Inter-comparison of microwave satellite soil moisture retrievals over the Murrumbidgee Basin, southeast Australia

Remote Sensing of Environment 134 (2013) 1–11

Contents lists available at SciVerse ScienceDirect

Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse

Inter-comparison of microwave satellite soil moisture retrievals over the Murrumbidgee Basin, southeast Australia Chun-Hsu Su a,⁎, Dongryeol Ryu a, Rodger I. Young a, Andrew W. Western a, Wolfgang Wagner b a b

Department of Infrastructure Engineering, University of Melbourne, Victoria 3010, Australia Department of Geodesy and Geoinformation, Vienna University of Technology, Austria

a r t i c l e

i n f o

Article history: Received 6 December 2012 Received in revised form 16 February 2013 Accepted 16 February 2013 Available online xxxx Keywords: Soil moisture Remote sensing AMSR-E SMOS ASCAT Validation

a b s t r a c t The use of satellite-based soil moisture retrievals for hydrologic, meteorological and climatological applications is advancing significantly due to increasing capability and temporal coverage of current and future missions. Characterisation of the relative skill of soil moisture products from different satellite sensors on a common spatial grid is crucial to achieve synergetic applications. This paper therefore evaluates three soil moisture products from AMSR-E (Advanced Microwave Scanning Radiometer — Earth Observing System), ASCAT (Advanced Scatterometer) and SMOS (Soil Moisture and Ocean Salinity) in absolute soil moisture units and on a common grid, against in-situ observations from southeast Australia. Before renormalisation, the three products yield correlations of 0.63–0.71 and a similar root-mean-square difference (RMSD) in the order of 0.1 m3 m−3, although showing different levels of error contributions from bias, variance and correlations. The results are compared with land and precipitation data to investigate the sensitivity of their errors to land surface features. Three renormalisation strategies – minimum–maximum matching, mean/standard-deviation (μ–σ) matching and cumulative distribution function (CDF) matching – are considered for correcting systematic differences between ground and satellite data. The renormalised satellite data is found to retain RMSDs of 0.04– 0.06 m3 m−3 on average. The CDF method produces only marginal further improvements to correlations (0.67–0.75) and RMSDs compared to the μ–σ approach. The renormalisations by μ–σ and CDF methods also bring three products into better agreements with each other, but lead to strong correlations between RMSD and the dynamic range of in-situ soil moisture. © 2013 Elsevier Inc. All rights reserved.

1. Introduction Soil moisture has a significant control on hydrological and meteorological responses of the land surface (Western et al., 2002 and references therein). Antecedent soil moisture influences rainfall-runoff response, groundwater recharge and catchment drainage. As a part of the interface between the land surface and the atmosphere, it regulates energy and water exchanges and constrains plant transpiration and photosynthesis. Routine and real-time observations of surface soil moisture as inputs to environmental modelling has great potential in improving predictability and understanding of short-term weather, extreme events and climate variability, and their impact on ecosystems and agriculture. In-situ monitoring is not feasible on large scales, and land surface models may suffer from predictive errors due to simplified model physics, input forcing and parameter errors and poor initialization (Ni-Meister et al., 2005). However global daily monitoring of topsoil moisture can be achieved practically with space-borne sensors.

⁎ Corresponding author. Tel.: +61 383449792; fax: +61 383446215. E-mail address: [email protected] (C.-H. Su). 0034-4257/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2013.02.016

Two prominent missions currently operational are the Soil Moisture and Ocean Salinity (SMOS) and the Advanced Scatterometer (ASCAT) on meteorological satellite MetOp-A. In 2012, the Advanced Microwave Scanning Radiometer (AMSR-2) on the GCOM-W (Global Change Observation Mission — Water) and ASCAT on the MetOp-B are crucial additions to this network, which is growing further with several highly anticipated missions. These include: the Soil Moisture Active/Passive mission planned for 11/2014 by National Aeronautics and Space Administration (NASA); MetOp-C, which is expected in 2016; and the launch of the Argentine Microwaves Observation Satellite series is also expected near term. These satellites will provide an uninterrupted stream of daily soil moisture observations well beyond 2020. The use of satellite-retrieved soil moisture estimates for scientific and operational geophysical applications is advancing rapidly due to ongoing improvements of sensor technologies and retrieval algorithms, as well as their relative success in providing usable data (Wagner et al., 2007a). Numerous studies have been dedicated to assessing the accuracy of the various soil moisture products by comparing them against ground measurements from monitoring networks around the world, e.g., in West Africa (Gruhier et al., 2010), Europe (Albergel et al., 2009; Brocca et al., 2011), Australia (Draper et al., 2009; Mladenova et al., 2011); and the

2

C.-H. Su et al. / Remote Sensing of Environment 134 (2013) 1–11

United States (Collow et al., 2012). Recently, Albergel et al. (2012) drew upon data from over 200 stations from all these regions to compare ASCAT and SMOS products. In general, these evaluations show that both radiometers and scatterometers yield products of promising performance with moderate-to-fair correlations but high levels of the root-meansquare difference (RMSD) well outside mission targets caused by a rather high bias. Biases and systematic differences in dynamic range in satellite data highlight the difficulties in both retrieval science and validation campaigns. Soil moisture is highly variable spatially and temporally, while satellite sensors have different sensing depths and spatial resolutions from ground probes and retrievals are vulnerable to land surface features. To overcome these difficulties, a dense monitoring network is needed (Jackson et al., 2010), or often, time stability restriction, statistics-matching methods, or geostatistical estimation methods must be invoked (Crow et al., 2012). Despite these difficulties, the utility of the surface soil moisture estimates from satellite appears promising. Several studies have shown that assimilating satellite data into land models yields improved root-zone soil moisture (Das et al., 2008; Draper et al., 2012; Reichle & Koster, 2005) and has potential to improve process-based models for simulations of carbon and water fluxes (Rebel et al., 2012). And one notable step is the operational use of ASCAT data in Numerical Weather Prediction (Albergel et al., 2010; de Rosnay et al., 2012; Dharssi et al., 2011). The increasing temporal coverage by current and near-future satellites is also expected to enhance their utility. At present, the existing constellation can provide up to 8 observations per day (by AMSR-2, SMOS, MetOp-A and B) over Australia with 2.5 to 5-hour time intervals. Such temporal resolution is of particular importance for rainfall-runoff studies, and it also enables cross product validation, error characterisation (Dorigo et al., 2010; Scipal et al., 2008), data gap infilling (Dumedah & Coulibaly, 2011; Wang et al., 2012), blending (Liu et al., 2012; Yilmaz et al., 2012), and correction of other retrieval product such as precipitation (Crow & Bolten, 2007). To meet this synergy, the correspondence between the datasets must be established. This study therefore expands on past evaluation studies to characterise the relative skills of three soil moisture products in a common spatial grid and units. The first part of the paper focuses on comparing against in-situ observations from 49 sites at the Murrumbidgee Basin in southeast Australia, to characterise the products in terms of errors, correlations, land surface characteristics, and error profiles. The evaluation area is also currently used for a comprehensive field assessment of SMOS (Peischl et al., 2012). The second part of the paper examines three commonly used renormalisation strategies for correcting systematic differences in satellite data. Their utilities and their implications for modelling are then briefly discussed. 2. Satellite products We consider data from ASCAT and SMOS for their distinctive characteristics in terms of sensors, retrieval algorithms, acquisition time, sensing depths, and spatiotemporal resolutions. Importantly SMOS and ASCAT have most potential for applying to modelling land surface processes in operational environments. The upcoming SMAP shares similarities with these sensors to operate in L-band with both radiometric and radar capabilities (Entekhabi et al., 2010). We also consider retrievals from the Advanced Microwave Scanning Radiometer (AMSR-E) onboard the Aqua satellite for its close relation to the newly launched AMSR-2 on GCOM-W, which inherits most of AMSR-E's characteristics. Since AMSR-2 has an improved calibration system, radio-frequency interference (RFI) identification capability and better spatial resolution (Maeda et al., 2011), the findings in this study can be compared with AMSR-2 in the immediate future to understand their utility and limitations. The temporal coverage of each data set is depicted in Fig. 1, which illustrates the differences in the coverage of the three products.

