Severe cloud contamination of MODIS Land Surface Temperatures over an Arctic ice cap, Svalbard

Remote Sensing of Environment 142 (2014) 95–102

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Severe cloud contamination of MODIS Land Surface Temperatures over an Arctic ice cap, Svalbard Torbjørn I. Østby ⁎, Thomas V. Schuler, Sebastian Westermann Institute of Geoscience, University of Oslo, PO Box 1047 Blindern, N-0316 Oslo, Norway

a r t i c l e

i n f o

Article history: Received 18 June 2013 Received in revised form 6 November 2013 Accepted 8 November 2013 Available online 14 December 2013 Keywords: MODIS Land Surface Temperature Glacier Snow Validation Austfonna Svalbard

a b s t r a c t Land Surface Temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) is among today's major tools for climate monitoring. After more than a decade of operation, there still remains considerable uncertainty about its performance in polar regions. We compare MODIS LST to eight years of in situ observations of surface and air temperatures on Austfonna, an Arctic ice cap located on Svalbard. From measurements of longwave radiation and air temperature, an in situ cloud index is derived to quantify the cloudiness at the study site and assess the possibility for LST being affected by erroneous cloud detection. According to this cloud index, only 26% of satellite-derived LST values are acquired under clear-sky conditions. In situations, when the cloud index indicates clouds, about 40% of the scenes are classified as clear-sky by MODIS during winter, while it is only about 20% in the summer period. The shortcomings of the MODIS cloud detection are reflected by a Root Mean Square Error (RMSE) of LST compared to in situ surface temperatures of 7.0 K under actual cloudy conditions, in contrast to 3.0 K under actual clear-sky conditions. The overall RMSEs of LST compared to surface and air temperatures are 5.3 K and 6.2 K, respectively. The bias under actual clear-sky conditions displays a clear seasonality, with MODIS LST being strongly cold-biased during winter and slightly warm-biased during summer. The study exemplifies the challenges of thermal remote sensing over snow and ice surface in areas with frequent cloudiness, especially during polar night. Nevertheless, remotely sensed LST offers a great, but hitherto largely unexploited opportunity for environmental monitoring in regions with sparse observations, in particular if the cloud detection can be improved. © 2013 Elsevier Inc. All rights reserved.

1. Introduction Since the early 1980s, satellite-derived surface temperatures have been employed for studies of climatology, meteorology, hydrology, ecology and glaciology in polar regions, where ground-based data sets are sparse (Coll et al., 2005; Frey, Kuenzer, & Dech, 2012; Hall et al., 2012; Serreze et al., 2000). From time series of remotely sensed Land Surface Temperatures (LST), Comiso (2003) deduced evidence for warming of the Arctic in the 1980s and 1990s. At present, the “Moderate Resolution Imaging Spectroradiometer” (MODIS) on board the satellites Terra and Aqua (launched in 2000 and 2002, respectively) is an important instrument providing remotely sensed LST. A number of studies have validated MODIS LST over snow and ice surfaces. However, these studies are geographically biased towards the interior of Greenland and Antarctica and may not be representative for the polar regions in general. While the interior of the ice sheets are characterized by low temperatures and dry snow, occasional and/or seasonal melt impacts e.g. snow and ice temperatures, water content, grain size or emissivity

⁎ Corresponding author. E-mail address: [email protected] (T.I. Østby). 0034-4257/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2013.11.005

in other polar regions. The atmospheric conditions such as water vapor and clouds physics are also different in a maritime setting like Svalbard compared to the more continental climate in the ice sheets interior. Thermal remote sensing in the polar regions is problematic due to spectral similarities of clouds and snow in the visual bands, which can lead to a contamination of LST with cloud top temperatures (e.g. Ackerman et al., 1998, 2008; Comiso, 2006; Liu, Ackerman, Maddux, Key, & Frey, 2010). While the target accuracy of MODIS LST measurements under clear-sky conditions is better than 1 K (Wan, 2008; Wan, Zhang, Zhang, & Li, 2002), considerably lower accuracies between 1 and 4 K are found for snow and ice surfaces in the polar regions (Hall, Key, Casey, Riggs, & Cavalieri, 2004; Hall et al., 2008; Koenig & Hall, 2010; Scambos, Haran, & Massom, 2006). In this study, we present a validation of MODIS LST with in situ measurements from the Austfonna ice cap on Svalbard spanning in total eight years, thus covering a large part of the MODIS acquisition period. We assess the accuracy of MODIS Land Surface Temperature, and provide a comprehensive performance evaluation of the cloud detection for the study site. In the following, we distinguish between MODIS cloud conditions (as derived from the MODIS cloud product, Section 2.2) and in situ cloud conditions (as derived from in situ measurements, Section 2.3.2).

