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Estimation of Water Level Changes of Large-Scale Amazon Wetlands Using ALOS2 ScanSAR Differential Interferometry

Department of Civil & Environmental Engineering, University of Houston, Houston, TX 77004, USA
National Center for Airborne Laser Mapping, Houston, TX 77204 USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Science Systems and Applications, Inc., Lanham, MD 20706, USA
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 966;
Received: 23 May 2018 / Revised: 13 June 2018 / Accepted: 15 June 2018 / Published: 17 June 2018
(This article belongs to the Special Issue Imaging Geodesy and Infrastructure Monitoring)
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Differential synthetic aperture radar (SAR) interferometry (DInSAR) has been successfully used to estimate water level changes (∂h/∂t) over wetlands and floodplains. Specifically, amongst ALOS PALSAR datasets, the fine-beam stripmap mode has been mostly implemented to estimate ∂h/∂t due to its availability of multitemporal images. However, the fine-beam observation mode provides limited swath coverage to study large floodplains and wetlands, such as the Amazon floodplains. Therefore, for the first time, this paper demonstrates that ALOS2 ScanSAR data can be used to estimate the large-scale ∂h/∂t in Amazon floodplains. The basic procedures and challenges of DInSAR processing with ALOS2 ScanSAR data are addressed and final ∂h/∂t maps are generated based on the Satellite with ARgos and ALtiKa (SARAL) altimetry’s reference data. This study reveals that the local ∂h/∂t patterns of Amazon floodplains are spatially complex with highly interconnected floodplain channels, but the large-scale (with 350 km swath) ∂h/∂t patterns are simply characterized by river water flow directions. View Full-Text
Keywords: SAR; InSAR; DInSAR; water level change; floodplain; wetland SAR; InSAR; DInSAR; water level change; floodplain; wetland

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Cao, N.; Lee, H.; Jung, H.C.; Yu, H. Estimation of Water Level Changes of Large-Scale Amazon Wetlands Using ALOS2 ScanSAR Differential Interferometry. Remote Sens. 2018, 10, 966.

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