Fig. 1. Temporal coverage of the near-surface soil moisture data sets from OzNET, and satellite retrievals from AMSR-E, ASCAT and SMOS. Measurements from first-generation (from 9/2001), second-generation (from 9/2003) and recently installed sites (from 9/2010) are combined to provide areal averaged topsoil moisture for 17 0.25° grid cells (Table 1).

2.1. AMSR-E The AMSR-E radiometer (18/6/2002–4/10/2011) uses C- (6.9 GHz) and X-band (10.65 and 18.7 GHz) radiance observations to derive near-surface soil moisture using a land-surface radiative transfer model. These bands have variable satellite footprints and sensing depths; C-band has a footprint of 74 × 43 km2 and topsoil sensing depth of 1–2 cm while X-band has smaller footprints but b 5 mm penetration depths (Owe et al., 2008). The host satellite Aqua operates in a polar, sun-synchronous orbit providing daily scans of Australia during the ascending (1330 h local time) and descending (0130 h) orbits. Currently while several AMSR-E products are available, we evaluate the Version 5 data set created by the Vrije Universiteit Amsterdam (VUA) in collaboration with NASA. The exact retrieval method is described in Owe et al. (2008). Draper et al. (2009) and Brocca et al. (2011) found that the VUA-NASA product exhibits stronger agreement with in-situ observations in Europe and Australia respectively. The combined Cand X-band data set is used as their difference is found to be small (Draper et al., 2009; Owe et al., 2008), particularly in regions such as Australia where regional radio-frequency interference (RFI) in X-band is small (Njoku et al., 2005). The VUA-NASA approach uses the Land Parameter Retrieval Model (LPRM) with C- or X-band radiances as input for retrievals of both soil moisture and vegetation water content. It is an iterative forward physical model inversion method, which simulates observed brightness temperature by varying three land surface variables (vegetation optical depth, topsoil dielectric constant and surface temperature) to partition surface emission into soil and canopy components. Once convergence is reached, the model uses a global database of soil physical properties and a soil dielectric mixing model to determine the absolute value of surface soil moisture. The results are made available as a global gridded product in the units of volumetric water content (m3 m−3) on a regular 0.25° global grid. Since the retrieval model was not calibrated with field observations of hydrological and biophysical variables, the model has limited regional dependence. With AMSR-E's dual polarisation sensing setup, vegetation development can be monitored to some extent. As the surface emission becomes depolarized with increasing vegetation cover, the algorithm uses normalised polarisation difference to infer vegetation water content and opacity indirectly, and account for their effects. However, as C- and X-band emissions have relatively poor penetration capability through vegetation, the retrievals in the presence of relatively dense vegetation cover are still likely to be erroneous (Ryu et al., 2007).

C.-H. Su et al. / Remote Sensing of Environment 134 (2013) 1–11

2.2. ASCAT The ASCAT sensor onboard the MetOp-A (from 19/10/2006) is a fixed fan-beam scatterometer that transmits and measures electromagnetic waves in C-band (5.3 GHz) in vertical polarisation. The physical basis for their soil moisture measurement capability is the strong dependence of backscatter echos on the topsoil moisture. Measurements over Australia are obtained about twice a day; once during the night-time descending orbit at around 1130 h UTC (2130 h local time) and once during the morning ascending orbit at 2230 h UTC (0830 h). The nominal spatial resolution of the backscatter measurement varies from 25 to 50 km, and the product grid spacing is 12.5 km for the higher resolution product (Wagner et al., 2012). While sensitive to soil moisture, C-band backscatter is also strongly dependent on surface roughness (Verhoest et al., 2008) and vegetation cover (Wagner et al., 2012), rendering direct estimates from radar backscatter inaccurate. Some of these difficulties can be overcome by using a time-series based change-detection algorithm (Wagner et al., 1999). By assuming land surface characteristics to be relatively static over long time periods under a given incidence angle, it compares the instantaneous backscatter coefficients to the historical lowest and highest values to attribute the relative differences to changes in soil water. The resultant soil moisture content is therefore measured in relative terms as the degree of saturation. Here we use the ASCAT soil moisture data that was produced using Water Retrieval Package (Version 5.4) by the Vienna University of Technology. 2.3. SMOS The MIRAS (Microwave Imaging Radiometer with Aperture Synthesis) radiometer (from 2/11/2009) on the SMOS satellite uses a Y-shape network of 69 dual-polarisation single-channel L-band (1.4 GHz) detectors to achieve a spatial resolution of ~432 km2 (Kerr et al., 2010). The platform provides global coverage twice every 3 days with a morning ascending orbit at 0600 h local time and an afternoon descending orbit at 1800 h. The advantages of operating in L-band compared with higher frequencies are that the instrument has a greater sensing depth (approximately 5 cm), which is comparable with the typical setup of in-situ sensors, and greater penetration through vegetation (Ryu et al., 2007). Similar to the VUA-NASA algorithm, the standard ESA (European Space Agency) product is derived using a forward physical model inversion, described by Kerr et al. (2012). The distinguishing feature of SMOS is its ability to provide 160 angular fully-polarised measurements that enable direct inference of both soil moisture and vegetation opacity. In this study, we use the most recently re-processed 1-day global soil moisture product (Version RE01) provided by Centre Aval de Traitement des Données. Their algorithm extends the ESA's algorithm to provide better estimations at revisited locations and to increase SMOS retrieval coverage (Jacquette et al., 2010). Using information from preceding and succeeding revisits enhances the angular sampling near the swath edge, and together with strong auto-correlations in vegetation optical thickness, reduces the number of parameters in retrieval algorithm. Multiple retrievals at revisited locations are filtered to choose the best value. This product is available as volumetric soil moisture gridded on a global Equal-Area Scalable Earth (EASE) grid in a cylindrical equal-area projection. 3. Study area The Murrumbidgee Soil Moisture Monitoring Network (OzNet) consists of 62 monitoring stations across the 82,000 km 2 Murrumbidgee River Catchment, southeast Australia (Smith et al., 2012). The measurement records up to 9/2011 are publicly available (http://www.oznet. org.au/). We identified quality-assured data from 49 sites for use in

3

this study, as tabulated in Table 1. 5 sites (labelled A*) are grouped in the 145 km 2 Adelong Creek catchment, 7 (K*) are in the 600 km 2 Kyeamba Creek catchment, 30 (Y*) sites are in a 60 × 60 km 2 sub-region near Yanco, and the remaining 7 (M*) are sparsely distributed around the catchment. All the stations are installed in grassland/cropping land cover areas. The climate across the basin varies from temperate with seasonally uniform rainfall and either a warm or hot summer (Köppen Cfb and Cfa respectively) to arid steppe with a cold winter (Köppen BsK) (Peel et al., 2007). Soil moisture varies seasonally from dry in summer (Dec–Feb) to wet winter (Jun–Aug) due to the seasonality of evapotranspiration forcing. We adopted a common regular 0.25° grid (Fig. 2a) for the inter-comparison of different satellite products and these sites are found to reside in 17 grid cells. The setups of the instruments are described by Smith et al. (2012). In general, the network uses a mixture of time-domain interferometer-based Campbell Scientifics (CS) 615 s to measure 0–8 cm topsoil and Stevens Hydraprobes of length 5 cm, with site-specific calibration accuracies that yield RMSD of 0.025 m 3 m − 3 and 0.033 m 3 m − 3, respectively. Fig. 2 identifies significant regional differences between the monitoring stations. The land cover/use map in Fig. 2b, produced by merging land cover (Lymburner et al., 2010) and land use (Australian Bureau of Rural Science, 2010) data sets, shows increasing forest and woodland coverage in the east, with pasture and cropping in the central region, and increasing grassland to the west. Specifically, Adelong Creek is on steep slopes dominated by pasture for grazing; Kyeamba Creek is located on gentle slopes with rainfed cropping and pasture; and the Yanco region is a large flat area with a mix of irrigation, cropping and pasture. Fig. 2c shows the distributions of saturated volumetric water content (θSAT) for the (surface soil) A-horizon reported in the McKenzie et al. (2000) interpretations of Digital Atlas of Australian Soils (Northcote et al., 1960–1968). The western plain is dominated by clay-loam soils and with decreasing clay content in the middle and eastern plains (Peischl et al., 2012). This corresponds to decreasing θSAT from the west to the east. The soil map also shows that the Yanco region generally has a more homogenous soil distribution of clay-loams whereas heterogeneity increases to the east with soils varying from sandy to loam. Finally Fig. 2d and e provide elevation and rainfall characteristics of the region. The latter is derived from Australia-wide daily gridded

