Faraday Rotation Effects in ALOS/PALSAR Data
Franz Meyer and Jeremy Nicoll, ASF, University of Alaska Fairbanks, Fairbanks Alaska, USA
Since the second half of 2006, ASF has been receiving, processing, archiving, and distributing data of ALOS PALSAR, a fully-polarized L-band SAR sensor. Among other applications, PALSAR data are used for sea ice and vegetation mapping, ice-motion analysis, volcano monitoring, crustal dynamics, and polarimetric studies. These applications demand high data-quality and signal-calibration standards.
In L-band SAR, the influence of the ionosphere on the radiometric, geometric, and polarimetric image quality is of major concern. Ionospheric effects in L-band strongly exceed those observed in C-band, and are, therefore, more significant in PALSAR images than in data of other spaceborne SARs. One of the most prevalent ionosphere-induced distortions in PALSAR data is Faraday rotation (FR), a rotation of the polarization vector of the radar signal. Rotations exceeding 45 degrees are likely to happen during the lifetime of PALSAR, which will significantly reduce the accuracy of geophysical parameter recovery from SAR, if uncorrected. Therefore, the estimation and correction of Faraday rotation effects is necessary to assure high and consistent data quality.
A model-driven FR prediction method, capable of forecasting the FR angles for every PALSAR image in ASF’s PALSAR archive, has been developed. This tool should aid in data selection for researchers wishing to avoid FR effects or for those deliberately targeting these effects. The method uses a physical model and ancillary information to come up with reliable predictions of FR angles for every PALSAR image. The predicted Faraday rotation for every PALSAR granule in the Americas ALOS Data Node (AADN) archive as of January 23, 2007, is shown in Figure 1. The histogram illustrates the expected range of FR for the next few years while solar activity is low.
During the later stages of the PALSAR mission, the solar activity will likely increase and raise the FR angle significantly. The FR prediction tool allows determination of the strength of ionospheric effects in a specific image before ordering. It will be operational on ASF’s order interface in the future.
In addition to FR prediction, ASF also supports empirically-derived estimation of FR angles from any full-polarimetric PALSAR data set. The estimator produces two-dimensional FR maps of high accuracy and spatial resolution. The example presented in Figure 2a) illustrates the richness of detail and the utility of the evaluation method. Analysis of an image acquired in the area north of Gakona, Alaska, is shown below. The left panel of Figure 2a displays the SAR image of the analyzed area, while the center panel shows the FR map as a grayscale image. The right panel is reserved for statistical information (e.g., histogram, as well as range and azimuth trends), which is meant to assist the user in data analysis.
This example is particularly interesting as it reveals strong changes in the ionospheric activity within a single SAR image. Several streaks of high FR are visible, which are oriented mainly in east-west directions. This disturbance is most likely caused by auroral activities during the time of the SAR acquisition. Figure 2b shows the along track profile of FR for this ionospheric event. The profile was derived by processing three consecutive images, where the center image is shown in Figure 2a. Besides being essential for the analysis of SAR data quality, FR maps provide valuable information for ionospheric science and can increase our understanding of ionospheric turbulence.
A second example, shown in Figure 3a, covers an area close to the geomagnetic North Pole. Again, variations in the FR indicate significant turbulence in the ionosphere. The undulations detected in Figure 3a are part of a large-scale disturbance covering an area from -82° to -100° longitude. An along-track profile of the disturbance, mapped from seven consecutive full-polarimetric PALSAR images of the same orbit, is presented in Figure 3b.
As shown in the examples in Figures 2 and 3, FR is present in PALSAR data, reducing data quality. If full-polarimetric data sets are available, FR effects can be compensated. ASF developed a correction algorithm that creates maps of the estimated FR and compensates the image appropriately to restore the original quality of the SAR data. The correction has proven successful for full-polarimetric data sets and can correct for both spatially constant and spatially varying ionospheric effects. Methods for restoring the quality of single- or dual-pol data are not yet available. For these data types, the FR prediction tool will facilitate the search for images with low ionospheric distortions.
Although FR is still a fairly new topic in remote sensing, enormous scientific progress was made during the last few years. FR-prediction methods assist users in selecting data sets suitable for their applications. FR detection and correction algorithms are available for full-polarimetric SAR data and are proven to be effective. Approaches for detecting FR in single and dual-pol data are currently under investigation.
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