A comparison of MODIS land surface temperature with in situ observations

Bosilovich, Michael G.

doi:10.1029/2006gl027519

Cited by 39 publications

(28 citation statements)

References 13 publications

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“…The assessment study of remotely sensed land surface temperature by Trigo et al [51] has found that the both SEVIRI and MODIS tend to underestimate local measurements, with colder values obtained with MODIS. It agrees with the results of the study [67] that compare MODIS LST with ground measurements over land. As an important input in the method, the cold bias of MODIS LST product will influence the values of the estimated soil and vegetation temperature.…”

Section: Validation and Discussionsupporting

confidence: 81%

A Synergetic Algorithm for Mid-Morning Land Surface Soil and Vegetation Temperatures Estimation Using MSG-SEVIRI Products and TERRA-MODIS Products

Zhao

Bian

et al. 2014

Remote Sensing

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Land surface is normally considered as a mixture of soil and vegetation. Many applications, such as drought monitoring and crop-yield estimation, benefit from accurate retrieval of both soil and vegetation temperatures through satellite observation. A preliminary study has been conducted in this study on the estimation of land surface soil and vegetation component temperature using the geostationary satellite data acquired by Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) and TERRA-MODIS data. A synergetic algorithm is proposed to derive soil and vegetation temperatures by using the temporal and spatial information in SEVIRI and MODIS products. The approach is applied to both simulation data and satellite data. For simulation data, the component temperatures are well estimated with root mean squared error (RMSE) close to 0 K. For satellite data application, reasonable spatial distributions of the soil and vegetation temperatures are derived for eight cloud-free days in the Iberian Peninsula from June to August 2009. An evaluation is performed for the estimated vegetation temperature against the near surface air temperature. The correlation analysis between two datasets is found that the R-squareds are from 0.074 to 0.423 and RMSEs are within 4 K. Considering the impact of fraction of vegetation cover (FVC) on the validation, the pixels with FVC less than 30% are excluded in the total data comparison, and an obvious improvement is achieved with R-squared from 0.231 to 0.417 OPEN ACCESSRemote Sens. 2014, 6 2214 and RMSE from 2.9 K to 2.58 K. The validation indicates that the proposed algorithm is able to provide reasonable estimations of soil and vegetation temperatures. It is a potential way to map soil and vegetation temperature for large areas.

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Section: Validation and Discussionsupporting

confidence: 81%

A Synergetic Algorithm for Mid-Morning Land Surface Soil and Vegetation Temperatures Estimation Using MSG-SEVIRI Products and TERRA-MODIS Products

Zhao

Bian

et al. 2014

Remote Sensing

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“…It reveals that the LSTs retrieved during daytime are closer to the GSTs than during nighttime with LSTs only 1 • C higher than GSTs, but the high variability shows that LSTs are very variable around this 1 • C (from (Table 5). It shows that at nighttime, problems appear that do not exist during daytime (Ackerman et al, 1998). Comparing data acquired within the same hour (not shown in tables) indicates that 13:00 and 14:00 h are the hours at which the mean differences between LST and GST are the smallest and correlations highest, independently of the station in Alaska, while it is at 12:00 h for the Québec stations.…”

