We demonstrate a method using lidar data fusion to improve the forest height estimation accuracy of multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR). Compared to single-baseline PolInSAR, multibaseline PolInSAR allows forest canopy height to be estimated more accurately across a wider range of height values. However, to arrive at a single forest height estimate, the estimates from the multiple baselines must be selected or weighted. A number of approaches to selecting between baselines have been proposed in the literature, but they are generally based on simple metrics of the PolInSAR data and do not necessarily capture the full range of characteristics that make one baseline produce more accurate forest height estimates than another. We solve this problem by treating baseline selection as a supervised classification problem that can be trained using a small amount of sparse lidar data located within the PolInSAR coverage area. We train a support vector machine classifier using a variety of coarse lidar sample spacings of 250 m and greater, to demonstrate that data from future spaceborne lidar missions will be sufficient for this purpose. We demonstrate results for multiple study areas in the country of Gabon using data collected by NASA's uninhabited aerial vehicle synthetic aperture radar and land, vegetation, and ice sensor lidar. The use of lidar fusion for PolInSAR baseline selection yields improved results compared to standard baseline selection methods, and further demonstrates the strong potential of PolInSAR and lidar fusion for remote sensing of forests.

The NASA-ISRO SAR (NISAR) mission will map the Earth’s surface every 12 days using a reflector-feed system with scan-on-receive (SweepSAR) capability. An alternative for continuous swath coverage is to employ staggered SAR operation, i.e., to vary the pulse repetition intervals over time. In this way, the blocked (missing) samples are spread in range and azimuth and can be interpolated prior to focusing. However, the relatively low azimuth oversampling intended for NISAR in combination with the strong non-uniformity of the staggered data grid forbids the use of conventional interpolation kernels. This paper discusses a processing approach for the NISAR staggered data and validates the strategy with simulated data. Moreover, the impact of the staggered operation for contrast-based and interferometric applications is discussed with examples.

Recent results have highlighted the potential ability of bi- and multistatic synthetic aperture radar (SAR) tomographers to measure vegetation structure and surface topography. However, the quality of SAR tomographic measurements with multiple platforms is impacted by the phase instability in each platform’s oscillator. The phase noise, if uncompensated, may lead to degradation in the SAR data products such as increased sidelobe levels, reduced peak amplitude of the impulse response, and a low frequency phase modulation, among others. In this work, we model and examine the effects of oscillator phase noise on tomographic SAR signals for spaceborne missions flying in formation. A synchronization process is also adopted to help mitigate oscillator phase errors by measuring and predicting relative phase offsets at prescribed temporal intervals. A simulation tool was developed to examine the point target response as seen by realistic satellite constellations in low Earth orbit using different quality oscillators, radar configurations, and synchronization configurations. A first analysis of a multi-platform tomographic SAR mission suggests that a system without a dedicated physical link with minimal effects on the point target response may be achievable using current oscillators. Our analysis also shows that phase noise has differing effects on multistatic radar modes. Tomograms formed with a system operating in SIMO mode are the most affected by oscillator phase noise error, followed by MIMO, with negligible effects on the SAR-SISO mode. These trade studies and the simulation tool can be used to help inform the design of future multistatic radar missions.

We use the 2017 Thomas fire and ensuing Montecito debris flow as a case study to demonstrate how application of various interferometric synthetic aperture radar postprocessing techniques can be used to highlight the fire scar and major damage areas from the debris flows. We use National Aeronautics and Space Administration's airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L‐band polarimetric interferometric synthetic aperture radar (InSAR) for this study. Results demonstrate that a single polarimetric radar image highlights burn areas. Coherence and interferometric radar pairs show the fire scar and debris flow. An unwrapped interferogram shows the location of the debris flow over the 101 Freeway in greater detail than a raw interferogram. We enhanced the interferogram and coherence product of a pair of images acquired before and after the fire and debris flows, which highlights areas of destroyed structures and major damage. We first despeckled the images and then converted the interferogram to four colors and the coherence map to two colors. Analysis of optical imagery between before and after images shows that the debris flows are best identified using visible bands and that near‐infrared better highlights the lower part of the flows where more mud was deposited. These simplified products provide more actionable products for disaster responders; the products were downloaded for use by the State of California.