Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 114417 |
| Seitenumfang | 23 |
| Fachzeitschrift | Remote sensing of environment |
| Jahrgang | 315 |
| Frühes Online-Datum | 9 Okt. 2024 |
| Publikationsstatus | Veröffentlicht - 15 Dez. 2024 |
Abstract
Climate change has intensified flooding in arid and semi-arid regions, presenting a major challenge for flood monitoring and mapping. While satellites, particularly Synthetic Aperture Radar (SAR), allow synoptically observing flood extents, accurately differentiating between sandy terrains and water for arid region flooding remains an open challenge. Current global flood mapping products exclude arid areas from their analyses due to the sand and water confusion, resulting in a critical lack of observations which impedes response and recovery in these vulnerable regions. This paper explores the full potential of Sentinel-1 SAR to improve near-real-time flood mapping in arid and semi-arid regions. By investigating the impact of various parameters such as polarization, temporal information, and interferometric coherence, the most important information sources for detecting arid floods were identified. Using three distinct arid flood events in Iran, Pakistan, and Turkmenistan, different scenarios were constructed and tested using RF to evaluate the effectiveness of each feature. Permutation feature importance analysis was additionally conducted to identify key elements that reduce computational costs and enable a faster response during emergencies. Fusing VV coherence and amplitude information in pre-flood and post-flood imagery proved to be the most suitable approach. Results also show that leveraging crucial features reduces computational time by ∼35% as well as improves flood mapping accuracy by ∼50%. With advancements in cloud processing capabilities, the computational challenges associated with interferometric SAR computations are no longer a barrier. The demonstrated adaptability of the proposed approach across different arid areas, offers a step forward towards improved global flood mapping.
ASJC Scopus Sachgebiete
- Agrar- und Biowissenschaften (insg.)
- Bodenkunde
- Erdkunde und Planetologie (insg.)
- Geologie
- Erdkunde und Planetologie (insg.)
- Computer in den Geowissenschaften
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in: Remote sensing of environment, Jahrgang 315, 114417, 15.12.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Unlocking the full potential of Sentinel-1 for flood detection in arid regions
AU - Garg, Shagun
AU - Dasgupta, Antara
AU - Motagh, Mahdi
AU - Martinis, Sandro
AU - Selvakumaran, Sivasakthy
N1 - Publisher Copyright: © 2024 The Authors
PY - 2024/12/15
Y1 - 2024/12/15
N2 - Climate change has intensified flooding in arid and semi-arid regions, presenting a major challenge for flood monitoring and mapping. While satellites, particularly Synthetic Aperture Radar (SAR), allow synoptically observing flood extents, accurately differentiating between sandy terrains and water for arid region flooding remains an open challenge. Current global flood mapping products exclude arid areas from their analyses due to the sand and water confusion, resulting in a critical lack of observations which impedes response and recovery in these vulnerable regions. This paper explores the full potential of Sentinel-1 SAR to improve near-real-time flood mapping in arid and semi-arid regions. By investigating the impact of various parameters such as polarization, temporal information, and interferometric coherence, the most important information sources for detecting arid floods were identified. Using three distinct arid flood events in Iran, Pakistan, and Turkmenistan, different scenarios were constructed and tested using RF to evaluate the effectiveness of each feature. Permutation feature importance analysis was additionally conducted to identify key elements that reduce computational costs and enable a faster response during emergencies. Fusing VV coherence and amplitude information in pre-flood and post-flood imagery proved to be the most suitable approach. Results also show that leveraging crucial features reduces computational time by ∼35% as well as improves flood mapping accuracy by ∼50%. With advancements in cloud processing capabilities, the computational challenges associated with interferometric SAR computations are no longer a barrier. The demonstrated adaptability of the proposed approach across different arid areas, offers a step forward towards improved global flood mapping.
AB - Climate change has intensified flooding in arid and semi-arid regions, presenting a major challenge for flood monitoring and mapping. While satellites, particularly Synthetic Aperture Radar (SAR), allow synoptically observing flood extents, accurately differentiating between sandy terrains and water for arid region flooding remains an open challenge. Current global flood mapping products exclude arid areas from their analyses due to the sand and water confusion, resulting in a critical lack of observations which impedes response and recovery in these vulnerable regions. This paper explores the full potential of Sentinel-1 SAR to improve near-real-time flood mapping in arid and semi-arid regions. By investigating the impact of various parameters such as polarization, temporal information, and interferometric coherence, the most important information sources for detecting arid floods were identified. Using three distinct arid flood events in Iran, Pakistan, and Turkmenistan, different scenarios were constructed and tested using RF to evaluate the effectiveness of each feature. Permutation feature importance analysis was additionally conducted to identify key elements that reduce computational costs and enable a faster response during emergencies. Fusing VV coherence and amplitude information in pre-flood and post-flood imagery proved to be the most suitable approach. Results also show that leveraging crucial features reduces computational time by ∼35% as well as improves flood mapping accuracy by ∼50%. With advancements in cloud processing capabilities, the computational challenges associated with interferometric SAR computations are no longer a barrier. The demonstrated adaptability of the proposed approach across different arid areas, offers a step forward towards improved global flood mapping.
KW - Coherence
KW - Disaster management
KW - Flood mapping
KW - Machine learning
KW - Remote sensing
KW - SAR
UR - http://www.scopus.com/inward/record.url?scp=85205672329&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2024.114417
DO - 10.1016/j.rse.2024.114417
M3 - Article
AN - SCOPUS:85205672329
VL - 315
JO - Remote sensing of environment
JF - Remote sensing of environment
SN - 0034-4257
M1 - 114417
ER -