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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 4, NO. 2, JUNE 2011

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Dense Temporal Series of C- and L-band SAR Datafor Soil Moisture Retrieval Over Agricultural CropsAnna Balenzano, Francesco Mattia, Senior Member, IEEE, Giuseppe Satalino, and Malcolm W. J. Davidson

AbstractThis paper investigates the potential of multi-temporal C- and L-band SAR data, acquired within a short revisitingtime (12 weeks), to map temporal changes of surface soil moisture) underneath agricultural crops. The analysed datacontent (consist of a new ground and SAR data set acquired on a weeklybasis from late April to early August 2006 over the DEMMIN(Durable Environmental Multidisciplinary Monitoring Information Network) agricultural site (Northern Germany) during theEuropean Space Agency 2006 AgriSAR campaign. The paperfirstly investigates the main scattering mechanisms characterizingthe interaction between the SAR signal and crops, such as winterwheat and rape. Then, the relationship between backscatter andsoil moisture content temporal changes as a function of differentSAR bands and polarizations is studied. Observations indicatethat rationing of the multi-temporal radar backscatter can be asimple and effective way to decouple the effect of vegetation andsurface roughness from the effect of soil moisture changes, whenvolume scattering is not dominant. The study also assesses towhich extent changes in the incidence angle between subsequentradar acquisitions may affect the radar sensitivity to soil moisturecontent. Finally, an algorithm based on the change detectiontechnique retrieving superficial soil moisture content is proposedand assessed both on simulated and experimental data. Resultsindicate that for crops relatively insensitive to volume scatteringin the vegetation canopy (as for instance winter wheat at C-bandcan be retrievedor winter rape and winter wheat at L-band),during the whole growing season, with accuracies ranging between3 ]. We also show that low incidence angles5% and 6% [ 3(e.g., 20 35 ) and HH polarization are generally better suited toretrieval than VV polarization and higher incidence angles.Index TermsSentinel-1, SMAP, soil moisture retrieval, synthetic aperture radar (SAR).

I. INTRODUCTION

T

HE major difficulties in monitoring bio-geophysicalparameters of agricultural surfaces using SAR sensorsare due to the dependence of the observed SAR data on multiple bio-geophysical parameters including volumetric soil, soil roughness, vegetation biomassmoisture contentand canopy structure [1][5]. Decoupling the effect ofsoil and vegetation on the scattered SAR signal is an open andManuscript received July 31, 2009; revised February 06, 2010; accepted May03, 2010. Date of publication August 16, 2010; date of current version May 20,2011. This work was supported by ESA under Grants 19558/06/NL/HE and19974/06/I/LG.A. Balenzano, F. Mattia, and G. Satalino are with the Consiglio Nazionaledelle Ricerche (CNR), Istituto di Studi sui Sistemi Intelligenti per lAutomazione (ISSIA), I-70126 Bari, Italy (e-mail: [email protected]).M. W. J. Davidson is with the European Space Agency, European Space Research and Technology Center (ESA-ESTEC), EOP/SMS, Nordwijk 2201 AG,The Netherlands.Digital Object Identifier 10.1109/JSTARS.2010.2052916

challenging task, particularly for agricultural areas where thegeometrical parameters of plant constituents span the typicalto L-band)range of microwave wavelengths (e.g., fromthroughout the growing season.Over the last 15 years, the use of decomposition theorems,applied to fully polarimetric SAR data [6], has been one ofthe most elegant and promising techniques investigated todecompose the scattered radar signal into surface and volumecontributions with applications, for instance, for the retrievalof soil moisture content underneath agricultural crops [7],[8]. However, most of the obtained results concerning soilmoisture retrieval are not sufficiently accurate to meet scientificrequirements [9], at least without the use of additional a prioriinformation on surface parameters [10], [11]. A second approach showing a strong potential for soil moisture monitoring,particularly in view of near future missions characterized byshort-repeating cycles (e.g., Sentinel-1 [12], SMAP [13]), isbased on change detection technique [14]. The rational of thismethod is that temporal changes of surface roughness, canopystructure and vegetation biomass take place at longer temporal scales than soil moisture changes (excluding cultivationpractices). Therefore, multi-temporal acquisitions with a shortrepeating cycle are expected to track changes in soil moisturecontent only, since other parameters affecting radar backscattercan be considered constant [15]. Change detection techniquefor soil moisture monitoring is applied operationally at lowspatial resolution using scatterometer data [16] and it has alsobeen investigated in previous studies dealing with SAR data[17], [18]. One of the main reasons hampering a more extensiveinvestigation of the method at high spatial resolutions has beenthe lack of long and dense time series of SAR data that arenecessary in this approach.In this context, the objective of this paper is to assess the potential of change detection technique applied to multi-temporalC- and L-band SAR data for monitoring soil moisture changesunderneath agricultural crops. The experimental data come fromthe European Space Agency (ESA) AgriSAR 2006 campaign,which encompassed airborne SAR and ground data acquiredroughly every week during the entire 2006 growing season overthe DEMMIN (Durable Environmental Multidisciplinary Monitoring Information Network) agricultural site in Germany.The paper firstly evaluates the radar sensitivity to soil moisture content at different frequencies, polarizations, incidence angles and for different crop types and vegetation growing stages.To support the interpretation of the observed relationships between the radar response and surface parameters and to assessretrieval strategy, a theoretical scatteringthe validity of themodel, based on Radiative Transfer (RT) approach, is employed.

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Fig. 1. Land cover map of the airborne SAR system East-West track. The studied rape (140-110-101) and wheat (221-230-250) fields are indicated as a functionof the incidence angle.

