Remote Sensing of Environment - ICDC€¦ · used for soil moisture retrieval from remote sensing: the Advanced SCATterometer, ASCAT, on-board the MetOp (Meteorological Opera-tional)

Remote Sensing of Environment 115 (2011) 3390–3408

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Remote Sensing of Environment

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Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparisonand validation study across Europe

L. Brocca a,⁎, S. Hasenauer b, T. Lacava c, F. Melone a, T. Moramarco a, W. Wagner b, W. Dorigo b, P. Matgen d,J. Martínez-Fernández e, P. Llorens f, J. Latron f, C. Martin g, M. Bittelli h

a Research Institute for Geo-Hydrological Protection, CNR, Perugia, Italyb Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Vienna, Austriac Institute of Methodologies for Environmental Analysis, CNR, Potenza, Italyd Public Research Centre—Gabriel Lippmann, CRP, Belvaux, Luxembourge Centro Hispano Luso de Investigaciones Agrarias, Universidad de Salamanca, Villamayor, Spainf Institute of Environmental Assessment and Water Research (IDAEA), CSIC, Barcelona, Spaing UMR-6012 ESPACE, Département de Géographie, Université de Nice-Sophia-Antipolis, Nice, Franceh Department of AgroEnvironmental Science and Technology, University of Bologna, Bologna, Italy

⁎ Corresponding author at: Research Institute forNational Research Council, Via Madonna Alta 126, 060755014418; fax: +39 0755014420.

E-mail address: [email protected] (L. Brocca).

0034-4257/$ – see front matter © 2011 Elsevier Inc. Aldoi:10.1016/j.rse.2011.08.003

a b s t r a c t
a r t i c l e i n f o
Article history:Received 2 March 2011Received in revised form 2 August 2011Accepted 7 August 2011Available online 30 August 2011

Keywords:Soil moistureRemote sensingASCATAMSR-EValidation

Global soil moisture products retrieved from various remote sensing sensors are becoming readily availablewith a nearly daily temporal resolution. Active and passive microwave sensors are generally considered as thebest technologies for retrieving soil moisture from space. The Advanced Microwave Scanning Radiometer forthe Earth observing system (AMSR-E) on-board the Aqua satellite and the Advanced SCATterometer (ASCAT)on-board the MetOp (Meteorological Operational) satellite are among the sensors most widely used for soilmoisture retrieval in the last years. However, due to differences in the spatial resolution, observation depthsand measurement uncertainties, validation of satellite data with in situ observations and/or modelled data isnot straightforward. In this study, a comprehensive assessment of the reliability of soil moisture estimationsfrom the ASCAT and AMSR-E sensors is carried out by using observed andmodelled soil moisture data over 17sites located in 4 countries across Europe (Italy, Spain, France and Luxembourg). As regards satellite data,products generated by implementing three different algorithms with AMSR-E data are considered: (i) theLand Parameter Retrieval Model, LPRM, (ii) the standard NASA (National Aeronautics and SpaceAdministration) algorithm, and (iii) the Polarization Ratio Index, PRI. For ASCAT the Vienna University ofTechnology, TUWIEN, change detection algorithm is employed. An exponential filter is applied to approachroot-zone soil moisture. Moreover, two different scaling strategies, based respectively on linear regressioncorrection and Cumulative Density Function (CDF) matching, are employed to remove systematic differencesbetween satellite and site-specific soil moisture data. Results are shown in terms of both relative soil moisturevalues (i.e., between 0 and 1) and anomalies from the climatological expectation.Among the three soil moisture products derived from AMSR-E sensor data, for most sites the highestcorrelation with observed and modelled data is found using the LPRM algorithm. Considering relative soilmoisture values for an ~5 cm soil layer, the TUWIEN ASCAT product outperforms AMSR-E over all sites inFrance and central Italy while similar results are obtained in all other regions. Specifically, the averagecorrelation coefficient with observed (modelled) data equals to 0.71 (0.74) and 0.62 (0.72) for ASCAT andAMSR-E-LPRM, respectively. Correlation values increase up to 0.81 (0.81) and 0.69 (0.77) for the two satelliteproducts when exponential filtering and CDF matching approaches are applied. On the other hand,considering the anomalies, correlation values decrease but, more significantly, in this case ASCAT outperformsall the other products for all sites except the Spanish ones. Overall, the reliability of all the satellite soilmoisture products was found to decrease with increasing vegetation density and to be in good accordancewith previous studies. The results provide an overview of the ASCAT and AMSR-E reliability and robustnessover different regions in Europe, thereby highlighting advantages and shortcomings for the effective use ofthese data sets for operational applications such as flood forecasting and numerical weather prediction.

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1. Introduction

Understanding soil moisture spatial–temporal variability at dif-ferent scales is a great topical matter in many scientific and

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3391L. Brocca et al. / Remote Sensing of Environment 115 (2011) 3390–3408

operational applications such as flood forecasting (Brocca et al., 2010;Koster et al., 2010), numerical weather prediction (Albergel et al.,2010; Entekhabi, 1995), climate and agricultural modelling (Weaver& Avissar, 2001; Koster & GLACE Team, 2004; Koster et al., 2009; DeWit & Van Diepen, 2007; Bolten et al., 2010). Soil moisture, in fact, isthe core of the system that controls the hydrological interactionsbetween soil, vegetation and climate forcing, and plays a key role ingoverning the water and energy balance between land surface andatmosphere. This is the main reason why soil moisture was recentlyintroduced among the “Essential Climate Variables” (ECV), which arethe key variables needed to properly characterize the Earth's climate(Global Climate Observing System, GCOS, 2010).

Soil moisture estimates can be obtained in different ways, throughin situ measurements, satellite data and hydrological models.However, a multidisciplinary approach considering the integrationof all the three techniques allows overcoming the drawbacks of eachone of the single methodologies, furnishing a proper and effectivemonitoring system of soil moisture variability at different scales.Ground measurements which arguably provide the most accurateestimates of soil moisture, are limited in terms of spatial extent (e.g.Brocca et al., 2010; Brocca et al., 2007), and therefore should be usedpreferably for calibrating and testing hydrological models thatprovide information on horizontal and vertical soil moisture variation.Satellite data, covering wider areas with a daily (or even longer)revisit time, can be applied for calibrating, evaluating and periodicallyupdating spatially distributed hydrologic models, as they offer asnapshot of the evolving processes at a given time (Houser et al.,2010).

As far as satellite techniques are concerned, in recent years thereliability of soil moisture estimates from microwave sensors, bothactive and passive, has been investigated in-depth. The launch of theSoil Moisture and Ocean Salinity (SMOS) satellite by the EuropeanSpace Agency (ESA) on the 5th of November 2009, and the schedulingof the Soil Moisture Active and Passive (SMAP) program by NationalAeronautics and Space Administration (NASA) for November 2014,give a clear evidence of the relevance of the topic within the scientificcommunity at international level (Entekhabi et al., 2010; Kerr et al.,2010; Schiermeier, 2010). Besides SMOS, two other sensors arewidelyused for soil moisture retrieval from remote sensing: the AdvancedSCATterometer, ASCAT, on-board the MetOp (Meteorological Opera-tional) satellite and the Advanced Microwave Scanning Radiometerfor Earth Observing System, AMSR-E, on-board NASA's Aqua satellite.These two sensors are characterized by a coarse spatial resolution(~25–50 km), but have a revisit time of 1 day (or less) over Europe,which can be considered an adequate temporal resolution for manyhydrological, meteorological and agricultural applications (De Langeet al., 2008; Walker & Houser, 2004).

The ASCAT sensor is a C-band scatterometer (5.255 GHz, VVpolarization) operating on-board the Metop since 2006. ASCATsucceeded the ERS-1/2 scatterometers that is operating since 1992.The algorithm proposed by Wagner et al. (1999), and improved byNaeimi et al. (2009), referred as the Vienna University of Technology(TUWIEN) change detection algorithm, is used for surface soilmoisture retrieval by scatterometer data. Besides the surface soilmoisture product, a root-zone soil moisture one, usually referred asSoil Water Index, SWI (Wagner et al., 1999), has been analyzed indifferent studies. It is derived by applying an exponential filter thatallows, in a simple and effective way, to obtain an estimate of theaverage value of soil moisture profile from the surface soil moistureproduct time series. In particular, the first validation of the ASCAT soilmoisture products was performed by Albergel et al. (2009) who foundsignificant correlation coefficient, R, values between ASCAT-derivedsurface (average R=0.50) and root-zone (average R=0.56 or 0.64excluding one of the sites) soil moisture products and in situobservations from 11 stations located in south-western France(SMOSMANIA network) considering a six-month period. Recently,

the same authors confirmed these results by considering an extendedtwo-year period (Albergel et al., 2010). Moreover, Brocca et al. (2010)and Sinclair and Pegram (2010) compared the soil moisturesimulations produced by two rainfall-runoff models, i.e., TOPographicKinematic APproximation and Integration, TOPKAPI (Ciarapica &Todini, 2002) and “Modello Idrologico Semi-Distribuito in continuo”,MISDc (Brocca et al., 2011), to the SWI obtaining a good linearagreement (R-values between 0.60 and 0.98) in the dynamicbehaviour of the two independent soil moisture estimates. However,all these studies tested the ASCAT soil moisture product produced innear-real-time by EUMETSAT, which was based on model retrievalparameters derived from the analysis of long-term ERS-1/2 scatte-rometer time series (Bartalis et al., 2007). As the radiometriccalibration of the ERS-1/2 scatterometer and Metop ASCAT turnedout to be slightly different, the use of ERS SCAT-derived modelparameters to retrieve soil moisture from Metop ASCAT has led toperiodic errors that added to the uncertainty of the ASCAT retrievals(Hahn & Wagner, 2011; Sinclair & Pegram, 2010). Recently, Brocca etal. (2010) analyzed an updated version of the ASCAT soil moistureproduct which was retrieved off-line by TUWIEN. It is based on twoyears (2007–2008) of only ASCAT observations, but considers thesame algorithm used by EUMETSAT for the near-real-time ASCATproduct. The authors obtained high performance of the new ASCATsoil moisture product with R-values ranging between 0.80 and 0.94when the SWI is compared with in situ and modelled soil moistureobservations for three sites located in central Italy.