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The area is characterized by humid conditions and frequent cloud cover (Schuler et al., 2013), but only few direct cloud observations are available. Lidar measurements from the western side of Svalbard reveal that fog and low clouds are frequent during summer and early autumn, while high clouds and clear-sky are dominant during winter (Shiobara, Yabuki, & Kobayashi, 2003).

2. Data and methods 2.1. Study area Austfonna is a ~7800 km2 ice cap centered at 80° north, 24° east on the island Nordaustlandet, northeast Svalbard in the Norwegian Arctic (Moholdt & Kääb, 2012). The ice cap has a simple domed shaped geometry gently rising from sea level up to the summit at approximately 800 ma.s.l. (Moholdt, Hagen, Eiken, & Schuler, 2010). Since 2004, an Automatic Weather Station (AWS) has been operated at an altitude of 369 ma.s.l. on the outlet glacier Etonbreen (621 km2), which flows westwards from the summit with a mean surface slope of 1° (Fig. 1). The AWS is situated at the average glacier equilibrium line, so that bare ice appeared at the surface for a few weeks in July/August in some years (Østby, Schuler, Hagen, Hock, & Reijmer, 2013). Located between the northern extent of the North Atlantic current and the southern edge of the multi-year sea ice, Svalbard is one of the climatically most sensitive regions of the world (Rogers, Yang, & Li, 2005). Both temperature and precipitation feature a large interannual variability, which strongly depends on the cyclone activity (HanssenBauer & Førland, 1998). Winters are relatively mild despite the northern latitude; air temperature rarely drops below − 40 °C and short melt events and rainfall are not uncommon even in midwinter (Nordli, 2010). Austfonna represents one of the coldest areas of Svalbard; the mean annual air temperature at the AWS is − 8.5 °C, with March being the coldest month at − 17 °C and July and August being the only months with averages above 0 °C. Precipitation is usually connected with southeasterly air flow advecting moist air from the Barents Sea. This precipitation pattern is reflected by the snow distribution across the ice cap (Schuler et al., 2007; Taurisano et al., 2007); in the northwestern sector where the AWS is located, winter snow thickness is usually less than 1 m, while it can be 2–3 times more extensive on the southeastern side of Austfonna (Dunse et al., 2009).

2.2. MODIS Land Surface Temperatures We employ the MODIS level 3 collection 5 LST products from the satellites Terra (MOD11A1) and Aqua (MYD11A1). LST is retrieved from several spectral bands using the Split-window algorithm (Wan, 2008; Wan & Dozier, 1996). The MODIS level 3 products provide two LST values per day for each of the two satellites. At high latitude, these LST values are selected from the much larger number of satellite measurements available due to the convergence of the satellite orbits. On Austfonna, LST measurements with close-to-nadir angles are usually acquired around 13:30 and 19:30 local time for Terra, while the corresponding Aqua acquisition times are around 07:30 and 02:30. The study area is located across the boundary between the two tiles h18v00 and h18v01 (downloaded from http://reverb.echo.nasa.gov/) (Fig. 1). Satellite-measurements of LST require clear-sky, and cloudcovered areas are masked out using the MODIS cloud mask products MOD35_L2 (Terra) and MYD35_L2 (Aqua) (Ackerman et al., 1998; Frey et al., 2008). In the MODIS process chain, quality control flags are automatically generated to represent the confidence level of the produced LST. Each pixel is assigned a quality flag either as good, suspicious or failed, if the presence of clouds precludes the production of LST. We have studied MODIS LST over a 131 × 136 km area covering most of Nordaustlandet. The LST product is examined in detail for the AWS on the Austfonna ice cap (Fig. 1), where LST is compared to in situ data and quality flags are analyzed.