Subsequently, the sensitivity of multi-temporal SAR intensityratios to changes of soil moisture content underneath agricultural crops is assessed and compared with model predictions.Furthermore, the impact of changes in incidence angle betweensubsequent radar acquisitions to soil moisture content sensitivityis investigated. Finally, the performance of the change detection technique to quantitatively retrieve temporal series of soilmoisture content values during the vegetation growing season isassessed using simulated and experimental data.II. MATERIALSA. Test Site and Ground DataThe ESA AgriSAR 2006 campaign was carried out over theDEMMIN agricultural site, in the Northern Germany from Aprilto August 2006. It encompassed multi-temporal airborne SARacquisitions together with extensive in situ measurements ofbio-geophysical parameters during the entire growing season[19]. The principal objective of the campaign was to assess theimpact of future ESA Sentinel-1 and -2 missions for land applications and to provide a well-documented database to investigate the bio-geophysical parameter retrieval.The study area is characterized by an almost flat topography) and large land parcels with an av(altitude variationserage area of 80 ha. The main crops for the test site were winterwheat, winter rape, winter barley, maize and sugar beet.The 12 weekly in situ measurements performed mostlysimultaneously to the radar flights include: total fresh and drybiomass, vegetation height and crop density, crop coverage,volumetric soil moisture content at depth 05 cm and surfaceroughness. Ancillary information about precipitation, soiltexture and phenological stage is also available.This study focuses on three winter wheat (221-230-250) andthree winter rape (140-110-101) fields. Fig. 1 shows a land covermap of the area imaged by the airborne SAR system along theEast-West track (10 km long and 3 km wide) and identifies thelocation of each rape and wheat field as a function of the incidence angle (from approximately 30 to 50 ).The intent has been to select, per each crop, homogeneousfields (i.e., similar ground conditions), located at increasingincident angles within the image swath (i.e., from near tofar range) in order to study the radar sensitivity to surface

Fig. 2. Temporal behavior of mean fresh biomass [kg=m ] (a) and volumetricsoil moisture content [%] (b) values over the two wheat and the two rape fieldsevaluated per three locations on each Day of Year (DoY). Standard deviationsare also reported. On the right of (b), precipitation rate [mm/day] is illustrated.

parameters at different incidence angles. Although grounddata were collected only over 2 out of 3 wheat (i.e., fields 230and 250) and rape fields (i.e., fields 140 and 101), the groundconditions of the fields 221 and 110 can be considered similarto those of fields 230, 250 and 140, 101, respectively. Such aconjecture is supported by Fig. 2(a) and (b), which show thatandvalues, both for wheatthe intra-field variability ofand rape crops, is larger than the inter-field variability observedduring the growing season. From April to August, there was anfrom approximately 27% to 5% in averageoverall drying of[Fig. 2(b)]. However, there were important and temporarilyconcentrated precipitation events from mid May to early June

BALENZANO et al.: DENSE TEMPORAL SERIES OF C- AND L-BAND SAR DATA FOR SOIL MOISTURE RETRIEVAL OVER AGRICULTURAL CROPS

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and from end June to early July, during which two considerablewere measured, i.e., 9.2% and 16.2% ofincrements ofincrement in average on DoYs 136 and 172, respectively.Concerning the soil surface roughness, winter crops (101-250fields) had similar soil roughness, approximately 1 cm in average, during the entire campaign [20] (though this figure requires a certain caution because it was estimated over a sampling area of 70 70 cm ).B. SAR DataIn this study, the SAR data acquired by the DeutschesZentrum fr Luft- und Raumfahrt (DLR) Experimental-SAR(E-SAR) airborne system at C and L-band have been analyzed.For each of the 12 flights, C- band data were acquired in a dualpolarization mode [horizontal-horizontal (HH) or vertical-vertical (VV) and cross-polarization (XP)] and L-band data usingthe fully polarimetric mode. On the 2nd of August rape field 140had already been harvested and no complete ground data werecollected on the other fields, therefore the last SAR acquisitionhas not been included in this analysis.The backscattering coefficients of the area correspondingto the monitored fields have been extracted from the DLRE-SAR multi-looked and geocoded products with 2 m pixelsize and averaged at field scale to make speckle noise negligible(the number of pixels per field ranged approximately between112000 and 280000) with respect to the radiometric accuracydB at C and L bands [21].which is withinIII. SENSITIVITY OF SAR DATA TO SOIL MOISTURE CONTENTIn this section the temporal series of the SAR data are analat differentysed in order to establish the SAR sensitivity tofrequencies, polarizations, incidence angles, and for significantchanges in the vegetation growing stages, i.e., before and afterheading on DoY 158 and development of fruits on DoY 164 forwheat and rape crops, respectively.Fig. 3 compares the temporal backscatter profiles of wheat(triangles) and rape (squares) fields at low incidence angle30 , at VV (continuous line), HH (dash-dotted line) andXP (dashed line) polarizations at both C [Fig. 3(a) and (b)]and L [Fig. 3(c) and (d)] bands. Rape field shows the highestbackscattering values at all frequencies and polarizations. Theradar temporal signature of the two crops is quite different atC-band, whereas at L-band there is a high correlation, approximately 0.8, in the temporal trends of winter wheat and winterrape regardless of polarization.A. Backscatter at C Band of Winter WheatFigs. 2(b) and 3(a) show that althoughincreases of 6.3%on DoY 136 (before heading) the backscattering coefficient doesnot significantly change, i.e., differences of 0.2 dB and of 0.5dB at VV and HH polarization have been measured, respectively. The relatively small variation of backscatter is due to thefact that at this early growing stage the wheat crop is characterized by a strong attenuation of backscatter [22]. On DoY 172(17.8%) and the(after heading), for the higher increment ofincreases of 1.9 dB at VVreduced vegetation attenuation,and of 1.7 dB at HH polarization. Moreover, the V polarizedincidence wave is also significantly more attenuated than the

Fig. 3. Comparison between the temporal behavior of backscattering coefficients ( ) of wheat (triangles) and rape (squares) fields at low ( 30 ) incidence angle, at VV (continuous line), HH (dash dotted line) and XP (dashedline) polarizations, at C (a), (b) and L (c), (d) bands, respectively. Error bar accounting for the calibration is 1 dB.

