VUA-NASA Land Parameter Retrieval Model
The Land Parameter Retrieval Model consitute a global database of:
The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived seperately from Ka-band. A unique feature of this method is that it may be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from historic satellites (see table below).
There is additionally also a merged 32 year soil moisture product available. This product combines both the active and passive microwave soil moisture products and is developed in close collaboration with TU Wien. This dataset is free available at the ESA Climate Change Initiative website
The VUA-NASA retrieval products are available through different web portals.
The AMSR-E and TRMM soil moisture products can be found on NASA's Global Change Master Directory which hosts both our level 2 (Swath) and Level 3 (gridded) products:
The SMMR and SSM/I soil moisture products are available through the following ftp site.
username : adaguest
pwd : downloader
Unfortunately this ftp site will soon disapear but we are currently developing a new ftp site..
Please refer to the following citation when using soil moisture data:
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
for the temperature data:
Holmes, TRH, R,A.M. De Jeu, M. Owe and A.J. Dolman, 2009, "Land Surface Temperature from Ka-band (37 GHz) Passive Microwave Observations;, J. Geophys. Res., 114, D04113, doi:10.1029/2008JD010257.
for the vegetation optical depth data:
Liu, Y, RAM de Jeu, MF McCabe, JP Evans and AIJM van Dijk, Global long-term passive microwave satellite based retrievals of vegetation optical depth, Geophysical Research Letters, 38, L18402, doi: 10.1029/2011GL048684
and for the soil moisture uncertainties:
Parinussa R, AGCA Meesters, Y Liu, W Dorigo, W Wagner and RAM De Jeu An Analytical Solution to Estimate the Error Structure of a Global Soil Moisture Dataset, IEEE Geoscience and remote sensing letters, 8, 779-783, doi: 10.1109/LGRS.2011.2114872