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Title
Japanese: 
English:Enhancing surface water extent time series during the rainy season through satellite data-fusion and gap-filling 
Author
Japanese: Declaro Alexis, 鼎 信次郎.  
English: Alexis Declaro, 鼎 信次郎.  
Language English 
Journal/Book name
Japanese:水文・水資源学会/日本水文科学会 2023年度研究発表会 
English: 
Volume, Number, Page        
Published date Jan. 2024 
Publisher
Japanese:水文・水資源学会 
English: 
Conference name
Japanese:水文・水資源学会/日本水文科学会 2023年度研究発表会 
English: 
Conference site
Japanese:長崎 
English:Nagasaki 
Official URL https://www.jstage.jst.go.jp/article/jshwr/36/0/36_70/_article/-char/en
 
DOI https://doi.org/10.11520/jshwr.36.0_70
Abstract In hydrology and flood inundation studies, high temporal and high spatial resolution of surface water extent (SWE) estimates derived from satellite-based remote sensing are crucial for better understanding of natural water variability. However, satellite imagery is often affected by cloud cover, partial satellite coverage, and sensor failures (Pekel et.al., 2016; Ju and Roy, 2008), which can result in gaps in observations of SWE. Therefore, it is necessary to optimize the available satellite information and address the gaps in observations caused by cloud-heavy conditions, partial satellite coverage, and other factors . In this study, we propose to achieve the following objectives: (1) conduct a simple optical-SAR data fusion approach to increase available useful information during the rainy season; and (2) establish a deep learning architecture for effective gap-filling of Landsat-8 and Sentinel-2 SWE observations against any potential causes of missing data. Specifically, the deep learning architecture will leverage the spatio-temporal-pseudo-spectral (STSp ) relationship between observations to fill missing SWE pixels, using a Unet-based convolutional neural networks.
Award 水文・水資源学会／日本水文科学会 2023年度 研究発表会優秀発表賞

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