The AMSR-E sensor is a radiometer (with six bands ranging from6.9 to 89 GHz at HH–VV polarization) operating on-board theAqua satellite since 2002. AMSR-E was the first satellite sensor toincorporate soil moisture as a standard product also specifying anaccuracy goal less than 0.06 m3/m3 (Njoku & Chan, 2006; Njoku et al.,2003). Several algorithms (using different physical formulations,parameters, and ancillary data) have been developed by NASA andJAXA (Japan Aerospace Exploration Agency) and other researchgroups (Jackson, 1993; Koike et al., 2004; Njoku et al., 2003; Oweet al., 2008; Paloscia et al., 2006; Pellarin et al., 2008) to retrieve soilmoisture from measured brightness temperature by AMSR-E. Amongthem, the simplest algorithm is the Polarization Ratio Index (PRI)which is frequently used to describe soil moisture variations (Pellarinet al., 2008; Wigneron et al., 2003). However, the most widely usedapproaches are the standard algorithm provided by NASA (Njokuet al., 2003), AMSR-E-NASA, and the Land Parameter Retrieval Model,AMSR-E-LPRM, developed at the “Vrije Universiteit Amsterdam”

(VUA) in collaboration with NASA (Owe et al., 2001; Owe et al.,2008). A large number of studies aiming at the validation of these twosoil moisture products have been published in recent years (see, e.g., ;Draper et al., 2009; Gruhier et al., 2010; Jackson et al., 2010; Rudiger etal., 2009; Wagner et al., 2007). Specifically, there seems to be anagreement that the AMSR-E-LPRM product gives the better perfor-mance compared against in situ observations, especially if thecorrelation coefficient is selected as evaluation score. Draper et al.(2009) and Wagner et al. (2007) obtained R-values greater than 0.80for an AMSR-E-LPRM product considering 12 and (the average of) 23stations located in southeast Australia and central Spain (REMEDHUSnetwork), respectively. Rudiger et al. (2009) compared modelled soilmoisture data obtained from the operational land surface model ISBA(Interaction between Soil, Biosphere and Atmosphere) of Météo-France (Noilhan & Planton, 1989) and different AMSR-E satelliteproducts over the whole French territory obtaining fairly goodcorrelation (average R=0.49) with the AMSR-E-LPRM product,especially in low altitude areas with low-to-moderate vegetationcoverage (1.5 to 3 kg/m2 vegetation water content). Gruhier et al.(2010) evaluated different satellite products against ground mea-surements obtained from the soil moisture network deployed in Mali(Sahel) in the framework of the African Monsoon MultidisciplinaryAnalysis (AMMA) project (Redelsperger et al., 2006). The authors

3392 L. Brocca et al. / Remote Sensing of Environment 115 (2011) 3390–3408

obtained R-values in the range 0.55–0.66 and 0.51–0.84 for AMSR-E-NASA and AMSR-E-LPRM products, respectively. Jackson et al. (2010)evaluated four different AMSR-E algorithms (including NASA andLPRMones) by using seven-year high-quality in situ observations fromfour experimental networks located in different climatic regions of theU.S. and found that each algorithm performs differently at each site.When site-specific corrections are applied, all algorithms hadapproximately the same error level and correlation (R=0.71–0.79)clearly showing that there is much room for improvement in thealgorithms currently in use. It has to be noted that these results can bedue to the use of X-band, that is often preferred to limit radiofrequency interferences, but is known to be less sensitive to soilmoisture than C-band observations.

One key issue in the validation of coarse-resolution satellite soilmoisture products with in situ observations is the disparity in spatialscales between them (Jackson et al., 2010). Based on in situ mea-surements, several studies that employed the temporal stabilityconcept introduced by the pioneering work of Vachaud et al. (1985),showed that point soil moisture data can be representative of largerareas (e.g. Brocca et al., 2009; Brocca et al., 2010; Loew & Mauser,2008; Loew & Schlenz, 2011; Martinez-Fernandez & Ceballos, 2005;Miralles et al., 2010; Wagner et al., 2008). This means that thetemporal pattern of local soil moisture measurements followsclosely the temporal pattern of the spatial average. On this basis, itis evident that if the correlation coefficient between in situ andsatellite time series is used, it can be expected that in situ datarepresent a good benchmark (Liu et al., 2011). However, systematicdifferences between remote sensing-derived and in situ observationsare usually detected even though the temporal dynamics are verysimilar (Loew& Schlenz, 2011). Consequently, tomatch the variabilityof satellite data with in situ ones, different techniques have beendeveloped: linear rescaling (Brocca et al., 2010; Draper et al., 2009;Scipal et al., 2008), linear regression correction (Jackson et al., 2010)and Cumulative Density Function, CDF, matching (Drusch et al., 2005;Lacava et al., 2010).

Another important issue in the validation of satellite soil moistureretrieval is related to the product considered in the comparison:(i) the original seasonal values or (ii) the anomalies. The first is acombination of the ability to detect soil moisture seasonality andanomaly while the latter is a reflection of the skill with regard todetecting single events, where the influence of the seasonal cycle isexplicitly removed. This means that the ranking in performancebetween various satellite products can differ if original values oranomalies are considered (Dorigo et al., 2010).

The main purpose of this study is to assess the potential ofdifferent ASCAT and AMSR-E-based products in retrieving reliablysurface soil moisture. This is pursued through a comprehensivevalidation analysis by using observed and modelled soil moisturedata for 17 sites located in four European countries: Italy, France,Luxembourg and Spain. The analysis also aims at addressing the twoimportant issues in the validation of satellite soil moisture retrievalreported above considering two different approaches for matchingsatellite data with site-specific ones and both the relative soil moisturevalues and anomalies. In particular, different products based on threealgorithms are used for AMSR-E: (i) the Land Parameter Retrieval Model,AMSR-E-LPRM, (ii) the standard algorithm, AMSR-E-NASA, and (iii) thePolarization Ratio Index, AMSR-E-PRI. For ASCAT, the TUWIEN changedetection algorithm is selected. Both the Surface Soil Moisture product,SSM, and the root-zone soil moisture product, SWI, obtained through theexponential filter proposed by Wagner et al. (1999), are tested.

The specific issues addressed in the study are the following:(i) What is the reliability of the soil moisture retrieval from the ASCATand AMSR-E sensors and how does it vary for different regions acrossEurope? (ii) How do results compare to those formerly published?(iii) How could the accuracy of the soil moisture retrieval algorithmsdeveloped for ASCAT and AMSR-E sensors be improved?

2. Study areas and soil moisture measurements

The different test-sites used for the validation of the four soilmoisture products are described in the sequel. A total of 17 sites acrossfour different countries (Italy, France, Spain and Luxembourg) areinvestigated. Table 1 synthesizes the main characteristics of each sitein terms of location, soil texture, land cover and climate (mean annualrainfall and temperature), while Fig. 1 shows the framework of thestudy sites. For most of the sites the climate is Mediterranean and,more specifically, it can be classified as semi-humid (all the Italiansites, the Valescure and LZC sites in France), humid (Vallcebre inSpain) and semi-arid (REMEDHUS network in Spain). For theLuxembourg site the climate is humid temperate climate while thetwo SMOSMANIA sites located in the western France (URG and PRG)are characterized by a more temperate continental climate. Overall,for all sites the soil moisture time series are characterized by a typicalseasonality with highwetness conditions inwinter and dry conditionsin summer.

Table 2 shows the main features of the site-specific soil moisturedata sets used for the validation of the satellite products in terms ofmeasurement technique, data period and measurement layer depth.The average vegetation optical depth obtained from AMSR-E data(Owe et al., 2008) and the difference between the maximum andminimum soil moisture value observed in the investigated period(used for the estimation of the relative soil moisture from the originalvolumetric soil moisture values) are also reported. For all the in-vestigated sites, except for SMOSMANIA stations, rainfall and tem-perature data are also available, thereby allowing to carry out thecomparison with both in situ observations and modelled data(computed for a depth of 5 cm, see below). On the other hand, fortwo Italian sites the in situ observed soil moisture data are availablefor a period different from that used in this study (January 2007–December 2008). Therefore, for these sites the in situ observations arenot directly compared with the different satellite soil moistureproducts. It has to be noted that some of the observed data sets(IRPI, Campania, Calabria, UMSUOL, REMEDHUS, and SMOSMANIA)can been downloaded directly from the International Soil MoistureNetwork (Dorigo et al., 2011) website available at: http://www.ipf.tuwien.ac.at/insitu. Observations downloaded from this networkhave been controlled and flagged for large outliers. Flagged observa-tions were not included in the analysis. A brief description of thedifferent test sites subdivided by country is reported in the sequel.Specifically, details about the calibration of the capacitance sensorsboth considering gravimetric and TDR (nowadays often consideredas benchmark for operational field monitoring of soil moisture)measurements are given. On this basis, it can be surmised that thequality of the employed in situ measurements is quite high; alsobecause a quality check of the soil moisture temporal patterns wascarried out for each site.

2.1. Italy

The three sites of the “IRPI”network are located in an inland regionofcentral Italy (Umbria Region): the Vallaccia (VAL), Cerbara (CER) andSpoleto (SPO) site. The VAL site was installed by the Research InstituteforGeo-Hydrological Protection in 2003 for researchpurposes related tothe understanding of the flood generation processes. Specifically, sixFrequency Domain Reflectometry (FDR) sensors measuring volumetricsoil moisture at 10, 20 and 40 cmare locatedwithin an area of ~20 km2;the average of the soilmoisture values at a depth of 10 cm is used for thisstudy (seeBrocca et al., 2010 formoredetails). TheCER sitewas installedin 2009 by the Umbria Region for Civil Protection activities aimed atfloodprediction and forecastingand ithas the sameconfigurationofVALsite. The SPO site was settled up in 2008 by the Department of AppliedBiology of the Perugia University to study the relation between soilmoisture conditions and the production of mycorrhized plants used in

http://www.ipf.tuwien.ac.at/insitu

http://www.ipf.tuwien.ac.at/insitu

Table 1Main characteristics of the test-sites used for this study.

