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River Mapping from Satellite Radar Altimeter Sigma0 Bramer, S.M.S. (1) , Berry, P.A.M. (1) (1) EAPRS Lab, De Montfort University, The Gateway, Leicester LE1 9BH, United Kingdom ([email protected]) ABSTRACT The ERS-1 Geodetic Mission yielded a unique, closely spaced mesh of measurements over the earth’s land surface. This unique dataset contains echoes obtained over inland water, with very dense spatial sampling along river courses. However, interpretation of these data is problematic, since environmental events also cause ephemeral bright targets to appear on the earth’s surface; also the sigma0 response over rivers is extremely variable, affected by surface roughness, off-ranging and the spatial extent of the flooded surface. Using an expert system approach to re-calculate sigma0 and screen the echo shapes, a methodology has been designed to create detailed sigma0 maps from these data, supplemented by ERS-2 data. Whilst this approach was initially developed to generate accurate sigma0 maps of desert regions for calibration purposes, the techniques for removal of environmental contamination have proved so effective that it has been possible to apply this technique over major river basins, obtaining precise maps of these river system locations. This work therefore demonstrates the extent to which pulse limited altimeters can retrieve inland water signatures over river networks and gives a valuable insight into the untapped potential of radar altimetry for river monitoring. METHOD The radar altimeter data used in this study have been reprocessed using a rule-based expert system [1] to optimise the recovery of the range-to-surface. This works by selecting one of eleven retracking algorithms based on the waveform characteristics. The system has been under development for several years and has been tuned for ERS-1/2, Envisat, TOPEX and Jason-1. After retracking, an automated method was used to remove environmental contamination from the processed sigma0 data, as part of a research programme designed to create accurate sigma0 models of selected desert areas for calibration/validation of multi-mission altimeter sigma0 [2,3]. In all cases the lowest values at any point are chosen as most closely representing a ‘dry earth’ value. Values from all tracks are reconciled in an iterative process, and the models are then created by use of triangulation and bilinear interpolation. The technique was then tested by modelling progressively wetter regions, and proved extremely robust, to the extent of providing remarkable levels of detail even over wet terrain such as rainforests. THE AMAZON To investigate the performance of this technique in difficult circumstances, the entire Amazon basin was used as a test area. The ERS-1 Geodetic Mission dataset was retracked, and the technique used for desert regions [3] was applied to the data, to produce a model. Whilst the environmental filter has screened out some data, a surprisingly high proportion of sigma0 data was retained, which allowed the successful calculation of a sigma0 model for the entire Amazon basin (Fig. 1.). The path of smaller tributaries within the Amazon river network are clearly revealed in greater detail using this technique than in the GLCC mapping of the region shown in Fig. 2. As a quantitative indicator of the internal consistency of the model, the sigma0 values at all locations where two altimeter tracks cross were calculated both before and after the track reconciliation and cleaning process. Crossover differences (given in Table 1.) do show that there is still a great deal of noise in the model, with standard deviations of crossover sigma0 differences in the region of 7dB. In contrast, typical values are in the region of 1dB over desert calibration zones. This does not, however, detract from the remarkable clarity of the mapping.

River Mapping from Satellite Radar Altimeter Sigma0earth.esa.int/hydrospace07/participants/09_04/09_04_Bramer.pdf · River Mapping from Satellite Radar Altimeter Sigma0 Bramer, S.M.S.(1),

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Page 1: River Mapping from Satellite Radar Altimeter Sigma0earth.esa.int/hydrospace07/participants/09_04/09_04_Bramer.pdf · River Mapping from Satellite Radar Altimeter Sigma0 Bramer, S.M.S.(1),

River Mapping from Satellite Radar Altimeter Sigma0Bramer, S.M.S.(1), Berry, P.A.M. (1)

(1)EAPRS Lab, De Montfort University, The Gateway, Leicester LE1 9BH, United Kingdom ([email protected])