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H one, because of the predominant vertical structure of wheatcanopy. In fact, the difference between HH and VV backscatterincreases up to 2.8 dB on DoY 158 when biomass reached 4.5 kg[Fig. 2(a)], then it keeps slowly decreasing because of vegetation drying. These observations would be in accordance withprevious experimental studies [2], [23], [24], which have shownthat the backscattering coefficient at C band of winter wheat decreases with the increase of fresh biomass during the first partof the growing season (i.e., until to heading). This is interpretedphysically in terms of soil backscatter attenuated by the canopy[3], [22], [25]. Therefore, SAR signal of winter wheat at C bandand variable vegetation atis influenced by both changes intenuation depending on crop status.B. Backscatter at C Band of Winter RapeFor winter rape, the comparable values of VV and HH[Fig. 3(a)] and the high values ofat cross polarization[Fig. 3(b)] indicate an important scattering contribution fromat C band is expectedthe crop canopy. As a consequence,to be relatively insensitive to changes induring the wholemonitored growing season. This is consistent with the experimental observations which show that, on DoY 136,increases of 11.6% whereasdecreases at all polarizationsand on DoY 172,increases by 16.4% whereasvaries byless than 1 dB. This would agree with previous theoretical [26]and experimental studies [23], [27] demonstrating that SARbackscatter of winter rape at C band is influenced principallyby the vegetation response (i.e., volume scattering).C. Backscatter at L Band of Winter WheatThe temporal backscatter profile of winter wheat [Fig. 3(c)]evolution [Fig. 2(b)] during the entire growingfollows theseason for all polarisations. For instance, on Doy 136 an increaseof 3.7 dB at VV and 5.4 dB at HH polarization were observedincrement of approximately 6.3%. However, it mayfor abe worth mentioning that the strong backscatter increase observed on DoY 136 as well as its substantial persistence untilDoY 172 can only partially be attributed to the moderatechanges recorded in the period (as it will be shown later on inSection III-E). On DoY 172, backscatter increments of 0.8 dBat VV and 2.2 dB at HH polarization were measured in coincrement of 17.8%. The differential attenuincidence ofation of HH and VV backscatter due to wheat canopy structure is also visible at L band, starting from DoY 131 when the. These observations inbiomass becomes higher than 2during thedicate that SAR signal at L band is sensitive toentire growing season and that at HH polarization it is more senthan at VV polarization.sitive toD. Backscatter at L Band of Winter RapeBackscatter increments of about 2.6 dB at VV and 3.5 dB atHH polarization are observed on DoY 136 [Fig. 3(c)] for aincrement of 11.6% [Fig. 2(b)]. Whereas increases of 0.8 dBat HH polarization and is stationary at VV polarization in coincidence with the second important precipitation event producing aincrement of 16.4%. Moreover, because of the higher valueswith respect to wheat[Fig. 2(a)], the differentialof rapevegetation attenuation of HH and VV backscatter is visible since

the early growing season for rape crop. It reaches 3.5 dB on DoY172 at the end of the development of fruits and starts decreasingat ripening.E. Radiative Transfer Model SimulationsTo better understand the nature and strength of the soil andcanopy contributions to rape and wheat backscatter, a theoretical model, based on RT first-order radiative transfer approach,has been employed. The model represents the crop canopy asa plane-stratified multi-layer of vegetation elements overlyinga rough half-space. Dielectric cylinders of circular cross section have been used to represent stems, branches and ears (atC band). Scattering of a finite length cylinder has been computed by using the infinite cylinder approximation [28]. Leaveshave been depicted by circular or elliptical disks and pods andears (at L band) by prolate spheroids. The scattering amplitudesof disks and spheroids have been computed by using the Generalized Rayleigh-Gans (GRG) approximation [28], and the smallspheroid approximation by the T-matrix approach [29], respectively. The direct backscatter from the soil surface is calculatedby the Integral Equation Model (IEM) [30]. The dielectric constants of each vegetation component and soil surface are evaluated using [31] and [32], respectively. The model has been validated [33] using the detailed ground data set collected over theMatera site in 2003 [22]. The total scattering (tot) consists ofthe soil surface response attenuated by the canopy (ground), thedirect scattering from the vegetation elements (veg) and doublebounce (db) between ground and crop canopy. In-depth comparisons between model simulations and SAR measurementsare beyond the limit of this paper, as no detailed geometricalmeasurements were available. The missing information concerning the length, width and thickness of geometrical canopyparameters and the distribution of fresh biomass among canopycomponents (e.g., leaves, stems, etc) has been inferred frommore detailed ground data collected during previous campaigns[22], [34].Fig. 4 shows the contributions to the simulated backscattering coefficients of winter wheat and rape at C (a), (b) and L(c), (d) bands and HH polarization, respectively. The E-SARdata are included for comparison. At C band, the simulatedtotal backscatters reproduce the experimental data with an accuracy of 1.1 dB. Model simulations confirm that the dominantbackscatter contribution is the direct soil response attenuatedby vegetation canopy for winter wheat and the direct vegetationresponse for winter rape. At L band, although simulated totalbackscatters reproduce the changes of the experimental data, ahigh root mean square error (rmse) has been found, i.e., 2.5 dBfor wheat [Fig. 4(c)] and 2.2 dB for rape [Fig. 4(d)]. The maindifferences between measured and simulated values are in theperiod from mid May to end of June, for both winter wheat andwinter rape, where a bias of approximately 4 dB is observed.Such a systematic increment can be also observed for themeasured backscatter at VV polarization although at least 1 dBlower with respect to HH polarization [Fig. 3(c)]. A similarbehavior is also evident over the other wheat and rape fields,imaged at higher incidence angles over the DEMMIN site, butit is not so evident at C-band [Fig. 3(a)]. As the discrepancyregards both winter wheat and winter rape, its causes are more


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Fig. 5. Backscattering coefficients versus 30 , 40 and 50 incidence angles,at C (a) and L (b) bands and VV polarization for wheat (dashed line) and rape(continuous line) fields. DoY, DoY.