Location Site Latitude(°)

Longitude(°)

Elevation(m a.s.l.)

Soil texture Land use Mean annualrainfall (mm)

Mean annualtemperature (°C)

Italy—IRPI VAL 43.22 12.15 315 Sandy loam Grass 900 13CER 43.56 12.38 300 Loamy sand Grass 900 13SPO 42.88 12.85 435 Sandy loam Grass 900 13

Italy—UMSUOL CAP 44.30 11.30 10 Sandy loam Grass 750 16Italy—Campania and Calabria BAG 40.83 15.06 560 Sandy loam Grass 920 17

MEL 41.16 14.51 180 Sandy clay loam Grass 920 17TOR 39.51 16.15 100 N/A N/A 850 21CHI 38.67 16.48 550 N/A N/A 850 21

Luxembourg BIB 49.63 6.23 270 Loam Grass 860 9Spain—REMEDHUS K10 41.35 −5.22 770 Sand Corn 380 12

F11 41.24 −5.54 830 Loamy sand Grass 380 12I06 41.38 −5.43 730 Sand Bare soil 380 12

Spain—Vallcebre VCE 42.17 1.83 1180 Silt loam Grass 860 9France—SMOSMANIA URG 43.64 −0.44 150 Silt Grass N/A N/A

LZC 43.17 2.73 70 Clay loam Grass N/A N/APRG 43.67 0.22 170 Silt Grass N/A N/A

France—Valescure VOB 43.79 4.35 430 Sandy Grass 1500 12

N/A information not available.


truffle cultivation (DiMassimo et al., 2008). At SPO site, eight boreholeswere established and FDR measurements were taken up to a depth of80 cm, every 10 cm. For all the “IRPI” network sites, at the beginning ofthe soil moisture campaign, the reliability of the measurements waspositively tested by comparison with gravimetric and Time DomainReflectometry (TDR) measurements, using a representative soil withdifferent moisture conditions. It's worth noting that these three sites(VAL, SPO and CER)were already used by Brocca et al. (2010) to test thereliability of the ASCAT soil moisture product.

Fig. 1. Location of the study sites (for the names see Table 1) over the vegetation optical deptDecember 2008 (ascending pass).

The San Pietro Capofiume (CAP) site, belonging to the “UMSUOL”network, is located in Northern Italy (Emilia-Romagna Region) andwas installed in 2004 by the Hydrology, Meteorology and ClimateService of the Regional Agency for Environmental Protection in Emilia-Romagna (ARPA-SIMC) mainly for agricultural purposes. For this site,TDR volumetric soil moisture measurements have been collected atseven depths between 10 and 200 cm.

In Southern Italy, four sites are selected: Bagnoli (BAG) andMelizzano (MEL) located in the Campania Region and Torano (TOR)

hmap obtained through the LPRM algorithm and averaged for the period January 2007–

Table 2Main characteristics of the in situ observed, obs, and modelled, mod, soil moisture data sets used for this study (TDR and FDR: Time and Frequency Domain Reflectometry, CAP:capacitance probe, OD: vegetation optical depth averaged over the period January 2007–December 2008, θe: difference between the maximum and minimum soil moisture valueobserved in the investigated period).

Location Site Type Technique Sensor Data period Time step (min) Depth (m) OD θε

Italy—IRPI VAL obs FDR EnviroSCAN, Sentek Technologies Apr2007–Jun2008 30 10 0.66 0.28VAL mod / Jan2007–Dec2008 5CER mod / Jan2007–Dec2008 5 0.62 0.3SPO mod / Jan2007–Dec2008 5 0.62 0.25

Italy—UMSUOL CAP obs TDR TDR100, Campbell Scientific Inc. Jun2007–Dec2008 60 10 0.66 0.43CAP mod / Jan2007–Dec2008 5

Italy—Campania and Calabria BAG obs TDR ThetaProbe ML2, Delta-T Device Jan2007–Dec2008 60 30 0.60 0.30BAG mod / Jan2007–Dec2008 5MEL obs TDR Jan2007–Dec2008 30 0.60 0.22MEL mod / Jan2007–Dec2008 5TOR obs TDR Jan2007–Dec2008 30 0.62 0.20TOR mod / Jan2007–Dec2008 5CHI obs TDR Jan2007–Dec2008 30 0.62 0.31CHI mod / Jan2007–Dec2008 5

Luxembourg BIB obs CAP ECH2O, DecagonTM Jan2007–Dec2008 60 5 0.64 0.31BIB mod / Jan2007–Dec2008 5

Spain—REMEDHUS K10 obs CAP Hydra Probes, Stevens Jan2007–Dec2008 60 5 0.60 0.22K10 mod / Jan2007–Dec2008 5F11 obs CAP Jan2007–Dec2008 5 0.44 0.18F11 mod / Jan2007–Dec2008 5I06 obs CAP Jan2007–Dec2008 5 0.40 0.10I06 mod / Jan2007–Dec2008 5

Spain—Vallcebre VCE obs TDR CS615, Campbell Scientific Inc. Jan2007–Dec2008 60 20 0.40 0.27VCE mod / Jan2007–Dec2008 5

France—SMOSMANIA URG obs TDR ThetaProbe ML2X, Delta-T Device Jan2007–Dec2008 12 5 0.40 0.50LZC obs TDR Jan2007–Dec2008 5 0.67 0.44PRG obs TDR Jan2007–Dec2008 5 0.64 0.30

France—Valescure VOB obs TDR TRIME®-PICO IPH/T3, IMKO company Jan2007–Dec2008 15 30 0.43 0.28VOB mod / Jan2008–Dec2008 5


and Chiaravalle (CHI) in the Calabria Region. These sites wereinstalled in 2000 by the “Centro Funzionale Decentrato” of Campaniaand Calabria Region for Civil Protection activities related to hydro-meteorological monitoring for flood and landslide riskmitigation. TDRvolumetric soil moisture measurements have been collected at 30, 60and 90 cm depth for each site.

2.2. Luxembourg

In the Southern part of Luxembourg, the experimental Bibeschbach(BIB) catchment (10.8 km2) is considered as test-site. Since 2005, thebasin has been equipped with a set of 40 sensors measuring thevolumetric soilmoisture of the topsoil layer at a depth of 4 to 7 cmusingthe capacitance principle. Specifically, a calibration with regular TDRpoint measurements is performed to convert the signal into volumetricsoil moisture content. The calibration equation is established for eachmeasurement site individually. The soil probes were installed atdifferent locations in accordance with land use, geology and pedology.For this study, the temporal pattern of the average soil moisture over allsites is used.

2.3. Spain

The “REMEDHUS” network (~1300 km2) is located in the centralsector of the Duero basin (Spain). In the spring of 1999, a network of23 soil moisture stations was set up in the area (Martinez-Fernandez& Ceballos, 2005). The distribution of the stations is irregular and isbased on the distribution of the main physiographic and pedologicalunits of the area. Each station has been equipped with capacitanceprobes installed horizontally at a depth of 5 cm. Comprehensivelaboratory analyses of soil samples were carried out to verify thecapacitance probes and to assess soil properties at each station(texture, bulk density, soil water retention curve, etc.). The data for

three representative stations (K10, F11 and I06) are used in this study.These three sites are selected considering the data reliability andcompleteness during the study period.

The Vallcebre (VCE) Research Catchments are an ensemble ofexperimental basins (0.15 to 4.17 km2) located in the headwaters ofthe Llobregat river, on the southernmargin of the Pyrenees (Catalonia,north-east Spain). A complete overview of general hydrologicalfindings in the Vallcebre research area can be found, for instance, inLatron et al. (2009). Volumetric soil moisture has been measuredweekly in the VCE site since 1993 using the TDR method at nineprofiles and at 4 different depths (0–20, 20–40, 40–60, 60–80 cm)distributed in the main geo-ecological units. For this study, hourly soilmoisture data measured by a continuous TDR probe installed in 2007at a depth of 20 cm are used.

2.4. France

The “SMOSMANIA” network was set up in support of calibrationand validation activities for SMOS. The network is operated by the“Centre National de Recherches Meteorologiques, Group d'Etudes del'Atmosphère Metéorologique” (CNRM/GAME) of “Météo-France”. Itis comprised by twelve stations that form a Mediterranean–Atlantictransect following the marked climatic gradient between the twocoastlines. More details of SMOSMANIA can be found in Albergel et al.(2008). Three representative stations from East to West (URG, PRGand LZC) are selected for this study thus having the soil moistureinformation for the different climate settings.

The Valescure (VOB) catchment (3.83 km2) is a small experimen-tal catchment located in the South of France, at the southern boundaryof the Cevennes mountain area (Tramblay et al., 2010). In 2005, 12TDR soil moisture probes were installed at different depths in fiveplots. For this study, soil moisture data collected by one representativeautomatic station at a depth of 30 cm are used.


3. Satellite products

3.1. ASCAT

Following from the ERS-1 and ERS-2 scatterometers, ASCAT is areal-aperture radar sensor measuring radar backscatter at C-band(5.255 GHz) in VV polarization with a radiometric accuracy betterthan about 0.3 dB (Verspeek et al., 2010). The equipment, mounted ontheMetop satellite, scans the globe in a push-broommode by six side-looking antennae (three left-hand and three right-hand beams). Eachantenna observes a specific location on the earth surface under adifferent azimuth and viewing angle. The swath width is approxi-mately 550 km and there are 14 orbit revolutions per day resulting ina global coverage achieved in ~1.5 days. For Western Europe, mea-surements are generally obtained twice a day, one in the morning(descending orbit) and one in the evening (ascending orbit), between08:00–11:00 and 17:00–21:00 UTC, respectively. The basic equipmentsampling distance is 12.5 km. For soil moisture, the processing isperformed at 50 km (operational) and 25 km (research) resolution.