Table 1 Monitoring stations used in this study, grouped in 17 0.25° cells, identified in Fig. 1. The locations of the stations can be retrieved from http://www.oznet.org.au. Asterisks denote the first-generation sites that use 0–8 cm CS615s sensors since 9/2001; otherwise 0–5 cm hydroprobes are used. † denotes the second-generation sites that measured topsoil moisture from late 2006, and the remaining sites are recently installed and generates quality-assured data from 6/2010. Grid cell

Monitoring stations

M-1 M-2 M-3 M-4 M-5 M-6 M-7 Y-1 Y-2 Y-3 Y-6 Y-9 Y-B Y-10 K-1 K-14 A

M1* M2* M3* M4* M5* M6* M7* Y1† Y2†, Y4†, YA1, YA3, YA4a, YA4b, YA4d, YA4e, YA5, YA7a, YA7b, YA9 Y3*, Y5† Y6† Y9†, YA7d, YA7e YB1, YB5a, YB5b, YB5e, YB7a, YB7b, YB7c Y10†, Y13†, YB7d, YB7e K1*, K2*, K3*, K4*, K5*, K7* K14† A1*, A2*, A3*, A4*, A5*

4

C.-H. Su et al. / Remote Sensing of Environment 134 (2013) 1–11

4. Methods 4.1. Data preparation

Fig. 2. Land surface characteristics of (a) the Murrumbidgee River Catchment, showing (b) land cover/use, (c) saturated volumetric water content, (d) elevation, and (e) mean annual rainfall. The 49 monitoring sites (marked with crosses) in 0.25° grid cells [squares, labelled in (a)] are used in this study.

rainfall during the evaluation period of 2002–2010 (Raupach et al., 2012). The maps show that the mountainous regions of the catchment are characterised with much higher rainfall and variance in elevation and rainfall. Soil moisture variability is generally dominated by land cover patterns such as topography, vegetation and soil factors (Grayson et al., 1997; Vinnikov et al., 1996; Wilson et al., 2004). These factors introduce sub-grid spatial variability of soil moisture that undermines the representativeness of in-situ observations. And dense vegetation that masks the soil emissions and non-soil surface can introduce brightness temperature uncertainties. Therefore insights into effects of land characteristics on retrieval performance can be developed by drawing upon these high-resolution ancillary data.

A rigorous evaluation of satellite observations requires a dense and reliable ground network and well-designed sensor setup corresponding with the shallow (typically 0–5 cm or less) sensing depth, minimal data latency and high temporal frequency (Jackson et al., 2010). The OzNet meets most of these criteria, except for the spatial density. The high temporal frequency allowed the time series of 0–5/8 cm 20/30 min interval moisture observations to be sub-sampled to match the overpass times of each satellite. However most grid cells do not meet the requirement of ≥5 sampling locations to yield accurate grid-scale estimates (Brocca et al., 2010; Famiglietti et al., 2008). Cells A and K-1 have been better instrumented since 2002, and some of the Yanco instrumentations at Y-2 and Y-B became operational from June 2010. These cells are expected to have a more reliable representation of the areal condition. The areal estimates are calculated using equal-weighted arithmetic averages of measurements from stations collocated in a given cell. This method can however introduce biases when a subset of the averaged sites is inactive and soil moisture is systematically different between sites. To overcome this problem, we employ a method analogous to a sensitivity test. The following steps are taken: i) coincident data from periods where all sites are active are used to determine which subsets of sites best replicate the average of all sites when combined. Each subset is given a score, i.e. RMSD. ii) The subsets with poor RMSDs that are more than the calibration errors of the sensors are rejected, and the remaining subsets of active sites form a lookup table. iii) To determine an areal estimate of soil moisture during periods when some sites were offline, the lookup table is consulted to determine which subsets of active stations can provide best estimate. We note that alternative geostatistical estimation methods such as ordinary kriging and block kriging are available. However these are unsuitable for our sites due to the limited number of sampling locations within each cell. To match the working spatial 0.25° grid for coherent analyses, the SMOS and ASCAT data are also spatially re-sampled. For SMOS we use area-weighted averages based on the overlap between satellite pixels. As SMOS' EASE grid resolutions at these latitudes commensurate with the working grid, the re-sampling does not significantly modify the original estimates. In contrast, as the ASCAT product is defined over a sinusoidal grid with ~ 12.5 km spacing, there are ≥ 6 retrieval points uniformly distributed within a grid cell. First, individual ASCAT retrievals in units of relative saturation θr are converted to volumetric units using ϑ = ϑAD + (ϑSAT − ϑAD)ϑr, where the static ancillary map of saturated volumetric water content (ϑSAT) is given in Fig. 2c. ϑAD denotes the residual soil water content under air dry condition. As accurate data is unavailable and they are typically smaller than wilting points, we assumed zero ϑAD. Second, the ASCAT is upscaled to 0.25° by taking arithmetic averages. Finally to eliminate outliers in the satellite data, we follow Hoaglin et al.(1986) and censor data points that lie outside the window defined by ϑ ∈ [ϑ0.25 − g(ϑ0.75 − ϑ0.25), ϑ0.75 + g(ϑ0.75 − ϑ0.25)] where ϑ0.25 (ϑ0.75) is the moisture value at 25% (75%) percentile of the data set and g = 2.2. Following these treatments, the resultant satellite data sets are regarded as the baseline — examples from K-1 are shown in Fig. 3. There is a good correspondence between the data sets by capturing the seasonal trend and multiple rainfall events, although short time-scale fluctuations and many gaps also exist in the data. 4.2. Performance metrics We first treat the satellite retrievals as an exact measure of near-surface soil moisture but at a coarse scale to enable direct

C.-H. Su et al. / Remote Sensing of Environment 134 (2013) 1–11

5

the satellite data is first renormalised (Section 4.3) with respect to the in-situ data before evaluation. For this work, we follow a more holistic approach by examining all the aforementioned performance metrics. As we will see, the baseline data provides valuable evidence to the impact of land cover on retrieval accuracy. Our evaluations also explore possible associations between error and moisture level by calculating the linear correlations between the residual (εi) and the ground moisture (Ii), to which we refer as the residual correlations. 4.3. Renormalisation methods Three renormalisation strategies are considered for removing the systematic differences between the ground and satellite data. They are commonly used to match the satellite data to the statistics of corresponding model states for soil water in modelling, and to in-situ data in evaluations. It is expected to improve RMSD by reducing biases and variance error. The first approach involves rescaling each satellite time series to match its minimum (ϑmin) and maximum (ϑmin) to those (Imin, Imax) of the in-situ time series, Fig. 3. Time series of the satellite soil moisture (grey curves) at K-1 during 2010, compared with the ground measurements (black).

comparisons with ground measurements. In particular, their overall mismatch is the root-mean-square difference, vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N u1 X 2 ε ; RMSD ¼ t N i¼1 i

ð1Þ

where εi = ϑi − Ii is the difference between the in-situ (Ii) and remotely-observed (ϑi) soil moisture at time step i, and N is the length of the time series. Differences in temporal mean (i.e. biases), dynamic range (related to variance difference) and temporal pattern (mismatch in timing and shape, i.e. relating to correlations) all contribute to the RMSD. In a step further to investigate the error profile, we consider a decomposition of RMSD or equivalently mean square error MSE = RMSD 2 into these individual error components by using (Gupta et al., 2009), MSE ¼ MSEcorr þ MSEbias þ MSE var ;

ð2Þ

where MSEcorr ¼ 2σ I σ ϑ ð1−RÞ;

