to air temperature [24
]. Variation in LST was likely influenced by cloud contamination, which likely disproportionately affected the ICM product because of its dependence on observations of maximum LST [36
]. Furthermore, cloud shadow can cause differences in LST [11
] across spatial and temporal scales and might also contribute to the influence of cloud contamination in LST.
Decoupling of air temperature and LST at higher temperatures, caused by low albedo vegetation cover, was likely playing a role in the high RMS error reported here, especially for the ICM product because its sole input originates from the warmest part of the day. This interpretation was supported by the two non-vegetated sites (glaciers) displaying the lowest RMSE values for the ICM. Meltwater ponds at the glacier sites are small compared to the LST grid cell. However the air temperature which was recorded over glacier, snow and ice was contained within a LST grid cell which contains high percentages of talus, is the probable reason for the large y-intercepts. As expected the MMM LST product had a lower RMS error likely due to the moderating effect of the minimum temperature. However, the daytime MODIS LST product has a larger confidence in identifying daytime cloud cover cloud mask compared to the night-time mask [36
], which should act to minimize cloud contamination in the ICM product compared to the MMM product.
Error could include the moderating effect on LST caused by surface water in tundra environments [13
] or soil heat or soil water storage [14
]. Neither the validation sites, nor the Haines Junction Environment Canada monitoring site contained large bodies of water within the MODIS grid cells. Nevertheless, the effects of small and ephemeral surface water bodies on soil heat or water storage remain unaccounted for in both models. Another error could be the larger error in day time LST caused by angular anisotropy [37
] when compared with night time LST. The larger RMS error values reported for the ICM product (compared to the MMM which included pre-dawn minimum LST values) likely accentuates angular effects, where the MMM minimizes them.
The differences in slope and y-intercept values between the daily average and the 8-day aggregation results from cloud cover influence of LST observations used in the 8-day period. The daily MMM values are calculated from the same day that has both clear sky minimum and maximum values, which suggests there is little cloud influence compared to the 8-day aggregation. Because the majority of values used in the 8-day aggregation did not have matching daily maximum or minimum values, we can deduce that cloud cover is influencing the LST value to a greater extent than the MMM method. This interpretation is supported by the 8-day values being warmer than the daily values when the temperature was below 0 °C and colder than the daily values when the temperature was above 0 °C. This effect should be considered when aggregating LST values for comparison to air temperature.
Application of the two models presented here can provide a relatively high resolution (1 km) spatial/climatic product with which to evaluate the glacier surface processes and tundra dynamics in the sub-arctic, either on their own or in conjunction with other modelled products in a gap filling capacity. For low statured arctic vegetation, LST is likely a better indicator of plant temperature than air temperature [38
] and there are well documented relationships between leaf temperature and photosynthesis of arctic tundra plants [39
Although improvements in cloud masking will reduce the negative effects of cloud contamination in LST data as a whole, the cost in terms of the reduction of data availability and quality could be significant in areas of the cryosphere that experience large amounts of cloud cover. Future work should be devoted to the identification of cloud contaminated LST data and the degree to which contaminated LST values are affected. By quantifying the degree of contamination, data volume will be preserved and quality improved. Furthermore, the effect that different LST aggregation schemes have on the aggregated products should also be investigated with consideration being given to land cover and regional climate. Statistical techniques such should be investigated to make the most use of limited ground station data. The implementation of the ICM method for every LST observation could provide a powerful technique to fill gaps and aggregate LST data. Integration with interpolated air temperature products or other forms of surface temperature, such as passive microwave, could provide other sources of data. However, mismatches in spatial and temporal scale will pose challenges, especially when trying to mitigate the influence of persistent cloud cover.