Alos-4 L-Band SAR Mission and Observation

Motohka, Takeshi; Kankaku, Yukihiro; Miura, Satoko; Suzuki, Shuichi

doi:10.1109/igarss.2019.8898169

Cited by 19 publications

(12 citation statements)

References 4 publications

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“…Unfortunately, dense time series of L-band SAR data are not easily available, although the future launches of L-band SAR satellites (e.g. NISAR [57] and ALOS-4 [58]) will allow for further exploitation of L-band SAR data for damage mapping. NISAR will also image selected regions using S-band radar, allowing the use of SAR data at multiple wavelengths to further increase the ability of the damage map to distinguish different types of surface change.…”

Section: Discussionmentioning

confidence: 99%

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Deep Learning-based Damage Mapping with InSAR Coherence Time Series

Stephenson

Köhne

Zhan

et al. 2021

Preprint

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<p>Satellite remote sensing is playing an increasing role in rapid mapping of damage after natural disasters. In particular, synthetic aperture radar (SAR) can image the Earth&#8217;s surface and map damage in all weather conditions, day and night. However, current SAR damage mapping methods struggle to separate damage from other changes in the Earth&#8217;s surface. In this study, we propose to map damage using the full time history of SAR observations of an impacted region from a single satellite constellation in order to detect anomalous variations in the Earth&#8217;s surface properties due to a natural disaster. We quantify Earth surface change using time series of sequential interferometric SAR coherence, then use a recurrent neural network (RNN) as a probabilistic anomaly detector on these coherence time series. The RNN is first trained on pre-event coherence time series, and then forecasts a probability distribution of the coherence between pre- and post-event SAR images. The difference between the forecast and observed co-event coherence provides a measure of the confidence in the identification of damage. The method allows the user to choose a damage detection threshold that is customized for each location, based on the local temporal behavior before the event. We apply this method to calculate estimates of damage for three earthquakes using multi-year time series of Sentinel-1 SAR acquisitions. Our approach shows good agreement with measured damage and quantitative improvement compared to using pre- to co-event coherence loss as a damage proxy.</p>

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Section: Discussionmentioning

confidence: 99%

“…Other SAR satellites also acquire data, however they do not have the same long time series of open access acquisitions that is available from Sentinel-1. Planned SAR missions, such as NISAR [57] and ALOS-4 [58] should improve the availability of frequently acquired SAR data.…”

Section: Near Real-time Deploymentmentioning

confidence: 99%

Deep Learning-based Damage Mapping with InSAR Coherence Time Series

Stephenson

Köhne

Zhan

et al. 2021

Preprint

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“…Although L-band ALOS-2 can retain sufficient coherence in these areas, the temporal sampling frequency is much lower and the detailed temporal evolution of the postseismic deformation cannot be revealed (Hashimoto 2020). The next-generation Lband SAR missions with high observation frequency such as ALOS-4 (Motohka et al 2019) or NASA-ISRO SAR (NISAR; Jet Propulsion Laboratory 2020) will help clarify the complex postseismic deformation, even in such densely vegetated regions. show some degree of subsidence.…”

Section: Postseismic Deformation Of the 2016 Kumamoto Earthquakementioning

confidence: 99%

“…It is difficult to detect the detailed deformation time series using existing L-band SAR data because the observation frequency for interferometry of the unique operating L-band satellite ALOS-2 is much lower (< 10 times/year) than that of Sentinel-1 (30-60 times/year). However, nextgeneration L-band SAR satellites such as ALOS-4 (Motohka et al 2019) and NISAR (Jet Propulsion Laboratory 2020), which have higher frequency observation capabilities comparable to Sentinel-1, will enable detailed deformation monitoring even in forested areas.…”

Section: Applicability To Other Areas Worldwidementioning

confidence: 99%

Nationwide urban ground deformation monitoring in Japan using Sentinel-1 LiCSAR products and LiCSBAS