ð3Þ

  Imax −I min ^ ¼I ϑ : min þ ðϑ−ϑmin Þ ϑmax −ϑmin

ð6Þ

This is mathematically equivalent to the approach by Albergel et al. (2012) and Brocca et al. (2011) where they rescaled satellite data sets to a standard range 0–1. We refer to this approach as minimum–maximum (MM) matching. The second approach μ–σ (e.g., Draper et al., 2009) is to match their means and standard deviations using, ^ ¼ μ þ σ I ðϑ−μ Þ: ϑ I θ σθ

ð7Þ

Lastly the cumulative distribution function (CDF) matching (Brocca et al., 2011; Reichle & Koster, 2004) is a nonlinear method to match all orders of statistical moments. These rescaling methods are applied to individual sites independently. Given the linearity of the transformations in Eqs. (6) and (7), the first two rescaling schemes will not improve the correlations. The μ–σ and CDF matching strategies will by definition eliminate bias and variance errors. By correcting the mismatch of the higher-order statistical moments, the CDF technique is the only method that may be capable of correcting mismatch in seasonality, modality and locally preferred moisture states observed in the ground data and has the most potential to improve all skill scores. 5. Results and discussion

2

MSEbias ¼ ðμ ϑ −μ I Þ ;

ð4Þ

2

ð5Þ

MSE var ¼ ðσ ϑ −σ I Þ :

μ* (σ*) denotes the temporal mean (standard deviation) of each time series, and R is the Pearson's linear correlation coefficient. The bias (μθ–μI) is also considered separately and the 95% confidence interval qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   (CI) of the bias is given by Δβ ¼ z2 =N σ 2I þ σ 2ϑ with a critical value z = 1.96; and the 95% CI of the correlation is calculated using Fisher's transform. It is noted that another school of thought objects to the notion of an exact physical correspondence between coarse-scale satellite and point-like ground observations, and the reliance on RMSD as the main criterion for evaluating the satellite data. Similarly one could limit the assessments to examining only the correlations by invoking temporal stability argument, where ground and satellite data follow similar temporal trends as local and regional atmospheric processes influence soil moisture over regional spatial scales. In both cases,

The baseline soil moisture products described in Section 4.1 are first compared directly to in-situ data. The entire data sets shown in Fig. 1 are used to achieve maximum statistical significance in the derived skill scores. However this may undermine fair comparisons between the three products with variable data periods, as soil moisture dynamics behave differently during different years. To address this concern, the performance of the products is also compared by considering a common data period; the 2010 calendar year. 5.1. Baseline satellite data Fig. 4 provides an overview on the soil moisture dynamics between the sites and the products. Box plots are used to summarise the statistics of individual time series during the wet (Jun–Aug) and the dry (Dec–Feb) months. Fig. 4a highlights some regional differences in the ground observations: M-1, M-2, M-5, M-6 and M-7 experienced less rainfall than most other sites, accounting for smaller soil moisture dynamic ranges during the wet and dry months. This

6

C.-H. Su et al. / Remote Sensing of Environment 134 (2013) 1–11

Fig. 4. Box plots showing the statistics of satellite and in-situ time series during the wet months (black) and dry months (red). The edges of the box are the 25th and 75th percentiles of the soil moisture variations and the central mark is the median. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

contrasts with bigger seasonal changes at higher rainfall sites at Adelong and Kyeamba. Other differences are likely to be due to soils, vegetation and topographic influences. Satellite retrievals in Fig. 4b–d tend to show less difference in the dynamic ranges between sites and seasons, than the ground sites. AMSR-E data exhibits much more distinct seasonal change in soil moisture than the ground sites and other satellite products, and it correlates with the vegetation (and rainfall) distribution in Fig. 2. In contrast, SMOS shows relatively smaller seasonal changes and the bigger within-season variances, particularly during the dry months. It should be noted that these observations remain valid when the analysis was repeated with single year data sets (not shown). This may be related to the lower attenuation of L-band emission from soil by vegetation and relatively deeper sensing depth of SMOS. Moreover, the spatial heterogeneity is not well represented at 40-km satellite footprint, potentially leading to narrower dynamic ranges for AMSR-E. Further diagnosis to ascertain the causes requires in-depth analyses of the direct sensor observations and ancillary data and is beyond the scope of this paper. Lastly, the ASCAT data also exhibits strong seasonal change and relates somewhat to rainfall patterns. However without good information on air dry residual moisture (ϑAD), soil water during the dry months is generally under-estimated and overall there is a larger variance. Table 2 provides another overview with a summary of the skill scores for the baseline data, averaged over all the grid cells with equal weighting. The confidence interval pffiffiffiffiffi (CI) for average bias is shown and was estimated using ∑j Δβj = M, where M is the number pffiffiffiffiffi of grid cells; while the CI for average R scores is given by ∑j ΔRj = M (Draper et al., 2012). AMSR-E shows substantial and statistically significant wet biases >0.03 m 3 m−3. This is consistent with evaluation findings by Jackson et al. (2010) in the U.S. While ASCAT and SMOS also exhibit some biases, they are not statistically significant on average. The average RMSDs and

correlation scores are similar between the products, with moderate correlations 0.63–0.71 and relatively high RMSD ~ 0.1 m 3 m −3. Fig. 5 provides a detailed breakdown of the scores for each grid cell: the RMSDs and R-values for the three products range between 0.05–0.19 m 3 m − 3 and 0.4–0.85, respectively, with substantive biases of magnitudes up to 0.18 m 3 m − 3. From comparing Fig. 5a and b, the large RMSDs are generally caused by rather high biases. The accuracy of the retrievals varies considerably across the network, and thus interpretation of the performance based on the land surface data in Fig. 2 may offer some insights. Starting with the AMSR-E, the worst performance with large biases and/or low correlations is found over the eastern grid cells M-1, M-2, M-5, and A with above-average biases. Over 60% of these cells are covered by trees (open, closed, sparse, scattered forests and dense woodlands). Moreover these regions have higher spatial heterogeneity. The mountainous areas (M-1, M-2, A) also have high variances of elevations and rainfall distributions and more heterogeneous soil. Water bodies are present at M-5 (at 3.7% coverage) and A (2.6%). It is known that a water presence of 2% of the footprint area can increase the root-mean-square (rms) errors above 0.04 m 3 m −3 due to the high dielectric constant of liquid water (Gouweleeuw et al., 2012; Loew, 2008). It is also interesting to find that, from Fig. 2e, the on-site (point scale) mean rainfalls at M-2 and M-3 are relatively lower than the grid-level rainfall statistics, potentially leading to the observed wet bias. This observation perhaps should be treated with caution because of the use of interpolated rainfall data. Validating against the ASCAT retrievals, the fine-scaled data does not produce better agreement than their upscaled counterparts. The most probable reason for the large biases in AMSR-E is vegetation-related, given that there is the strong connection between retrieval accuracy and vegetation density due to the vegetation-induced attenuation of short-wavelength soil emissions

Table 2 Skills of each satellite products after renormalisation. The average scores are calculated as the mean of the scores of 17 grid cells. Bias and RMSD are in volumetric units m3m−3. The baseline satellite data is rescaled using minimum–maximum (MM) matching, mean and standard deviation (μ–σ) matching, and CDF matching schemes. N is the total number of the coincident data points between the in-situ and satellite data used in the evaluation across all the cells. Square brackets denote averaged 95% confidence intervals (see text), e.g., 0.058[27] refers to 0.058 ± 0.027. Sites

AMSR-E ASCAT SMOS

Time

0130 1330 0830 2130 0600 1800

N

26,200 27,452 10,331 10,422 3513 3469

μ–σ

CDF

Bias [CI]

RMSD

R [CI]

Bias [CI]

RMSD

RMSD

RMSD

R [CI)

0.058[27] 0.072[24] 0.021[44] 0.026[45] −0.012[63] −0.024[64]

0.103 0.100 0.094 0.093 0.093 0.088

0.71[11] 0.69[12] 0.67[19] 0.68[18] 0.63[33] 0.71[33]

0.003[19] 0.036[19] 0.013[33] 0.013[33] −0.024[44] −0.028[46]

0.049 0.060 0.063 0.060 0.058 0.055

0.047 0.048 0.050 0.049 0.052 0.045

0.047 0.046 0.050 0.049 0.049 0.043

0.72[11] 0.71[12] 0.68[18] 0.69[17] 0.67[30] 0.75[30]

Baseline

MM

C.-H. Su et al. / Remote Sensing of Environment 134 (2013) 1–11

Fig. 5. Comparisons between in-situ and different (baseline) satellite soil moisture data in terms of (a) bias, (b) RMSD, and (c) Pearson's correlation. The brackets in (a) denote the cells with multiple monitoring stations. The error bars of the biases and correlations in (a,c) denote confidence intervals for 95% significance level. Note that there are insufficient data to evaluate SMOS at the cell A.