ABSTRACT

The ERS-1 Geodetic Mission yielded a unique, closely spaced mesh of measurements over the earth’s land surface.This unique dataset contains echoes obtained over inland water, with very dense spatial sampling along river courses.However, interpretation of these data is problematic, since environmental events also cause ephemeral bright targets toappear on the earth’s surface; also the sigma0 response over rivers is extremely variable, affected by surface roughness,off-ranging and the spatial extent of the flooded surface. Using an expert system approach to re-calculate sigma0 andscreen the echo shapes, a methodology has been designed to create detailed sigma0 maps from these data, supplementedby ERS-2 data. Whilst this approach was initially developed to generate accurate sigma0 maps of desert regions forcalibration purposes, the techniques for removal of environmental contamination have proved so effective that it hasbeen possible to apply this technique over major river basins, obtaining precise maps of these river system locations.This work therefore demonstrates the extent to which pulse limited altimeters can retrieve inland water signatures overriver networks and gives a valuable insight into the untapped potential of radar altimetry for river monitoring.

METHOD

The radar altimeter data used in this study have been reprocessed using a rule-based expert system [1] to optimise therecovery of the range-to-surface. This works by selecting one of eleven retracking algorithms based on the waveformcharacteristics. The system has been under development for several years and has been tuned for ERS-1/2, Envisat,TOPEX and Jason-1.After retracking, an automated method was used to remove environmental contamination from the processed sigma0data, as part of a research programme designed to create accurate sigma0 models of selected desert areas forcalibration/validation of multi-mission altimeter sigma0 [2,3]. In all cases the lowest values at any point are chosen asmost closely representing a ‘dry earth’ value. Values from all tracks are reconciled in an iterative process, and themodels are then created by use of triangulation and bilinear interpolation.The technique was then tested by modelling progressively wetter regions, and proved extremely robust, to the extent ofproviding remarkable levels of detail even over wet terrain such as rainforests.

THE AMAZON

To investigate the performance of this technique in difficult circumstances, the entire Amazon basin was used as a testarea. The ERS-1 Geodetic Mission dataset was retracked, and the technique used for desert regions [3] was applied tothe data, to produce a model. Whilst the environmental filter has screened out some data, a surprisingly high proportionof sigma0 data was retained, which allowed the successful calculation of a sigma0 model for the entire Amazon basin(Fig. 1.). The path of smaller tributaries within the Amazon river network are clearly revealed in greater detail usingthis technique than in the GLCC mapping of the region shown in Fig. 2. As a quantitative indicator of the internalconsistency of the model, the sigma0 values at all locations where two altimeter tracks cross were calculated bothbefore and after the track reconciliation and cleaning process. Crossover differences (given in Table 1.) do show thatthere is still a great deal of noise in the model, with standard deviations of crossover sigma0 differences in the region of7dB. In contrast, typical values are in the region of 1dB over desert calibration zones. This does not, however, detractfrom the remarkable clarity of the mapping.

Page 2: River Mapping from Satellite Radar Altimeter Sigma0earth.esa.int/hydrospace07/participants/09_04/09_04_Bramer.pdf · River Mapping from Satellite Radar Altimeter Sigma0 Bramer, S.M.S.(1),

Fig. 1. Altimeter Sigma0 (dB), Amazon Region 0-10S 55-70W

Fig. 2. GLCC Mapping of Amazon Region, 0-10S 55-70W [4]

Page 3: River Mapping from Satellite Radar Altimeter Sigma0earth.esa.int/hydrospace07/participants/09_04/09_04_Bramer.pdf · River Mapping from Satellite Radar Altimeter Sigma0 Bramer, S.M.S.(1),

Table 1. Sigma0 Crossover Statistics for Amazon Region 0-10S 55-70WSigma0 Crossover Statistics Standard Deviation of Crossover

Differences dBNumber of Crossovers

GM prior to modification 7.483 1045703

35-day crossovers with GM prior tomodification

7.893 1689232

GM after cleaning 6.447 957260

35-day after cleaning 7.272 1599084

THE CONGO

Following the successful application of this automated technique to the Amazon basin, the methodology was applied tothe Congo basin. Within the Congo region the sigma0 plot (Fig. 3.) reveals fascinating detail. The river courses aremore clearly defined than in the Google Earth plot of the region shown in Fig. 4. Orthometric heights derived from theERS-1 Geodetic Mission and plotted as Fig. 5. show good correspondence with the river courses revealed in the sigma0map. Raw crossover statistics (see Table 2.) are broadly comparable with those of the Amazon region, although thefinal results of the modelling process are marginally better.

