Estimation of water level changes of large-scale Amazon wetlands using ALOS2 ScanSAR differential interferometry

Ning Cao, Hyongki Lee, Hahn Chul Jung, Hanwen Yu

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number966
JournalRemote Sensing
Volume10
Issue number6
DOIs
Publication statusPublished - 2018 Jun 1

Bibliographical note

Publisher Copyright:
© 2018 by the authors.

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

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