1. Height error “scaling” factor Temporal Decorrelation and Topographic Layover Impact on Ka-band Swath Altimetry for Surface Water Hydrology Delwyn Moller, Remote Sensing Solutions Ernesto Rodriguez, Jet Propulsion Laboratory, California Institute of Technology Overview: Traditional radar altimetry has demonstrated the ability to retrieve surface water heights with decimeter accuracies along the nadir path. However, a profiling sensor is insufficient to provide global monitoring of fresh water bodies. In response, to address both hydrologic and oceanographic needs, the NRC Decadal Survey has recommended the Surface Water Ocean Topography (SWOT) mission. During this mission a swath-based imaging altimeter (shown at right in Figure 1) will provide the key hydrologic variables needed for comprehensive river discharge and storage observations, specifically temporal height change, slope and spatial extent [1]. In addition, key imaging capabilities provide classification masks and data for topographic corrections. As part of a “virtual mission,” we have developed a high-fidelity instrument simulator capable of predicting the radar response and error characteristics over dynamically modeled study regions [2]. In this poster we specifically address two error sources: 1.The effects of water decorrelation on the SAR image formation. 2.Predictions of layover contamination in the high relief topography. Coherence Time Characterization and Effects: To provide proof-of-concept validating data for the SWOT concept, a Ka-band radar breadboard (see Figure 2), developed for the Mars Science Laboratory project, was fielded to three diverse locations in Ohio (consisting of a small fast-flowing river, a reservoir, and a large slow-flowing river). Measurements of backscatter as a function of incidence angle and Doppler measurements were collected at each site. In situ measurements of wind and current were taken. Temporal coherence was derived from the phase variability of the cross-correlated “pulse-pair” returns. This quantity is important because, for a synthetic aperture system, the scene must remain correlated over the aperture synthesis time in order to achieve full resolution. The Ka-band radar was not able to directly measure coherence times of the surface, but does measure the complex pulse-pair product, the phase statistics of which can be used to estimate coherence times. The phase standard- deviation can be expressed as: We assume  = exp(-(t/  c ) 2 ) for the surface, as is often assumed for the ocean. A least-squares fit to the standard-deviation of the phase as a function of lag yields an estimate of the decorrelation time  c. Figure 3 shows the experimental results where the coherence time can vary by over an order of magnitude. The implication of this for an imaging radar is that the effective resolution can be fundamentally limited by the scene coherence such that: The effects of a finite decorrelation are shown in Figure 4, where a simulation indicates a) the reach of a river required to converge on a mean “width” and b) the bias of that mean as a function of decorrelation time. Note however, that this should not affect the height accuracy - only the along-track resolution. Figure 3: Temporal coherence estimates derived from phase statistics of pulse-pair measurements. The coherence estimates did not change significantly at an incidence angle of 5 o. Figure 4: Simulation of the effect of water coherence time for a simple test case. The upper figure shows the reach averaging required to converge on an estimate of the width where: The lower plot shows the mean width bias as a function of correlation time. Note we are ultimately limited by finite pixel sizes in estimating width even as the decorrelation -> infinity. The next step is to work on an algorithm and sensitivity analysis for correcting the bias -Radar point-target response can be characterized -In the mission we may be able to process to different aperture lengths to estimate the correlation time from the azimuth widths Temporal decorrelation needs to be better understood and characterized => important to get more experimental data/statistics Figure 2: Bridge-based radar experimental configuration. The antenna is mounted at the end of a stiff boom on a precision positioner for scanning in elevation about nadir. An anemometer is mounted on the structure for ground-truth, and current measurements were made from the bridge. Layover Due to Topography: Although significant height contamination can occur in cases of either high topography or extensive vegetation, it may be possible to identify the contaminated regions and exclude them from the height product. For example, Figure 5 indicates height error due to layover in the high topographic relief of the Pacific Northwest area, but one can see that this is localized. A further area of study is to examine classification schemes utilizing correlation properties to identify these areas of layover and thereby exclude them. Figure 5: Layover due to topography in the Pacific Northwest region. The regions of layover are localized and predictable. The relative power ratio accounts for: 1. Projected area of the land relative to the water. 2. The dot product between the normal to the 2d facet and the incident wave. 3. The relative  0 between the land and water. Note a 10dB water/land  0 ratio is assumed at nadir, which is then corrected for the local angle of incidence. The magnitude of the additive error is typically very small (>99% of pixels have l r <1.1) Classification of these regions should avoid significant contamination. References: [1] Alsdorf, D. E., E. Rodríguez, and D. P. Lettenmaier (2007), Measuring surface water from space, Reviews of Geophysics, 45, RG2002, doi:10.1029/2006RG000197. [2] Andreadis, K. M., E. A. Clark, D. P. Lettenmaier, and D. E. Alsdorf (2007), Prospects for river discharge and depth estimation through assimilation of swath altimetry into a raster-based hydrodynamics model, Geophysical Research Letters, 34, L10403, doi:10.1029/2007GL029721. 47 45 46 -124-123-122 Height error “scaling” factor Figure 1: SWOT’s swath-based imaging altimeter.