Estimating Temperature Fields from MODIS Land Surface Temperature and Air Temperature Observations in a Sub-Arctic Alpine Environment

Williamson, Scott; Hik, David S.; Gamon, John A.; Kavanaugh, Jeffrey L.; Flowers, G. E.

doi:10.3390/rs6020946

Cited by 79 publications

(57 citation statements)

References 34 publications

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“…Although Li et al [14,17] used satellite observation to explore the effect of forest globally and found that forests decrease local temperatures in Europe, they focused on spatial patterns and did not consider the effects of background climate on forests during different yeas. Additionally, using two time observations to represent daily averages may also lead to some uncertainties [33]. Further studies should emphasize the effects of forests on the climate in Europe using both kinds of observations to explore the spatiotemporal patterns of forest effects and the effect of the background climate.…”

Section: Introductionmentioning

confidence: 99%

Local Effects of Forests on Temperatures across Europe

Tang

Zhao

2018

Remote Sensing

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Forests affect local climate through biophysical processes in terrestrial ecosystems. Due to the spatial and temporal heterogeneity of ecosystems in Europe, climate responses to forests vary considerably with diverse geographic and seasonal patterns. Few studies have used an empirical analysis to examine the effect of forests on temperature and the role of the background climate in Europe. In this study, we aimed to quantitatively determine the effects of forest on temperature in different seasons with MODIS (MODerate-resolution Imaging Spectroradiometer) land surface temperature (LST) data and in situ air temperature measurements. First, we compared the differences in LSTs between forests and nearby open land. Then, we paired 48 flux sites with nearby weather stations to quantify the effects of forests on surface air temperature. Finally, we explored the role of background temperatures on the above forests effects. The results showed that (1) forest in Europe generally increased LST and air temperature in northeastern Europe and decreased LST and air temperature in other areas; (2) the daytime cooling effect was dominate and produced a net cooling effect from forests in the warm season. In the cold season, daytime and nighttime warming effects drove the net effect of forests; (3) the effects of forests on temperatures were mainly negatively correlated with the background temperatures in Europe. Under extreme climate conditions, the cooling effect of forests will be stronger during heatwaves or weaker during cold spring seasons; (4) the background temperature affects the spatiotemporal distribution of differences in albedo and evapotranspiration (forest minus open land), which determines the spatial, seasonal and interannual effects of forests on temperature. The extrapolation of the results could contribute not only to model validation and development but also to appropriate land use policies for future decades under the background of global warming.

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Section: Introductionmentioning

confidence: 99%

Local Effects of Forests on Temperatures across Europe

Tang

Zhao

2018

Remote Sensing

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“…Some studies have successfully estimated high spatial resolution air temperature using satellite remote sensing [10][11][12][13][14][15][16]. Prihodko and Goward [8] used the temperature-vegetation index (TVX) from the Advanced Very High Resolution Radiometer (AVHRR) to estimate air temperature during the growing season of 1987 in Kansas, and found a strong correlation (r = 0.93) with a mean error of 2.92 °C.…”

Section: Introductionmentioning

confidence: 99%

Estimating High Spatial Resolution Air Temperature for Regions with Limited in situ Data Using MODIS Products

Rhee

2014

Remote Sensing

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Abstract:The use of land surface temperature and vertical temperature profile data from Moderate Resolution Imaging Spectroradiometer (MODIS), to estimate high spatial resolution daily and monthly maximum and minimum 2 m above ground level (AGL) air temperatures for regions with limited in situ data was investigated. A diurnal air temperature change model was proposed to consider the differences between the MODIS overpass times and the times of daily maximum and minimum temperatures, resulting in the improvements of the estimation in terms of error values, especially for minimum air temperature. Both land surface temperature and vertical temperature profile data produced relatively high coefficient of determination values and small Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values for air temperature estimation. The correction of the estimates using two gridded datasets, National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and Climate Research Unit (CRU), was performed and the errors were reduced, especially for maximum air temperature. The correction of daily and monthly air temperature estimates using the NCEP/NCAR reanalysis data, however, still produced relatively large error values compared to existing studies, while the correction of monthly air temperature estimates using the CRU data significantly reduced the errors; the MAE values for estimating monthly maximum air temperature range between 1.73 °C and 1.86 °C. Uncorrected land surface temperature generally performed better for estimating monthly minimum air temperature and the MAE values range from 1.18 °C to 1.89 °C. The suggested OPEN ACCESSRemote Sens. 2014, 6 7361 methodology on a monthly time scale may be applied in many data sparse areas to be used for regional environmental and agricultural studies that require high spatial resolution air temperature data.

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“…Various methods for estimating T a from satellite optical-IR and passive microwave remote sensing have been widely implemented since the 1990s [1,2,[6][7][8][9][10][11][12][13]. Satellite remote sensing estimation methods for T a can be broadly classified as: (1) exploiting relationships between optical-infrared retrievals of land surface "skin" temperature (LST) and spectral vegetation indices such as the normalized difference vegetation index (NDVI) [1,11,14,15]; (2) exploiting relationships between LST and T a [16][17][18][19][20][21]; (3) the use of infrared soundings of atmospheric temperatures [22,23]; (4) the use of lower frequency (≤37 GHz) brightness temperatures (T b ) and surface emissivities from passive microwave radiometry [7,[24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Retrievals of All-Weather Daily Air Temperature Using MODIS and AMSR-E Data

et al. 2014

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Satellite optical-infrared remote sensing from the Moderate Resolution Imaging Spectroradiometer (MODIS) provides effective air temperature (T a ) retrieval at a spatial resolution of 5 km. However, frequent cloud cover can result in substantial signal loss and remote sensing retrieval error in MODIS T a . We presented a simple pixel-wise empirical regression method combining synergistic information from MODIS T a and 37 GHz frequency brightness temperature (T b ) retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) for estimating surface level T a under both clear and cloudy sky conditions in the United States for 2006. The instantaneous T a retrievals showed favorable agreement with in situ air temperature records from 40 AmeriFlux tower sites; mean R 2 correspondence was 86.5 and 82.7 percent, while root mean square errors (RMSE) for the T a retrievals were 4.58 K and 4.99 K for clear and cloudy sky conditions, respectively. Daily mean T a was estimated using the instantaneous T a retrievals from day/night overpasses, and showed favorable agreement with local tower measurements (R 2 = 0.88; RMSE = 3.48 K). The results of this study indicate that the combination of MODIS and AMSR-E sensor data can produce T a retrievals with reasonable accuracy and relatively fine spatial resolution (~5 km) for clear and cloudy sky conditions. OPEN ACCESSRemote Sens. 2014, 6 8388

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Estimating Temperature Fields from MODIS Land Surface Temperature and Air Temperature Observations in a Sub-Arctic Alpine Environment

Cited by 79 publications

References 34 publications

Local Effects of Forests on Temperatures across Europe

Local Effects of Forests on Temperatures across Europe

Estimating High Spatial Resolution Air Temperature for Regions with Limited in situ Data Using MODIS Products

Retrievals of All-Weather Daily Air Temperature Using MODIS and AMSR-E Data

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