Soil moisture is retrieved from the ASCAT backscatter measure-ments using a time series-based change detection approach previ-ously developed for the ERS-1/2 scatterometer by Wagner et al.(1999);Wagner et al., 1999;Wagner et al., 1999). In this approach soilmoisture is considered to have a linear relationship to backscatter inthe decibel space, while the noise sources include the instrumentnoise, speckle and azimuthal anisotropies. The surface roughnessis assumed to have a constant contribution in time, and therefore isnot accounted for in the change detection algorithm. By knowing thetypical yearly vegetation cycle and how it influences the backscatter-incidence angle relationship for each location on the Earth, the veg-etation effects can be removed (Wagner et al., 1999), revealing thesoil moisture variations. As a last step, the historically lowest andhighest values of observed backscatter are assigned to the 0% (dry)and 100% (wet) references respectively, thereby yielding timeseries of relative soil moisture percentage values for the first fewcentimetres of the soil. The ASCAT surface soil moisture product usedfor this study (provided by TUWIEN) was obtained by processing2 years (2007–2008) of 25 km ASCAT backscatter measurementsusing the algorithm described by Naeimi et al. (2009) and consideringtogether both ascending and descending overpasses. Finally, we notethat ASCAT soil moisture retrievals might benefit from its slightlylower observation frequency (5.255 GHz) if compared with AMSR-E(6.9 GHz).

3.2. AMSR-E

The AMSR-E sensor on-board the NASA's Aqua satellite hasprovided passive microwave measurements at 6.9 GHz (C-band)and five higher frequencies (including 36.5 GHz Ka-band) since May2002, with daily ascending (13:30 equatorial local crossing time) anddescending (01:30 equatorial local crossing time) overpasses, over aswath width of 1445 km. For this study, both ascending, descendingand ascending plus descending passes are preliminary tested to selectthe configuration providing the better agreement with in situ obser-vations. As mentioned earlier, three soil moisture products derivedfrom AMSR-E data, based on different algorithms, are tested in thisstudy.

The simplest algorithm is the Polarization Ratio Index, PRI=(Tbv−Tbh)/(Tbv+Tbh), which uses the vertical (Tbv) and horizontal (Tbh)polarization measurements. It is frequently used to describe soilmoisture variations (Pellarin et al., 2008;Wigneron et al., 2003) becauseat low frequency (b10 GHz) the main part of the microwave emissionsignal comes from soilmoisture and soil temperature and the PRI allowsfiltering the effect of the soil temperature. In fact, as soil moistureincreases, the horizontal brightness temperature decreases morerapidly than the vertical one leading to an increase of the PRI. This

algorithm is very simple to be implemented as it requires no additionalinformation (as for the LPRM and NASA ones) and is not dependent onthe availability of a long satellite data time series. In this study, the PRI isderived from the AMSR-E AE_Land3 data (data version V06) (Njoku,2010), which are distributed as gridded 0.25° product (EASE gridprojection). The 6.9 GHz frequency (C-band), that was found to be notinfluenced by the Radio Frequency Interference (RFI) problem overEurope, is employed for the PRI computation.

The LPRM (Owe et al., 2001; 2008) is a three-parameter retrievalmodel (soil moisture, vegetation optical depth, and soil/canopy tem-perature) from passive microwave data based on a microwaveradiative transfer model. It uses the dual polarized channel (either6.9 or 10.6 GHz) for the retrieval of both surface soil moisture andvegetation optical depth. The land surface temperature is derivedseparately from the vertically polarized 36.5 GHz channel (Holmeset al., 2009). Here, we use the data provided at http://geoservices.falw.vu.nl/adaguc_portal_dev/. This soil moisture is a gridded 0.25°product (EASE grid projection) derived from the NASA griddedbrightness temperatures.

The currently implemented NASA algorithm uses normalizedpolarization ratios of the AMSR-E channel brightness temperaturesand it is significantly different from its earlier version described inNjoku et al. (2003) (see Jackson et al., 2010). Vegetation and soilroughness are accounted for using polarization ratios at 10.65 and18.7 GHz in empirical relationships (Njoku & Chan, 2006). Thevegetation/roughness parameter combines the effects of vegetationand roughness, that are assumed to have the same functional form(exponential) respect to the normalized polarization differences inthe simplified model used in the retrieval algorithm. Soil moisture iscomputed using the deviation of polarization ratio at 10.65 GHz froma baseline value (Njoku & Chan, 2006). Baseline values are establishedfrom the monthly minima at each grid cell. The soil moisture productscurrently available in the archive of NSDIC (National Snow and Icedata Center) are based on variations of the soil moisture algorithmand the brightness temperature products. Here, we extracted thelevel-3 soil moisture products directly from the AMSR-E AE_Land3data.

In the sequel of the paper, the three AMSR-E products are referredto as: AMSR-E-PRI, AMSR-E-LPRM and AMSR-E-NASA, respectively.

4. Methods

The different satellite soil moisture products derived from ASCATand AMSR-E are characterized by different measurement units. In thisstudy, we choose to normalize all the data between 0 and 1 to carryout a coherent analysis. Specifically, the ASCAT product already rangesbetween 0 and 1 as it is expressed in terms of relative soil moisture(or degree of saturation). The AMSR-E-LPRM product is originallyexpressed in volumetric terms (m3/m3) and for this study it is re-scaled between the maximum and minimum values observed in theinvestigated period. The AMSR-E-NASA product, expressed in g/cm3,is rescaled considering the soil moisture at saturation and residualprovided with the product itself. The AMSR-E-PRI product, on theother hand, is rescaled between the 2.5 and 97.5% percentiles (95%confidence interval) in order to exclude any outliers that arepresumably caused by instrumental noise. Both modelled andobserved data sets are rescaled by using the maximum and minimumvalues of each individual times series over the investigation period. Inthe remainder of this paper the terms soil moisture or relative soilmoisture are used indifferently even though they actually refer to thedegree of saturation (value between 0 and 1). Moreover, the surfaceproduct is defined as Surface Soil Moisture, SSM, and the root-zoneone as SWI.

The satellite products employed in this study, based on C- andX-band observations, are representative of a layer depth of at most2 cm (Escorihuela et al., 2010), while they are compared with in situ

http://geoservices.falw.vu.nl/adaguc_portal_dev/

http://geoservices.falw.vu.nl/adaguc_portal_dev/


observations at a depth of 5, 10, 20 and 30 cm and modelled data for alayer depth of 5 cm. Due to the difference in sensing depth betweensatellite and in situ sensors, the semi-empirical approach proposed byWagner et al. (1999), also named as exponential filter, is used toobtain the SWI values from the SSM directly sensed by satellitesensors. Additionally, as already stated in the introduction, in order tomatch satellite time series (both SSM and SWI) to site-specific ones,two strategies are considered: linear regression correction (e.g.Jackson et al., 2010) and Cumulative Density Function (CDF)matching(e.g. Drusch et al., 2005).

Specifically, for each site-specific (observed and/or modelled soilmoisture) data set (29) and for each satellite soil moisture product(4) the following processing steps have been carried out:

1) the satellite pixel whose centroid is nearest to the site location ischosen and the corresponding relative Surface Soil Moisture (SSM)time series is retrieved from the database;

2) the site-specific soil moisture data corresponding to the acquisi-tion time of the satellite sensor are selected;

3) the satellite root-zone soil moisture product, i.e. the SWI, iscomputed by optimizing the value of the characteristic time lengthparameter, T, obtained by maximizing the correlation between theSWI and the site-specific time series;

4) a linear regression correction is applied to satellite data byminimizing the squared differences between satellite (SSM andSWI) and site-specific data, thereby yielding SSM-REG and SWI-REGtime series;

5) a CDF matching approach is applied to satellite data (SSM andSWI) to match their CDF to site-specific ones, thereby yieldingSSM-CDF and SWI-CDF time series.

6) soil moisture anomalies of all time series (SSM-REG, SSM-CDF,SWI-REG, and SWI-CDF) are finally computed.

4.1. Exponential filter

The profile soil moisture values are often reasonably wellcorrelated with surface soil moisture values (e.g. Penna et al., 2009)because both are affected by the weather pattern during thepreceding few days to weeks. The exponential filter proposed byWagner et al. (1999) is a simple and effective method to retrieveprofile soil moisture values from surface ones and it relies on theanalytical solution of a differential equation assuming that thevariation in time of the average value of the soil moisture profile islinearly related to the difference between the surface and the profilevalues. Despite its simplicity, the algorithm was found reliable inpredicting profile soil moisture values based on surface soil moistureinformation both using in situ observations (Albergel et al., 2008;Albergel et al., 2010) and modelled data (Bisselink et al., 2011). Fora detailed description of the method the reader may refer to Wagneret al. (1999) and Ceballos et al. (2005). In this study, the simplerecursive formulation of the method is used (Albergel et al., 2009):

SWIn = SWIn−1 + Kn⌊ SSM tnð Þ−SWIn−1⌋ ð1Þ

with the gain Kn at time tn given by (in a recursive form):

Kn =Kn−1

Kn−1 + e−

tn−tn−1

T

� � ð2Þ

where T is a parameter named characteristic time length, thatcharacterizes the temporal variation of soil moisture within theroot-zone profile and the gain Kn ranges between 0 and 1. For theinitialization of this filter, K0 and SWI0 are set to 1 and SSM(t0),respectively. Eqs. (1) and (2) are applied to SSM for all four

investigated satellite products derived from ASCAT and AMSR-Esensors.

The application of the exponential filter, and hence the use of theSWI, allows to compare satellite observations with site-specific datareferring to a layer depth higher than 5 cm which can be consideredrepresentative of the satellite observations. However, as it can be seenin the sequel, the comparison with surface soil moisture observationswill have more emphasis in the analysis and evaluation of the results.

4.2. Linear regression correction and CDF matching

Systematic differences between remote sensing-derived and site-specific data of soil moisture prevent an absolute agreement betweenthe two time series. Consequently, comparison of remotely sensedand site-specific time series is often aided by normalizing theremotely sensed data to better match the distribution of ground data.In this study, the linear regression correction and the CDF matchingapproaches are implemented.

The first one is based on the application of a regression equationbetween satellite and in situ soil moisture values (see Fig. 2a and bfor a numerical example). It has to be noted that this approach isslightly different from the linear rescaling one used in Draper et al.(2009) and Brocca et al. (2010). In fact, the latter allows the two timeseries (in situ and satellite) to have the same mean and variance,whereas the linear regression correction approach (Jackson et al.,2010), by minimizing the Root Mean Squared Differences, RMSD,between the two time series, allows only to have the same mean.

