Comparison of MODIS-derived land surface temperatures with ground surface and air temperature measurements in continuous permafrost terrain

Hachem, Sonia; Duguay, Claude R.; Allard, Michel

doi:10.5194/tc-6-51-2012

Cited by 139 publications

(111 citation statements)

References 43 publications

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“…Hall et al 2012), to monitor melt events on the ice sheet (Hall et al, 2013), and as input for surface mass balance or snowpack modeling (Fréville et al, 2014;Shamir and Georgakakos, 2014;Navari et al, 2016). A number of validation studies present results acquired over various time scales and in different locations to 130 determine the accuracy of the MODIS surface temperature products in the cryosphere (Hall et al, 2004(Hall et al, , 2008Koenig and Hall, 2010;Westermann et al, 2012;Hachem et al, 2012;Shuman et al, 2014;Østby et al, 2014;Shamir and Georgakakos, 2014;Hall et al, 2015;Williamson et al, 2017). validation studies and is discussed in further detail in the discussion section.…”

Section: Remote Sensing Of Surface Temperaturementioning

confidence: 99%

Near-surface thermal stratification during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

Adolph

Albert

Hall

2017

Preprint

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10As rapid warming of the Arctic occurs, it is imperative that climate indicators such as temperature be monitored over large areas to understand and predict the effects of climate changes. Temperatures are traditionally tracked using in situ 2 m air temperatures, but in remote locations where few ground-based measurements exist, such as on the Greenland Ice Sheet, temperatures over large areas are assessed using remote sensing techniques. Because of the presence of surface-based temperature inversions in ice-covered areas, differences between 2 m air temperature and the temperature of the actual snow 15 surface (referred to as "skin" temperature) can be significant and are particularly relevant when considering validation and application of remote sensing temperature data. We present results from a field campaign extending from 8 June through 18July 2015, near Summit Station in Greenland to study surface temperature using the following measurements: skin temperature measured by an infrared (IR) sensor, thermochrons, and thermocouples; 2 m air temperature measured by a NOAA meteorological station; and a MODerate-resolution Imaging Spectroradiometer (MODIS) surface temperature product. Our 20 data indicate that 2 m air temperature is often significantly higher than snow skin temperature measured in-situ, and this finding may account for apparent biases in previous surface temperature studies of MODIS products that used 2 m air temperature for validation. This inversion is present during summer months when incoming solar radiation and wind speed are both low. As compared to our in-situ IR skin temperature measurements, after additional cloud masking, the MOD/MYD11 Collection 6 surface-temperature standard product has an RMSE of 1.0°C, spanning a range of temperatures from -35°C to -5°C. For our 25 study area and time series, MODIS surface temperature products agree with skin surface temperatures better than previous studies indicated, especially at temperatures below -20°C where other studies found a significant cold bias. The apparent "cold bias" present in others' comparison of 2 m air temperature and MODIS surface temperature is perhaps a result of the nearsurface temperature inversion that our data demonstrate. Further investigation of how in-situ IR skin temperatures compare to MODIS surface temperature at lower temperatures (below -35°C) is warranted to determine if this cold bias does indeed exist. 30The Cryosphere Discuss., https://doi

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Section: Remote Sensing Of Surface Temperaturementioning

confidence: 99%

Near-surface thermal stratification during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

Adolph

Albert

Hall

2017

Preprint

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“…Such requirements are, however, extremely difficult to fulfill at Arctic sites, where logistical constraints limit the number of field stations. Hachem et al (2012) compared MODIS LST to ground and air temperatures over herbaceous and shrub tundra sites across North America. MODIS LST errors found by Hachem et al (2012) are in the range of errors found in this study.…”

Section: Uncertainties and Errors Of Modis Lst Evaluationmentioning

confidence: 99%

“…Hachem et al (2012) compared MODIS LST to ground and air temperatures over herbaceous and shrub tundra sites across North America. MODIS LST errors found by Hachem et al (2012) are in the range of errors found in this study. Alternatively to radiometer measurements, measurements of near-surface ground and air temperature might therefore serve as validation data for MODIS LST.…”

Section: Uncertainties and Errors Of Modis Lst Evaluationmentioning

confidence: 99%

“…Efforts have recently increased to quantify LST uncertainty in the high latitudes by consolidating LST data sources (Soliman, Duguay, Saunders, & Hachem, 2012) and by comparing LSTs to ground and air temperatures over large regions, including herbaceous and shrub tundra sites in northern Quebec, Canada, and on the North Slope of Alaska, USA (Hachem, Duguay, & Allard, 2012). Comparisons have also been made with in situ radiometer measurements at a polygonal tundra site in Siberia (Langer et al, 2010), at a barren site (Westermann, Langer, & Boike, 2011 and an Arctic ice cap on Svalbard (Østby et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal sensitivity of MODIS land surface temperature anomalies indicates high potential for large-scale land cover change detection in Arctic permafrost landscapes