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East UTM33X (km) Fig. 1. Map of the Austfonna ice cap (100 m elevation contours) indicating the location of the automatic weather station (AWS). Elevation after Moholdt and Kääb (2012) and glacier outline after Nuth et al. (2013). The border separating the h18v00 and h18v01 tiles of MODIS at 80°N is shown in red. The inset shows the location of Austfonna in the Svalbard archipelago.

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2.3. In situ meteorological measurements at the AWS

2.4. Comparing MODIS with in situ temperatures

The AWS on Austfonna (Fig. 1) has been operated almost continuously since 2004 and is maintained during annual field visits in April/ May. At 1 to 2 m height above the snow or ice surface, air temperature, relative humidity, wind speed and direction, as well as incoming and outgoing short- and longwave radiations are measured at intervals of 2 to 6 min and hourly averages are stored on a Campbell Scientific datalogger (Schuler et al., 2013). A description of the records and analysis of data quality is presented by Schuler et al. (2013). Low battery voltage, damages inflicted by polar bears and instrument failure caused three major data gaps: March to April 2005, July 2007 to July 2008, and after August 2011.

Time series of MODIS LST are extracted for the location of the AWS. Individual measurements are compared to the closest hourly value of temperature recorded by the AWS. Since the temperature of a melting glacier surface cannot exceed 0 °C, in situ temperatures are capped at this value when compared to TMODIS. For the period of synchronous air temperature and longwave radiation measurements at the AWS, there are in total 3941 successful (quality label good & suspicious) LST observations available. 3. Results 3.1. MODIS landmask for the tile h18v00

2.3.1. In situ temperature measurements, Tpyrg and Tair Surface temperatures at the AWS are derived from pyrgeometer measurements of downwelling (L↓) and upwelling (L↑) longwave radiations using the Stefan–Boltzmann law,  T pyrg ¼

 L↑ −L↓ ð1−Þ −4 σ

ð1Þ

where σ = 5.670373 × 10 − 8 Wm − 2 K − 4 represents the Stefan– Boltzmann constant and  = 0.99 the surface emissivity of snow and ice (Dozier & Warren, 1982; Snyder, Wan, Zhang, & Feng, 1998). The manufacturer of the CNR1 net radiometer reports a 10% uncertainty for the daily sums of net radiation (all four radiation components combined). However, Michel, Philipona, Ruckstuhl, Vogt, and Vuilleumier (2008) found the downward looking CNR1 pyrgeometer to have a RMSE less than 2 W m−2 on a glacier in the Alps. An erroneous longwave radiation of 2 W m−2 imposes an uncertainty of about 0.5 K to Tpyrg. The air temperature Tair is measured by a non-ventilated Vaisala HMP-45D sensor inside a radiation shield. While the site is exposed to prevalent winds providing ventilation, the temperature uncertainty is likely to be larger than the 0.2 °C sensor accuracy on the few calm and sunny days due to radiative heating. In addition, unsupervised measurements in high-Arctic conditions may be affected by icing or riming of the sensors, which can inflict measurement errors that are hard to detect and quantify. Such conditions have been documented at the study site during short periods, but the time series is not strongly affected (Schuler et al., 2013). 2.3.2. In situ cloud index A cloud index n is derived from measured longwave radiation following Giesen, van den Broeke, Oerlemans, and Andreassen (2008), see also van den Broeke, van As, Reijmer, and van de Wal (2004). The method is based on the principle that under cloudy conditions, net longwave radiation is balanced due to thermal equilibrium between surface and cloud base so that both are radiating at similar temperatures (van den Broeke, Reijmer, & van de Wal, 2004). Fully overcast skies (n = 1) are assumed when L↓ equals the black body radiation at the given air temperature. In contrast, under clear-sky conditions L↓ is anticipated to be significantly smaller than under a cloudy sky at the same air temperatures. To represent a clear-sky (n = 0) characteristic for the local atmospheric conditions, a second-order polynomial is fitted through the lower 5th percentile of L↓ in each 1 K air temperature interval. In doing so, the extreme values of n are defined for a given temperature and for each pair of Tair and L↓, the corresponding value of n is obtained by linear interpolation between the 1 K intervals. We classify the cloud conditions into clear-sky conditions when n b 0.2, overcast conditions for n N 0.8 and mixed conditions for 0.2 b n b 0.8 (Giesen et al., 2008). While visually observed cloudiness usually represents the fractional cloud cover, the cloud index employed here is symptomatic for cloud optical thickness (van den Broeke, Reijmer, van As, & Boot, 2006).

