Authors:
Diana Orlandi
1
;
Federico Galatolo
1
;
Mario Cimino
1
;
Carolina Pagli
2
;
Nicola Perilli
3
;
Joao Pompeu
4
and
Itxaso Ruiz
4
Affiliations:
1
Dept. of Information Engineering, University of Pisa, Italy
;
2
Dept. of Earth Science, University of Pisa, Italy
;
3
Dept. of Civil and Industrial Engineering, University of Pisa, Italy
;
4
BC3, Basque Centre for Climate Change, Leioa, Spain
Keyword(s):
Hydrological Remote Sensing, River Water Surface Mapping, Radar Backscatter, Convolutional Neural Network, Attention.
Abstract:
In the last decades, the effects of global warming combined with growing anthropogenic activities have caused a mismatch in the water supply-demand, resulting in a negative impact on numerous Mediterranean rivers regime and on the functionality of related ecosystem services. Thus, for water management and mitigation of the potential hazards, it is fundamental to efficiently map areal extents of river water surface. Synthetic Aperture Radar (SAR) is one of the satellite technologies applied for hydrological studies, but it has a spatial resolution which is limited for the study of rivers. On the other side, deep learning technology exhibits a high modelling potential with low spatial resolution data. In this paper, a method based on convolutional neural networks is applied to the SAR backscatter coefficient for detecting river water surface. Our experimental study focuses on the lower reach of Mijares river (Eastern Spain), covering a period from Apr 2019 to Sept 2022. Results suggest
that radar backscattering has high potential in modelling water river trends, contributing to the monitoring of the effects of climate change and impacts on related ecosystem services. To assess the effectiveness of the method, the output has been validated with the Normalized Difference Water Index (NDWI).
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