Section: Comparison Of Daytime Nighttime and Daily Mean Gst With Lstmentioning

confidence: 96%

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

2012

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Abstract. Obtaining high resolution records of surface temperature from satellite sensors is important in the Arctic because meteorological stations are scarce and widely scattered in those vast and remote regions. Surface temperature is the primary climatic factor that governs the existence, spatial distribution and thermal regime of permafrost which is a major component of the terrestrial cryosphere. Land Surface (skin) Temperatures (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellite platforms provide spatial estimates of near-surface temperature values. In this study, LST values from MODIS are compared to ground-based nearsurface air (T air ) and ground surface temperature (GST) measurements obtained from 2000 to 2008 at herbaceous and shrub tundra sites located in the continuous permafrost zone of Northern Québec, Nunavik, Canada, and of the North Slope of Alaska, USA. LSTs (temperatures at the surface materials-atmosphere interface) are found to be better correlated with T air (1-3 m above the ground) than with available GST (3-5 cm below the ground surface). As T air is most often used by the permafrost community, this study focused on this parameter. LSTs are in stronger agreement with T air during the snow cover season than in the snow free season. Combining Aqua and Terra LST-Day and LST-Nigh acquisitions into a mean daily value provides a large number of LST observations and a better overall agreement with T air . Comparison between mean daily LSTs and mean daily T air , for all sites and all seasons pooled together yields a very high correlation (R = 0.97; mean difference (MD) = 1.8 • C; and standard deviation of MD (SD) = 4.0 • C). The large SD can be explained by the influence of surface heterogeneity within the MODIS 1 km 2 grid cells, the presence of undetected clouds and the inherent difference between LST and T air . Retrieved over several years, MODIS LSTs offer a great potential for monitoring surface temperature changes in high-latitude tundra regions and are a promising source of input data for integration into spatially-distributed permafrost models.

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“…The latter study found that the night-time Aqua-MODIS LST had smaller differences in surface air temperature than the Terra-MODIS LST. Comparing these results, as well as reports from other, less-representative studies (e.g., [25]), one has to be cautious about systematic urban biases in the MODIS LST data. This question is rather unclear in the present context because, as mentioned above, high-latitude cities generally have no in situ observational networks and no detailed UHI studies have been conducted there previously.…”

Section: Methodsmentioning

confidence: 81%

“…It has high calibration accuracy in multiple thermal infrared bands designed for retrievals of LST and atmospheric properties [23]. Despite some noted difficulties and inaccuracies [24,25], the LST data retrieved by the MODIS sensors aboard the Terra (EOS AM) and Aqua (EOS PM) NASA satellites are widely used for UHI studies [16,26,27].…”

Section: Introductionmentioning

confidence: 99%

Seasonal and Spatial Characteristics of Urban Heat Islands (UHIs) in Northern West Siberian Cities

Miles

Esau

2017

Remote Sensing

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Anthropogenic heat and modified landscapes raise air and surface temperatures in urbanized areas around the globe. This phenomenon is widely known as an urban heat island (UHI). Previous UHI studies, and specifically those based on remote sensing data, have not included cities north of 60 • N. A few in situ studies have indicated that even relatively small cities in high latitudes may exhibit significantly amplified UHIs. The UHI characteristics and factors controlling its intensity in high latitudes remain largely unknown. This study attempts to close this knowledge gap for 28 cities in northern West Siberia (NWS). NWS cities are convenient for urban intercomparison studies as they have relatively similar cold continental climates, and flat, rather homogeneous landscapes. We investigated the UHI in NWS cities using the moderate-resolution imaging spectroradiometer (MODIS) MOD 11A2 land surface temperature (LST) product in 8-day composites. The analysis reveals that all 28 NWS cities exhibit a persistent UHI in summer and winter. The LST analysis found differences in summer and winter regarding the UHI effect, and supports the hypothesis of seasonal differences in the causes of UHI formation. Correlation analysis found the strongest relationships between the UHI and population (log P). Regression models using log P alone could explain 65-67% of the variability of UHIs in the region. Additional explanatory power-at least in summer-is provided by the surrounding background temperatures, which themselves are strongly correlated with latitude. The performed regression analysis thus confirms the important role of the surrounding temperature in explaining spatial-temporal variation of UHI intensity. These findings suggest a climatological basis for these phenomena and, given the importance of climatic warming, an aspect that deserves future study.

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A comparison of MODIS land surface temperature with in situ observations

Cited by 39 publications

References 13 publications

A Synergetic Algorithm for Mid-Morning Land Surface Soil and Vegetation Temperatures Estimation Using MSG-SEVIRI Products and TERRA-MODIS Products

A Synergetic Algorithm for Mid-Morning Land Surface Soil and Vegetation Temperatures Estimation Using MSG-SEVIRI Products and TERRA-MODIS Products

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

Seasonal and Spatial Characteristics of Urban Heat Islands (UHIs) in Northern West Siberian Cities

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