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| Titel |
Mapping air temperature using time series analysis of LST: the SINTESI approach |
| VerfasserIn |
S. M. Alfieri, F. Lorenzi, M. Menenti |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 20, no. 4 ; Nr. 20, no. 4 (2013-07-13), S.513-527 |
| Datensatznummer |
250018987
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| Publikation (Nr.) |
 copernicus.org/npg-20-513-2013.pdf |
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| Zusammenfassung |
This paper presents a new procedure to map time series of air temperature
(Ta) at fine spatial resolution
using time series analysis of satellite-derived land surface temperature
(LST) observations. The method assumes that air temperature is known at a
single (reference) location such as in gridded climate data with grid size of
the order of 35 km × 35 km. The LST spatial and temporal pattern
within a grid cell has been modelled by the pixel-wise ratios r (x,y,t) of
the LST at any location to the LST at a reference location. A preliminary
analysis of these patterns over a decade has demonstrated that their
intra-annual variability is not negligible, with significant seasonality,
even if it is stable throughout the years. The intra-annual variability has
been modeled using Fourier series. We have evaluated the intra-annual
variability by theoretically calculating the yearly evolution of LST (t)
for a range of cases as a function of terrain, land cover and hydrological
conditions. These calculations are used to interpret the observed LST
(x,y,t) and r (x,y,t). The inter-annual variability has been evaluated by
modeling each year of observations using Fourier series and evaluating the
interannual variability of Fourier coefficients. Because of the negligible
interannual variability of r (x,y,t), LST (x,y,t) can be reconstructed in
periods of time different from the ones when LST observations are available.
Time series of Ta are generated using the ratio r (x,y,t) and a
linear regression between LST and Ta. Such linear regression is
applied in two ways: (a) to estimate LST at any time from observations or
forecasts of Ta at the reference location; (b) to estimate
Ta from LST at any location. The results presented in this paper
are based on the analysis of daily MODIS LST observations over the period
2001–2010. The Ta at the reference location was gridded data at
a node of a 35 km × 35 km grid. Only one node was close to our
study area and was used for the work presented here. The regression of
Ta on LST was determined using concurrent observations of
Ta at the four available weather stations in the Valle Telesina
(Italy), our study area.
The accuracy of our estimates is consistent with literature and with the
combined accuracy of LST and Ta. We obtained comparable error
statistics when applying our method to LST data during periods different but
adjacent to the periods used to model of r (x,y,t). The method has also
been evaluated against Ta observations for earlier periods of
time (1984–1988), although available data are rather sparse in space and
time. Slightly larger deviation were obtained. In all cases five days of
averages from estimated and observed Ta were compared, giving a
better accuracy. |
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