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. 2012 Aug 15:432:85-92.
doi: 10.1016/j.scitotenv.2012.05.095. Epub 2012 Jun 20.

Temporal and spatial assessments of minimum air temperature using satellite surface temperature measurements in Massachusetts, USA

Itai Kloog  1 , Alexandra Chudnovsky, Petros Koutrakis, Joel Schwartz
Affiliations

Temporal and spatial assessments of minimum air temperature using satellite surface temperature measurements in Massachusetts, USA

Itai Kloog et al. Sci Total Environ. .

Abstract

Although meteorological stations provide accurate air temperature observations, their spatial coverage is limited and thus often insufficient for epidemiological studies. Satellite data expand spatial coverage, enhancing our ability to estimate near surface air temperature (Ta). However, the derivation of Ta from surface temperature (Ts) measured by satellites is far from being straightforward. In this study, we present a novel approach that incorporates land use regression, meteorological variables and spatial smoothing to first calibrate between Ts and Ta on a daily basis and then predict Ta for days when satellite Ts data were not available. We applied mixed regression models with daily random slopes to calibrate Moderate Resolution Imaging Spectroradiometer (MODIS) Ts data with monitored Ta measurements for 2003. Then, we used a generalized additive mixed model with spatial smoothing to estimate Ta in days with missing Ts. Out-of-sample tenfold cross-validation was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with available Ts and days without Ts observations (mean out-of-sample R(2)=0.946 and R(2)=0.941 respectively). Furthermore, based on the high quality predictions we investigated the spatial patterns of Ta within the study domain as they relate to urban vs. non-urban land uses.

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Figures

Fig. 1
Fig. 1
Map of the study area showing the full surface temperature grid and all air temperature monitor station across Massachusetts.
Fig. 2
Fig. 2
A scatter plot of the correlation between the weather underground/weatherbug stations and NCDC stations.
Fig. 3
Fig. 3
A scatter plot of the air temperature–surface temperature relationship before (A) and after (B) the daily 2003 calibrations.
Fig. 4
Fig. 4
A density plot exhibiting the daily variation of surface temperature slopes by time for 2003.
Fig. 5
Fig. 5
Mean air temperature in each 10×10 km grid during 2003 predicted by the surface temperature models.
Fig. 6
Fig. 6
Selected 4×4 km representative regions exploring differences in predicted annual mean air temperature between regions. The box represents the predicted air temperature while the whisks represent the standard deviation.
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References

    1. Ackerman SA, Strabala KI, Menzel WP, Frey RA, Moeller CC, Gumley LE. Discriminating clear sky from clouds with MODIS. J Geophys Res. 1998;103:32, 141–57.
    1. Barry RG, Chorley RJ. Atmosphere, weather, and climate. Psychology Press; 2003.
    1. Basu R, Feng WY, Ostro BD. Characterizing temperature and mortality in nine California counties. Epidemiology. 2008;19:138. - PubMed
    1. Chudnovsky A, Ben-Dor E, Saaroni H. Diurnal thermal behavior of selected urban objects using remote sensing measurements. Energy Build. 2004;36:1063–74.
    1. Dash P, Göttsche FM, Olesen FS, Fischer H. Land surface temperature and emissivity estimation from passive sensor data: theory and practice-current trends. Int J Remote Sens. 2002;23:2563–94.

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