Error characterisation of global active and passive microwave soil moisture datasets

Hydrol. Earth Syst. Sci., 14, 2605–2616, 2010 www.hydrol-earth-syst-sci.net/14/2605/2010/ doi:10.5194/hess-14-2605-2010 © Author(s) 2010. CC Attribution 3.0 License. Hydrology and Earth System Sciences Error characterisation of global active and passive microwave soil moisture datasets W. A. Dorigo1 , K. Scipal2 , R. M. Parinussa3 , Y. Y. Liu3,4,5 , W. Wagner1 , R. A. M. de Jeu3 , and V. Naeimi1,6 1 Vienna University of Technology, Institute of Photogrammetry and Remote Sensing, Vienna, Austria 2 ESA, ESTEC, Noordwijk, The Netherlands 3 VU University Amsterdam, Faculty of Earth and Life Sciences, Department of Hydrology and Geo-Environmental Sciences, The Netherlands 4 School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia 5 CSIRO Land and Water, Black Mountain Laboratories, Canberra, Australia 6 German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Wessling, Germany Received: 23 July 2010 – Published in Hydrol. Earth Syst. Sci. Discuss.: 13 August 2010 Revised: 13 December 2010 – Accepted: 14 December 2010 – Published: 16 December 2010 Abstract. Understanding the error structures of remotely sensed soil moisture observations is essential for correctly interpreting observed variations and trends in the data or assimilating them in hydrological or numerical weather prediction models. Nevertheless, a spatially coherent assessment of the quality of the various globally available datasets is often hampered by the limited availability over space and time of reliable in-situ measurements. As an alternative, this study explores the triple collocation error estimation technique for assessing the relative quality of several globally available soil moisture products from active (ASCAT) and passive (AMSR-E and SSM/I) microwave sensors. The triple collocation is a powerful statistical tool to estimate the root mean square error while simultaneously solving for systematic differences in the climatologies of a set of three linearly related data sources with independent error structures. Prerequisite for this technique is the availability of a sufficiently large number of timely corresponding observations. In addition to the active and passive satellite-based datasets, we used the ERA-Interim and GLDAS-NOAH reanalysis soil moisture datasets as a third, independent reference. The prime objective is to reveal trends in uncertainty related to different observation principles (passive versus active), the use of different frequencies (C-, X-, and Ku-band) for passive microwave observations, and the choice of the independent reference dataset (ERA-Interim versus GLDAS-NOAH). Correspondence to: W. A. Dorigo () The results suggest that the triple collocation method provides realistic error estimates. Observed spatial trends agree well with the existing theory and studies on the performance of different observation principles and frequencies with respect to land cover and vegetation density. In addition, if all theoretical prerequisites are fulfilled (e.g. a sufficiently large number of common observations is available and errors of the different datasets are uncorrelated) the errors estimated for the remote sensing products are hardly influenced by the choice of the third independent dataset. The results obtained in this study can help us in developing adequate strategies for the combined use of various scatterometer and radiometerbased soil moisture datasets, e.g. for improved flood forecast modelling or the generation of superior multi-mission longterm soil moisture datasets. 1 Introduction In recent years, an increasing number of global soil moisture products have become available from past and present passive and active coarse resolution satellite microwave sensors. Altogether, these datasets span a period of more than 30 years (Table 1). Knowing the quality of the different datasets and understanding the various error sources (sensor calibration, retrieval errors, model parameterisation, etc.) contributing to the observed soil moisture variations is indispensable if one wishes to draw conclusions on trends or anomalies in the datasets, e.g. in relation to climate change (Liu et al., 2009). But also other applications, like the assimilation of remotely Published by Copernicus Publications on behalf of the European Geosciences Union. 2606 W. A. Dorigo et al.: Error characterisation of global active and passive microwave soil Table 1. Operational products in the field of global monitoring of soil moisture using active and passive satellite microwave instruments (sorted according to product release date). Sensor Producer soil moisture product Dataset availability First product release ERS-1/2 scatterometer Vienna University of Technology (TU Wien) 1991–2007 2002 Scipal et al. (2002); Wagner et al. (2007) AMSR-E radiometer US National Snow and Ice Data Center (NSIDC) 2002–present 2003 Njoku et al. (2003) AMSR-E radiometer Japanese Aerospace Exploration Agency (JAXA) 2002–present 2004 Koike et al. (2004) AMSR-E and TRMM-TMI radiometers United States Department of Agriculture (USDA) 2002–present 2007 Jackson (1993) ERS-1/2 scatterometer Centre d’Etudes des Environnements Terrestre et Planétaires 1991–present 2008 Zribi et al. (2003) Windsat radiometer US navy 2003–present 2008 Li et al. (2010) SMMR, SSM/I, TRMM-TMI, AMSR-E and WindSat radiometers Vrije Universiteit Amsterdam (VUA) and NASA 1979–present 2008 Owe et al. (2008) Soil Moisture and Ocean Salinity mission (SMOS) European Space Agency (ESA) 2010–present 2010 Wigneron et al. (2007) sensed soil moisture in flood forecasting (Brocca et al., 2010) or numerical weather prediction models (Drusch, 2007; Scipal et al., 2008a; Mahfouf, 2010) require accurate estimates of the quality of the observations. Most of the globally available microwave-based soil moisture products have been intensively validated using in-situ observations (e.g. Wagner et al., 2007; Jeu et al., 2008; Gruhier et al., 2010; Jackson et al., 2010). Even though the quality of the datasets can be established fairly accurately for the locations of the in-situ stations, available ground observations are restricted to a few locations worldwide and often cover only limited observation periods. In addition, reliable error estimation is complicated by representativeness and scaling errors, which can be larger than the actual retrieval error (Martı́nez-Fernández and Ceballos, 2005). Also, differences in observation times and depths, and inaccuracies of the in-situ measurements may lead to faulty interpretations of the obtained validation results (Gruhier et al., 2010). In contrast to the locally confined in-situ validations, error propagation methods can provide a more global picture of the uncertainty of soil moisture datasets. Error propagation techniques assess the uncertainty of model estimates resulting from errors in the input variables. Naeimi et al. (2009) (...truncated)