In this work, temporal series of Synthetic Aperture Radar (SAR) data are used to estimate water elevations. The proposed method is based on a Sub-Pixel Offset Tracking (technique) to retrieve the displacement of the double-bounce scattering effect of man-made structures located in the proximity of the water surface. The experimental setup is focused on the cases of the Mosul dam in Iraq and the Missouri river in Kansas City. The proposed approach is applied to real data from the COSMO-SkyMed program. Results validated with in-situ and satellite radar altimeter measurements prove the effectiveness of the proposed method in measuring the water levels

Addabbo, P.

Biondi, Filippo

Clemente, Carmine

Orlando, Danilo

Tarpanelli, Angelica

English

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Water Level measurement using COSMO-SkyMed Synthetic Aperture Radar

University of Strathclyde Institutional Repository

Water Level measurement usingCOSMO-SkyMed Synthetic Aperture RadarFilippo BiondiUniversity of L’AquilaAngelica TarpanelliConsiglio Nazionale delle Ricerche(CNR)Pia AddabboUniversita` degli studi“Giustino Fortunato”Carmine ClementeUniversity of StrathclydeDanilo OrlandoUniversita` degli Studi“Niccolo` Cusano”Abstract—In this work, temporal series of Synthetic ApertureRadar (SAR) data are used to estimate water elevations. Theproposed method is based on a Sub-Pixel Offset Tracking(technique) to retrieve the displacement of the double-bouncescattering effect of man made structures located in the proximityof the water surface. The experimental setup is focused on thecases of the Mosul dam in Iraq and the Missouri river in Kansascity. The proposed approach is applied to real data from theCOSMO-SkyMed program. Results validated with in-situ andsatellite radar altimeter measurements prove the effectiveness ofthe proposed method in measuring the water levels.Index Terms—Synthetic Aperture Radar, COSMO-SkyMed,Water Elevation, Sub-Pixel Offset TrackingI. INTRODUCTIONThe flow of water in rivers and streams is of great interestbecause it represents the easiest access to water, consideredto be a fundamental natural resource. The ability to quantifythe flow of water in terms of river discharge and flow volumedepends on the capability to monitor water surface height ofwater bodies. Even more important is to monitor the waterlevel of dams which represent critical infrastructure for se-curity with social, economic, and environmental implications.Unfortunately, a shared and worldwide database containingthe historical and reliable data concerning water levels isnot available and satellite remote sensing represents a validobservation tool to solve this lack of information.In fact, in-situ gauges, installed along surface water bodiescourse, can be used to measure the water height and to describeits variation both in space and time; but the ground networkis not globally and uniformly distributed, i.e., many riversin developing countries are still unmonitored, and, since the1980s, the decline of gauge stations, also in the developedcountries, gets worse and worse the in-situ measurementsavailability [1]. For this reason, the advanced capability ofsatellite sensors to monitor inland water and the direct accessto their data motivated scientists to integrate and reinforcethe traditional monitoring of surface water with this newsource of measurements. Thus, the satellite remote sensingmay represent a valid observation tool to solve the lack ofinformation as before explained, especially when different datasources are combined [2].Nadir altimetry has been largely used in the past formeasuring the river surface height from space [3]–[8]. Fromthe first studies carried out with Geosat [3] to the more recentanalyses with Jason-2 and SARAL [8], [9], the improvedcapability of the altimeters allowed to monitor even narrowrivers, e.g. the Po river (∼300 m wide) or the Garonne River(∼200 m wide). Despite these encouraging results, the useof radar altimetry for narrow rivers is still limited because ofuncertainty in the evaluation of the water surface elevation dueto the local topography that contaminates the returned radarsignal. With the synthetic aperture radar (SAR) technologyapplied to the altimeters, reliable measurements of water levelare obtained for rivers of 200 m width [10] and according tothe requirements of the next SWOT mission, rivers of 100 mwidth will be accurately observed by the new Karin sensors[11]. However, the nominal orbit of the satellites often doesnot guarantee the global coverage of all narrow rivers. Thedesigned inter-track diamond distances and the revisit timerepresent obstacles to the monitoring of water courses. Toovercome these issues, low cost satellite constellations areinvestigated in order to provide global coverage and finertemporal resolution. Moreover, the use of high resolutionmeasurements is fundamental to ensure the level informationof small water