(Dorigo et al., 2010; Parinussa et al., 2011). In contrast, the SMOS and ASCAT data do not show above-average errors in some of these areas (M-1, M-5, A). This is consistent with the expectations of greater penetration through vegetation at L-band and the relative insensitivity of the change-detection based ASCAT algorithm to vegetation-induced scattering (Wagner et al., 2012). ASCAT shows statistically significant positive bias at several cells but to a lesser extent than AMSR-E. Of the sites, the retrievals at M-3 and Y-B are the worst in terms of bias and correlations. The result at M-3 may also be related to the poor representation of the monitoring station because of heterogeneous rainfall distribution. For Y-B, as we will discuss later, the upscaling is responsible for its poor scores. More importantly, the comparable biases and RMSDs support our approach of upscaling and converting ASCAT to volumetric terms. SMOS shows a weak tendency of dry bias in Fig. 5a, which appears to agree with observations by Collow et al. (2012) and Al Bitar et al. (2012) in the U.S. region. Fig. 5b however shows the relatively high RMSDs at M-2 (Canberra) and K-14 (Wagga Wagga). These cells have significant urban land covers of 30% and 17%, respectively. RFI is a likely cause (Skou et al., 2010) as man-made sources like air-traffic-control radars can contribute to increased brightness temperature and thus a dry bias. However high RMSD at Canberra is variance related, with the morning time series showing larger variance. Ye et al. (2011) found that urban areas behave like a target with a higher dielectric constant and/or lower surface roughness than dry soils. While this may have contributed to the wet-biased AMSR-E retrievals over Canberra, further work will be needed to investigate cause of the larger variance error in the SMOS ascending overpasses at the site. Finally while the SMOS

7

retrieval is highly impacted by water bodies (Al Bitar et al., 2012), the retrieval at M-5 with 3.7% water fraction is not noticeably degraded. Consider briefly now the associations between residual and the moisture level in Fig. 5d. The eastern cells (M-1, M-2, M-3, A) report positive correlations Re of around 0.5 across the products. Conversely, the Yanco and Kyeamba regions tend to show negative Re. Specifically, there are significant differences for AMSR-E over the Yanco region based on the timing, with mid-day retrievals having greater tendency to over-estimate wet moisture conditions and under-estimate dry conditions. Interestingly SMOS displays similar behaviours but to a smaller extent. Other differences in retrieval accuracy at different observation times can also be observed. While the difference is not statistically significant on average across the network (Table 2), they are statistically significant at selected cells. Passive microwave retrievals have been known to produce different performance depending on the timing of the satellite overpass. Since they rely on accurate estimations of land surface temperature (LST), retrievals are expected to be more accurate during the night when emission surfaces (soil and canopies) are at thermal equilibrium. For AMSR-E, the mid-day retrievals have higher biases at A-1, M-3 and K-1, and lower R over the Yanco and M-5, M-6 and M-7. These results are similar to the findings of Draper et al. (2009) on C-band retrievals. At the AMSR-E overpass times, surface heating could play an important role, which we investigated by examining the recorded temperature changes at these times. Fig. 6 shows the rates of soil heating/cooling at 2–4 cm depth. There is slow cooling at 0130 h (at a median rate − 0.2 °C/h on average), with faster heating (~ 0.4 °C/h) at 1330 h. Since the rate of temperature change decreases as the soil tends to thermal equilibrium with the surrounding, one may argue that the soil at 0130 h is closer to thermal equilibrium than near mid-day. At the same time, given that there are only small differences between the ascending and descending skill scores and contrasting findings from previous studies (Brocca et al., 2011), there is likely an interplay of other factors such as increased transparency of drier vegetation at midday. For SMOS, evening retrievals yielded higher correlations than the morning retrievals over multiple areas, such as M-7, Y-2, Y-3 and Y-10. Again, Fig. 6 offers some insights, showing that the soil was in the cooling phase at both times but slower in the morning. This difference can be attributed to either the environment being closer to thermal equilibrium or the presence of increasing solar radiance in the morning. The latter would explain the poorer retrieval accuracy as the

Fig. 6. Mean rate of LST change measured at the 49 monitoring stations at the overpass times of the satellites, namely AMSR-E at 0130/1330, ASCAT at 0830/2130, and SMOS at 0600/1800 h local time. Linear regression is used to estimate the rate using the 0–5/8 cm temperature measurements within a 2-hour window centred on each overpass times. The notations follow Fig. 4.

8

C.-H. Su et al. / Remote Sensing of Environment 134 (2013) 1–11

canopy and the soil surface are not heated uniformly, affecting LST estimation. As a further step, we investigate the correlations between the soil moisture at two retrieval times, and compared them against the in-situ observations. Insets in Fig. 7 show that in-situ soil moisture changes between two time periods on the same day are usually very small, with very strong correlations of 0.98–0.99. On average, the ground measurements show that the soils during 0600–1330 h are marginally wetter than at 1800–0130 h respectively. Some care is required in interpreting these exact values as there is a small temperature effect in the ground data and the average difference is quite small. While the differences are small, it is more common to find instances where the soil has become significantly wetter during 1330–2130 h compared with the previous time period on the same day, perhaps linking to the timing of diurnal precipitation frequency maxima. Over inland areas of New South Wales, precipitation is characterised by late afternoon maxima between 1500 and 1600 h (Griffiths et al., 1993), due to destabilization of atmospheric boundary layer caused by daytime insolation. In contrast, the satellite retrievals show larger soil moisture change and weaker correlations between the two overpass times. This is particularly striking for ASCAT and SMOS, which typically find that soils during 1800–2130 h are drier (by 20 and 30%) than during 0600–0830 h. Errors due to biases, dynamic range and correlations can have different contributions to the RMSD score. Using MSE (Eq. 2), Fig. 8 provides the means to examine this carefully. Two sets of results are presented here; Fig. 8a presents the error profiles derived from using entire data sets, whereas Fig. 8b uses data from a common one-year period to provide more coherent comparisons. Both sets indicate that for AMSR-E the relative contribution of bias is substantial, while the contributions of variance and correlation errors are more prominent in SMOS and ASCAT. In applications where retrieved soil moisture is treated only as an indicator of the wetness condition, it is more important that the trend of soil moisture be reproduced rather than the actual values. For instance in data assimilation in hydrological models, satellite data is typically transformed (e.g., Section 4.3) to match the mean and variance of the model states (Crow & Ryu, 2009; Koster et al., 2009). In this respect, the best indicator to assess data usability is the correlation, and AMSR-E marginally outperforms the other two products. In other applications where the knowledge of absolute soil water values is needed, SMOS appears to provide better estimates than the other products. Fig. 9 examines the effect of re-sampling the original ASCAT (~0.125° resolution) data set for the analyses so far. The skill scores derived from using the aggregated data set (0.25°) are compared with the results analysed with original 0.125° scaled data, which are selected based on the least-distance rule. For most areas, the upscaled data shows improved agreement with the in-situ data, indicating the usefulness of the aggregated product in possibly removing noise. We are also

able to explain the low scores observed at Y-9 and Y-B. Fig. 9a shows that upscaling increases biases at a number of sites, particularly at Y-B where the retrievals nearest to station YB1 show a significantly lower bias of 0.045 m 3 m −3 than the areal averaged data (0.09 m 3 m −3). The retrievals at stations Y9 and YA7d (in cell Y-9) also outperform areal averages with R > 0.6 in comparison with 0.45 (Fig. 9b). Yanco is an area with significant irrigation leading to high spatial soil moisture heterogeneity between irrigated and non-irrigated patches of land, which may contribute to these results.

