Soil moisture from AMSR-E
Topics
- Access
- Description
- Parameters
- Coverage, spatial and temporal resolution
- Data quality
- Contact person
- References
- Data citation
Access
RESTRICTED: This link to the data set is only available for a restricted user group. The data set is only accessible in CEN/MPI net or accessible from external nets with a customer account. Please contact ICDC if you would like to access this data from outside the network.
- View soil moisture data at LAS
- Access soil moisture data via OPeNDAP
- Data access via file system: /data/icdc/land/amsre_soilmoisture
Description
This soil moisture data set is based on dual-polarization brightness temperature measurements carried out with the polar orbiting Advanced Microwave Scanning Radiometer aboard EOS (AMSR-E) at frequencies of 6.9 GHz, 10.7 GHz, and 36.5 GHz. A radiative transfer model is used to simulate the brightness temperature based on the soil surface and canopy emissivity and the surface temperature which is derived using the AMSR-E 36.5 GHz data. Simulated brightness temperatures are compared to the measured ones while iteratively changing the variable parameters until convergence is achieved. The parameters are the soil dielectric constant (depends on soil type and moisture) and the canopy optical depth (depends on canopy type, density, and water content). Once convergence between modeled and measured brightness temperature is achieved (difference below 0.25 K) the soil moisture is derived using a soil dielectric mixing model and a global data set of soil physical properties. The derived quantity is the relative volumetric water content and is given in percent. Soil moisture (and its uncertainty) is computed for both frequencies (6.9 GHz and 10.7 GHz) separately.
We offer data for years 2002-2011, i.e. the full AMSR-E measurement period. Data are offered as daily swath composites, separated for descending and ascending overpasses, i.e. there are two files per day available; one contains data from all ascending overpasses of the respective day, the other data from all descending overpasses. In principle the data set offered here via ICDC is a re-formatted (Soil moisture data are now in landscape mode), inconsistency-corrected (Inconsistencies w.r.t. scale factors are corrected and netCDF-files are following the CF conventions) version of the LPRM_AMSRE_SOILM3_V002 data set of the VU Amsterdam.
Last update of data set at ICDC: August 6, 2015.
Parameters
| Name | Unit |
|---|---|
| Skin temperature | K |
| Relative volumetric soil moisture (C-Band, 6.9 GHz) | % |
| Uncertainty of relative volumetric soil moisture (C-Band, 6.9 GHz) | % |
| Canopy optical depth (C-Band, 6.9 GHz) | - |
| Relative volumetric soil moisture (X-Band, 10.7 GHz) | % |
| Uncertainty of relative volumetric soil moisture (X-Band, 10.7 GHz) | % |
| Canopy optical depth (X-Band, 10.7 GHz) | - |
| Quality flag | - |
Coverage, spatial and temporal resolution
Period and temporal resolution:
- June 19, 2002 to Oct. 03, 2011
- Daily
Coverage and spatial resolution:
- Global, separately for ascending and descending satellite overpasses
- Spatial resolution: 0.25° x 0.25°, cartesian grid
- Geographic longitude: 179.875°W to 179.875°E
- Geographic latitude: 89.875°S to 89.875°N
- Dimension: 720 rows x 1440 columns
- Altitude: following terrain
Format:
- NetCDF
Data quality
This data set contains quality flags, a retrieval error and canopy optical depth separately for soil moisture data sets of the two used AMSR-E frequency channels (see parameters).
The data set is completed with the retrieved surface temperature (derived from 36.5 GHz brightness temperatures, see Holmes et al. in the references).
Due to lacking documentation we cannot give complete information about the quality flag. It is clear, though, that these flag open water, ice/snow/frozen ground, and (too) dense vegetation. For the latter we recommend to consider the canopy optical depths which are also provided along with the data sets.
We note, that the soil moisture retrieval method used here, is of limited use particularly in regions covered by dense vegetation such as rain forests. These areas are therefore often flagged as unreliable data and/or show a large retrieval noise.
We recommend to take a look at the references for more details about data quality, validation, and inter-comparison studies.
Contact
Richard De Jeu & Thomas Holmes
Department of Eco-Hydrology, Faculty of Earth and Life Science
Vrjie Universiteit Amsterdam, Amsterdam, The Netherlands
E-Mail: richard.de.jeu (at) falw.vu.nl
E-Mail: thomas.holmes (at) falw.vu.nlStefan Kern
ICDC / CEN / University of Hamburg
E-Mail: stefan.kern"AT"uni-hamburg.de
References
Literature:
- Short description AMSR LPRMSM L3 Soilmoisture (pdf, not barrier free)
- de Jeu, R.A.M., Wagner, W., Holmes, T.R.H. et al. Global Soil Moisture Patterns Observed by Space Borne Microwave Radiometers and Scatterometers. Surv Geophys 29, 399–420 (2008). https://doi.org/10.1007/s10712-008-9044-0
- Owe, M., de Jeu, R., and Holmes, T. (2008), Multisensor historical climatology of satellite‐derived global land surface moisture, J. Geophys. Res., 113, F01002, doi:10.1029/2007JF000769. https://doi.org/10.1029/2007JF000769
- A. G. C. A. Meesters, R. A. M. De Jeu and M. Owe, "Analytical derivation of the vegetation optical depth from the microwave polarization difference index," in IEEE Geoscience and Remote Sensing Letters, vol. 2, no. 2, pp. 121-123, April 2005, doi: 10.1109/LGRS.2005.843983. https://doi.org/10.1109/LGRS.2005.843983
- Holmes, T. R. H., De Jeu, R. A. M., Owe, M., and Dolman, A. J. (2009), Land surface temperature from Ka band (37 GHz) passive microwave observations, J. Geophys. Res., 114, D04113, doi:10.1029/2008JD010257. https://doi.org/10.1029/2008JD010257
- Brocca et al. (2011), Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe, Remote Sensing of Environment, 115, 12, 3390-3408, https://doi.org/10.1016/j.rse.2011.08.003
- R. M. Parinussa, A. G. C. A. Meesters, Y. Y. Liu, W. Dorigo, W. Wagner and R. A. M. de Jeu, "Error Estimates for Near-Real-Time Satellite Soil Moisture as Derived From the Land Parameter Retrieval Model," in IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 4, pp. 779-783, July 2011, doi: 10.1109/LGRS.2011.2114872. https://doi.org/10.1109/LGRS.2011.2114872
- Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., and Evans, J. P.: Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals, Hydrol. Earth Syst. Sci., 15, 425–436, https://doi.org/10.5194/hess-15-425-2011 , 2011.
Data citation
Please cite the data as follows:
Owe, M., R. A. M., de Jeu, and T. R. H. Holmes (2008). Multi-Sensor historical Climatology of satellite-derived global land surface moisture, J Geophys. Res., 113, F01002, doi:1029/2007JF000769, distributed in rotated netCDF format by ICDC, CEN, University of Hamburg.