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Open AccessArticle

Wetland Surface Water Detection from Multipath SAR Images Using Gaussian Process-Based Temporal Interpolation

1
Graduate School of Engineering, Tohoku University, Aoba-Ku, Sendai 980-8752, Japan
2
Precision Forestry Cooperative, Remote Sensing and Geospatial Analysis Lab, School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98118, USA
3
International Research Institute of Disaster Science, Tohoku University, Aoba-Ku, Sendai 980-8752, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1756; https://doi.org/10.3390/rs12111756
Received: 31 March 2020 / Revised: 9 May 2020 / Accepted: 11 May 2020 / Published: 29 May 2020
Wetlands provide society with a myriad of ecosystem services, such as water storage, food sources, and flood control. The ecosystem services provided by a wetland are largely dependent on its hydrological dynamics. Constant monitoring of the spatial extent of water surfaces and the duration of flooding of a wetland is necessary to understand the impact of drought on the ecosystem services a wetland provides. Synthetic aperture radar (SAR) has the potential to reveal wetland dynamics. Multitemporal SAR image analysis for wetland monitoring has been extensively studied based on the advances of modern SAR missions. Unfortunately, most previous studies utilized monopath SAR images, which result in limited success. Tracking changes in individual wetlands remains a challenging task because several environmental factors, such as wind-roughened water, degrade image quality. In general, the data acquisition frequency is an important factor in time series analysis. We propose a Gaussian process-based temporal interpolation (GPTI) method that enables the synergistic use of SAR images taken from multiple paths. The proposed model is applied to a series of Sentinel-1 images capturing wetlands in Okanogan County, Washington State. Our experimental analysis demonstrates that the multiple path analysis based on the proposed method can extract seasonal changes more accurately than a single path analysis. View Full-Text
Keywords: Synthetic aperture radar; wetlands; drought; Gaussian process; time series analysis Synthetic aperture radar; wetlands; drought; Gaussian process; time series analysis
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MDPI and ACS Style

Endo, Y.; Halabisky, M.; Moskal, L.M.; Koshimura, S. Wetland Surface Water Detection from Multipath SAR Images Using Gaussian Process-Based Temporal Interpolation. Remote Sens. 2020, 12, 1756. https://doi.org/10.3390/rs12111756

AMA Style

Endo Y, Halabisky M, Moskal LM, Koshimura S. Wetland Surface Water Detection from Multipath SAR Images Using Gaussian Process-Based Temporal Interpolation. Remote Sensing. 2020; 12(11):1756. https://doi.org/10.3390/rs12111756

Chicago/Turabian Style

Endo, Yukio; Halabisky, Meghan; Moskal, L. M.; Koshimura, Shunichi. 2020. "Wetland Surface Water Detection from Multipath SAR Images Using Gaussian Process-Based Temporal Interpolation" Remote Sens. 12, no. 11: 1756. https://doi.org/10.3390/rs12111756

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