5.2. Renormalised soil moisture Renormalisation of the baseline satellite retrievals bring them to a better agreement with the ground data by reducing or eliminating systematic differences in temporal mean and variance. Fig. 10a and b summarises the improvements to skill scores through the application of three renormalisation methods. The MM method generally improves RMSDs; however, at some sites the RMSDs actually increased by up to 0.04 m 3 m −3. The overall biases of the SMOS data have also marginally increased to become greater than the ASCAT counterparts (Table 2). This relative difference in bias between SMOS and ASCAT agrees with findings by Albergel et al. (2012) where MM method was used. MM matching is unlikely to be robust because it is inherently susceptible to outliers. One possible improvement is to use 90% CI to define the minima and maxima (Albergel et al., 2010). Similarly, by matching first and second statistical moments, the μ–σ and CDF based methods are reliable in reducing RMSDs. Surprisingly, the additional enhancement to correlations by the CDF method is only marginal, even when mismatches in higher-order moments and modality of the in-situ statistics are corrected. In summary, Table 2 reflects progressive improvements of the baseline satellite data through renormalisation. The most significant reduction in RMSD is attributed to the correction of the bias and variance errors by MM and μ–σ approaches that yielded RMSDs of 0.49–0.63, and 0.45–0.52 m 3 m−3, respectively. The additional correction of correlations by CDF produces 0.43–0.50 m 3 m−3. Fig. 10c depicts the dramatic changes to the residual correlations following μ–σ and CDF based rescaling. We find that the remaining errors have become mainly negatively correlated with the moisture level, with Re in the range of −0.5 to −0.3 across the study areas and products. The corrected data sets therefore display small-to-moderate tendency of overestimation during drier periods and underestimation during wetter periods. This is likely due to the fact that the data contains short time scale fluctuations attributable to inherent noise of the radiance measurements, and environmental and sensor disturbances. This is perhaps the reason that CDF matching does not produce significant improvements to correlations, and thus it is possible that removal of the noise before renormalisation will yield better agreement.

Fig. 7. Comparisons of retrieved soil moisture (SM) at different overpass times. Insets make same comparisons with ground observations. Solid curves are the least-square fit to the data, and dashed curves are mere guides to the eye.

C.-H. Su et al. / Remote Sensing of Environment 134 (2013) 1–11

9

Fig. 8. Breakdown of the MSE in terms of bias (MSEbias), variance (MSEvar) and correlations (MSEcorr) for the different products using (a) the entire data sets, and (b) only data over 2010 calendar year. The results are only presented for grid cells with at least 100 coincident data points.

Re-examining the regional dependence of retrieval skills, Fig. 11 shows that all three products now share similar RMSDs at most locations following the CDF and μ–σ (not shown) rescaling. These results can in fact be inferred from Fig. 8, where MSEcorr are of similar magnitudes between the products. In other words, the different influences of the tree cover, water bodies, and urban areas on different products are no longer apparent. The exception is at Y-9 where SMOS retains much lower RMSDs than AMSR-E and ASCAT, with the latter caused by upscaling. The eastern sites now show the smallest RMSDs ~ 0.03 m 3 m − 3, contrasting with the Yanco and Kyeamba areas that retain higher errors. Since the satellite products are independent, the reasons for the similarity in the unbiased RMSD scores are likely to relate to product-independent factors (Wagner et al., 2007b). Indeed, Fig. 12 compares the biased-corrected RMSDs with the standard deviation of the in-situ statistics, revealing strong linear correlations measured at 0.89 (MM) and 0.94 (CDF, μ− σ). This in fact should not be surprising, as the removal of bias and variance pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi errors leads to RMSD ¼ 2ð1−RÞσ I and similar levels of correlation amongst the products lead to similar RMSDs. This result is a statistical truism from the fact that RMSD, σI and R are closely related (Entekhabi et al., 2009) but may be often overlooked. The implications for validation, data assimilation, and blending of these satellite soil moisture products should be highlighted. Renormalisation leads to strong coupling between the error variance of the normalised satellite data and variance of a reference data set or model state. Thus

Fig. 9. Skill scores derived from using 0.25° pixel averaged ASCAT soil moisture estimates, compared with that derived from using nearest-neighbour ASCAT values. The lines are mere guides — in (a) the data points above the line correspond to instances where biases and RMSDs are reduced via upscaling, and in (b) the data points below the line show improvements in correlations.

the resultant error variance should be interpreted with particular attention in either correcting other products through multi-satellite blending or correcting model states through data assimilation, and also in interpreting retrieval performance in evaluation studies. 6. Conclusions This study has characterised the relative skill of the three soil moisture retrieval products on an identical spatial grid, using ground observations from southeast Australia. Thus, the findings are particularly pertinent for future cross-validation and blending of these products to create a consistent long-term, sub-daily data set. In particular, the ASCAT product was combined with an Australian soil data before upscaling to 0.25° spatial grid. The aggregated data set shows good agreement, and in some case better agreement with the ground data than the 0.125° data set. The results therefore demonstrate both the usability of this soil data set and the upscaled ASCAT data set in this region. AMSR-E, ASCAT and SMOS can provide reasonable soil moisture information over a wide range of land surface and climatic conditions, and will continue to complement each other with recent AMSR-2/GCOM and ASCAT/MetOp-B missions. The 17 0.25° grid cells in the region provide a variety of conditions in elevation, rainfall, vegetation cover, and land cover/use. Without renormalisation, the products show similar levels of performance, showing moderate-fair levels of correlations and RMSDs in the order of 0.1 m 3 m −3. Our analyses offer some evidence of significant effect of spatial heterogeneity and the presence of trees, urban areas and open water on the retrieval accuracy of SMOS and AMSR-E. The ASCAT's change-detection-based product shows greater consistency across the network. By decomposing RMSDs, the AMSR-E product is found to be prone to bias-related error, while contributions of variance and correlation errors are more prominent in SMOS and ASCAT. This may influence the choice of product for a given application. The impact of retrieval timing can be seen in AMSR-E data with the mid-day pass showing marginally poorer results than mid-night pass. The most obvious timing difference is found in the SMOS data where the evening retrievals are superior, possibly because the morning retrievals are affected by increasing surface heating. Renormalisation methods of matching partial or full statistics of in-situ data are shown to bring satellite data into better agreement. On average, similar RMSDs ~ 0.05 m 3 m − 3 remains, largely due to reduction of bias and variance errors. The most sophisticated CDF-matching method produces only marginal improvements to

10

C.-H. Su et al. / Remote Sensing of Environment 134 (2013) 1–11

Fig. 10. Improvements to the skill scores through the application of different renormalisation methods. The lines are mere guides — in (a) the data points below the line correspond to instances where RMSDs are reduced after renormalisation, and in (b) the data points above the line show improvements in R-values.

This has serious implications for error characterisation and the resulting model-data weighting during data assimilation. Acknowledgements

Fig. 11. Comparisons of RMSDs of different satellite data sets after CDF matching. The legend follows Fig. 5.

correlations and RMSDs in comparisons to the μ–σ approach, likely due to noise in the data. These points to the need for further developments in retrieval techniques and post-retrieval error correction or noise filtering techniques. In particular, for the latter, one could explore the application of low-pass filters to the time series to remove high-frequency noise, e.g., moving average filter (Draper et al., 2009) and exponential filter (Ceballos et al., 2005) to remove high-frequency noise or using a Fourier filter to attenuate or amplify specific frequencies (Du, 2012). Finally renormalisations using the μ–σ and CDF methods bring the three products into better agreements with each other. This suggests that the impacts of land cover on retrievals are mainly manifested as bias and variance errors. Furthermore they lead to strong correlations between the unbiased RMSDs and the variance of the reference data.

Fig. 12. Comparisons of the standard deviation of the ground data and RMSDs of the satellite data before and after MM and CDF matching. Solid lines are fit to the normalised data. Dashed line is a mere guide plotting the RMSDs between two uncorrelated random variable of values from an identical normal distribution N(0,σI).