For the tile h18v00, an erroneous landmask is employed in the MODIS processing chain. As evident from Fig. 6, the land-sea mask is displaced by approximately 10 km westward for this tile, while a correct mask is used for the southern tile h18v01. As exemplified by Fig. 2, the LST-data are correctly geolocated in h18v00, but LST is produced over sea areas, while missing over land on the other hand: in November 2004, the sea north of Austfonna was ice free, resulting in a temperature difference of approximately 20 K between the sea and the much colder land areas. 3.2. MODIS LST and in situ temperatures Fig. 3 displays TMODIS and daily averages of the radiometric surface temperature Tpyrg at the AWS from spring 2004 to autumn 2011. The shaded area in the figure display periods when validation of MODIS has not been performed due to AWS failure or suspicious AWS data quality. The two temperature records closely follow each other, but TMODIS is usually cold-biased. Melting conditions at the surface are accurately reproduced by MODIS during summer, at least in the upper bound, as TMODIS rarely exceeds 0.2 °C, while values above 0.3 °C never occur. The scatter plots in Fig. 4 directly compare TMODIS to in situ measurements of Tpyrg and Tair at the MODIS acquisition time. On average, TMODIS is 3.3 K lower than the surface temperature and 5.2 K lower than the air temperature. TMODIS is rarely higher than Tair, while it is often higher than Tpyrg, especially for Tpyrg N − 10∘C. A root-mean-square error

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Fig. 2. TMODIS for November 2004 when the sea was mostly ice-free, indicating the displacement of the MODIS land mask. The temperature contrast between the sea and colder land areas coincides with the actual shape of the coast line. The scene corresponds to the upper left quadrant of the study area.

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Fig. 3. Comparison of TMODIS at the AWS and daily averages of Tpyrg from April 2004 to August 2011. Shaded areas: no MODIS validation due to instrument failure or suspicious data quality.

(RMSE) of 5.3 °C and a correlation of 0.84 is found between TMODIS and Tpyrg, while for Tair the corresponding numbers are 6.2 °C and 0.89, respectively. The color of the markers in Fig. 4 represent the in situ cloud index n (Section 2.3). In general, large TMODIS errors are associated with cloudy conditions.

3.3. MODIS acquisition and clouds To test the performance of the MODIS cloud detection, we study the LST acquisition frequency under different cloud conditions, as determined by the in situ cloud index (Section 2.3). In total, 9442 MODIS LST measurements are analyzed for which a simultaneous in situ cloud index is available. Furthermore, for about half of the potential LST acquisitions, clouds are detected by the MODIS cloud product and no LST values are produced. In these cases, no specific time stamp exists and we use the average cloud index for the 12 hour interval containing the satellite overpass to represent this cloudiness. Fig. 5 shows the frequency of successfully produced LST and failures for 10 different

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classes of n, where n b 0.2 represents in situ clear-sky conditions and n N 0.8 in situ cloudy conditions. As expected, the MODIS LST production rate decreases with cloudiness. In periods when the in situ cloud index indicates cloudy conditions, MODIS LST is produced in 37% of the cases, whereas LST is produced in 94% of the in situ clear-sky cases. The fractions of LST flagged good and suspicious are fairly similar under in situ clear-sky conditions, but during in situ cloudy conditions about two thirds of the produced LST are flagged as suspicious. Whereas the in situ cloudiness exhibits a seasonal pattern with fewer clouds between April and August and more overcast during the fall season, the rate of successful LST acquisition displays little seasonal variability. Table 1 presents MODIS cloud misidentification rates during polar night (Nov–Feb) and polar day (Jul–Aug). When the in situ cloud index suggests overcast conditions during winter, MODIS produces LST during ~ 40% of the time. This suggests that the MODIS cloud detection fails in almost half of the cases during the dark winter period. In contrast, clear-sky conditions are usually not misidentified as cloudy.