bodies. In literature, some examples show theuse of SAR images to derive information of water level. Forexample, [12] showed the use of SAR images from ENVISATand RADARSAT to indirectly estimate water level of theSevern River (UK) and the Red River (US).On the other hand, the GNSS monitoring systems, combinedwith geotechnical, hydraulic and structural ones, could allowthe monitoring of real-time dam displacements, with highaccuracy, to ensure the correct functionality and the durabilityover time. Recent efforts in monitoring of dams using InSARcan be found in [13]–[15]. For a whole understanding of thedam’s infrastructure deformation it is worth to study the rela-tion between water levels and displacements. In this context,InSAR measurements allow possible continuous investigationof water levels over a wide area that includes the entirereservoir as well as the surrounding area, offering a clearpicture of the dynamics of deformations.Fig. 1. The observation geometry.The main objective of this work is to contribute in solvingthe information-gap problem of water levels monitoring, forrivers or dam’s reservoirs, through a SAR signal processingtechnique. Particularly, a pixel tracking technique, proposed in[16] and based on the localized spectral analysis, is here usedwith the objective to track in time the water level temporalvariations. To achieve water level measurements, the proposedmethod exploits the measurement of the distance betweenbridges or other man-made objects on the embankments. Theheight estimation is obtained by measuring the cross-slant-range distance between the edge of the structure and the echoof second bounce reflected by the water surface. This physicalphenomenon is tracked in time allowing observations of thetrend of water level variations. The two analysed case studiesprove the effectiveness of the method in estimating water levelsboth over dams and rivers.The remainder of this paper is organized as follows. InSection II, the proposed methodology is presented, whereasin Section III, the performance of the proposed method areassessed. Finally, in Section IV the conclusions are drawn.II. THE PROPOSED METHODOLOGYIn this section, the proposed methodology is described. Thegeometry of the observation is depicted in Figure 1. Theessential element for the estimation is represented by a man-made structure, a bridge in Figure 1 located in the proximity ofthe water surface (i.e., the river). The correspondence betweenthe height-range coordinates and the azimuth range is alsoshown in the figure. Without loss of generality the water issupposed to flow along the range direction from left to right.A reference point, indicated in figure as ”Ref” represents thefocused single-bounce backscattered echo, which is constitutedby the front-edge of the bridge that is visible on the SARimage. Its position is supposed constant during the entiretime series measurements. The positions of points P1, P2and P3 on the projection screen, represent the double-bouncebackscattered echoes of the same target. The distance of thetwo-way propagation system depends on the heights of thewater levels L1, L2 and L3, which are variable and dependingon the time.The architecture of the whole proposed estimation techniqueis depicted in Figure 2. Input data consist in a long temporalseries of InSAR measurements and the processing schemeis composed by four processing blocks. The co-registrationstage is performed at first. After co-registration, the secondstep exploits the range shifts for an initial coarse estimationof the double-bounce shifts and error correction. To extractrange shifts, the offset components of the sub-pixel normalizedcross-correlation is considered, which, according to [17], isgiven byDitot =Didispl +Ditopo +Diorbit +Dicontrol +Diatmo+Dinoise, i = 1, . . . , G, (1)for each interferometric complex pair, indexed by i andbelonging to a time series of length G. The offset termscomprises Di that is the component generated by the variationof the water level and detected as a sub-pixel misalignmentexisting between the first SAR image (master) and the slaveSAR image. Ditopo is the offset component generated by theearth displacement when located on highly sloped terrain,Diorbit is the offset caused by residual errors of the satelliteorbits, Dicontrol is the offset component generated by generalattitude and control errors of the flying satellite trajectory,Diatmo and Dinoise are the contributions accounting for changein the atmospheric and ionospheric dielectric constant andfor decorrelation phenomena (spatial, temporal, thermal, etc.),respectively. After the compensation of the different offsetterms, the displacement component can be isolated.The region of interest (ROI) extraction is, then, performedby the third block and the last step consists in range shifts andwater level estimation.The interested reader can refer to [16] for further detailsabout the estimation