Fig. 3. Altimeter Sigma0 (dB), Congo Region, 4S-4N 16-26E

Page 4: River Mapping from Satellite Radar Altimeter Sigma0earth.esa.int/hydrospace07/participants/09_04/09_04_Bramer.pdf · River Mapping from Satellite Radar Altimeter Sigma0 Bramer, S.M.S.(1),

Fig. 4. Google Earth View of Congo Region, 4S-4N 16-26E

Fig. 5. Altimeter Orthometric Heights (m), Congo Region, 4S-4N 16-26E

Page 5: River Mapping from Satellite Radar Altimeter Sigma0earth.esa.int/hydrospace07/participants/09_04/09_04_Bramer.pdf · River Mapping from Satellite Radar Altimeter Sigma0 Bramer, S.M.S.(1),

Table 2. Sigma0 Crossover Statistics for the Congo Region 4S-4N 16-26ESigma0 Crossover Statistics Standard Deviation of Crossover

Differences dBNumber of Crossovers

GM prior to modification 7.465 484490

35-day crossovers with GM prior tomodification

7.947 733574

GM after cleaning 5.992 430955

35-day after cleaning 6.998 709992

LUNDA REGION

To investigate the use of this technique in a more complex environment with varying terrain, the Lunda region of Africawas then modelled. The northerly flow of rivers in the Lunda region is clear in Fig, 6. and corresponds well with theorthometric height model as depicted in Fig. 7. Crossover statistics in Table 3. show,, marginally less noise, prior toadjusting, than is found in the rainforest regions, and a slightly better final result..

Fig. 6. Altimeter Sigma0 (dB), Lunda Region, 5010S 15-25ETable 3. Sigma0 Crossover Statistics for the Lunda Region 5-10S 15-25E

Sigma0 Crossover Statistics Standard Deviation of CrossoverDifferences dB

Number of Crossovers

GM prior to modification 7.133 249074

35-day crossovers with GM prior tomodification

7.533 483697

GM after cleaning 5.639 210230

35-day after cleaning 6.842 472777

Page 6: River Mapping from Satellite Radar Altimeter Sigma0earth.esa.int/hydrospace07/participants/09_04/09_04_Bramer.pdf · River Mapping from Satellite Radar Altimeter Sigma0 Bramer, S.M.S.(1),

Fig. 7. Altimeter Orthometric Heights (m), Lunda Region, 5-10S 15-25E

DISCUSSION

The technique developed originally for modelling altimeter sigma0 in desert regions for calibration purposes has beenshown to be very successful at modelling wetter regions. Within these plots, river systems, which have high sigma0values, are found to show up very clearly, and it is evident that mapping the course of river systems is possible usingthis technique. The models generated show finer scale detail of the river systems than is available in global datasetssuch as the GLCC database. Additionally, floodplains show up clearly. This technique will now be applied to a largerselection of river systems, to determine the limits of applicability, and to estimate the potential contribution of thismethod to the mapping of river systems.

ACKNOWLEDGMENTS:

The authors would like to thank the European Space Agency for providing the ERS data used in this study.

REFERENCES:[1] P.A.M. Berry, A. Jasper, H. Bracke, “Retracking ERS-1 altimeter waveforms overland for topographic height

determination: an expert system approach,” ESA Pub. SP414, Vol. 1, 403-408., 1997[2] S.M.S. Bramer, P.A.M. Berry, “Cross Calibration of Multi-Mission Altimeter and TRMM PR Sigma0 Over

Natural Land Targets”, Proceedings of the Symposium on 15 Years Progress in Radar Altimetry, Venice, Italy,2006

[3] S.M.S. Bramer, P.A.M. Berry, J.A. Freeman, B. Rommen, ” Global Analysis of Envisat Ku and S Band Sigma0over all Surfaces”, Proceedings, ESA: ENVISAT Symposium, Montreux, Switzerland, 2007

[4] T.R. Loveland et.al., “Development of a Global Land Cover Characteristics Database and IGBP DISCover from1-km AVHRR Data”, International Journal of Remote Sensing, v. 21, no. 6/7, p. 1,303-1,330, 2000