131 = stars

186 = triangles

likely related to soil changes (i.e., surface roughness or soilhomogeneity) rather than to crop canopy changes. In fact, theimportant precipitation events occurred between mid May andend of June may have determined abrupt changes in the verticalprofile distribution of soil moisture content with an importantincrease of L-band backscatter, i.e., higher at HH than atVV polarization [35]. An alternative interpretation could berelated to a change in the surface roughness due to the strongprecipitations. However, due to the scarcity of detailed groundinformation, no conclusion can be drawn on the cause of theobserved phenomenon.Regarding the backscattering mechanisms at L band, forwinter wheat [Fig. 4(c)], the main scattering contributioncomes from the ground (dashed line), whereas for rape, nopredominant scattering contributions to the total backscattercan be recognized [Fig. 4(d)]. Indeed, vegetation and groundcontributions are roughly equal for a well-developed rapecanopy.F. Effect of Incidence Angle

Fig. 4. Simulated scattering mechanisms at C (a), (b) and L (c), (d) bands andHH polarization for wheat and rape fields, respectively: Total (i.e., tot, continuous line), ground (dashed line), vegetation (i.e., veg., dotted line), doublebounce (i.e., db, dot dashed line) scattering. Measured data (continuous linetriangles) are reported for comparison.

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In the following, the scattering mechanisms playing a roleat higher incidence angles are investigated by interpreting theSAR data behavior both at C and L bands as a function ofincidence angle. Fig. 5 compares the VV backscatter valuesobserved at approximately 30 , 40 and 50 incidence angles[Fig. 1] and at C [Fig. 5(a)] and at L [Fig. 5(b)] bands over

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winter wheat (dashed line) and winter rape (continuous line).Two dates, DoYs 131 (stars) and 186 (triangles), referring tocritical phonological stages, i.e., heading for winter wheat anddevelopment of fruits for winter rape, have been selected.1) C Band: On DoY 131 wheat backscatter at VV from30 to 50 incidence angles decreases of approximately 6 dB[Fig. 5(a)], indicating that also at higher incidence angle the attenuated soil contribution is prevailing. Moreover, the declineis steeper in the interval between 30 and 40 than between40 and 50 . This means that beyond 40 incidence angle thedirect volume scattering of wheat canopy emerges as alreadynoted in [22]. On DoY 186 (after heading) a significantly different behavior is observed. Backscatter decreases by approximately 1 dB from 30 to 40 and then increases at 50 by1.2 dB. This behavior can be explained by the fact that afterheading, between 30 and 40 incidence angles, the positivevegetation contribution tends to compensate the decrease of attenuated soil scattering, whereas beyond 40 there is a changein the dominant scattering mechanism from soil backscatter tocanopy backscatter, as already noted in [3], [22], [25], [26]. As aconsequence, before heading there is a significant sensitivity tosoil moisture changes irrespective to incidence angle. Whereas,sensitivity tois considerably reducedafter heading theat incidence angles beyond 40 .For winter rape, a moderate backscatter decreasing rate withthe incidence angle is observed regardless to the phenologicalstage, i.e., the backscatter decline from 30 to 50 is within 2 dBboth before and after the development of fruits. Such a behavioris in agreement with the results reported in [26] and [27] and itindicates that the volume scattering is the dominant mechanismfor winter rape at medium-high incidence angles. Therefore,of winter rape is poorly sensitive tochanges irrespectivelyto the incidence angle.2) L Band: At L band [Fig. 5(b)], winter wheat and winteron DoY 131 show a substantially similar angular berapehavior, even though the decline of winter wheat backscatter from30 to 40 is slightly higher than the one of winter rape. However, beyond 40 there is a reduction of the decreasing rate indicating a positive contribution of vegetation scattering. The latteris more evident on DoY 186 for both winter wheat and winterrape, as the angular dependence of from 30 to 50 decreases.These considerations are in agreement with the SAR data analysis reported in [27]. Moreover, winter rape backscatter is alwayshigher than winter wheat backscatter because of the higher direct volume contribution, due to the higher biomass values, anddouble bounce contributions, related to the higher stem height.Despite the increment of vegetation contribution at higher incidence angle, the SAR signal at L band can keep its sensitivityto soil moisture changes because of the significant contributionof the ground and the double bounce scattering mechanisms.Previous observations can be summarized as follows:1) for winter wheat, at C band and relatively low incidenceangle (i.e., 30 ) it is confirmed that the principal scatteringmechanism is the attenuated soil contribution for the entire growing season and that VV polarization is more attenuated than HH polarization; whereas, at higher incidence angles (e.g., 40 50 ), the direct volume contribution from the canopy starts to be significant after heading.

