Titelaufnahme

Titel
Frozen soil detection based on advanced scatterometer observations and air temperature data as part of soil moisture retrieval
VerfasserZwieback, Simon In der Gemeinsamen Normdatei der DNB nachschlagen ; Paulik, Christoph ; Wagner, Wolfgang In der Gemeinsamen Normdatei der DNB nachschlagen
Erschienen in
Remote sensing, 2015, Jg. 7, H. 3, S. 3206-3231
Erschienen2015
Ausgabe
Published version
SpracheEnglisch
DokumenttypAufsatz in einer Zeitschrift
Schlagwörter (EN)freeze/thaw / soil moisture / radar / scatterometer / classification
URNurn:nbn:at:at-ubtuw:3-2210 Persistent Identifier (URN)
DOI10.3390/rs70303206 
Zugriffsbeschränkung
 Das Werk ist frei verfügbar
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Frozen soil detection based on advanced scatterometer observations and air temperature data as part of soil moisture retrieval [9.26 mb]
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Zusammenfassung (Englisch)

Surface soil moisture is one of the operational products derived from Advanced Scatterometer (ASCAT) data. The reliability of its estimation depends on the detection of predominantly frozen conditions of the landscape (including soil and vegetation) and the presence of wet snow, which would otherwise impede the estimation. As the robust determination of the freeze/thaw (F/T) state using exclusively scatterometer measurements on a global basis is complicated due to the myriad of different climatic and land cover conditions; we propose to support the retrieval using ERA Interim temperature data. The approach is based on a probabilistic time series model, whereby backscatter and temperature data are combined to estimate the freeze/thaw state. The method is assessed with proxy F/T states derived from modeled and in situ air and soil temperature data on a global basis. These analyses show an improved consistency compared to a previously published ASCAT F/T algorithm, with typical agreements between the external data and the results of the algorithm exceeding 80%. The quantitative interpretation of these comparisons is, however, hampered by discrepancies between the F/T state derived from temperature data and the one pertinent to radar remote sensing, as the former does not account for, e.g., wet snow conditions. The inclusion of the ERA Interim temperature data can improve the accuracy of the algorithm by more than 10 percentage points in regions where freezing conditions are rare.