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| Titel |
The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations |
| VerfasserIn |
R. M. Parinussa, T. R. H. Holmes, M. T. Yilmaz, W. T. Crow |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 15, no. 10 ; Nr. 15, no. 10 (2011-10-17), S.3135-3151 |
| Datensatznummer |
250012992
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| Publikation (Nr.) |
 copernicus.org/hess-15-3135-2011.pdf |
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| Zusammenfassung |
| For several years passive microwave observations have been used to retrieve
soil moisture from the Earth's surface. Low frequency observations have the
most sensitivity to soil moisture, therefore the current Soil Moisture and
Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP)
satellite missions observe the Earth's surface in the L-band frequency. In
the past, several satellite sensors such as the Advanced Microwave Scanning
Radiometer-EOS (AMSR-E) and WindSat have been used to retrieve surface soil
moisture using multi-channel observations obtained at higher microwave
frequencies. While AMSR-E and WindSat lack an L-band channel, they are able
to leverage multi-channel microwave observations to estimate additional land
surface parameters. In particular, the availability of Ka-band observations
allows AMSR-E and WindSat to obtain coincident surface temperature estimates
required for the retrieval of surface soil moisture. In contrast, SMOS and
SMAP carry only a single frequency radiometer and therefore lack an
instrument suited to estimate the physical temperature of the Earth.
Instead, soil moisture algorithms from these new generation satellites rely
on ancillary sources of surface temperature (e.g. re-analysis or near real
time data from weather prediction centres). A consequence of relying on such
ancillary data is the need for temporal and spatial interpolation, which may
introduce uncertainties. Here, two newly-developed, large-scale soil
moisture evaluation techniques, the triple collocation (TC) approach and the
Rvalue data assimilation approach, are applied to quantify the
global-scale impact of replacing Ka-band based surface temperature
retrievals with Modern Era Retrospective-analysis for Research and
Applications (MERRA) surface temperature output on the accuracy of WindSat
and AMSR-E based surface soil moisture retrievals. Results demonstrate that
under sparsely vegetated conditions, the use of MERRA land surface
temperature instead of Ka-band radiometric land surface temperature leads to
a relative decrease in skill (on average 9.7%) of soil moisture anomaly
estimates. However the situation is reversed for highly vegetated conditions
where soil moisture anomaly estimates show a relative increase in skill (on
average 13.7%) when using MERRA land surface temperature. In addition, a
pre-processing technique to shift phase of the modelled surface temperature
is shown to generally enhance the value of MERRA surface temperature
estimates for soil moisture retrieval. Finally, a very high correlation
(R2 = 0.95) and consistency between the two evaluation techniques lends
further credibility to the obtained results. |
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