TABLE ICORRELATION COEFFICIENTS (R) BETWEEN THE CO-POLARIZED CHANGES [dB] AND SOIL MOISTUREON TWO CONSECUTIVE DATES () AND FRESH BIOMASS () RATIOS MEASURED OVER WHEAT AND RAPEFIELDS, AT C AND L BANDS AND AT APPROXIMATELY 30 . THE SIGNIFICANCE( ) OF THE CORRELATION (R) IS ALSO REPORTED (THE SIZE SAMPLE IS 10)

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At L band, HH polarization and lower incidence angle,the wheat canopy is almost transparent to the SAR signal,whereas at VV polarization and incidence angles higherthan 40 the volume contribution is observed;2) for winter rape, the backscatter at C-band is mostly inirrespective to the incidence angle. Thissensitive tomeans that there is an important direct volume contributionfrom the canopy that tends to mask the soil response sinceearly phenological stages. At L band, there is always asignificant attenuated soil contribution, through direct anddouble-bounce mechanisms, together with an important direct volume term.For both wheat and winter rape fields, the HH backscatter at Lfor all the investigatedband shows the higher sensitivity toincidence angles.IV. SAR VERSUS

CHANGES

On the basis of the interpretation of the main scattering mechanisms characterizing wheat and rape crops, the ratio of SARbackscatters on two consecutive dates both at HH and VV polarizations is expected to mainly depend on soil and not on vegetation changes for wheat at C and L-bands and for rape at L-bandbut not at C band. Indeed, this is what we observe in Table I thatreports the correlation coefficients (R) between the temporal seratios on two consecutive dates, indiries of co-polarizedcated by superscripts (1) and (2), i.e.,, or difference ofwhen expressed in decibels, i.e.,, andthe correspondent temporal series ofand ratios measuredon wheat and rape crops imaged at approximately 30 incidencechanges are significantly correlated during theangle. andentire growing season (0.70 at VV and 0.77 at HH) for winterandwheat, whereas there is almost no correlation betweenchanges. For winter rape, no significant correlation betweenchanges and bothand changes is observed. At L band,the highest correlation between changes andchanges canbe observed at HH polarization both for wheat (0.70) and rape(0.64) crops.


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TABLE IIRMSE AND CORRELATION COEFFICIENTS (R) BETWEEN MEASURED AND RTMODEL AND ALPHA APPROXIMATION CO-POLARIZED CHANGES ON TWOCONSECUTIVE DATES [dB] , AT C BAND, AT APPROXIMATELY30 AND AT BOTH VV AND HH POLARIZATIONS FOR WHEAT CROP

0

Fig. 6. Temporal behavior of E-SAR (continuous line) and RT model (dashedline) and alpha approximation (dotted line) backscatter changes on two consec [dB]), at C band, at HH polarization, and at approxutive dates (i.e imately 30 incidence angle for wheat crop.

0

Under the simplifying assumption that the backscatter response is just due to ground response attenuated by vegetationratiocanopy and that roughness surface is unchanged, thebetween two consecutive acquisitions, can be expressed as a function of the dielectric constant , the incidenceangle and the polarizationor VV [36], i.e.,(1)where

Equation (1) holds for smooth and medium rough surfaces[37] with respect to the observation wavelength (as usually thecase at C-band and for agricultural soils after sowing [38]) and itis appealing because not only it does not depend on vegetation,but it also is independent of surface roughness parameters.In the following, the validity of this assumption is assessed.A. C BandFor the wheat field 221, Fig. 6 compares the temporal behavior of the ratio between backscatters on two consecutivedates at C-band and HH polarization, measured by E-SAR (continuous line) and simulated by the RT model (dashed line) andby means of (1) (dotted line) (hereafter referred to as alpha approximation). Table II reports the rmse and correlation coefficients (R) between SAR and simulated values at both HH andVV polarizations. The good agreement between observed andsimulated values using both RT model and alpha approximation confirms that by using a change detection approach the influence of wheat canopy is remarkably reduced.B. L BandFor the winter wheat (field 221) and winter rape (field 140),Fig. 7 compares the temporal behavior of the ratio between

Fig. 7. Temporal behavior of E-SAR (continuous line) and RT model (dashedline) and alpha approximation (dotted line) backscatter changes on two consecutive dates (i.e [dB]), at L band, at HH polarization, and at approximately 30 incidence angle for wheat (a) and rape (b) crops.

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backscatters on two consecutive dates at L band and HH polarization, measured by E-SAR (continuous line) and simulatedby the RT (dashed line) and by the alpha approximation (dottedline). Table III reports the rmse and correlation coefficients (R)between SAR and simulated values, at both VV and HH polarizations. Simulated ratios obtained by using (1) show the highestcorrelation with observed SAR ratios at HH polarization, forboth wheat (i.e., 0.85) and rape crops (i.e., 0.69). The highestdiscrepancies are observed for the third and the eighth ratios(i.e., DoY 136 over DoY 131 and DoY 186 over DoY 172) forthe wheat field, which includes the dates when variations in soilground conditions have been observed.A first important consideration is that the agreement betweenobserved and simulated data of Fig. 7 is better than the oneof Fig. 4(c) and (d) where the comparison between observedand simulated backscattering coefficients is shown. In addition,Fig. 7 demonstrates that observed ratios between backscatters

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TABLE IIIRMSE AND CORRELATION COEFFICIENTS (R) BETWEEN MEASURED AND RTMODEL AND ALPHA APPROXIMATION CO-POLARIZED CHANGES ON TWO [dB] , AT L BAND, AT APPROXIMATELYCONSECUTIVE DATES 30 AND BOTH VV AND HH POLARIZATIONS FOR WHEAT AND RAPE CROPS

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V. IMPLICATION FOR

RETRIEVAL

In this section, it is investigated under which conditions soilmoisture content can be retrieved from dense temporal seriesof radar backscatter ratios. The explored method is based onthe alpha approximation for the HH polarization reported in(1). Settingresult inlinear equations in, i.e.,