The CDF matching approach (e.g. Drusch et al., 2005) can beconsidered as an enhanced non-linear technique for removingsystematic differences between two data sets (see Fig. 2c, d, and efor a numerical example). Through this method the satellite data arerescaled in such a way that its CDF matches the CDF of in situmeasurements (Fig. 2e). The differences in soil moisture valuesbetween the corresponding elements of each ranked data set areconsecutively computed (Fig. 2d). These differences are then plottedagainst the satellite data and a polynomial fitting function is used tocalculate the bias-corrected soil moisture data sets. Both the linearregression correction and CDF matching are performed on the SSM(and SWI) time series, yielding SSM-REG (and SWI-REG) and SSM-CDF (and SWI-CDF). The methods are applied to all four investigatedsatellite products.

4.3. Soil moisture anomalies

In order to avoid seasonal effects that can artificially enhance thecorrelations (Scipal et al., 2008), soil moisture anomalies are alsocomputed following Albergel et al. (2009). Considering a 5-weeksliding window, the soil moisture anomaly, SManom(t), is computed asfollows:

SManom tð Þ = SM tð Þ−SM t−17 : t + 17ð Þσ SM t−17 : t + 17ð Þ½ � ð3Þ

where SM(t) is the relative soil moisture value at time t obtained fromsatellite sensor or in situ observation or modelled data, the overbarand σ are the temporal mean and standard deviation operators,respectively, for a time window of 35 days (5-week) centred on time tand defined by t±17 days. According to Eq. (3), the SManom(t) are notcomputed for the first and the last 17 days.

4.4. Soil water balance model

In addition to in situ observations, modelled soil moisture data fora layer depth of 5 cm are used for validation thus allowing, also for thesites for which in situ observations are available for a depth of 30 cm,to compare satellite data with site-specific data (modelled) for a layer

Fig. 2. Example based on AMSR-E-PRI product for the K10 site in Spain illustrating the regression correction (REG) and the cumulative distribution functionmatching approach (CDF)implemented to rescale satellite products, SSM, against site-specific soil moisture data, OBS. (a, c) Time series of OBS and SSM together with the rescaled product applying: a) theregression correction, SSM-REG, and c) the CDF matching approach, SSM-CDF. The SSM-REG product is obtained by applying the regression equation reported in (b). The SSM-CDFproduct is derived by applying a 5th-order polynomial fitting (d) to the difference between the ranked OBS and SSM data. Therefore, the CDF of OBS and SSM-CDF time series areequivalent (e).


depth similar to that sensed by satellite sensors. The structure of thesoil water balance model used in this study was derived by using soilmoisture observations carried out in an experimental catchmentlocated in central Italy (Brocca et al. 2008). The model has beenapplied in different test-sites located across Europe obtaining a goodperformance in all of them (Brocca et al., 2010; Brocca et al., 2010).The soil water balance model employs the Green–Ampt equation forinfiltration, a gravity driven non-linear relationship for percolationand a linear relation between the actual and the potential evapo-transpiration, this latter computed through the Blaney and Criddleformula (Blaney & Criddle, 1950). The model requires as input datathe meteorological variables routinely measured (rainfall and airtemperature) and incorporates only five parameters; the usual timestep is hourly (or less). Moreover, because the parameters arephysically based and their value range is limited, the model wasfound reliable evenwhen it was calibrated onlywith a limited numberof observations (Brocca et al., 2008). These two characteristics allowto confidently use the model over large areas and for periods differentof those employed for parameters calibration.

4.5. Masking methodology

As it is known that orographic effects and the vicinity to the coastcan potentially hamper the observation of soil moisture from space,some pre-processing is required before applying the previouslydescribed methodology.

The orography that influences both ASCAT and AMSR-E observa-tions needs to be taken into account, especially for the VOB and VCEsites, located in France and Spain, respectively. The corresponding soilmoisture products are characterized by high retrieval noise, which issuggested to be a result due to the orographic effect. Moreover,frequent snow cover hampers soil moisture retrievals from satellitemeasurements. To investigate these aspects, the R-values between insitu observed data for VCE and VOB sites and the ASCAT/AMSR-Epixels surrounding these sites are computed for a radius of 100 km. Asignificant decrease of the R-values in the proximity of mountainous

areas is observed (for all satellite products but, mainly, for ASCATsensor) with a clear negative trend between R-values and mean pixelelevation. This result can also be due to the high spatial variability ofsoil moisture in mountainous areas characterized by high variabilityof both precipitation and temperature. Generally speaking, it is noteasy to understand and discriminate what arises directly from theretrieval and what from natural effects, so that deeper investigationsdeserve to better understand the reliability of satellite soil moistureproducts over mountainous regions. In this study, we masked allASCAT and AMSR-E pixels with a mean elevation higher than 800 m a.s.l. (at the scale of 25 km pixels) and, then, the pixels (not masked)closest to the VCE and VOB sites are selected.

The second problem, i.e. the “coast effect”, only impacts AMSR-Emeasurements, arguably because of its lower spatial resolution. ForMEL, BAG, TOR and CHI sites in Southern Italy, for which the closestpixel is overlapping the sea coast, low performance values are ob-tained. By removing all pixels that are overlapping the sea, per-formances are significantly improved. Therefore, for this study, wemasked all the AMSR-E pixels neighbouring with the sea.

4.6. Performance index

For each comparison the two following statistical scores are usedto evaluate the soil moisture product accuracy:

RMSD =ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiSMSAT−SMINSITUð Þ2

qð4Þ

R =

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1− SMSAT−SMINSITUð Þ2

SMINSITU−SMINSITU

� �vuut ð5Þ

where RMSD is the root mean squared difference between in situ(observed and modelled), SMINSITU, and the different satellite soilmoisture products, SMSAT, the overbar is themean operator and R is thecorrelation coefficient. It has to be noted that instead of RMSE (where E

image of Fig.�2


stands for ‘error’) we used RMSD to underline that also groundmeasurements contain errors (instrumental and representativeness)and, hence, they cannot be considered as the “true” soil moisture.

5. Results

In the following, the results of the comparison between satelliteand site-specific time series are reported firstly considering therelative soil moisture temporal patterns and, then, the correspondingsoil moisture anomalies.

A preliminary analysis was carried out for the selection of the bestorbit to be used for the AMSR-E soil moisture products. Fig. 3 showsthe R-values averaged over all the comparisons between the 21 site-specific data sets for a 5 cm layer depth and the three AMSR-E soilmoisture algorithms (LPRM, NASA and PRI) split up according to theorbit: ascending, descending and ascending plus descending. Overall,it is quite clear that data from ascending passes provide highercorrelations with site-specific data (even though the differences inperformance could be considered not very significant). The sameresults were obtained for the soil moisture anomalies (not shown forsake of brevity). These results are in accordance with Loew et al.(2009) who employed AMSR-E data over Europe but, on the otherhand, are in contrast to many other AMSR-E studies (Draper et al.,2009; Gruhier et al., 2010; Liu et al., 2011; Rudiger et al., 2009;Wagner et al., 2007) that have used descending overpass (01:30 am).In fact, it is well-known that over night time the negative effectrelated to the difference between the surface and the canopy tem-perature is reduced. At the same time, the ascending passes have thepositive effect that during the day the vegetation is more transparent,being dryer around peak temperatures of the day, and, therefore, thequality of the ascending passes might be better at certain vegetationdensities. Considering the results of Fig. 3, the ascending passes seemto be more accurate over Europe, but more in-depth investigationsare needed to clarify this interesting and important issue. In thisstudy, for sake of summarizing the results, the ascending overpassesare selected and analyzed in details in the sequel. For ASCAT, theperformance of ascending and descending passes was found quitesimilar and, hence, both passes are used thus improving the temporalcoverage of this product.

All performance measures are given for the satellite products afterthe application of either the linear regression correction or the CDFmatching approach, i.e. after that the variability of satellite data ismatched to the site-specific data. In fact, while for the ASCAT productthe direct comparison with site-specific data in terms of relativesoil moisture values is rather straightforward (and the performanceare very similar to the ones obtained after the linear regression cor-rection), this is more difficult for AMSR-E products because of thepresence of outliers in the time series that cannot be easily removedby an automatic procedure. For this reason, we decided not to showany results of the comparison between the original satellite products,

Fig. 3. Average values of the correlation coefficients obtained between the 21 site-specific daSWI-REG, SWI-CDF) derived through the three AMSR-E algorithms (LPRM, NASA and PRI). Reand both. Note that the y-axis range of the three figures is not the same.

SSM, and site-specific data. In this context, it has to be noted that theadjustment of satellite time series to match in situ variability couldnot represent a strong limitation. When remote sensing data areassimilated into a hydrological or meteorological model they need tohave the same range of variability of modelled data and, hence, alinear (or non-linear) transformation is required (Brocca et al., 2010;Entekhabi et al., 2010; Koster et al., 2009; Liu et al., 2011; Miralleset al., 2010). Moreover, both the modelled and the observed data donot represent the actual soil moisture value but only an indicatorof the wetness condition that, surely, is affected by a certain degree ofuncertainty.

5.1. Relative soil moisture

The results of all the comparisons in terms of RMSD and R-valuesare reported in Tables 3–6 for ASCAT, AMSR-E-LPRM, AMSR-E-NASAand AMSR-E-PRI products, respectively, all modified through theapplication of the CDF matching approach. The sample size and theoptimized value of the characteristic time length parameter, T, ofthe SWI algorithm are also shown. We have to highlight that verysimilar results are obtained when the linear regression correction isapplied. Tables 3–6 show that all four satellite surface soil moistureproducts provide a good agreement with in situ observations cor-responding to a layer depth of 5 cm, which are considered as the bestbenchmark for evaluating satellite data. In particular, R-values arein the range 0.64–0.81, 0.46–0.78, 0.21–0.64, and 0.54–0.71 (withaverage values of 0.71, 0.62, 0.44 and 0.66) for ASCAT, AMSR-E-LPRM,AMSR-E-NASA and AMSR-E-PRI products, respectively. Due to thelarge sample size (N400), the obtained R-values have a very highsignificance level (p-valueb0.0001). It has to be noted that the ratherlow overall performance of the AMSR-E-NASA product is also causedby the very low performance over the two French sites LZC and PRG.On average, ASCAT and AMSR-E-PRI products provide better results.Also the AMSR-E-LPRM product furnishes satisfactory results exceptfor the two French sites URG and PRG with a decrease of R-value of~0.16 respect to the AMSR-E-PRI product. Moreover, it's worth notingthat AMSR-E-LPRM outperforms ASCAT for the Luxembourg site andSpanish site F11; while the contrary occurs for the French sites. Thisinsight can be also inferred by comparing the outcomes of Wagneret al. (2007) addressing the AMSR-E-LPRM performance in Spanishsites with the one obtained by Rudiger et al. (2009) in French sites.This different behaviour is also pointed out by Dorigo et al. (2010) asshown in the next paragraph.