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Fig. 4. TMODIS vs. Tpyrg (a) and Tair (b) at the AWS. The color of the markers represents the in situ cloud index (n) at the LST acquisition time. Additionally, the 1:1-line (dashed) and a linear fit (solid) are shown.

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average RMSE of 6.8 °C and 7.5 °C. Therefore, these flags effectively mask out strongly erroneous TMODIS, but are only operating under cold conditions (Tair b −20 ° C). As shown in Fig. 7, there is a pronounced seasonality for the TMODIS error, both for in situ clear-sky and overcast conditions. While TMODIS is cold-biased throughout the year under in situ cloudy conditions, MODIS LST is slightly warm-biased from May to August during in situ clear-sky conditions. The annual mean TMODIS is 4.0 K lower than Tpyrg and 6.1 K lower than Tair. Taking the MODIS sampling bias into account, TMODIS is 5.1 K too low compared to allsky Tpyrg. Both Tair and Tpyrg are generally lower under in situ clear-sky conditions while TMODIS is generally lower under in situ cloudy conditions.

Good, N=1643 Suspicious, N=2871 Failed, N=4928

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4. Discussion

Fig. 5. MODIS LST acquisition vs. in situ cloud index n (bins of 0.1) at the AWS. The stacked bars show the MODIS internal quality check where each measurement is flagged as either: good, suspicious or failure.

The MODIS cloud detection is influenced not only by solar illumination, but surface characteristics also play a role (Fig. 6). In August, the production rate over glacier-covered areas is about twice as high as compared to the adjacent snow-free tundra (Fig. 6b). For the snowfree tundra, there are almost no MODIS LST acquisitions labeled good by the internal quality check. 3.4. TMODIS error under different conditions In Fig. 6 the average difference ΔT between TMODIS and Tpyrg is shown as a function of the in situ cloud index n and the sun angle. ΔT increases with in situ cloudiness from about 0 K for clear-sky conditions to 6 K for cloudy conditions. The difference decreases with increasing insolation. The average RMSE of TMODIS is 5.3 K and 6.2 K compared to Tpyrg and Tair, respectively. As indicated by Fig. 6c, the difference increases with cloudiness, with a RMSE of 7.0 K when cloudy and 3.0 K under clearsky. There is also a pronounced dependency on daylight (sun angle N0°), yielding RMSEs of 6.0 K and 4.3 K for night and day, respectively. Table 2 shows the differences between TMODIS and both Tair and Tpyrg for different meteorological conditions and internal quality flags. The difference between TMODIS and Tair is larger than between TMODIS and Tpyrg, which can be explained by the prevalent near-surface temperature inversions over snow (Serreze & Barry, 2005; Westermann, Lüers, Langer, Piel, & Boike, 2009). Near surface inversions occur most of the time at the AWS, though being negligible under windy and cloudy conditions. On average, the surface temperature is 2.2 K lower than the air temperature at the AWS, with the strongest inversions occurring during clear-sky nights with low wind speeds. The data flagged by the internal quality check (QC) as good have a slightly smaller RMSE (4.7 K) than those flagged as suspicious (5.5 K). Little is gained in terms of accuracy by the main QC-flag and more than half of the data are discarded if one only uses the ones flagged good. The LST flagged other quality and LST error N 2 K feature an

Table 1 MODIS cloud misidentification in percent for summer (Jul–Aug) and winter (Nov–Feb). When MODIS detects clouds, LST is not produced and no specific time stamp exists for the LST retrieval. In these cases we use the average cloud index for the 12 hour intervals 12 a.m.–12 p.m. (Aqua) and 12 p.m.–12 a.m. (Terra). Number of cases in brackets. Season

Clear-skya when cloudyb

Cloudya when clear-skyb

Summer Winter

16.6 (673) 42.1 (2738)

2.4 (247) 7.2 (279)

a b

MODIS cloud algorithm. AWS cloud index.