, SAR acquisitionsunknown Fresnel coefficients

(2)

TABLE IVRMSE AND CORRELATION COEFFICIENTS (R) BETWEEN MEASURED ANDRT MODEL AND ALPHA APPROXIMATION CO-POLARIZED CHANGESON TWO CONSECUTIVE DATES AND AT DIFFERENT INCIDENCE ANGLES, (40 ) (30 ) [dB], AT L BAND AND HH POLARIZATIONS FORWHEAT AND RAPE CROPS

0

on consecutive dates can be fairly well simulated for both winterwheat and winter rape by using the very simple alpha approximation approach, completely disregarding the presence of vegetation. This is not the case when comparing the backscatteringcoefficients as in Fig. 4(d) where the disagreement between observed and simulated backscatter response of winter rape wouldhave been of several dBs if the simulation model had includedthe soil contribution only.In other words, Figs. 6 and 7 convey the message that themulti-temporal backscatter ratio, between close subsequentdates, is a feature little sensitive to surface roughness andvegetation canopy contributions whereas it is significantly andsimply related to soil moisture changes.C. Changes at Different Incidence AnglesThe effect of changes in the incidence angle between twoconsecutive acquisitions has also been investigated.Best results are obtained for ratios at L band and HH polarization over both wheat and rape. Table IV summarizes the correspondent rmse and correlation coefficients (R) between meachanges on two consecutive dates andsured and simulatedat different incidence angles, namely at approximately 40 and30 , i.e.,.The rmse are higher than those observed for unchanged incidence angles (Table III) and get worst for higher incidence, however the correlation coefangle, i.e.,ficients are higher than 0.7 indicating that the backscatter ratio isstill sensitive to soil moisture changes. Therefore, it appears feasible to retrieve soil moisture content using L-band backscatterratios even when the backscattering coefficients have been acquired at different angle.

with,, and. The numberranges betweenand,of equationsdepending on whether the ratios between two consecutivebackscatter values only or all possible ratios between two subsequent backscatters are considered, respectively. It is worthemphasizing that the entire approach is based on the hypothesisthat surface roughness conditions remain constant during theacquisitions, so that the choice of thevalue is strictlyconnected to the time-span of theacquisitions and to thetemporal behavior of the soil surfaces. In this respect, we haveselected, as it is plausible that at least betweenconsecutive and close SAR acquisitions surface conditions donot significantly change. Under these conditions, the resultingequations inunknowns islinear system ofunder determined and an infinite number of solutions satisfy(2). In our case, the solution is found subject to the constraintsthatfor. Indeed, at Cand L bands and at 30 40 incidence angles, varying soilmoisture content from 3% to 40%, thecoefficients, rangeapproximately betweenandover aloamy sand soil, which is one of the main soil texture classesover the DEMMIN site.Oncecoefficient is retrieved for each date, the relativedielectric constantcan be analytically derived and then thesoil moisture content can be estimated, by using the inverse ofthe empirical expression of Hallikainen et al. [32]. The performance of the algorithm has been firstly assessed over data setssimulated by the RT model, which allows to explore the applicability of the alpha approximation in a very large numberof cases, and then on experimental data. In the simulation, theSAR measurement error, whereis theSAR system radiometric accuracy andis the SAR systemradiometric resolution, has been accounted for by adding to themodelled backscattering coefficient a Gaussian noise with zeromean and a standard deviation equal to 0.7 dB. Such a choicecorresponds todB (e.g., Sentinel-1 [12]) and a radiometric resolution ofdB (e.g., assuming fully developed speckle and considering a confidence interval with 95%probability, it amounts to average over approximately 500 independent samples [39], which roughly corresponds to a linearresolution of 220 m for the Sentinel-1 system [12] in the interferometric wide swath acquisition mode). The observed temporalseries of backscatter ratios has been simulated by means of theRT model, i.e.,, both for wheat andrape canopies at different andvalues (covering the entirerange of values observed over the DEMMIN site). It is worthnoting that IEM model, adopted in the RT approach to simulate


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TABLE VSIMULATED (hxi) AND RETRIEVED (hy i) MEAN m VALUES, THE A ANDB PARAMETERS OF THE LINEAR FIT (I.E., Y = A + Bx), THE RMSEAND CORRELATION COEFFICIENT (R) BETWEEN THE SIMULATED AND THERETRIEVED m VALUES, USING N = 3, N = 6 HH SAR ACQUISITIONS ATC BAND AND AT 30 INCIDENCE. THE TOTAL (MODEL AND MEASUREMENT)ERROR BETWEEN RT MODEL AND ALPHA APPROXIMATION BACKSCATTERRATIOS OF WHEAT AND RAPE CROPS AT DIFFERENT FRESH BIOMASSVALUES (f b) IS ALSO REPORTED

TABLE VIAS TABLE V BUT AT L-BAND

Fig. 8. Scatter plot between observed and retrieved m values obtained byusing N = 11 SAR acquisitions at C band over field 221 (wheat) (a) and N =3 SAR acquisitions at L band over fields 221 and 230 (wheat) and 140 and110 (rape) (b). The observed (hxi) and retrieved (hy i) mean m values, thelinear fit parameters, (i.e., y = A [%] +Bx), the rms errors and the correlationcoefficients (R) are also reported at the upper left corner. At the lower rightcorner, results obtained neglecting retrieved m values beyond 2- confidenceinterval (1 and 2 outliers surrounded by squared have been excluded for C andL band, respectively) are illustrated.

