An Improved Retrieval Algorithm as a Stepping Stone towards the Development of a Consistent CCI Soil Moisture Record
De Jeu, Richard1; Parinussa, Robert1; Holmes, Thomas2
1VU University Amsterdam, NETHERLANDS; 2USDA ARS, UNITED STATES

One of the more successful global scale soil moisture products are retrieved from the Land Parameter Retrieval Model (LPRM). The LPRM is based on a radiative transfer model that links soil moisture, land surface temperature and vegetation optical depth to brightness temperatures observed by space-borne radiometers. The basic concept of the LPRM was presented in Owe et al. (2001) and was applied to various satellite platforms carrying low frequency passive microwave radiometers aboard. These products became publically available in 2007 and were extensively validated by a large user community (i.e. De Jeu et al., 2008; Draper et al., 2009; Brocca et al., 2011), they were used in many environmental studies (e.g. Jung et al., 2010; Taylor et al., 2012) and may become an important dataset in climate change studies.
Within ESA's Water Cycle Multi-Mission Observation Strategy (WACMOS) project a multi-decadal soil moisture record has been developed which was partially based on soil moisture retrievals retrieved by the LPRM. These products were combined with those from the TU Wien which were retrieved from active microwave observations. Together, these soil moisture products were used in a harmonization routine to create a 32 year record. This harmonized soil moisture product will be further improved within ESAs Climate Change Initiative for soil moisture.
The source code of the LPRM has been essentially the same since 2001 and is well documented in literature (e.g. Meesters et al., 2005; Owe et al., 2008; Holmes et al., 2009). The previously mentioned validation studies, which vary from very local- to global studies, revealed that the quality of soil moisture retrievals (from both active- and passive observations) vary significantly in space and time. Based on these studies, combined with the extensive feedback from the user community, it is now time to further improve the LPRM. These improvements will make LPRM soil moisture products more ready for integration into the long term record and will further improve the quality of the long term soil moisture climate record.
Within this paper we demonstrate the steps we took to improve the LPRM. We explored the possibilities to produce a degree of saturation instead of volumetric soil moisture product. With this procedure we simplify the model and avoid errors associated to soil maps. In addition, this change could become an important step towards the elimination of a modeled reference dataset in the climate record harmonization procedure (Liu et al., 2012). Second, we analyzed the impact of the use of new innovations in the estimation of roughness, effective temperature and atmospheric contributions. Finally a new model convergence approach was analyzed with an improved masking routine for active precipitation events and radio frequency interference. The impact of these changes were validated over 150 in situ sites from the International Soil Moisture Network (ISMN; Dorigo et al. 2010) and globally evaluated through a series of statistical methods.

Brocca, L., et al. (2011). Soil moisture estimation through ASCAT and AMSR-E sensors: an intercomparison and validation study across Europe. Remote Sensing of Environment, 115, 3390-3408, doi:10.1016/j.rse.2011.08.003.

De Jeu, R.A.M., et al. (2008) Global Soil Moisture Patterns Observed by Space Borne Microwave Radiometers and Scatterometers, Surveys in Geophysics, 28, 399-420 doi 10.1007/s10712-008-9044-0

Dorigo, W., et al. (2011) The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrology and Earth System Sciences, 15, 1675 - 1698.

Draper, C.S., et al. (2009) Evaluation of AMSR-E derived soil moisture over Australia, Remote Sensing of Environment, 113, 703-710, doi:10.1016/j.rse.2008.11.011

Holmes, T.R.H., et al. (2009) Land Surface Temperatures from Ka-Band (37 GHz) Passive Microwave Observations. Journal of Geophysical Research, 114, D04113, doi:10.1029/2008JD010257.

Jung, M., et al., (2010) A recent decline in the global land evaportranspiration trend due to limited moisture supply,Nature, 467, 951-954, doi:10.1038/nature09396

Liu, Y., et al. (2012), Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment,123, 280-297

Meesters, A.G.C.A., et al. (2005), Analytical Derivation of the Vegetation Optical Depth from the Microwave Polarization Difference Index, IEEE Geoscience and Remote Sensing Letters, 2, 121-123.

Owe, M., et al., (2008) Multi-Sensor Historical Climatology of Satellite-Derived Global Land Surface Moisture, Journal of Geophysical Research, 113, F01002, doi:10.1029/2007JF000769

Owe, M., et al., (2001) A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval Using the Microwave Polarization Difference Index. IEEE Transactions on Geoscience and Remote Sensing, 39, 1643-1654

Taylor, C., (2012) Afternoon rain more likely over drier soils, Nature, 489, 282 - 286.