99

4.1. Uncertainties and error sources The uncertainty of our analysis originates from: i) uncertainty of in situ measurements and ii) uncertain representativeness of point measurements for 1 km MODIS pixels. We regard the representativeness of the AWS site for the surrounding 1 km area as good: the glacier surface is homogeneous and gently sloping (1.1°) westwards. The glacier ice at Austfonna generally features a low content of dust and debris, with a satellite-derived summer ice albedo of Etonbreen as high as 0.44 (Greuell et al., 2007). Still, spatially variable dust concentrations may occur during summer, but they are not likely to affect surface temperatures which are confined to 0 °C under melting conditions. Stronger temperature heterogeneities may occur during freeze-up or nocturnal freezing, when ponds, surface streams and saturated snow can be significantly warmer than dry snow or bare ice due to the release of latent heat. The main uncertainty regarding this study is most likely related to the in situ measurements. The uncertainty in Tpyrg is controlled by the pyrgeometer accuracy described in Section 2.3; Tair uncertainty is dominated by the measuring setup rather than the sensor accuracy. Radiative heating of passively ventilated air temperature sensors is a known problem under calm and sunny conditions, which is most likely a main source of uncertainty for Tair. When Hall et al. (2008) validated MODIS for a Greenland site using air temperatures they excluded conditions with insolation exceeding 240 W m−2 and wind speed less than 4 m s−1. Applying the same selection criterion at Austfonna showed larger discrepancies between TMODIS and Tair. This is most likely attributed to the deficient cloud mask rather than indicative of radiative heating of the sensor. Scambos et al. (2006) found strong near surface inversion over Antarctic sea ice and questioned the usefulness of air temperature for validating LST. At Austfonna, Tair is measured 1–2 m above the surface and was on average 3.6 K warmer than Tpyrg during in situ clear-sky and 1.4 K warmer during allsky. We report a slightly stronger near-surface inversion than other studies conducted in the polar region (Hall et al., 2008; Hudson & Brandt, 2005; Koenig & Hall, 2010). The strong observed inversions may partly be explained by measurements errors, since the inversion is weaker when calm and sunny situations are excluded (radiative heating). One can not preclude that riming or other unforeseen events have reduced the data quality of the autonomous AWS. However, shorter episodes with degraded measurement performance do not attain a large weight with regard to the long times series and do not affect the main findings of the paper, i.e. a significantly higher RMSE under in situ cloudy conditions. 4.2. Cloud contamination Cloud detection with imagery in the visible and infrared spectrum is known to be difficult in the polar regions, especially during night (e.g. Ackerman et al., 2008; Liu et al., 2010; Wang & Key, 2003, 2005). Deficient cloud masking has been identified as one of the main factors limiting the accuracy of LST products over snow and ice (Hall et al.,

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Fig. 6. Monthly fraction of successfully produced LST for 2004 to 2011 for April (a) and August (b). Mean temperature difference ΔT = Tpyrg − TMODIS as a function of c) cloudiness (bins of 0.1) and d) sun angle (bins of 2.5°). N is the number of observation in each bin. The dark bar indicates negative sun angles (sun below the horizon).

2004; Westermann, Langer, & Boike, 2012). In general, MODIS LST is smooth and homogeneous over the ice cap, largely reflecting the homogeneity of the glacier in terms of topography, temperature and emissivity. In contrast, on the tundra, the number of observations and LST values exhibits a larger heterogeneity. In August when the tundra most likely is free of snow, there are twice as many observations on the glacier compared to adjacent tundra (Fig. 6b). The abrupt change in numbers of observations is closely aligned with the glacier margin, suggesting that surface characteristics influence the MODIS cloud detection. In situ clear-sky conditions are reliably detected by MODIS. During summer at the AWS, less than 3% of the in situ clear-sky days were mapped as cloudy by the MODIS algorithm. The major shortcoming is the detection of cloudy conditions over snow and ice, in particular during the polar night in winter (e.g. Liu, Key, Frey, Ackerman, & Menzel, 2004): during summer, 17% of the in situ cloudy days at the AWS are interpreted as clear-sky by MODIS, while it is 42% during polar night conditions. This in contrast to Hall, Williams, Casey, DiGirolamo, and Wan (2006) who found that the MODIS cloud mask is restrictive and maps too many clouds over snow and ice. Cloudiness frequently occurs at Austfonna, and due to shortcomings in cloud detection, 40% of the successfully produced LST values are acquired during in situ cloudy, 35% during an in situ mixed sky and only 26% during in situ clear-sky conditions. The effect of failed cloud detection is highlighted by a RMSE of 7.0 K under in situ cloudy, in contrast to 3.0 K under in situ clear-sky conditions. The erroneous MODIS cloud mask leads to a considerable contamination with cloud