Concerning the comparison with modelled surface soil moisturedata (5 cm depth), Tables 3–6 show that they are slightly morecorrelated with satellite data than in situ observations. This supportsthe use of modelled data to estimate the accuracy of the satelliteproducts when in situ observations are not available. Considering allsites with modelled data, the results in terms of R-values reveal that

ta sets for a 5 cm layer depth and the four soil moisture products (SSM-REG, SSM-CDF,sults are subdivided considering the selection of different orbits: descending, ascending

image of Fig.�3

Table 3Summary of the results of the comparison between site-specific and satellite soilmoisture for the ASCAT soil moisture product with the application of the CDF matchingapproach (SSM: Surface Soil Moisture, SWI: Soil Water Index, R: correlation coefficient,RMSD: root mean square difference, T: characteristic time length, N: sample size).

SITE SSM-CDF SWI-CDF T(days)

N

R RMSD R RMSD

In situ observed at 5 cm depthLU-BIB-obs05 0.642 0.219 0.907 0.110 10.0 814SP-K10-obs05 0.686 0.120 0.768 0.101 2.0 550SP-F11-obs05 0.653 0.171 0.752 0.142 4.5 594SP-I06-obs05 0.661 0.113 0.672 0.113 1.0 434FR-URG-obs05 0.787 0.152 0.898 0.105 10.5 625FR-LZC-obs05 0.807 0.096 0.841 0.085 1.0 651FR-PRG-obs05 0.721 0.164 0.824 0.130 5.0 638Average 0.708 0.148 0.809 0.112 4.9 615

In situ observed at N5 cm depthIT-VAL-obs10 0.711 0.222 0.924 0.112 19.0 340IT-CAP-obs10 0.752 0.145 0.893 0.094 17.0 544IT-BAG-obs30 0.746 0.141 0.815 0.118 2.5 461IT-MEL-obs30 0.647 0.279 0.883 0.158 25.0 572IT-TOR-obs30 0.741 0.166 0.859 0.122 11.0 532IT-CHI-obs30 0.705 0.120 0.842 0.085 16.0 545SP-VCE-obs20 0.443 0.265 0.579 0.218 6.5 551FR-VOB-obs30 0.547 0.189 0.738 0.128 10.0 689Average 0.662 0.191 0.817 0.129 13.4 529

Modelled at 5 cm depthIT-VAL-mod05 0.848 0.139 0.877 0.125 1.5 663IT-CER-mod05 0.821 0.136 0.840 0.128 1.0 666IT-SPO-mod05 0.721 0.188 0.778 0.162 2.0 658IT-CAP-mod05 0.720 0.143 0.815 0.115 6.5 678IT-BAG-mod05 0.832 0.120 0.842 0.116 1.0 572IT-MEL-mod05 0.842 0.169 0.907 0.129 4.5 579IT-TOR-mod05 0.854 0.100 0.877 0.092 1.5 534IT-CHI-mod05 0.806 0.108 0.824 0.103 1.0 547LU-BIB-mod05 0.634 0.188 0.919 0.088 12.0 814SP-K10-mod05 0.693 0.107 0.778 0.089 2.0 667SP-F11-mod05 0.746 0.134 0.861 0.098 3.5 633SP-I06-mod05 0.705 0.125 0.749 0.113 1.0 653SP-VCE-mod05 0.546 0.181 0.635 0.156 6.5 656FR-VOB-mod05 0.638 0.125 0.669 0.117 0.5 356Average 0.743 0.140 0.812 0.117 3.2 620

Table 4As in Table 3 but for the AMSR-E-LPRM soil moisture product (ascending passes).

SITE SSM-CDF SWI-CDF T(days)

N

R RMSD R RMSD





ASCAT and AMSR-E-LPRM product provide the better performance,with average R equal to 0.74 and 0.72, respectively.

Even considering the data for a layer depth of 5 cm, Tables 3–6highlight that the satellite product reliability increases when theexponential filter is applied for both benchmarks used in this study,i.e., observed and modelled soil moisture data. For instance, for in situobservations, the relative increase of the R-value is of ~14% and 10%after the application of the filter for ASCAT and AMSR-E-LPRM soilmoisture products, respectively, thus underlying that the noise levelsof the original soil moisture values retrieved from active and passivemicrowave observations are found to be nearly the same. A possibledrawback of the application of the exponential filter methodologyat global scale is related to the need of an additional parameter, T,which has to be regionalized. However, assuming a constant T valueequal to 3.5 days, the average R-value decreases from 0.81 (0.74) to0.79 (0.73) for ASCAT (AMSR-E-LPRM) product. Therefore, the effectof setting a constant T value is not significant in terms of performance(Brocca et al., 2010; Brocca et al., 2010).

For a quick look at the performance of the employed soil moistureproducts, Fig. 4 illustrates the R-values with respect to site-specificdata sets and for both the linear regression correction and the CDFmatching. As mentioned above, the two rescaling procedures providevery similar results and the better performance of ASCAT and AMSR-E-LPRM products is further visually confirmed. Moreover, Fig. 5 showsthe time series of SSM-CDF and SWI-CDF (obtained through theapplication of the CDF matching approach) derived by ASCAT and

AMSR-E-LPRM products versus modelled and observed data at 5 cmdepth for MEL and F11 site, respectively. Notwithstanding the highvariability of surface soil moisture, and hence the complexity tocorrectly estimate this variability, both ASCAT and AMSR-E-LPRMsoil moisture products are able to accurately reproduce the temporalpattern of ground observations, even at a fine temporal scale. Similarresults are also obtained for all the other data sets as it can be found inthe supplementary material available with the electronic version ofthe paper. In the supplementary material a figure showing the resultsfor each satellite product and site-specific data set is available (also forsoil moisture anomalies).

In terms of RMSD very similar conclusions can be drawn. In thisstudy these values are reported in term of relative soil moisturevalues (between 0 and 1) but they can be converted into volumetricsoil moisture by taking the difference between the maximum andminimum soil moisture values observed in the measurement periodindicated in Table 2 into account. For in situ observed and modelleddata at 5 cm depth, the average RMSD-values are equal to 0.143(0.039 m3/m3 in volumetric terms), 0.149 (0.042 m3/m3), 0.229(0.064 m3/m3), and 0.171 (0.047 m3/m3) for ASCAT, AMSR-E-LPRM,AMSR-E-NASA and AMSR-E-PRI products, respectively. These resultsindicate that the accuracy of both ASCAT and AMSR-E-LPRM productsmatch the target value of 0.04 m3/m3 that is the accuracy goal ofthe SMOS and SMAP missions (Entekhabi et al., 2010; Kerr et al.,2010) with even better accuracy (0.031 m3/m3 for ASCAT) if the SWIproduct is analyzed. However, it has to be noted that the linearregression correction and the CDF matching approaches employed inthis study permit to significantly reduce bias and RMSD-values. Byperforming a direct comparison between satellite and in situ

Table 5As in Table 3 but for the AMSR-E-NASA soil moisture product (ascending passes).

SITE SSM-CDF SWI-CDF T (days) N

R RMSD R RMSD


In situ observed at N5 cm depthIT-VAL-obs10 0.610 0.260 0.582 0.268 2.5 255IT-CAP-obs10 −0.131 0.303 −0.151 0.306 0.5 327IT-BAG-obs30 −0.005 0.276 −0.071 0.283 25.0 441IT-MEL-obs30 0.041 0.438 0.056 0.433 7.5 450IT-TOR-obs30 0.239 0.286 0.628 0.200 25.0 424IT-CHI-obs30 0.217 0.194 0.595 0.139 25.0 424SP-VCE-obs20 −0.031 0.353 −0.037 0.353 1.0 437FR-VOB-obs30 −0.012 0.243 −0.011 0.242 0.5 521Average 0.116 0.294 0.199 0.278 10.9 410

Modelled at 5 cm depthIT-VAL-mod05 0.479 0.270 0.478 0.270 0.5 455IT-CER-mod05 0.141 0.304 0.139 0.305 0.5 217IT-SPO-mod05 0.383 0.274 0.408 0.267 1.5 359IT-CAP-mod05 −0.153 0.284 −0.173 0.286 0.5 424IT-BAG-mod05 0.036 0.292 0.250 0.257 25.0 512IT-MEL-mod05 0.079 0.406 0.150 0.389 8.0 452IT-TOR-mod05 0.270 0.231 0.604 0.171 25.0 425IT-CHI-mod05 0.180 0.231 0.523 0.176 25.0 425LU-BIB-mod05 0.665 0.179 0.711 0.166 2.5 505SP-K10-mod05 0.677 0.110 0.674 0.110 0.5 580SP-F11-mod05 0.558 0.175 0.556 0.174 0.5 580SP-I06-mod05 0.454 0.172 0.445 0.173 0.5 580SP-VCE-mod05 −0.174 0.281 −0.181 0.281 0.5 519FR-VOB-mod05 0.227 0.186 0.226 0.185 0.5 252Average 0.273 0.243 0.344 0.229 6.5 449

Table 6As in Table 3 but for the AMSR-E-PRI soil moisture product (ascending passes).

