Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

Hydrol. Earth Syst. Sci., 15, 425–436, 2011 www.hydrol-earth-syst-sci.net/15/425/2011/ doi:10.5194/hess-15-425-2011 © Author(s) 2011. CC Attribution 3.0 License. Hydrology and Earth System Sciences Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals Y. Y. Liu1,2,4 , R. M. Parinussa2 , W. A. Dorigo3 , R. A. M. De Jeu2 , W. Wagner3 , A. I. J. M. van Dijk4 , M. F. McCabe1 , and J. P. Evans5 1 School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia 2 Department of Hydrology and Geo-Environmental Sciences, Faculty of Earth and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands 3 Institute for Photogrammetry and Remote Sensing, Vienna University of Technology, Vienna, Austria 4 CSIRO Land and Water, Black Mountain Laboratories, Canberra, Australia 5 Climate Change Research Centre, University of New South Wales, Sydney, Australia Received: 2 August 2010 – Published in Hydrol. Earth Syst. Sci. Discuss.: 2 September 2010 Revised: 18 January 2011 – Accepted: 25 January 2011 – Published: 1 February 2011 Abstract. Combining information derived from satellitebased passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a methodology that takes advantage of the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates to produce an improved soil moisture product. First, volumetric soil water content (m3 m−3 ) from AMSR-E and degree of saturation (%) from ASCAT are rescaled against a reference land surface model data set using a cumulative distribution function matching approach. While this imposes any bias of the reference on the rescaled satellite products, it adjusts them to the same range and preserves the dynamics of original satellite-based products. Comparison with in situ measurements demonstrates that where the correlation coefficient between rescaled AMSR-E and ASCAT is greater than 0.65 (“transitional regions”), merging the different satellite products increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT, respectively, are used for the merged product. Therefore the merged product carries the advantages of better spatial coverage overall and increased number of observations, particularly for the transitional regions. The combination method developed has the potential to be applied Correspondence to: Y. Y. Liu () to existing microwave satellites as well as to new missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles. 1 Introduction Passive and active microwave satellites have been shown to provide useful retrievals of near-surface soil moisture variations at regional and global scales (Wagner et al., 2003; Wen et al., 2003; Njoku et al., 2003; Owe et al., 2008; Gao et al., 2006; McCabe et al., 2005). They can penetrate cloud cover and are sensitive to soil water. A series of operational satellite-based passive microwave sensors have been available since 1978, including the Scanning Multichannel Microwave Radiometer (SMMR) (1978–1987), the Special Sensor Microwave Imager (SSM/I) of the Defense Meteorological Satellite Program (since 1987), the microwave imager from the Tropical Rainfall Measuring Mission (TRMM) (since 1997), and more recently the Advanced Microwave Scanning Radiometer – Earth observing system (AMSR-E) onboard the Aqua satellite (since 2002). In terms of active microwave sensors, the European Remote Sensing (ERS-1) scatterometer began its operation from 1992, ERS-2 started collecting data from March 1996, and the Advanced Scatterometer (ASCAT) onboard the Meteorological Operational satellite programme (MetOp) was launched in October 2006. Published by Copernicus Publications on behalf of the European Geosciences Union. 426 Y. Y. Liu et al.: Developing an improved soil moisture dataset The Soil Moisture and Ocean Salinity (SMOS) satellite launched in November 2009 carries the low frequency Lband sensor. While currently at the calibration stage, it is expected to continue the developing record of globally retrieved soil moisture data. In the coming years, numerous new satellite missions with microwave instruments are scheduled for launch (e.g., Soil Moisture Active Passive (SMAP), Aquarius, Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI), and the Argentine Microwaves Observation Satellite (SAOCOM)). These are expected to bring soil moisture retrievals with further enhanced accuracy. Various retrieval algorithms have been developed to estimate soil moisture from microwave observations (e.g., Owe et al., 2008; Njoku et al., 2003; Jackson, 1993; Wagner et al., 1999). Here we consider soil moisture products derived from two algorithms, one using passive microwave and the other active microwave observations. The algorithm developed by VU University Amsterdam in collaboration with the National Aeronautics and Space Administration (VUA-NASA) can be used for all bands in the passive microwave domain (Owe et al., 2008), allowing data collected by different satellites to be combined. The change detection algorithm developed by Vienna University of Technology (TU-Wien) has been applied on ERS-1/2 and ASCAT (Wagner et al., 1999; Bartalis et al., 2007), and provides a global satellite-based active microwave soil moisture product starting 1992. A number of previous studies (Vischel et al., 2008; Brocca et al., 2010; Albergel et al., 2009; Gruhier et al., 2010; Rüdiger et al., 2009; Draper et al., 2009; Wagner et al., 2007) evaluated these passive and active microwave soil moisture products against in situ measurements and found that VUA-NASA passive microwave product performs better over sparsely vegetated regions, whereas the TU-Wien active microwave product shows better agreement for regions of moderate vegetation density. Over the sparsely to moderately vegetated regions, both products have similar performances. Scipal et al. (2008) and Dorigo et al. (2010) applied the triple collocation approach with VUA-NASA passive microwave, TU-Wien active microwave and model simulated soil moisture products to estimate the relative error of each product at global scale. These three products are derived from different approaches and can be considered as having independent error characteristics, the key requirement for this approach. The results confirmed that the errors of VUA-NASA passive microwave are smaller than those of the TU-Wien active microwave product for sparsely vegetated regions and larger over moderately vegetated regions. Their errors are comparable over the regions with l (...truncated)