top temperatures in the LST-product (Hall et al., 2013). The seasonal variation in ΔT cannot be explained by seasonality in the cloud coverage, since the seasonal trend in ΔT occurs under both in situ clear-sky and cloudy conditions. The cloud type may play a role in explaining the seasonality of ΔT under cloudy conditions. Fog is common during summer at Svalbard (Shiobara et al., 2003). The cloud top temperature of fog/low clouds is most likely closer to the surface temperature than those of high clouds. Hence, cloud contamination by fog may impose a smaller error on the MODIS product than most other cloud types. The warm bias of MODIS LST during in situ clear-sky conditions in summer remains unexplained, but might be caused by deficiencies in the atmospheric correction. Unlike most other surfaces, strong near surface inversion over snow and ice may occur when the sun is strong, since air temperatures may exceed freezing point, while a melting surface cannot. Further studies should examine the seasonality in the MODIS bias. 4.3. Other TMODIS validation studies As displayed in Table 3, the present study finds larger RMSE between in situ measurements and MODIS LST than those reported by other validation efforts over snow and ice surfaces. While most of the previous studies have evaluated TMODIS under actual clear-sky conditions (as determined by in situ observations), we have performed validation whenever MODIS LST is produced (i.e. when MODIS cloud detection indicates clear-sky conditions). Furthermore, most previous studies were conducted in the continental setting of large ice sheets,

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Table 2 Averages (overlines), discrepancies (Δ), root-mean-square errors (RMSE) and correlations (r) of MODIS LST TMODIS, air temperature Tair and surface temperature from the pyrgeometer Tpyrg, under different conditions and MODIS Quality Check flags (QC). Subscripts a and p refer to air and pyrgeometer temperatures, respectively. Tair N 0 ° C was capped at 0 °C when computing ΔT. Hall et al. (2008) excludes data when S↓ N 240 W m−2 or wind speed b 4 m s−1. N is number of data points. Data

T MODIS

Tpyrg

−14.6 −12.1 −18.6 −15.7 −9.9 −19.7 −13.5 −15.2 −13.4 −31.6 −13.7 −14.2 −32.9

All In situ clear-sky In situ cloudy Hall et al. (2008) criterion Sun No sun QC: good QC: suspicious QC: good quality QC: other quality QC: LST err b1 QC: LST err b2 QC: LST err b3

Tair

Tp

ΔT p

RMSEp

r2p

−11.3 −11.8 −12.8 −11.8 −8.1 −14.8 −11.0 −11.6 −10.3 −26.3 −11.1 −10.6 −27.2

3.3 0.3 5.8 3.9 1.8 4.8 2.6 3.7 3.1 5.4 2.6 3.6 5.7

5.3 3.0 7.0 5.5 4.3 6.0 4.7 5.5 5.1 6.8 4.7 5.4 7.5

0.84 0.93 0.84 0.85 0.81 0.84 0.83 0.85 0.82 0.45 0.83 0.84 0.40

where e.g. the atmospheric correction in the MODIS LST algorithm may perform better than in the more maritime climate of Svalbard, resulting in a lower uncertainty there. It must be emphasized that the uncertainty estimates obtained by the various studies in Table 3 are not directly comparable, due to; i) different in situ temperature instrumentations, ii) different MODIS products, i.e. L2, L3, as well as collections 4 and 5. As suggested by Hall et al. (2012) we can confirm the temperature trend in the MODIS uncertainty previously indicated by Koenig and Hall (2010). In Fig. 4 the slopes of the regressions between TMODIS and Tpyrg and between TMODIS and Tair are 1.03 and 1.00, respectively. When corrected for the deficient MODIS cloud mask by adopting the in situ cloud index we find slopes of 1.14 and 1.15 to Tpyrg and Tair, respectively. As a result, TMODIS under actual clear-sky is cold-biased for temperatures below − 12 °C and warm-biased for temperatures above, which is closely linked to the seasonal bias. On average, MODIS is only slightly cold-biased by 0.9 K under in situ clear-sky conditions, similar to the findings of (Westermann et al., 2012) for another Svalbard location.