the soil contribution, does not analytically predict the decoupling between the term depending on dielectric constant and onthe roughness [30]. Under these circumstances, the performedsimulation, which is based on the alpha approximation, i.e.,(3)accounts for the presence of model errors concerning both thevegetation and the soil layer.Tables V and VI report the simulated and retrieved meanvalues (i.e.,[%] and[%], respectively), the linear fit parameters A [%] and B (i.e.,), the rmse, and the correlation coefficient (R) between 209520 simulated and retrievedvalues, at C and L-band, respectively. Two sets of simulations, referring toand, at 30 incidence angle,at HH polarization and at differentvalues (i.e., 0.6,

2.6and 5) have been performed. The total rmseerrors (i.e., including model and measurement error) are alsoreported. In general, they increase withand are larger at Cthan at L-band and for winter rape than winter wheat. More precisely, for winter wheat, both at C and L band, the total error onbackscatter is not larger than 1.1 dB, whereas for winter rape atC-band it can exceed 2.0 dB. As a consequence, at C-band soilmoisture values can be retrieved with accuracies between 5%and 6% during the entire winter wheat phenological cycle andduring the first phenological stages of winter rape. Conversely,at L-band,can be retrieved over wheat and rape fields duringthe entire growing season with comparable accuracies. In general, better results onretrieval are achieved as increasesfrom 3 to 6 irrespective to the value. This is probably becausethe higher , the better the ratio between theindependentequations and theunknowns, under the hypothesis that soilroughness anddo not significantly change. It may be worthmentioning that for larger values of a sort of saturation in thermse was observed.Concerning the AgriSAR experimental data, Fig. 8(a) showsvalues obthe scatter plots between observed and retrievedtained usingacquisitions at C band over wheat field 221

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and Fig. 8(b) shows the scatter plot obtained usingacquisitions at L band over wheat fields 221, and 230 and rape fieldsand retrievedmean140 and 110. The observedvalues, the linear fit parameters, i.e.,, the rmserrors and the correlation coefficients (R) are also reported atthe upper left corner. At the lower right corner, results obtainedafter eliminating one [Fig. 8(a)] and two [Fig. 8(b)] outliers surrounded by squares (i.e., actual errors larger than 2- confidenceinterval) are illustrated. Disregarding the outliers, at C band thermse error reduces from 7.6% to 5.1%, whereas at L-band theerror decreases from 6.5% to 5.5%. These figures are in overallgood agreement with the simulation results reported in Tables Vand VI. In fact, at L-band, unlike simulation results, better results have been obtained forthanacquisitions.This inconsistency is probably related to the fact that, as notedin Section III-E, the precipitations occurred between mid Mayand end of June may have determined changes in the soil surface conditions, so that the algorithm assumption of unchangedrathersurface is better satisfied considering groups ofthanSAR acquisitions., retrieved by using the aboveIn summary, the accuracy ofdescribed change detection approach, is expected to range between 5% and 6%, depending on the characteristics of the SARtemporal series and of the imaged surfaces.VI. CONCLUSIONThe paper describes the potential of dense temporal series ofSAR data at C and L band to follow changes of soil moisturecontent underneath winter wheat and winter rape crops. Thestudy is based on E-SAR and ground data acquired, approximately every week from late April to early August 2006, overthe DEMMIN (Germany) site during the ESA AgriSAR camhas shownpaign. The sensitivity analysis of backscatter tothat at C band the radar signal is considerably influenced bythe canopy structure and the phenological stage of agriculturalcrops; whereas at L band the backscatter is less sensitive to thecrop canopy and HH polarization shows the highest sensitivityfor both wheat and rape crops.toA significant improvement in decoupling the effect of vegetation and surface roughness from the effect of soil moisturechanges can be achieved by considering the backscatter ratiobetween two close subsequent dates. In particular, it has beenassessed to which extent it is feasible to model the observedbackscatter ratio in terms of soil contribution only. For wheatcrops, a high (0.74) correlation and a small rmse (approximately1 dB) have been found between observed and simulated C-bandHH polarized backscatter ratios. On the contrary, the same investigation for winter rape has shown that backscatter ratios arelittle or not correlated with soil moisture changes.At L-band, the backscatter ratio, particularly at HH polarchanges underlying both winterization, is sensitive towheat and winter rape crops and this holds true for low andmedium incidence angles (i.e., within 40 incidence). At 30incidence, a correlation of 0.85 and 0.69 has been found between observed and simulated (considering soil contributiononly) HH backscatter ratios for winter wheat and winter rape,respectively.

