Spatio-temporal analysis of the relationship between LST from MODIS and air temperature in New Zealand

• Published in: Theoretical and applied climatology Vol. 119; no. 3-4; pp. 567 - 583
• Main Authors: Sohrabinia, M., Zawar-Reza, P., Rack, W.
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01-02-2015; Springer; Springer Nature B.V
• Subjects: Air temperature; Ambient temperature; Annual variations; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Climate change; Climatology; Correlation coefficient; Earth and Environmental Science; Earth Sciences; Land surface temperature; Meteorological satellites; Noise reduction; Original Paper; Temperature; Time series; Waste Water Technology; Water Management; Water Pollution Control; Wind speed; PSE, New Zealand; Land Surface Temperature; Mean Absolute Error; Single Pixel; Local Solar Time; Large Window
• ISSN: 0177-798X; 1434-4483
• DOI: 10.1007/s00704-014-1106-2

The ambient air temperature (T
a
) is an important environmental parameter which can be estimated from satellite observations of the land surface temperature (LST) using a linear regression model. This paper attempts to answer the question of whether the series of a single pixel or a spatially averaged series over several pixels should be used for modelling T
a
from remotely sensed LST data. Sensitivity of LST-T
a
relationship to the moderate resolution imaging spectroradiometer (MODIS) window size, which determines the number of pixels contributed in the correlations, over a number of test sites in New Zealand was analysed. LST series of a single pixel over a period of 10 years gave a correlation coefficient
r
≥
0.80 with T
a
measurements. Bootstrapping by random resampling from seasonal subsets of both time-series was applied to determine seasonal and inter-annual variability of LST-T
a
relationship. A fast Fourier filtering was applied for noise reduction and detection of dominant spectra in LST series. Spatially averaged time-series from larger windows, which included more pixels, showed slightly higher agreement with T
a
measurements. We considered the effects of wind speed (WS) and wind direction (WD) on the LST-T
a
relationship. Highest correlation between T
a
and LST time-series was achieved using a 25 × 25 window at 2 ≤ WS < 8 ms
−1
. No significant effect due to WD was found in the results. MODIS-Terra nighttime (∼10:30 PM) observations showed the highestwhile MODIS-Aqua nighttime (∼1:30 AM) observations showed the lowest agreement with T
a
measurements. These results indicate that the best approach for modelling T
a
based on LST observations from MODIS in the long-term is to use a spatially averaged LST series over a window of 5 × 5 to 25 × 25 pixels, with a consideration of WS effects and observation times.