SITE SSM-CDF SWI-CDF T (days) N

R RMSD R RMSD





observations, the RMSD-values will be certainly higher and, hence, thetarget values of 0.04 m3/m3 could not be matched. On the other hand,a direct comparison requires porosity andwilting point information toconvert the satellite-derived values in a soil moisture range. Thisinformation can be derived from soil maps. However, such data arenot available at a global scale and existing maps are often not veryaccurate.

As for hydrological andmeteorological applications the knowledgeof the average soil moisture for a soil depth greater than 5 cm isrequired, the reliability of the SWI was tested through in situ data at10, 20 and 30 cm depth. Tables 3–6 show that the average R-valuesare equal to 0.82, 0.81, 0.20 and 0.63 for ASCAT, AMSR-E-LPRM, AMSR-E-NASA and AMSR-E-PRI products, respectively, thus confirmingthe better performance of the ASCAT and AMSR-E-LPRM. As expected,the application of the exponential filter provide a clear improvementrespect with the SSM products with a relative increase in the R-valuesof ~23% and 17% for ASCAT and AMSR-E-LPRM, respectively.

The obtained results, both in terms of R and RMSD, are in a goodagreement with those reported in scientific literature for ASCAT(Albergel et al., 2009; Albergel et al., 2010; Brocca et al., 2010) andAMSR-E (Draper et al., 2009; Gruhier et al., 2010; Jackson et al., 2010;Owe et al., 2008; Rudiger et al., 2009;Wagner et al., 2007). In particular,the higher performance of the new ASCAT product is highlightedby comparing the results obtained for three sites of SMOSMANIAnetwork with those given in Albergel et al. (2010). These authorsreported R-values in the range 0.59–0.64whereas values in the range0.72–0.81 for SSM are obtained in this study. Also for AMSR-E, thehigher performance of the AMSR-E-LPRM product is confirmed aswell.

Therefore, ASCAT outperforms AMSR-E on all sites except forSpanish ones where AMSR-E-LPRM gives a slightly better perfor-mance. On the other hand, considering all sites, AMSR-E-LPRM is thebetter AMSR-E product except for French sites where the simplerAMSR-E-PRI product produces better agreement with observations.The analysis of the study sites in different countries reveals no sig-nificant differences in model performance. Finally, it can be inferredthat the AMSR-E-NASA product is less reliable for Italian and Frenchsites.

5.2. Soil moisture anomalies

An additional test of the satellite products is carried out byanalyzing the soil moisture anomalies obtained considering a 5-weeksliding window. For sake of brevity, Table 7 displays the results ofall comparisons in terms of RMSD and R-values only for ASCAT andAMSR-E-LPRM products (modified through the CDF matchingapproach) while Fig. 6 summarizes the results in terms of R-valuesfor all products but considering only the 5 cm depth data sets(observed and modelled). It has to be mentioned that the expo-nential filter, the linear regression correction and the CDF matchingare not re-computed for the comparison of the anomalies; i.e. theanomalies are computed on the same time series obtained in theprevious analysis.

Comparing Fig. 6 with Fig. 4, for the surface soil moistureanomalies the correlations decrease for all satellite products withaverage R-values here equal to 0.51 and 0.38 for ASCAT and AMSR-E-LPRM products, respectively. Most notably, the accuracy of the dif-ferent products can be quite different if anomalies are used instead of

Fig. 4. Correlation coefficient obtained between site-specific (observed, obs, and modelled, mod) at 5 cm depth and satellite relative soil moisture time series for each site andproduct (SSM: Surface Soil Moisture, SWI: Soil Water Index, REG: linear regression correction, CDF: CDF matching).


relative soil moisture values. For instance, concerning the SWI derivedfor ASCAT and AMSR-E-LPRM products, for MEL site the R-valuesfor relative soil moisture and anomalies are equal to 0.91 and 0.93 (seeTables 3 and 4) and 0.69 and 0.57 (see Table 7), respectively; as it isvisualized comparing Fig. 7b with Fig. 5b. Therefore, for this site,AMSR-E-LPRM gives the better results with respect to the relativesoil moisture values, while ASCAT performs better with respect toanomalies. Except over Spanish sites, ASCAT outperforms all AMSR-Eproducts when anomalies are examined (Fig. 6) whereas for therelative soil moisture values ASCAT and AMSR-E provided similarresults (Fig. 4). As shown in the two examples of Fig. 7, ASCAT is able

to reproduce quite well the temporal pattern of the soil moistureanomalies thus having the capability to correctly detect single rainfallevents (see also the supplementary material). These results highlightthe importance of carefully looking for the accuracy of satelliteproducts according to the main feature of interest for the particularoperational or scientific application.

6. Discussions

Due to the current availability of different satellite soil moistureproducts, it is interesting to understand how these products can be

image of Fig.�4

Fig. 5. Time series of relative Surface Soil Moisture, SSM-CDF, and Soil Water Index, SWI-CDF, (obtained through the application of the CDF matching approach) derived by ASCAT(a, c) and AMSR-E-LPRM (b, d) products versus: (a, b) modelled data for a layer depth of 5 cm and for MEL site in Italy, (c, d) observed data for a layer depth of 5 cm and for F11 site inSpain.


further improved. Moreover, it is important to assess the validity ofusing in situ observations, which are known to be representative of(very) small scales, as benchmark in such validation studies.

In the previous section, problems related to the orography effectand to the closeness to the coast were highlighted. As a result, allpixels with a mean elevation (at 25 km scale) higher than 800 m a.s.l.were masked. The same was done with AMSR-E pixels neighbouringto the sea. Even though these two practical approaches can be eff-iciently used at a global scale, in-depth analyses to understand theeffects of orography and sea on active and passive microwave signalare certainly needed. For mountainous regions the errors are arguablylinked to a deficit in the representativeness of in situ measurementsand, hence, denser and better distributed networks should be set up toobtain more accurate soil moisture estimates at satellite pixel scale.

Moreover, the use of a threshold that is only based on mean elevationis possibly not sufficient and an analysis considering slope and aspectshould be performed. For instance, the REMEDHUS network is locatedon a near flat plateau and even though the mean elevation is around700 m a.s.l. (close to the threshold) the satellite measurements overthis site are seemingly not affected by any orography-related effect.

Furthermore, by looking at the time series, two other importantaspects can be underlined and they are exemplified in Fig. 8 where theSWI-CDF for ASCAT and AMSR-E-LPRMproducts are comparedwith insitu observed and modelled data for the BIB site in Luxembourg.

The first one is related to the representativeness of in situ mea-surements at the satellite pixel scale and, in particular, to the benefitof performing a combined analysis by using both in situ observationsand modelled data. Looking at the dashed rectangle in Fig. 8a it can be

image of Fig.�5

Table 7Summary of the results of the comparison between in situ and satellite soil moistureanomalies for the ASCAT and AMSR-E-LPRM soil moisture products with the applicationof the CDF matching approach (R: correlation coefficient, RMSD: root mean squaredifference, SSM: Surface Soil Moisture, SWI: Soil Water Index).

SITE SSM SWI

ASCAT AMSR-E-LPRM

ASCAT AMSR-E-LPRM

R RMSD R RMSD R RMSD R RMSD

In situ observed at 5 cm depthLU-BIB-obs05 0.435 0.931 0.113 1.182 0.663 0.659 0.317 0.966SP-K10-obs05 0.577 0.869 0.611 0.796 0.757 0.651 0.661 0.739SP-F11-obs05 0.476 0.896 0.460 0.889 0.656 0.698 0.694 0.648SP-I06-obs05 0.577 0.846 0.578 0.820 0.580 0.839 0.594 0.802FR-URG-obs05 0.529 0.848 0.330 1.021 0.609 0.714 0.204 0.930FR-LZC-obs05 0.527 0.879 0.176 1.144 0.580 0.823 0.269 1.022FR-PRG-obs05 0.517 0.887 0.231 1.128 0.635 0.734 0.250 0.948Average 0.520 0.879 0.357 0.997 0.640 0.731 0.427 0.865

In situ observed at N5 cm depthIT-VAL-obs10 0.558 0.838 0.252 1.124 0.704 0.635 0.406 0.869IT-CAP-obs10 0.390 0.930 0.092 1.178 0.540 0.707 0.083 1.081IT-BAG-obs30 0.500 0.901 0.362 1.003 0.563 0.833 0.185 1.033IT-MEL-obs30 0.389 0.949 0.163 1.044 0.490 0.719 0.235 0.785IT-TOR-obs30 0.234 1.009 0.232 0.962 0.448 0.755 0.449 0.745IT-CHI-obs30 0.180 1.028 0.063 1.122 0.227 0.865 0.164 0.985SP-VCE-obs20 0.057 1.217 0.165 1.186 0.183 1.077 0.457 0.867FR-VOB-obs30 0.387 0.996 0.258 1.094 0.357 0.943 0.405 0.874Average 0.337 0.984 0.198 1.089 0.439 0.817 0.298 0.905

Modelled at 5 cm depthIT-VAL-mod05 0.696 0.728 0.418 1.011 0.720 0.696 0.499 0.925IT-CER-mod05 0.666 0.771 0.400 1.029 0.710 0.716 0.458 0.970IT-SPO-mod05 0.481 0.975 0.248 1.174 0.528 0.924 0.421 0.988IT-CAP-mod05 0.456 0.932 0.206 1.142 0.597 0.757 0.337 0.981IT-BAG-mod05 0.594 0.850 0.385 1.004 0.622 0.815 0.411 0.959IT-MEL-mod05 0.538 0.870 0.348 1.014 0.692 0.682 0.566 0.776IT-TOR-mod05 0.543 0.836 0.291 1.074 0.611 0.763 0.427 0.940IT-CHI-mod05 0.464 0.931 0.270 1.105 0.509 0.881 0.347 1.020LU-BIB-mod05 0.414 0.953 0.251 1.077 0.694 0.624 0.494 0.822SP-K10-mod05 0.526 0.885 0.642 0.749 0.646 0.757 0.750 0.619SP-F11-mod05 0.523 0.853 0.666 0.705 0.720 0.640 0.796 0.541SP-I06-mod05 0.561 0.898 0.683 0.753 0.635 0.818 0.704 0.727SP-VCE-mod05 0.117 1.170 0.194 1.178 0.115 1.093 0.180 1.094FR-VOB-mod05 0.552 0.923 0.432 1.016 0.584 0.888 0.566 0.875Average 0.509 0.898 0.388 1.002 0.599 0.790 0.497 0.874


seen that both satellite products are able to reproduce the modelleddata with good accuracy. On the other hand, from Fig. 8b it can beinferred that in the same period both satellite products underestimatein situ observation (for the same time period). Such an effect canbe related to the non-representativeness of in situ observations atthe satellite pixel scale, probably due to higher rainfall close to the siteif compared with the average rainfall over the whole pixel. Similarresults (see supplementary material) are evident also for other datasets, e.g. for in situ data at VAL site. Therefore, the combined use oftwo satellites products might highlight the systematic or event-specific malfunctioning of in situ networks (due to measurement orrepresentativeness errors) and they can potentially be used to correctsite-specific time series at satellite pixel scale.