Morishita

2021

Prog Earth Planet Sci

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Ground subsidence in urban areas is a significant problem because it increases flood risk, damages buildings and infrastructure, and results in economic loss. Continual monitoring of ground deformation is important for early detection, mechanism understanding, countermeasure implementation, and deformation prediction. The Sentinel-1 satellite constellation has globally and freely provided frequent and abundant SAR data and enabled nationwide deformation monitoring through InSAR time series analysis. LiCSAR, an automatic Sentinel-1 interferometric processing system, has produced abundant interferograms with global coverage, and the products are freely accessible and downloadable through a web portal. LiCSBAS, an open source InSAR time series analysis package integrated with LiCSAR, enables users to obtain the deformation time series easily and quickly. In this study, spatially and temporally detailed deformation time series and velocities from the LiCSAR products using LiCSBAS for 73 major urban areas in Japan during 2014–2020 were derived. All LiCSBAS processing was automatically performed using predefined parameters. Many deformation signals with various temporal and spatial features, such as linear subsidence in Hirosaki, Kujyukuri, Niigata, and Kanazawa, episodic subsidence in Sanjo, annual vertical fluctuation in Hirosaki, Yamagata, Yonezawa, Ojiya, and Nogi, and linear uplift in Chofu were detected. Unknown small nonlinear uplift signals were found in Nara and Osaka in 2018. Complex postseismic deformations from the 2016 Kumamoto earthquake were also revealed. All the deformation data obtained in this study are available on an open repository and are expected to be used for further research, investigation, or interpretation. This nationwide monitoring approach using the LiCSAR products and LiCSBAS is easy to implement and applicable to other areas worldwide.

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“…In contrast, if the PRI is continuously varied and data are sufficiently oversampled in azimuth-the overall concept is referred to as staggered SAR -it is possible to get rid of blind ranges and map a wide continuous swath [9]- [12]. The multiple-elevation-beam SAR concept is well established and currently being considered as the baseline acquisition mode for the Tandem-L mission proposal and the NASA-ISRO SAR (NISAR) and ALOS-4 missions [13]- [17]. It furthermore represents an attractive solution for the future SAR missions of European Space Agency's Copernicus Program [18].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Nadir Echoes in Multiple-Elevation-Beam SAR With Constant and Variable Pulse Repetition Interval

Villano

Peixoto

2022

IEEE Trans. Geosci. Remote Sensing

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Multiple-elevation-beam synthetic aperture radar (SAR) is a concept based on digital beamforming (DBF) in elevation and simultaneous recording of the echoes of multiple transmitted pulses. It enables high-resolution imaging of wide areas and is therefore ideal for the systematic observation of dynamic processes on the Earth's surface. Furthermore, if the pulse repetition interval (PRI) is continuously varied (staggered SAR), it is possible to map a wide continuous swath rather than multiple subswaths separated by "blind" ranges. Within the design of multiple-elevation-beam SAR, however, it is fundamental to consider how nadir echoes affect the mapping capabilities of systems with constant PRI and the image quality of staggered SAR systems, where nadir echoes are intrinsically smeared due to the PRI variation. This article addresses the characterization of nadir echoes in multiple-elevation-beam SAR with constant and variable PRI by presenting a parametric model for the nadir echo profile based on real radar measurements, a formulation of the nadir echo location and smearing in staggered SAR, and realistic simulations based on TerraSAR-X data, which show that nadir echo are likely to be barely visible in staggered SAR images. The results of this work are relevant to both the design of future SAR systems and the interpretation of the acquired data.

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Alos-4 L-Band SAR Mission and Observation

Cited by 19 publications

References 4 publications

Deep Learning-based Damage Mapping with InSAR Coherence Time Series

Deep Learning-based Damage Mapping with InSAR Coherence Time Series

Nationwide urban ground deformation monitoring in Japan using Sentinel-1 LiCSAR products and LiCSBAS

Characterization of Nadir Echoes in Multiple-Elevation-Beam SAR With Constant and Variable Pulse Repetition Interval

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