The authors thank the staff at the University of Melbourne and Jeff Walker and his colleagues at Monash University who have been involved in the OzNet programme. CHS acknowledges the valuable discussions with Tim Peterson on performance metrics, and Angelika Xaver and Rocco Panciera on data quality. We would also like to thank Shelly Chua, Yuan Li and four reviewers for their valuable comments on the manuscript. The SMOS level 3 data were obtained from the “Centre Aval de Traitement des Données SMOS” (CATDS), operated for the “Centre National d'Etudes Spatiales” (CNES, France) by IFREMER (Brest, France). ASCAT level 3 soil moisture data were produced by the Vienna University of Technology (TU-WIEN) within the framework of EUMETSAT's Satellite Application Facility on Support of Operational Hydrology and Water Management (H-SAF) from MetOp-A observations. National soil data were provided by the Australian Collaborative Land Evaluation Program ACLEP, endorsed through the National Committee on Soil and Terrain NCST (www. clw.csiro.au/aclep). This research was conducted with financial support from the Australian Research Council (ARC Linkage Project No. LP110200520). References Al Bitar, A., Leroux, D., Kerr, Y. H., Merlin, O., Richaume, P., Sahoo, A., & Wood, E. F. (2012). Evaluation of SMOS soil moisture products over continental U.S. using the SCAN/SNOTEL network. IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1572–1586. Albergel, C., Calvet, J. -C., de Rosnay, P., Balsamo, G., Wagner, W., Hasenauer, S., Naeimi, V., Martin, E., Bazile, E., Bouyssel, F., & Mahfouf, J. -F. (2010). Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France. Hydrology and Earth System Sciences, 14, 2177–2191. Albergel, C., de Rosnay, P., Gruhier, C., Muñoz Sabater, J., Hasenauer, S., Isaksen, L., Kerr, Y. H., & Wagner, W. (2012). Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations. Remote Sensing of Environment, 118, 215–226. Albergel, C., Rudiger, C., Carrer, D., Calvet, J. C., Fritz, N., Naeimi, V., Bartalis, Z., & Hasenauer, S. (2009). An evaluation of ASCAT surface soil moisture products with in-situ observations in Southwestern France. Hydrology and Earth System Sciences, 13(2), 115–124. Australian Bureau of Rural Science (2010). Land Use of Australia, version 4, 2005/2006 http://adl.brs.gov.au/landuse (last access: September 2012) Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P., Latron, J., Martin, C., & Bittelli, M. (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. Brocca, L., Melone, F., Moramarco, T., & Morbidelli, R. (2010). Spatial-temporal variability of soil moisture and its estimation across scales. Water Resources Research, 46, W02516. Ceballos, A., Scipal, K., Wagner, W., & Martínez-Fernández, J. (2005). Validation of ERS scatterometer-derived soil moisture data over the central part of the Duero Basin, Spain. Hydrological Processes, 19, 1549–1566.

C.-H. Su et al. / Remote Sensing of Environment 134 (2013) 1–11 Collow, T. W., Robock, A., Basara, J. B., & Illston, B. G. (2012). Evaluation of SMOS retrievals of soil moisture over the central United States with currently available in situ observations. Journal of Geophysical Research, 117, D09113. Crow, W. T., Berg, A. A., Cosh, M. H., Loew, A., & Mohanty, B. P. (2012). Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products. Reviews of Geophysics, 50, RG2002. Crow, W. T., & Bolten, J. D. (2007). Estimating precipitation errors using spaceborne surface soil moisture retrievals. Geophysical Research Letters, 34(8), L08403. Crow, W. T., & Ryu, D. (2009). A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals. Hydrology and Earth System Sciences, 13, 1–16. Das, N. N., Mohanty, B. P., Cosh, M. H., & Jackson, T. J. (2008). Modeling and assimilation of root zone soil moisture using remote sensing observations in Walnut Gulch Watershed during SMEX04. Remote Sensing of Environment, 112(2), 415–429. de Rosnay, P., Drusch, M., Vasiljevic, D., Balsamo, G., Albergel, C., & Isaksen, L. (2012). A simplified Extended Kalman Filter for the global operational soil moisture analysis at ECMWF. Quarterly Journal of the Royal Meteorological Society. http://dx.doi.org/ 10.1002/qj.2023. Dharssi, I., Bovis, K. J., Macpherson, B., & Jones, C. P. (2011). Operational assimilation of ASCAT surface soil wetness at the Met Office. Hydrology and Earth System Sciences, 15, 2729–2746. Dorigo, W. A., Scipal, K., Parinussa, R. M., Liu, Y. Y., Wagner, W., de Jeu, R. A. M., & Naeimi, V. (2010). Error characterisation of global active and passive microwave soil moisture datasets. Hydrology and Earth System Sciences, 14, 2605–2616. Draper, C. S., Reichle, R. H., De Lannoy, G. J. M., & Liu, Q. (2012). Assimilation of passive and active microwave soil moisture retrievals. Geophysical Research Letter, 39, L04401. Draper, C. S., Walker, J. P., Steinle, P. J., de Jeu, R. A. M., & Holmes, T. R. H. (2009). An evaluation of AMSR-E derived soil moisture over Australia. Remote Sensing of Environment, 113(4), 703–710. Du, J. (2012). A method to improve satellite soil moisture retrievals based on Fourier analysis. Geophysical Research Letters, 39, L15404. Dumedah, G., & Coulibaly, P. (2011). Evaluation of statistical methods for infilling missing values in high-resolution soil moisture data. Journal of Hydrology, 400(1–2), 95–102. Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., et al. (2010). The Soil Moisture Active Passive (SMAP) mission. Proceedings of the IEEE, 98(5), 704–716. Entekhabi, D., Reichle, R. H., Koster, R. D., & Crow, W. T. (2009). Performance metrices for soil moisture retrievals and application requirements. Journal of Hydrometeorology, 11, 832–840. Famiglietti, J. S., Ryu, D., Berg, A. A., Rodel, M., & Jackson, T. J. (2008). Field observations of soil moisture variability across scales. Water Resources Research, 44, W01423. Gouweleeuw, B. T., van Dijk, A. I. J. M., Guerschman, J. P., Dyce, P., & Owe, M. (2012). Space-based passive microwave soil moisture retrievals and the correction for a dynamic open water fraction. Hydrology and Earth System Sciences, 16, 1635–1645. Grayson, R. B., Western, A. W., Chiew, F. H. S., & Bloschl, G. (1997). Preferred states in spatial soil moisture patterns: Local and nonlocal controls. Water Resources Research, 33(12), 2897–2908. Griffiths, D. J., Colquhoun, J. R., Batt, K. L., & Casinader, T. R. (1993). Severe thunderstorms in New South Wales: Climatology and means of assessing the impact of climate change. Climate Change, 25, 369–388. Gruhier, C., de Rosnay, P., Hasenauer, S., Holmes, T., de Jeu, R., Kerr, Y., Mougin, E., Njoku, E., Timouk, F., Wagner, W., & Zribi, M. (2010). Soil moisture active and passive microwave products: Intercomparison and evaluation over a Sahelian site. Hydrology and Earth System Sciences, 14(1), 141–156. Gupta, H., Kling, H., Yilmaz, K., & Martinez, G. (2009). Decomposition of the mean square error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1–2), 80–91. Hoaglin, D. C., Iglewiez, B., & Tukey, J. W. (1986). Performance of some resistant rules for outlier labelling. Journal of the American Statistical Association, 81(396), 991–999. Jackson, T. J., Cosh, M. H., Bindlish, R., Starks, P. J., Bosch, D. D., Seyfried, M., Goodrich, D. C., Moran, M. S., & Du, J. (2010). Validation of advanced microwave scanning radiometer soil moisture products. IEEE Transactions on Geoscience and Remote Sensing, 48(12), 4256–4272. Jacquette, E., Al Bitar, A., Mialon, A., Kerr, Y., Quesney, A., Cabot, F., & Richaume, P. (2010). SMOS CATDS level 3 global products over land, Proceeding SPIE7824. Remote Sensing for Agriculture, Ecosystems, and Hydrology, XII, 78240K. Kerr, Y. H., Waldteufel, P., Wigneron, J. -P., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M. -J., Font, J., Reul, N., Gruhier, C., Juglea, S. E., Drinkwater, M. R., Hahne, A., Martin-Neira, M., & Mecklenburg, S. (2010). The SMOS mission: New tool for monitoring key elements of the global water cycle. Proceedings of the IEEE, 98(5), 666–687. Kerr, Y. H., et al. (2012). The SMOS soil moisture retrieval algorithm. IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1384–1403. Koster, R. D., Guo, Z., Yang, R., Dirmeyer, P. A., Mitchell, K., & Puma, M. J. (2009). On the nature of soil moisture in land surface models. Journal of Climate, 22(16), 4322–4335. Liu, Y. Y., Dorigo, W. A., Parinussa, R. M., de Jeu, R. A. M., Wagner, W., McCabe, M. F., Evans, J. P., & van Dijk, A. I. J. M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sensing of Environment, 123, 280–297. Loew, A. (2008). Impact of surface heterogeneity on surface soil moisture retrievals from passive microwave data at the regional scale: The Upper Danube case. Remote Sensing of Environment, 112(1), 231–248. Lymburner, L., Tan, P., Mueller, N., Thackway, R., Lewis, A., Thankappan, M., Randall, L., Islam, A., & Senarath, U. (2010). 250 metre Dynamic Land Cover Dataset of Australia (1st ed.). Canberra: Geoscience Australia. Maeda, T., Imaoka, K., Kachi, M., Fujii, H., Akira, S., Naoki, K., Kasahara, M., Ito, N., & Nakagawa, K. (2011). Status of GCOM-W1/AMSR2 development, algorithms, and products. Proceedings of SPIE, 8176, 8176N.

