Pan-Arctic Land Surface Temperature from MODIS and AATSR: Product Development and Intercomparison

Soliman, Abdel-Hamid; Duguay, Claude R.; Saunders, William; Hachem, Sonia

doi:10.3390/rs4123833

Cited by 38 publications

(29 citation statements)

References 40 publications

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“…One of the special features of the AATSR instrument is its use of a conical scan to give a dual-view of the Earth's surface. AATSR LST product has also been available over a decade and many studies have been conducted to assess and validate this product [9,26,54]. The validation result indicates that AATSR LST data has a fairly good accuracy, e.g., an average error of −0.9 K with a STD of 0.9 K was reported by Coll et al [26] using ground measurements from experimental site in an area of rice crops.…”

Section: Cross Satellite Comparison Methodsmentioning

confidence: 77%

“…Wan [54] clearly described the underestimation of MODIS v5 LST over bare soil sites. Three possible sources are considered for the large LST error: (1) The original split window algorithm does not well cover the wide range of LSTs; (2) the large errors in surface emissivity values in bands 31 and 32 estimated from land-cover types; (3) effect of dust aerosols that has not been considered in the R-based validation.…”

Section: Comparison With Data From Gobabeb Namibiamentioning

confidence: 99%

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Quality Assessment of S-NPP VIIRS Land Surface Temperature Product

Liu

et al. 2015

Remote Sensing

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Abstract:The VIIRS Land Surface Temperature (LST) Environmental Data Record (EDR) has reached validated (V1 stage) maturity in December 2014. This study compares VIIRS v1 LST with the ground in situ observations and with heritage LST product from MODIS Aqua and AATSR. Comparisons against U.S. SURFRAD ground observations indicate a similar accuracy among VIIRS, MODIS and AATSR LST, in which VIIRS LST presents an overall accuracy of −0.41 K and precision of 2.35 K. The result over arid regions in Africa suggests that VIIRS and MODIS underestimate the LST about 1.57 K and 2.97 K, respectively. The cross comparison indicates an overall close LST estimation between VIIRS and MODIS. In addition, a statistical method is used to quantify the VIIRS LST retrieval uncertainty taking into account the uncertainty from the surface type input. Some issues have been found as follows: (1) Cloud contamination, particularly the cloud detection error over a snow/ice surface, shows significant impacts on LST validation; (2) Performance of the VIIRS LST algorithm is strongly dependent on a correct classification of the surface type; (3) The VIIRS LST quality can be degraded when significant brightness temperature OPEN ACCESS Remote Sens. 2015, 7 12216 difference between the two split window channels is observed; (4) Surface type dependent algorithm exhibits deficiency in correcting the large emissivity variations within a surface type.

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Section: Cross Satellite Comparison Methodsmentioning

confidence: 77%

Section: Comparison With Data From Gobabeb Namibiamentioning

confidence: 99%

Quality Assessment of S-NPP VIIRS Land Surface Temperature Product

Liu

et al. 2015

Remote Sensing

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“…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%

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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“…In spite of the recognized importance of LST, in situ measurements of LST over continents are not yet adequate for resolving diurnal cycles or for analyzing synoptic, seasonal and interannual variability, because of strong spatiotemporal variations in the data and difficulties in accurate measurements. Remote sensing instruments onboard satellites working in the thermal infrared channels are the only available operational systems capable of collecting cost-effective LST data at spatial and temporal resolutions that are appropriate for various applications [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Land Surface Temperature Operationally Retrieved from Korean Geostationary Satellite (COMS) Data

Cho

Suh

2013

Remote Sensing

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Abstract:We evaluated the precision of land surface temperature (LST) operationally retrieved from the Korean multipurpose geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS). The split-window (SW)-type retrieval algorithm was developed through radiative transfer model simulations under various atmospheric profiles, satellite zenith angles, surface emissivity values and surface lapse rate conditions using Moderate Resolution Atmospheric Transmission version 4 (MODTRAN4). The estimation capabilities of the COMS SW (CSW) LST algorithm were evaluated for various impacting factors, and the retrieval accuracy of COMS LST data was evaluated with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. The surface emissivity values for two SW channels were generated using a vegetation cover method. The CSW algorithm estimated the LST distribution reasonably well (averaged bias = 0.00 K, Root Mean Square Error (RMSE) = 1.41 K, correlation coefficient = 0.99); however, the estimation capabilities of the CSW algorithm were significantly impacted by large brightness temperature differences and surface lapse rates. The CSW algorithm reproduced spatiotemporal variations of LST comparing well to MODIS LST data, irrespective of what month or time of day the data were collected from. The one-year evaluation results with MODIS LST data showed that the annual mean bias, RMSE and correlation coefficient for the CSW algorithm were −1.009 K, 2.613 K and 0.988, respectively.

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Pan-Arctic Land Surface Temperature from MODIS and AATSR: Product Development and Intercomparison

Cited by 38 publications

References 40 publications

Quality Assessment of S-NPP VIIRS Land Surface Temperature Product

Quality Assessment of S-NPP VIIRS Land Surface Temperature Product

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

Evaluation of Land Surface Temperature Operationally Retrieved from Korean Geostationary Satellite (COMS) Data

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