Jeremy Nicoll

Prediction, Detection, and Correction of Faraday Rotation in Full-Polarimetric L-Band SAR Data

With the synthetic aperture radar (SAR) sensor PALSAR onboard the Advanced Land Observing Satellite, a new full-polarimetric spaceborne L-band SAR instrument has been launched into orbit. At L-band, Faraday rotation (FR) can reach significant values, degrading the quality of the received SAR data. One-way rotations exceeding 25 deg are likely to happen during the lifetime of PALSAR, which will significantly reduce the accuracy of geophysical parameter recovery if uncorrected. Therefore, the estimation and correction of FR effects is a prerequisite for data quality and continuity. In this paper, methods for estimating FR are presented and analyzed. The first unambiguous detection of FR in SAR data is presented. A set of real data examples indicates the quality and sensitivity of FR estimation from PALSAR data, allowing the measurement of FR with high precision in areas where such measurements were previously inaccessible. In examples, we present the detection of kilometer-scale ionospheric disturbances, a spatial scale that is not detectable by ground-based GPS measurements. An FR prediction method is presented and validated. Approaches to correct for the estimated FR effects are applied, and their effectiveness is tested on real data.

Correction and Characterization of Radio Frequency Interference Signatures in L-Band Synthetic Aperture Radar Data

Radio frequency interference (RFI) is a known issue in low-frequency radar remote sensing. In synthetic aperture radar (SAR) image processing, RFI can cause severe degradation of image quality, distortion of polarimetric signatures, and an increase of the SAR phase noise level. To address this issue, a processing system was developed that is capable of reliably detecting, characterizing, and mitigating RFI signatures in SAR observations. In addition to being the basis for image correction, the robust RFI-detection algorithms developed in this paper are used to retrieve a wealth of RFI-related information that allows for mapping, characterizing, and classifying RFI signatures across large spatial scales. The extracted RFI information is expected to be valuable input for SAR-system design, sensor operations, and the development of effective RFI-mitigation strategies. The concepts of RFI detection, analysis, and mapping are outlined. Large-scale RFI mapping results are shown. In case studies, the benefit of detailed RFI information for customized RFI filtering and sensor operations is exemplified.

The Impact of the Ionosphere on Interferometric SAR Processing

The impact of ionospheric propagation effects on the signal properties of SAR systems is significant and increases with decreasing carrier frequency. Besides polarimetric applications, also interferometric SAR processing can be significantly affected. Relative range shifts, internal image deformations, range and azimuth blurring, and interferometric phase errors are the most significant effects to be considered. In this paper we provide the theoretical background for ionospheric effects on InSAR. We quantify expected magnitudes of the respective effects for various existing SAR sensors and discuss methods for their detection and correction. Real data examples, mainly stemming from the ALOS PALSAR mission, are presented to verify the derived theory.

Mapping the Ionosphere Using L-Band SAR Data

The influence of the ionosphere on spaceborne SAR signals can be significant, especially approaching wavelengths at L-band or larger. By measuring these effects and inverting the underlying physical model, these effects can be used to measure and map aspects of the ionosphere. ALOS PALSAR data is employed to demonstrate this capability. Single-pol data is used to determine second-order gradients of the iononsperic Total Electron Content (TEC) (the second derivative of intensity). Dual-pol can yield lateral TEC variations in homogeneous areas. Finally, Faraday rotation measurements from full-pol data can be used to create 2-D maps of the absolute TEC.

Prediction and detection of Faraday rotation in ALOS PALSAR data

Faraday rotation can degrade the quality of low- frequency spaceborne SAR data, making an estimation and correction of these effects a prerequisite for data quality continuity. In this paper, methods for predicting and estimating Faraday rotation are presented and tested on ALOS/PALSAR data. A first example for unambiguous detection of Faraday rotation in SAR is shown. In addition, the improvement after correcting for FR is proven using a real data example.

Mapping aurora activity with SAR — a case study

Auroral physics is an exceedingly rich and complex subject. However, due to a lack of high resolution data of ionospheric activity during auroral events, not all phenomena in the high latitude ionosphere are fully understood. Recent research has proven that L-band SAR data is significantly affected by the ionosphere and can be used for mapping its activity. With this paper we will prove and unambiguously verify the potential of L-band SAR to capture auroral activity. We will present examples of aurora signatures mapped from ALOS PALSAR data and will verify the results from SAR with observations provided from ground based measurements.

Pre-processing compensation for saturation power loss in SAR data

In this paper, we describe a method for compensating data for saturation power loss due to the analog to digital converter (ADC) in spaceborne synthetic aperture radar (SAR) instruments. The algorithm counts the number of saturated points in a block of raw signal data and adjusts the data to regain the lost power. The algorithm compensates well for lost power, showing a correction of up to 7 dB, and agreeing within 1 dB with unsaturated data. If care is not taken In the compensation sort order image artifacts can occur. We describe the methods used and discuss limitations in the application of the algorithm.

Characterization and extent of randomly-changing radio frequency interference in ALOS PALSAR data

This paper analyzes severe, broadband, radio frequency interference (RFI) signatures that are commonly observed in L-band ALOS PALSAR images acquired close to the North American Arctic coast. These RFI signals are caused by military over-the-horizon radar systems. We introduce the specifics of the interfering signals and demonstrate that standard RFI filters used in the operational ALOS PALSAR processor are insufficient to remove their influence. A new approach for filtering the identified RF interferences is presented and its performance is analyzed by a cross comparison with reference methods. The capabilities of the new correction method are emphasized by presenting several processing results. A statistical and geographical analysis of the strength and distribution of observed RFI signatures is shown, indicating widespread contamination of imagery across the American Arctic coast.

Development and application of a SAR training processor

In order to make the concepts of SAR more accessible to a general audience, a SAR Training Processor (STP) was developed. The SAR Training Processor was used in lecture and laboratory exercises for graduate-level Remote Sensing classes and for public seminars. The software allows the user to interactively visualize data and explore the effects of processing options. Users can view any intermediate processing results, modify the processing steps, and change processing parameters. The processor can be run either from the command line or from a graphical user interface. Examples of exercises that demonstrate SAR principles and strategies for using the STP are given.

Large scale characterization of Radio Frequency Interference signatures in L-band SAR

Radio Frequency Interference (RFI) is a known issue of low-frequency Synthetic Aperture Radar (SAR) data. In this paper, a processing system is described that is successful in detecting, characterizing, and mitigating RFI signatures in data of the L-band SAR system ALOS PALSAR. An RFI analysis system that is integrated into the SAR processor is logging a wealth of RFI-related information that is used to map, characterize, and classify RFI signatures across large spatial scales. The concept of the RFI detection and mitigation algorithm is described, the RFI analysis concept is outlined, and examples of spatial RFI maps are shown.