11

McKenzie, N. J., Jacquier, D. W., Ashton, L. J., & Cresswell, H. P. (2000). Estimation of soil properties using the Atlas of Australian Soils. CSIRO Land and Water Technical Report 11/00 (Available Online at http://www.clw.csiro.au/aclep/documents/tr11-00.pdf) Mladenova, I., Lakshmi, V., Jackson, T. J., Walker, J. P., Merlin, O., & de Jeu, R. A. M. (2011). Validation of AMSR-E soil moisture using L-Band airborne radiometer data from National Airborne Field Experiment 2006. Remote Sensing of Environment, 115(8), 2096–2103. Ni-Meister, W., Walker, J. P., & Houser, P. R. (2005). Soil moisture initialization for climate prediction: Characterization of model and observation errors. Journal of Geophysical Research, 110, D13111. Njoku, E., Ashcroft, P., Chan, T., & Li, L. (2005). Global survey of statistics of radiofrequency interference in AMSR-E land observations. IEEE Transactions on Geoscience and Remote Sensing, 43, 938–947. Northcote, K. H., Beckmann, G. G., Bettenay, E., Churchward, H. M., Van Dijk, D. C., Dimmock, G. M., Hubble, G. D., Isbell, R. F., McArthur, W. M., Murtha, G. G., Nicolls, K. D., Paton, T. R., Thompson, C. H., Webb, A. A., & Wright, M. J. (1960–1968). Atlas of Australian Soils, Sheets 1 to 10. With explanatory data. Melbourne: CSIRO Aust. and Melbourne University Press. Owe, M., de Jeu, R., & Holmes, T. (2008). Multi-sensor historical climatology of satellite-derived global land surface moisture. Journal of Geophysical Research, 113, F01002. Parinussa, R. M., Meesters, A. G. C. A., Liu, Y. Y., Dorigo, W., Wagner, W., & de Jeu, R. A. M. (2011). Error estimates for near-real-time satellite soil moisture as derived from the Land Parameter Retrieval Model. IEEE Geoscience and Remote Sensing Letters, 8(4), 779–783. Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen– Geiger climate classification. Hydrological and Earth System Science, 11, 1633–1644. Peischl, S., Walker, J. P., Rudiger, C., Ye, N., Kerr, Y. H., Kim, E., Bandara, R., & Allahmoradi, M. (2012). The AACES field experiments: SMOS calibration and validation across the Murrumbidgee River catchment. Hydrology and Earth Sciences, 16(6), 1697–1708. Raupach, M. R., Briggs, P. R., Haverd, V., King, E. A., Paget, M., & Trudinger, C. M. (2012). Australian Water Availability Project. Canberra, Australia: CSIRO Marine and Atmospheric Research (http://www.csiro.au/awap (last assessed: March 2011)) Rebel, R. T., de Jeu, R. A. M., Ciais, P., Viovy, N., Piao, S. L., Kiely, G., & Dolman, A. J. (2012). A global analysis of soil moisture derived from satellite observations and a land surface model. Hydrology and Earth System Sciences, 16(3), 833–847. Reichle, R. H., & Koster, R. D. (2004). Bias reduction in short records of satellite soil moisture. Geophysical Research Letters, 31, L19501. Reichle, R. H., & Koster, R. D. (2005). Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model. Geophysical Research Letters, 32, L02404. Ryu, D., Jackson, T. J., Bindlish, R., & Le Vine, D. M. (2007). L-band microwave observations over land surface using a two-dimensional synthetic aperture radiometer. Geophysical Research Letters, 34, L14401. Scipal, K., Holmes, T., de Jeu, R., Naeimi, V., & Wagner, W. (2008). A possible solution for the problem of estimating the error structure of global soil moisture data sets. Geophysical Research Letters, 35, L24403. Skou, N., Misra, S., Balling, J. E., Kristensen, S. S., & Søbjærg, S. S. (2010). L-band RFI as experienced during airborne campaigns in preparation for SMOS. IEEE Transactions on Geoscience and Remote Sensing, 48(3), 1398–1407. Smith, A. B., Walker, J. P., Western, A. W., Young, R. I., Ellett, K. M., Pipunic, R. C., Grayson, R. B., Siriwardena, L., Chiew, F. H. S., & Richter, H. (2012). The Murrumbidgee soil moisture network data set. Water Resources Research, 48(7), W07701. Verhoest, N. E. C., Lievens, H., Wagner, W., Alverez-Mozos, J., Moran, M. S., & Mattia, F. (2008). On the soil roughness parameterization problem in soil moisture retrieval of bare surfaces from Synthetic Aperture Radar. Sensors, 8(7), 4213–4248. Vinnikov, K. Y., Robock, A., Speranskaya, N. A., & Schlosser, A. (1996). Scales of temperal and spatial variability of midlatitude soil moisture. Journal of Geophysical Research, 101, 7163–7174. Wagner, W., Blöschl, G., Pampaloni, P., Calvet, J. -C., Bizzarri, B., Wigneron, J. -P., & Kerr, Y. (2007). Operational readiness of microwave remote sensing of soil moisture for hydrologic applications. Nordic Hydrology, 38, 1–20. Wagner, W., Lemoine, G., & Rott, H. (1999). A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment, 70(2), 191–207. Wagner, W., Naeimi, V., Scipal, K., de Jeu, R., & Martinez-Fernandez, J. (2007). Soil moisture from operational meteorological satellites. Hydrogeology Journal, 15, 121–131. Wagner, W. et al. (2012). The ASCAT soil moisture product: Specifications, validation, results, and emerging applications. Submitted to Meteorologische Zeitschrift. Wang, G., Garcia, D., Liu, Y., de Jeu, R., & Dolman, A. J. (2012). A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations. Environmental Modelling & Software, 30, 139–142. Western, A. W., Grayson, R. B., & Blöschl, G. (2002). Scaling of soil moisture: A hydrologic perspective. Annual Review of Earth and Planetary Sciences, 30, 149–180. Wilson, D. J., Western, A. W., & Grayson, R. B. (2004). Identifying and quantifying sources of variability in temporal and spatial soil moisture observations. Water Resources Research, 40(2), W02507. Ye, N., Walker, J. P., Rudiger, C., Ryu, D., & Gurney, R. J. (2011). The effect of urban cover fraction on the retrieval of space-borne surface soil moisture at L-band. 19th International Congress on Modelling and Simulation (pp. 3398–3404). Yilmaz, M. T., Crow, W. T., Anderson, M. C., & Hain, C. (2012). An objective methodology for merging satellite- and model-based soil moisture products. Water Resources Research, 48, W11502.