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| Titel |
Estimating Air Temperature over the Tibetan Plateau Using MODIS Data |
| VerfasserIn |
Fangfang Huang, Weiqiang Ma, Yaoming Ma, Maoshan Li, Zeyong Hu |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250122903
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| Publikation (Nr.) |
 EGU/EGU2016-2045.pdf |
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| Zusammenfassung |
| Estimating Air Temperature over the Tibetan Plateau Using MODIS Data
Fangfang Huang1,2, Weiqiang Ma3,4, Yaoming Ma3,4, Maoshan Li1,2, Zeyong Hu1,4
1Key Laboratory for Land Process and Climate Change in Cold and Arid Regions, Cold and
Arid Region Environmental and Engineering Research Institute, Chinese Academy of
Sciences, Lanzhou, 730000, China
2University of Chinese Academy of Sciences, Beijing, 100049, China
3A Key Laboratory of Tibetan Environment Changes and Land Surface Processes,
Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101,
China
4CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, 100101,
China
Abstract Time series of MODIS land surface temperature (LST) data and normalized
difference vegetation index (NDVI) data, combined with digital elevation model (DEM) and
meterological data for 2001-2012, were used to estimate and map the spatial distribution of
monthly mean air temperature over the Tibatan Plateau (TP). Time series and regression
analysis of monthly mean land surface temperature (Ts) and air temperature (Ta) were both
conducted by ordinary liner regression (OLR) and geographical weighted regression (GWR)
methods. Analysis showed that GWR method had much better result (Adjusted
R2 > 0.79, root mean square error (RMSE) is between 0.51˚ C and 1.12˚ C)
for estimating Ta than OLR method. The GWR model, with MODIS LST, NDVI
and altitude as independent variables, was used to estimate Ta over the Tibetan
Plateau. All GWR models in each month were tested by F-test with significant level of
α=0.01 and the regression coefficients were all tested by T-test with significant
level of α=0.01. This illustrated that Ts, NDVI and altitude play an important role
on estimating Ta over the Tibetan Plateau. Finally, the major conclusions are as
follows: (1) GWR method has higher accuracy for estimating Ta than OLR (Adjusted
R2=0.40∼0.78, RMSE=1.60∼4.38˚ C), and the Ta control precision can be up to
1.12˚ C. (2) Over the Northern TP, the range of Ta variation in January is -29.28 ∼
-5.0˚ C, and that in July is -0.53 ∼ 14.0˚ C. Ta in summer half year (from May to
October) is between -15.92 ∼ 14.0˚ C. From October on, 0˚ C isothermal level
is gradually declining from the altitude of 4∼5 kilometers, and hits the bottom
with altitude of 3200 meters in December, and Ta is all under 0˚ C in January.
10˚ C isothermal level gradually starts rising from the altitude of 3200 meters
from May, and reaches the highest level with altitude of 4∼5 kilometers in July. In
addition, Ta in south slope of the Tanggula Mountains is obviously higher than
that in the north slope. Ta in east of Qinghai-Tibet Railway is higher than that in
the west, and Ta shows an increasing tendency from northwest to southeast. (3)
Over the Northern TP, the variation range of the difference between surface and air
temperature (DT) in January is -6.52∼6.0˚ C, and that in July is -6.32∼6.0˚ C.
DT in summer half year (from May to October) is between -6.34∼8.0˚ C, and
DT along Qinghai-Tibet Railway is greater than that in the east and west areas of
Qinghai-Tibet Railway. However, except of the southeastern area of the Northern TP, where
DT is under 0˚ C, DT values in other areas are all more than 0˚ C in winter half
year. In summer half year, the altitude of the area lower than 0˚ C rises to 4∼5
kilometers, and DT in south of the Tanggula Mountains is between -2.0˚ C and 0˚ C. |
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