The second aspect is related to the well-known problem to obtaintrustworthy soil moisture estimates when soil freezing occurs. Soilfreezing leads to changes in the dielectric constant, so not only thebackscattering coefficient (and the brightness temperature) changesabruptly and significantly, but also the in situ observations carried outwith TDR (or FDR) techniques. In fact, looking at the grey rectangle inFig. 8b, a sudden decrease in relative soil moisture values can beobserved for both in situ and ASCAT data, whereas AMSR-E-LPRMproduct seems to be less affected by this problem. If modelled data areconsidered (Fig. 8a), this fast decrease is not observed since the adoptedsoil water balance model does not account of soil freezing in itsformulation. Therefore, it can be inferred that themodel ismore reliableif one is interested to the total water content of the soil layer, whereas insitu and ASCAT observations are able to detect only liquid water. Also inthis case, to improve the quality of satellite products, an air temperaturethreshold can be used to mask periods affected by soil freezing.

Finally, in order to attempt to generalize the results, the rela-tionship between the obtained performance and the land use char-acteristics of each site has been analyzed. Previous studies (De Jeuet al., 2008; Dorigo et al., 2010; Jackson et al., 1982; Parinussa et al.,2011) showed a strong connection between the quality of satelliteretrieved soil moisture and vegetation density. In general, the theoryindicates that as the vegetation biomass increases, the observedsoil emission decreases, and therefore the soil moisture informa-tion contained in the microwave signal decreases. Fig. 9a shows therelationship between the vegetation optical depth retrieved by theLPRM algorithm (and averaged for the period January 2007–December 2008) and the RMSD-values of the SSM-REG productsobtained by ASCAT and AMSR-E-LPRM considering only site-specificdata at 5 cm depth. In fact, the comparison with deeper layers wouldproduce an additional error linked to the differences in thesensing depth between satellite and in situ observations. Overall,the expected increasing trend between the soil moisture productsperformance and the vegetation optical depth is evident for bothASCAT and AMSR-E-LPRM; interestingly, the two products show avery similar pattern indicating that the effect of vegetation is nearlysimilar. This relationship is also in very good accordance with theresults of Parinussa et al. (2011) and it can be used to extrapolate theobtained results for larger areas and for the estimation of the temporalvariability of errors, an important factor for data assimilation studies.However, a more detailed analysis with a larger number of sites willbe required to test and validate this hypothesis.

Another interesting point relates to the comparison of the ob-tained results with the results reported by Dorigo et al. (2010) whoapplied the Triple Collocation Method, TCM (Scipal et al., 2008) toASCAT, AMSR-E-LPRM soil moisture products along with ERA-Interim(or GLDAS-NOAH) reanalysis soil moisture data sets (as third in-dependent data set) to characterize, at a global scale, the error struc-ture of the different data sources. For the sites investigated in thisstudy, Fig. 9b shows the comparison of the difference (ASCAT minusAMSR-E-LPRM) between the errors derived through the TCM ap-proach (Dorigo et al., 2010) and the root mean square differences,RMSDs, obtained in this study for the comparison of site-specific data

at 5 cm depth and the respective satellite products. If these differencesare positive (i.e., the error of ASCAT is higher than the AMSR-E-LPRMone), theAMSR-E-LPRMoutperformsASCAT and vice versa. Specifically,Dorigo et al. (2010) found that ASCAT outperforms AMSR-E-LPRMnearly forwhole Europe except over Central and South Spain. Looking atFig. 9b the points in the upper-right part of the figure, which are thosefor which AMSR-E-LPRMoutperforms ASCAT, exactly correspond to theREMEDHUS sites in Spain. For all the other sites, it can be clearly seenthat both methods confirm that the ASCAT soil moisture productoutperforms the AMSR-E-LPRM one. Moreover, Fig. 9b illustrates thatthe TCM seems to show lower differences in performance between thetwo products than the analysis performed in this study. Finally, theseresults are also in accordancewithDe Jeu et al. (2008)whopointed out astrong similarity between AMSR-E and ERS scatterometer soil moisturedata (the predecessor of ASCAT) in sparsely to moderately vegetatedregions.

7. Conclusions

Based on the obtained results the following conclusions canbe drawn:

1) the satellite soil moisture products derived fromASCAT and AMSR-Eprovide a good agreement with in situ observed and modelled dataacross several test sites located in four different European countries;

Fig. 6. As in Fig. 4 but for soil moisture anomalies.


2) among the three AMSR-E products investigated in this study, theAMSR-E-LPRM provides the better results;

3) in term of relative soil moisture values, ASCAT and AMSR-E-LPRMsurface soil moisture products offer similar performance withaverage correlation values equal to 0.71 (0.74) and 0.62 (0.72),respectively, for the comparison with observed (modelled) data at5 cm depth;

4) the exponentially filtered Soil Water Index, SWI, product worksbetter than the Surface Soil Moisture, SSM, one with averagecorrelation values equal to 0.81 (0.81) and 0.69 (0.77) for ASCATand AMSR-E-LPRM, respectively, for the comparison with ob-served (modelled) data at 5 cm depth;

5) considering soil moisture anomalies, correlation values decreasebut, most notably, ASCAT outperforms all the other products for allsites, except over Spain, and this last insight is in accordance withprevious studies;

6) in the different study sites, the ASCAT performance is rathersimilar whereas the reliability associated with AMSR-E productsdecreases both in Southern Italy and in France;

7) three different issues concerning the orographic effect, the closenessto the coast and the soil freezing are highlighted and somepreliminary approaches are described to reduce these problems;

8) due to their uncertainties, in situ measurements and modelleddata should be integrated to achieve a more comprehensive and

image of Fig.�6

Fig. 7. As in Fig. 5 but for soil moisture anomalies.


efficient assessment of the reliability of satellite soil moistureproducts.

Overall, the validation activity conducted allowed to determinethe reliability of ASCAT and AMSR-E soil moisture products througha robust and standardized comparison with ground observationfor 17 sites across four different countries (Italy, France, Spain andLuxembourg) in Europe. The obtained results are encouraging forthe efficient use of these products to support operational hydrolog-ical, meteorological and water management activities. In fact, notwith-standing the spatial mismatch between ground data and satelliteproducts (point measurements against ~25 km pixel average), thecorrelation between the data sets is satisfactory over all investigatedsites.

Finally, the accordance of the results obtained in this study withthose in Dorigo et al. (2010), who employed the Triple CollocationMethod to characterize the ASCAT and AMSR-E-LPRM soil moisture

products at a global scale, further corroborates the validity of theperformed analysis which could allow to generalize the results overwhole Europe, also considering the dependence of soil moistureproducts reliability with vegetation density. Moreover, preliminaryresults from the SMOS satellite showed that passive microwaveobservations in L-band are affected by Radio Frequency Interferencein many regions over Europe (Camps et al., 2010; Matgen et al., 2011).Therefore the products compared and validated in this work could beused as an alternative in these regions.

Acknowledgments

Wewould like to acknowledge the Umbria Region (Italy), the ARPAEmilia-Romagna (Italy), the Functional Centre of Campania and CalabriaRegion (Italy), the University of Salamanca (Spain), the Public ResearchCentre—Gabriel Lippmann (Luxembourg), the Centro Hispano Luso deInvestigaciones Agrarias, Universidad de Salamanca (Spain), the

image of Fig.�7

Fig. 8. BIB site in Luxembourg: time series of Soil Water Index, SWI-CDF, (obtained through the application of the CDF matching approach) derived by ASCAT and AMSR-E-LPRMproducts versus: (a) modelled data, and b) observed data for a layer depth of 5 cm. The two rectangles highlight two different issues raised by the comparison of satellite data withobserved and modelled data (see text for details).

Fig. 9. a) Relationship between the vegetation optical depth averaged for the periodJanuary 2007–December 2008 and the root mean square difference, RMSD, of the SSM-REG product obtained by ASCAT and AMSR-E-LPRM considering only the site-specificdata at 5 cm depth. b) Comparison of the difference (ASCAT minus AMSR-E-LPRM)between the errors obtained through the Triple Collocation Method, TCM, and theRMSD between in situ data at 5 cm depth and the respective satellite products (R2:determination coefficient).


Institute of Environmental Assessment andWater Research, IDÆA-CSIC(Spain) and the Département de Géographie, Université de Nice-Sophia-Antipolis (France) for providing the in situ soil moisture andhydrometeorological data. This work was funded by the NationalResearch Council of Italy and by the project “EUMETSAT SatelliteApplication Facility on Support to Operational Hydrology and WaterManagement (H-SAF)”. The contribution of the University ofSalamanca was supported by the AYA2010-22062-C05-02 projectfrom the Spanish Ministry of Science and Innovation. The contribu-tion of the Institute of Environmental Assessment and WaterResearch, IDÆA-CSIC was supported by the RespHiMed (CGL2010-18374) and Montes (CSD2008-00040) projects financed by SpanishMinistry of Science and Innovation. The authorswould like to expressa special acknowledgment to four anonymous reviewers and tothe Associate Editor for their valuable comments and remarkswhich helped to significantly improve the manuscript. We are alsogratefully to Richard De Jeu who provided the vegetation opticaldepth data set and gave useful support for the AMSR-E-LPRM soilmoisture product.

Appendix A. Supplementary data

Supplementary data to this article can be found online at doi:10.1016/j.rse.2011.08.003.

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