Muster

Langer

Abnizova

et al. 2015

Remote Sensing of Environment

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The accelerated warming of the Arctic climate may alter the local and regional surface energy balances, for which changing land surface temperatures (LSTs) are a key indicator. Modeling current and anticipated changes in the surface energy balance requires an understanding of the spatio-temporal interactions between LSTs and land cover, both of which can be monitored globally by measurements from space. This paper investigates the accuracy of the MODIS LST/Emissivity Daily L3 Global 1 km V005 product and its spatio-temporal sensitivity to land surface properties in a Canadian High Arctic permafrost landscape. The land cover ranged from fully vegetated wet sedge tundra to barren rock. MODIS LSTs were compared with in situ radiometer measurements from wet tundra areas collected over a 2-year period from July 2008 to July 2010 including both summer and winter conditions. The accuracy of the MODIS LSTs was −1.1°C with a root mean square error of 3.9°C over the entire observation period. Agreement was lowest during the freeze-back periods where MODIS LST showed a cold bias likely due to the overrepresentation of clear-sky conditions. A multi-year analysis of LST spatial anomalies, i.e., the difference between MODIS LSTs and the MODIS LST regional mean, revealed a robust spatiotemporal pattern. Highest variability in LST anomalies was found during freeze-up and thaw periods as well as for open water surface in early summer due to the presence or absence of snow or ice. The summer anomaly pattern was similar for all three years despite strong differences in precipitation, air temperature and net radiation. Summer periods with regional mean LSTs above 5.0°C showed the greatest spatial diversity with four distinct 2.0°C classes. Summer anomalies ranged from −4.5°C to 2.6°C with an average standard deviation of 1.8°C. Dry ridge areas heated up the most, while wetland areas and dry areas of sparsely vegetated bedrock with a high albedo remained coolest. The observed summer LST anomalies can be used as a baseline against which to evaluate both past and future changes in land surface properties that relate to the surface energy balance. Summer anomaly classes mainly reflected a combination of albedo and surface wetness. The potential to use this tool to monitor surface drying and wetting in the Arctic should therefore be further explored. A multi-sensor approach combining thermal satellite measurements with optical and radar imagery promises to be an effective tool for a dynamic, process-based ecosystem monitoring scheme.

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“…Tomlinson et al [12] compared the nighttime LST from MODIS with ground-measured air temperature across a conurbation and found that the measured air temperature was always greater than the MODIS-derived LST. Hachem et al [26] found that the mean daily LST was more strongly correlated with near-surface air temperature in an area with continuous permafrost when the TERRA/AQUA MODIS data were combined than when these values were considered separately (TERRA or AQUA, daytime or nighttime). Zhu et al [13] showed that daily maximum and minimum air temperatures could be retrieved effectively from MODIS LST products by using temperature-vegetation index method in the Xiangride River basin of the northern Tibetan Plateau.…”

Section: Introductionmentioning

confidence: 99%

Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data

et al. 2015

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Air temperature is one of the most important factors in crop growth monitoring and simulation. In the present study, we estimated and mapped daily mean air temperature using daytime and nighttime land surface temperatures (LSTs) derived from TERRA and AQUA MODIS data. Linear regression models were calibrated using LSTs from 2003 to 2011 and validated using LST data from 2012 to 2013, combined with meteorological station data. The results show that these models can provide a robust estimation of measured daily mean air temperature and that models that only accounted for meteorological data from rural regions performed best. Daily mean air temperature maps were generated from each of four MODIS LST products and merged using different strategies that combined the four MODIS products in different orders when data from one product was unavailable for a pixel. The annual average spatial coverage increased from 20.28% to 55.46% in 2012 and 28.31% to 44.92% in 2013.The root-mean-square and mean absolute errors (RMSE and MAE) for the optimal image merging strategy were 2.41 and 1.84, respectively. Compared with the OPEN ACCESS Remote Sens. 2015, 7 8729 least-effective strategy, the RMSE and MAE decreased by 17.2% and 17.8%, respectively. The interpolation algorithm uses the available pixels from images with consecutive dates in a sliding-window mode. The most appropriate window size was selected based on the absolute spatial bias in the study area. With an optimal window size of 33 × 33 pixels, this approach increased data coverage by up to 76.99% in 2012 and 89.67% in 2013.

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Comparison of MODIS-derived land surface temperatures with ground surface and air temperature measurements in continuous permafrost terrain

Cited by 139 publications

References 43 publications

Near-surface thermal stratification during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

Near-surface thermal stratification during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

Spatio-temporal sensitivity of MODIS land surface temperature anomalies indicates high potential for large-scale land cover change detection in Arctic permafrost landscapes

Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data

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