procedure.III. PERFORMANCE ANALYSISThe procedure is tested processing two long temporal seriesof CSK data. The processed data belongs to the COSMO-SkyMed mission, conceived by ASI (Agenzia Spaziale Ital-iana), and funded by the Italian Ministry of Research andthe Italian Ministry of Defense, Italy. For the analysis, along temporal dataset of CSK images are considered basedon two case studies whose locations are shown in Figure 3superimposed to the optical images over the two areas ofinterest: the Mosul dam in Iraq (on the left) and the Missouririver in Kansas city (on the right).The specifications of the datasets used for study areas arereported in Table I. The number of images varies from 83images for the study area at Kansas City and 113 for Mosul,and, the temporal interval of the images is different, rangingfrom about 6 to 8 years, respectively.The water levels estimated by the proposed method arevalidated by comparison with the supposed ground-truthsFig. 2. The block-scheme of the proposed procedure.Fig. 3. Optical images of the analysed case studies: the Mosul dam in Iraq (on the left) and the Missouri river at Kansas city (on the right).TABLE ISATELLITE DATASETS USED IN THE ANALYSIS AND CORRESPONDING GROUND TRUTHSLocation Ground truth COORDINATES (WGS-84) Time of obs. Images[yyyy/mm/dd] numberKansas City USGS 39◦ 6’ 42.20” N, 94◦ 35’ 17.30” W 2012/12/31 - 2019/03/30 83Mosul Hydroweb 36◦ 40’ 49.67” N, 42◦ 54’ 6.36” E 2011/06/07 - 2019/09/09 133TABLE IIPERFORMANCE INDICATORSLocation R NS RMSE [m]Mosul 0.97 0.93 1.71Kansas City 0.96 0.93 0.67available over the study areas during a consistent period withrespect to the CSK orbits. Specifically, for the Missouri river atKansas city, the water levels ground-truths are provided by theUnited States Geological Survey (USGS) dataset [18]. For theMosul dam, because of the lack of the in-situ data, water levelsfrom satellite radar altimetry, derived by Hydroweb dataset[19], are supposed as ground-truth in this case.The trend of the estimated water levels are depicted inFigure 4. The blue lines represent the water-levels measuredby using the proposed approach on CSK data (W obsi ) and thered ones are the supposed ground-truths (W truthi ).In Figure 5, the scatterplots are reported for both casestudies representing the comparison between the water levelsestimated using CSK and the supposed ground truths. For bothcases, a good agreement is found because the scatter pointslie in the proximity of the linear regression represented by thered line in the figure. This is confirmed by the performanceparameters (reported in Table II):• the Pearson correlation coefficient (R)R =N∑i=1(W obsi − µobs) (W truthi − µtruth)√N∑i=1(W obsi − µobs)2√ N∑i=1(W truthi − µtruth)2 ,withµobs =1NN∑i=1W obsiandµtruth =1NN∑i=1W truthi ,Fig. 4. Water levels for the analysed case studies: the Mosul dam in Iraq (on the top) and the Missouri river at Kansas city (on the bottom). Red lines arethe retrieved values of water levels whereas the blue lines represents the ground-truths.• the Nash-Sutcliffe efficiency (NS) [20]NS = 1−N∑i=1(W obsi −W truthi)2N∑i=1(W obsi − µobs)2 ,• the root-mean square error (RMSE)RMSE =√√√√ 1NN∑1(W obsi −W truthi).IV. CONCLUSIONS AND FINAL REMARKSThis research demostrated that the information-gap problemof river flow monitoring could be solved through water flowelevation estimation with a synthetic aperture radar (SAR)signal processing technique.This paper proposed the application of a robust techniquefor tracking the double-bounce reflections of some referencestructure crossing the water surface and to measure the gapspace existing between the water surface and the structure.Specifically, the difference in position between the single andFig. 5. Scatterplots of the estimated water levels and the ground-truths’ measurements: the Mosul dam in Iraq (on the top) and the Missouri river at Kansascity (on the bottom).double bounce is suitably measured over the time.The experiments have been evaluated processing real dataconsisting of a long temporal series of COSMO-SkyMed data.Two case studies are analysed in this paper: the Mosul damin Iraq and the Missouri river at Kansas city.Finally, the high correlation between the estimated CSKresults and the ground-truths confirms the effectiveness of theproposed method in both the case studies.REFERENCES[1] D. M. Hannah, S. Demuth, H. A. J. van Lanen, U. Looser, C. Prud-homme, G. Rees, K. Stahl, and L. M. Tallaksen, “Large-scale river flowarchives: importance, current status and future needs,” Hydrol Process.,vol. 25, no. 7, pp. 1191–1200, March 2011.[2] P. Addabbo, M. Focareta, S. Marcuccio, C. Votto, and S. 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Water level measurements using COSMO-SkyMed synthetic aperture radar

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Water level measurements using COSMO-SkyMed synthetic aperture radar

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