Publications in Scientific Journals:

C. Paulik, W. Dorigo, W. Wagner, R. Kidd:
"Validation of the ASCAT Soil Water Index using in situ datafrom the International Soil Moisture Network";
International Journal of Applied Earth Observation and Geoinformation, 30 (2014), 1 - 8.

English abstract:
tSoil moisture is an essential climate variable and a key parameter in hydrology, meteorology and agricul-ture. Surface Soil Moisture (SSM) can be estimated from measurements taken by ASCAT onboard Metop-Aand have been successfully validated by several studies. Profile soil moisture, while equally important,cannot be directly measured by remote sensing but must be modeled. The Soil Water Index (SWI) productdeveloped for near real time applications within the framework of the GMES project geoland2 aims toprovide such a modeled profile estimate using satellite data as input. It is produced from ASCAT SSMestimates using a two-layer water balance model which describes the relationship between surface andprofile soil moisture as a function of time. It provides daily global data about moisture conditions foreight characteristic time lengths representing different depths.The objective of this work was to assess the overall quality of the SWI data. Furthermore we tested theassumptions of the used water balance model and checked if ancillary information about topography,water fraction and noise information are useful for identifying observations of questionable quality. SWIdata from January 1st 2007 until the end of 2011 was compared to in situ soil moisture data from 664stations belonging to 23 observation networks which are available through the International Soil MoistureNetwork (ISMN). These stations delivered 2081 time series at different depths which were compared tothe SWI values.The average of the significant Pearson correlation coefficients was 0.54 while being greater than 0.5 for64.4% of all time series. It was found that the characteristic time length showing the highest correlationincreases with in situ observation depth, thus confirming the SWI model assumptions. Relationships ofthe correlation coefficients with topographic complexity, water fraction, in situ observation depth, andsoil moisture noise were found.

Created from the Publication Database of the Vienna University of Technology.