Left: 5-Day composite of the global soil moisture distribution derived from EUMETSAT MetOp-ASCAT data for August 18, 2007. Right: Retrieval noise. Pixels marked in red denote areas with unreliable data due to missing satellite data, mixed pixel effects, or weather and/or vegetation effects. Pixels marked in pink denote areas flagged as snow covered, frozen or saturated with water / flooded. Black areas over land denote lakes or permanent ice. Olive, light and dark blue mark low, medium and high soil moisture or soil moisture retrieval noise, respectively. Blue areas in the right image denote areas where the retrieved soil moisture values should be given less confidence because of a substantial retrieval noise (complex topography, dense vegetation like the rain forest in South America).
This data set is only available for a restricted user group, please contact us if you want to access these data.
RESTRICTED only accessable in ZMAW net or via CliSAP login What does that mean?
This soil moisture data set is based on radar backscatter measurements (at C-Band) of the Advanced SCATterometer (ASCAT) aboard the EUMETSAT MetOp satellite. This data is first normalized to a common incidence angle (40°) using a radar backscatter model. The obtained radar backscatter coefficient is a function of the soil moisture: low values correspond to a low soil moisture, high values are associated with a high soil moisture. Radar backscatter values are scaled between 0 % (dry soil) and 100% (wet soil, saturated with water). The obtained relative soil moisture values represent the moisture in the topmost 5 cm of the soil. A short introduction to the data set is given in the data sheet; for detailed information we recommend the given references (see below).
This is version 2.0 of this data set.
We offer data for years 2007-2011 as a 5-day gridded composite in order to achieve an as complete as possible data coverage. Additionally, the data have been interpolated into a simple cylindrical grid for easy use in models and for display (see section: coverage, spatial and temporal resolution down below). Original data come from single ASCAT overpasses and are organized as time series per 12.5 km x 12.5 km grid cell organized in 5° x 5° tiles. We also offer these original data upon request.
Coverage, spatial and temporal resolution
Period and temporal resolution:
Coverage and spatial resolution:
Original data are organized in 5° x 5° tiles. Each tile comprises a certain number of 12.5 km grid cells. The data are stored as time series per grid cell per tile in binary format.
This data set contains a number of quality flags and additional information. One is the retrieval noise. This is estimated using Gaussian error propagation of the input uncertainties like the variability of the used radar backscatter values due to measurement noise and the variability of the sensitivity of the radar backscatter values to soil moisture for different soil and vegetation types.
We note, that the soil moisture retrieval method used here, is of limited use particularly in polar regions and regions covered by dense rain forest like in South America. These areas are therefore often flagged as unreliable data and/or show a large retrieval noise.
Soil moisture retrieval is not possible for areas covered with snow and ice, for areas with frozen soil and for wetlands/lakes/rivers. In order to allow identification of dubious soil moisture values (which have perhaps not been flagged as being unreliable), the product contains the percentage fractions of snow, frozen soil, and wetlands for every grid cell. Those for wetland are static, assuming that the fraction does not change over time, while those of snow and frozen soil stem from a climatology and therefore vary with season but don't have interannual variation.
Regions of a strongly variable topography are also problematic because of the highly variable local incidence angle in these cases. This causes problems to normalize the measured radar backscatter values to the common incidence angle and thus to retrieve the soil moisture. Therefore, the data set contains the normalized standard deviation of the altitude in each grid cell (in relative units) as a measure of the topographic complexity.
We recommend to check the references (see below) for details.
Institute of Photogrammetry and Remote Sensing (IPF)
Vienna University of Technology (TU Wien)
E-Mail: ww@ ipf.tuwien.ac.at
ICDC, CliSAP, Universität Hamburg
E-Mail: stefan.kern@ zmaw.de
Upon using this data please cite as follows:
ASCAT Soil Moisture 2007-2010, Institute of Photogrammetry and Remote Sensing (IPF), Vienna Institute of Technology (TU Wien), Vienna, Austria,
provided as 5-Day composites by: Integrated Climate Data Centre (ICDC, icdc.zmaw.de), University of Hamburg, Hamburg, Germany.