Finally, a soil moisture retrieval algorithm transforming tembackscatter ratios intosoil moistureporal series ofcontent values has been devised and assessed over simulated andexperimental data. The use of multi-temporal data is beneficialfor the accuracy of the retrieval algorithm, under the conditionthat roughness remains constant during the time span of theacquisitions. The experimental analysis confirms that the choiceof is strictly connected to the condition of the soil surfaces.The accuracies of the retrieved soil moisture values range between 5% and 6% when the dominant scattering mechanism isnot the volume scattering.In summary, our findings suggest that:1) at C-band there is a potential to use dense time series of HHor (HH and HV) SAR data (as those that will be acquiredby the forthcoming Sentinel-1 mission) to monitor soilmoisture changes underlying, for instance, cereal crops, forwhich the dominant scattering mechanism is the soil attenuated contribution. Under these circumstances, systematicretrieval of soil moisture would first require a classification step to identify crop classes whose backscatter is notdominated by the volume scattering. An option, which deserves future studies, is to use a threshold in the level ofcross polarized backscatter to identify the areas for whichthe algorithm can work at C-band. In addition, it is worthmentioning that the incidence angle is a critical issue because it should not exceed moderate values (e.g., 30 35 );2) at L-band, the change detection technique is expectedto be able to track soil moisture changes underlying abroad range of agricultural crops and to be less sensitiveto changes in the incidence angle between the first and thesecond acquisition.ACKNOWLEDGMENTThe authors are grateful to the AgriSAR 2006 team (InstitutoNacional de Tcnica Aeroespacial, ITRES Research Limited,German Aerospace Center German Remote Sensing DataCenter, Leibniz Centre for Agricultural Landscape Research,Geo-Informatics, University of Kiel, Friedrich-Schiller University, Laboratory of Hydrology and Water Management,Istituto di Studi sui Sistemi Intelligenti per lAutomazione,Danish Technical University, University of Alicante, Universityof Valencia, Ludwig Maximilians University, University ofNaples Federico II, Free University of Berlin, InternationalInstitute for Geo-Information Science and Earth Observation)for providing the ground and SAR data collected during thecampaign and ESA for supporting the activities.REFERENCES[1] T. Le Toan, F. Ribbes, L.-F. Wang, N. Floury, K. H. Ding, J. A. Kong,M. Fujita, and T. Kurosu, Rice crop mapping and monitoring usingERS-1 data based on experiment and modeling results, IEEE Trans.Geosci. Remote Sens., vol. 35, pp. 4156, Jan. 1997.[2] P. Saich and M. Borgeaud, Interpreting ERS SAR signatures of agricultural crops in Flevoland, 19931996, IEEE Trans. Geosci. RemoteSens., vol. 38, pp. 651657, Mar. 2000.[3] G. Picard, T. Le Toan, and F. Mattia, Understanding C-band radarbackscatter from wheat canopy using a multiple scattering coherentmodel, IEEE Trans. Geosci. Remote Sens., vol. 41, pp. 15831591,Jul. 2003.


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Anna Balenzano received the Laurea degree (cumlaude) in physics from the University of Bari, Italy,in 2003, where she worked with the AstroparticlePhysics Group, the Master degree in informationtechnology from the University of Benevento, Italy,in 2004, and the Ph.D. degree in physics from theUniversity of Bari in 2010.From June 2004 through January 2005 she workedas Java analyst programmer at Italdata S.p.A.,Italy (Siemens Business Services). In summer2005, she worked with the INTEC Services webdesign/development team in Glasgow, Scotland, as participant to the EuropeanCommunitys Vocational Training Programme (LEONARDO). Since 2006, shehas been grantholder of Italian National Council of Research (CNR)Instituteof Intelligent Systems for Automation (ISSIA) in Bari, Italy. Her research workfocuses on direct modeling of electromagnetic scattering from vegetated andbare soils and retrieval of bio-geophysical parameters from SAR images.

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Francesco Mattia received the Laurea degree inphysics and the Master degree in signal processingfrom the University of Bari, Italy, in 1990 and 1994,respectively. He received the Ph.D. degree from theUniversity Paul Sabatier, Toulouse, France, in 1999.From 1991 to 1994 he has been grantholder of theItalian National Council of Research (CNR) and ofthe European Commission at the Institute for RemoteSensing Applications of the Joint Research Centre,Ispra, Italy. From 1995 to 2003, he was a researchscientist at the Institute for Information and SpaceTechnology (ITIS) of CNR, Matera, Italy. In 2003, he joined the Institute forIntelligent Systems and Automation (ISSIA) of CNR, Bari, Italy, where he ispresently a senior research scientist. During 1996, 1997, 1998, and 1999, he wasa visiting scientist at the Centre dEtudes Spatiales de la BIOsphere (CESBIO),Toulouse, France. His scientific interests include the direct and inverse modeling of microwave scattering from land surfaces and the use of information derived from Earth observation sensors to improve land surface process models.On these themes, he has been involved as CI or PI in several national and international scientific proposals concerning SAR sensors aboard ERS-1/2, ENVISAT,ALOS and COSMO-SkyMed satellites.In 2007, Dr. Mattia was co-organizer of the 5th International Symposium onRetrieval of Bio- and Geophysical Parameters from SAR Data for Land Applications held in Bari, Italy.

Giuseppe Satalino received the Laurea (cum laude)degree in computer science from the Universityof Bari, Italy, in 1991. In the same year, he was asummer student at the European Organization forNuclear Research (CERN), Geneva, Switzerland,where he worked on applications of neural networksto high energy physics.From 1993 to 1996, he was grantholder of Aleniaand Consiglio Nazionale delle Ricerche (CNR).Since 1996, he has been with the Institute of Intelligent Systems for Automation (ISSIA) of theCNR, Bari, Italy. He worked in several national and international researchprojects concerning image processing, data classification and remote sensingapplications. He participated in several SAR and ground radar experiments forstudies concerning the use of remote sensing for agricultural and hydrologicapplications. His main research field concerns data classification techniquesand methods for the retrieval of geo-physical parameters from SAR and opticalremote sensed data.

Malcolm W. J. Davidson was born in Canberra,Australia, in 1968. He received the B.Sc. degree(with Honours) in physics from the University ofToronto, Canada, in 1990, the M.Sc. degree (withdistinction) in image processing and remote sensingfrom the University of Edinburgh, Scotland, in 1992,and the Ph.D. degree in physics from the RheinischeFriedrich-Wilhelms-Universitt Bonn, Germany, in1997. From 1997 to 2001 he worked at the CentreDEtudes de la Biosphere (CESBIO) in Toulouse,France, as a research associate.He is currently affiliated with the Mission Science Division of the European Space Research and Technology Centre (ESA-ESTEC). At ESA, he holdsthe role of Mission Scientist for the future GMES Sentinel-1 mission (C-BandSAR). His main responsibilities concern the formulation and of mission requirements for these missions and chairing mission advisory groups. He has also beenappointed Head of the Campaigns section in 2009 and is responsible in definingand implementing airborne campaigns in support of Earth Observation (EO)mission programs.

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