Interferometric synthetic aperture radar (InSAR) is a key tool for the analysis of displacement and stress changes caused by large crustal earthquakes, particularly in remote areas. A challenge for traditional InSAR has been its limited spatial and temporal coverage especially for very large events, whose dimensions exceed the typical swath width of 70–100 km. This problem is addressed by the ALOS‐2 satellite, whose PALSAR‐2 instrument operates in ScanSAR mode, enabling a repeat time of 2 weeks and a swath width of 350 km. Here we present InSAR line‐of‐sight displacement data from ALOS‐2/PALSAR‐2 observations covering the Mw 7.8 Gorkha, Nepal earthquake and its Mw 7.3 aftershock that were acquired within 1 week of each event. The data are made freely available and we encourage their use in models of the fault slip and associated stress changes. The Mw 7.3 aftershock not only extended the rupture area of the main shock toward the east but also left a 20 km gap where the fault has little or no coseismic slip. We estimate this unslipped fault patch has the potential to generate a Mw 6.9 event.
Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks.
The Advanced Land Observing Satellite-2 (ALOS-2, "DAICHI-2") has been observing Nepal with the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) in response to an emergency request from Sentinel Asia related to the Mw 7.8 Gorkha earthquake on April 25, 2015. PALSAR-2 successfully detected not only avalanches and local crustal displacements but also continental-scale deformation. Especially, by the use of the ScanSAR mode, we are able to make interferograms that cover the entire displacement area of the earthquake. However, we did encounter some fundamental problems with the ScanSAR and incorrect settings of PALSAR-2 operation that have now been fixed. They include (1) burst overlap misalignment between two ScanSAR observations, which limits the number of pairs available and the quality of the interferogram, (2) non-crustal fringes which are derived from co-registration error and/or ionospheric effect and, (3) incorrect setting of the center frequency in the Stripmap beam F2-6. In this paper, we describe their problems and solutions. The number of interferometric pairs are limited by (1) and (3). The accuracy of the interferograms are limited by (2) and (3). The experimental results showed that current solutions for (2) and (3) work appropriately.
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