2024-03-29T07:18:46Zhttp://doidb.wdc-terra.org/oaip/oaioai:doidb.wdc-terra.org:61222017-03-03T13:31:05ZDOIDBDOIDB.GFZ
10.5880/GFZ.1.1.2015.001
Supplement to: Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: A case study for Sutherland, South Africa
Vey, Sibylle; Güntner, Andreas; Wickert, Jens; Blume, Theresa; Ramatschi, Markus
Supplement to: Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: A case study for Sutherland, South Africa
2015
Potsdam, Germany
GFZ Data Services
http://dx.doi.org/10.5880/GFZ.1.1.2015.001
EARTH SCIENCE
CLIMATE INDICATORS
LAND SURFACE/AGRICULTURE INDICATORS
SOIL MOISTURE
geoscientificInformation
soil moisture
GNSS
reflectometry
soil moisture
signal-to-noise ratio
-32.38
-32.38
20.81
20.81
Deutsches GeoForschungsZentrum GFZ
GFZ
DATA CENTER CONTACT
Deutsches GeoForschungsZentrum GFZ
We provide data of a case study from the GNSS station Sutherland, South Africa (SUTM). This data set contains soil moisture derived from GNSS data using reflectometry. It covers a time period from January 1, 2008 to September 1, 2014 and gives the integral soil moisture over an area of 60 by 60 m for the uppermost surface (max. down to 10 cm. depth) The data are daily averages based on daily measurements from 6 different satellites. The GNSS derived soil moisture was validated by Time Domain Reflectometry (TDR) observations. The detailed description of the processing, the evaluation with TDR and the discussion of the results is described in Vey et al. (2015).
The data are provided in ASCII format with four colums: (1) year (YEAR) (2) day of the year (DOY) (3) volumetric soil moisture as average over all satellite tracks (SM Vol %) (4) accuracy, root mean square error of soil moisture from a single satellite track compared to the mean of all satellites (RMSE Vol %).
DIF
9.8.2