5. Conclusions The MODIS LST level 3 product is compared to eight years of meteorological data of an automatic weather station located on the Austfonna ice cap, Svalbard. In total 3941 MODIS LST measurements are compared to in situ surface and air temperatures. In addition, an in situ cloud index is derived from the AWS measurements to evaluate the performance of the MODIS cloud mask. The study suggests a severe contamination of

Clear sky Mixed Cloudy

3 2 1

ΔT (°C)

0 −1 −2 −3 −4 −5 −6 −7 J

F

M

A

M

J

J

A

S

O

N

D

Ta

ΔT a

RMSEa

−9.4 −8.0 −12.2 −10.4 −5.3 −13.9 −8.7 −9.8 −8.3 −25.1 −8.8 −8.8 −26.0

5.2 4.0 6.4 5.3 4.7 5.7 4.8 5.4 5.1 6.6 4.8 5.3 6.9

6.2 4.6 7.6 6.4 5.6 6.8 5.7 6.5 6.1 7.7 5.7 6.3 8.4

0.89 0.95 0.84 0.88 0.89 0.85 0.90 0.89 0.88 0.51 0.90 0.88 0.44

3941 1065 1487 3468 2052 1866 1429 2512 3680 261 1444 2365 132

the remotely sensed LST by cloud-top temperatures due to a deficient cloud detection (Hall et al., 2013), especially during polar night. Of the successfully produced MODIS LST, 38% are acquired under in situ cloudy conditions and only 26% under in situ clear-sky conditions. The deficient MODIS cloud detection is reflected in a RMSE of 7.0 K under in situ cloudy conditions, in contrast to 3.0 K under in situ clear-sky conditions. The cloud mask generally underdetects clouds over snow and ice, especially during nighttime. An overall RMSE of 5.3 K is obtained in this study for MODIS LST. The internal quality flags may be used to improve the accuracy, but at the cost of reduced data amount. Utilizing only data of good quality reduces the data amount from 52% to 17% of the initial data amount, although many of the omitted data agree well with in situ temperatures. MODIS is cold-biased by 0.9 K under in situ clearsky conditions, but exhibits a strong seasonality with a − 3 K bias in February and + 2 K in June. Taking the sampling bias into account, MODIS is cold biased by 5.1 K and 6.2 K compared to in situ measurements of surface and air temperature, respectively. For most applications of the MODIS LST-product in similar environments, our new uncertainty estimate is more appropriate than the previous ones. We compare in situ temperatures with MODIS LST whenever MODIS assumes clear-sky conditions, even if cloud-cover is indicated by in situ measurements. Hence, we estimate uncertainty, as if in situ cloud observations were not available, i.e. the common situation in most applications. In contrast, previous studies were exclusively performed under in situ clear-sky, thus obtaining MODIS uncertainties under optimal atmospheric conditions. Despite a much lower accuracy than the 1 K target provided in Wan et al. (2002), MODIS LST can provide an important climate record in the data-sparse Arctic cryosphere.

Table 3 MODIS LST validation studies over snow and ice surfaces. Most uncertainties are given as RMSE, see footnotes for specifications. The type of in situ measurements used for TMODIS validation is given in the first column. Data

Location

Uncertainty (°C)

N

References

Tpyrg Tpyrg Tair Tair Several Tair Tair Tpyrg Tpyrg Tair

Snow, CA-USA Antarctic sea ice Arctic buoys South Pole Greenland Greenland Greenland Greenland Svalbard Svalbard

0.5 ±1.0 3.7 (1.6)a 1.7a 2b 2.1a 4.1a 3.1a 5.3 (3.0)a 6.2 (4.6)a

1 17 25 255 ? 48 250 62 3941 3941

Wan et al. (2002) Scambos et al. (2006) Hall et al. (2004) Hall et al. (2004) Hall et al. (2006) Hall et al. (2008) Koenig and Hall (2010) Koenig and Hall (2010) This study This study

a

Fig. 7. Mean temperature difference ΔT = TMODIS − Tpyrg under different cloud conditions.

N r2a

b

RMSE (extra cloud inspection). Qualitative analysis/manual inspections.

102

T.I. Østby et al. / Remote Sensing of Environment 142 (2014) 95–102

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