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 CEN/MPI net or via CliSAP login What does that mean?
View data via LAS
Data access via OPeNDAP
Data access via file system /data/icdc/land/ascat_soilmoisture
Coverage, spatial and temporal resolution
Period and temporal resolution:
- 2007-01-01 to 2017-12-31
- daily as sliding 5-day composites, separately for descending & ascending satellite overpasses
- 2007-01 to 2017-12
Coverage and spatial resolution:
- Spatial resolution: about 0.1° x 0.1°, cartesian grid
- Geographic longitude: 179.944°W to 179.944°E
- Geographic latitude: 89.9437°S to 89.9437°N
- Dimension: 1599 rows x 3207 columns
- Altitude: following terrain
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 (variable: "snow_cover"), frozen soil (variable: "frozen_soil"), and wetlands (variable: "wetland") 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 (variable: "topography").
A so-called "wet correction" is applied for dry regions classified as climate BW(h or k) according to Köppen; this is driven by the observation, that the time-series approach used to derive the soil moisture fails to provide realistic soil moisture values in those regions / cases where within the time period considered the soil moisture never reached 100%. Application of this wet correction and of regions where the sensitivity of the radar backscatter to the soil moisture is below 1% or where the soil moisture error is larger than 50% can be tracked in the quality flag.
We recommend to check the references (see below) for details.
Department of Geodesy and Geoinformation (GEO), Research Group Remote Sensing
http://rs.geo.tuwien.ac.at, Vienna University of Technology (TU Wien)
email: ww (at) ipf.tuwien.ac.at or radar (at) ifp.tuwien.ac.at
Hydrology - Satellite Application Facility (H-SAF)
email: us_hsaf (at) meteoam.it
ICDC / CEN / University of Hamburg
email: stefan.kern (at) uni-hamburg.de
Upon using this data please cite as follows:
EUMETSAT-HSAF Metop ASCAT soil moisture time series DR2018, HSAF product H113, http://hsaf.meteoam.it, last accessed July 30, 2019, provided as [daily / monthly] global maps of 5-day composite / mean separately for descending & ascending Metop overpasses by: Integrated Climate Data Centre (ICDC, icdc.cen.uni-hamburg.de), University of Hamburg, Hamburg, Germany.
In addition you need to cite:
Wagner, W., G. Lemoine, and H. Rott (1999), A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment, 70(2), 191-207, doi:10.1016/S0034-4257(99)00036-X
Naeimi, V., K. Scipal, Z. Bartalis, S. Hasenauer, and W. Wagner, 2009, An improved soil moisture retrieval algorithm for ERS and MetOp scatterometer observations, IEEE Transactions on Geoscience and Remote Sensing, 47(7), 1999-2013, doi:10.1109/TGRS.2008.2011617
Naeimi, V., C. Paulik, A. Bartsch, W. Wagner, R. Kidd, S.-E. Park, K. Eiger, and J. Boike, 2012, ASCAT surface state flag (SSF): Extracting information on surface freeze/thaw conditions from backscatter data using an empirical theshold-analysis algorithm. IEEE Transactions on Geoscience and Remote Sensing, 50(7), 2566-2582, doi:10.1109/TGRS.2011.2177667
Because the data are based on measurements of the EUMETSAT METOP-ASCAT sensor EUMETSAT has to be mentioned:
All H-SAF products are owned by EUMETSAT, and the EUMETSAT SAF Data Policy applies. They are available for all users free of charge.
Users should recognise the respective roles of EUMETSAT, the H-SAF Leading Entity and the H-SAF consortium when publishing results that are based on H-SAF products. EUMETSAT's ownership and intellectual property rights into the SAF data and products is best safeguarded by simply displaying the words "(C) EUMETSAT" under each of the SAF data and procuts shown in a publication of website.