Flood Mapping Using Multi-temporal Sentinel-1 SAR Images: A Case Study—Inaouene Watershed from Northeast of Morocco

Benzougagh, Brahim; Frison, Pierre‐Louis; Meshram, Sarita Gajbhiye; Boudad, Larbi; Dridri, Abdallah; Sadkaoui, Driss; Mimich, Khalid; Khedher, Khaled Mohamed

doi:10.1007/s40996-021-00683-y

Cited by 21 publications

(9 citation statements)

References 20 publications

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“…The increasing rate of urbanisation and the increased severity of environmental hazards underscore the criticality of incorporating deep-learning methodologies in the disciplines of disaster studies and environmental hazard management (Benzougagh et al 2021). These methodologies provide a proactive and evidence-based strategy for reducing risks, improving readiness, and ultimately aiding in the development of urban environments that are more sustainable and resilient.…”

Section: Deep-learning Approaches For Urban Environmental Hazards And...mentioning

confidence: 99%

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

Kumar,

Bassill,

Ghosh

2024

International Journal on Smart Sensing and Intelligent Systems

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Deep learning has changed the approach of urban environmental risk assessment and management. These methods enable solid models for large data sets, enabling early identification, prediction, and description of environmental risks. The current work analyses the advances in deep learning for urban environmental hazard assessments and disaster studies to provide monitoring, management, and mitigation measures. It reports the improvement in self-supervised learning, transformer architectures, persistent learning, attention mechanisms, adversarial robustness, associated learning, meta-learning, and multimodal learning within the domain of urban environmental hazard analysis. These approaches allow the creation of robust models for handling vast data volumes, facilitating early detection, prediction, and characterisation of diverse environmental threats. This trends analysis for urban applications will bring insights for connecting deep-learning models for effective and proactive approaches to tackle urban environmental hazards and disasters.

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Section: Deep-learning Approaches For Urban Environmental Hazards And...mentioning

confidence: 99%

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

Kumar,

Bassill,

Ghosh

2024

International Journal on Smart Sensing and Intelligent Systems

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“…The Sentinel-1 A/B satellite of the European Space Agency (ESA) provides highresolution (10 × 10 m) Synthetic Aperture Radar (SAR) data and orbits with a revisit cycle of 12 d. The SAR backscattering coefficient, which is the magnitude of the microwave signal received from the surface, is closely related to the soil moisture content [38]. Therefore, the characteristics of the backscattering coefficient have been utilized for flood mapping in various areas for natural risk management and land use planning [39,40]. Additionally, studies have been conducted for soil moisture mapping at the regional and field scales through linear regression analysis between the backscattering coefficient and the soil moisture observed at the surface [41][42][43][44].…”

Section: Spatial Distribution Of Soil Parameters Based On Remotely Se...mentioning

confidence: 99%

Analysis of Effects of Spatial Distributed Soil Properties and Soil Moisture Behavior on Hourly Streamflow Estimate through the Integration of SWAT and LSM

Lee,

Lim,

Kim

2024

Sustainability

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This study addresses the challenge of accurately estimating hourly flow and soil moisture by integrating the Soil and Water Assessment Tool (SWAT) with a Land Surface Model (LSM). Our approach enhances SWAT by incorporating spatially distributed soil properties and a physically-based soil moisture process, using the Noah LSM for hourly soil moisture estimation. This integration captures spatial variations in soil moisture and hydraulic properties from remote sensing across the watershed. The parameter sensitivity analysis and the calibration of hourly flow were significantly impacted by the physically-based hourly soil moisture routing and the incorporation of spatially distributed soil properties. Consequently, the modified SWAT model showed improved accuracy in hourly flow simulations for long-term and multiple rainfall events. Validation results showed significant improvements, with Coefficient of Determination (R2) and Nash and Sutcliffe Efficiency (NSE) increasing by 25.95% and 33.3%, respectively, and Percent Bias (PBIAS) decreasing by 85.8%. Notably, the average error for peak flows across eight events decreased by 49%. These findings highlight the importance of initializing soil parameters based on spatial soil moisture distribution and incorporating physical process-based moisture routing to enhance hourly flow simulation accuracy. Future research should focus on validating the physical feasibility of the soil parameter set in the study area with detailed hourly flow and soil moisture data and exploring its applicability in various regions. This study provides valuable insights for the scientific community, water resources, and agricultural decision-makers regarding integrated modeling of soil moisture and hourly flow, which can inform dam operation management, disaster planning, and crop yield improvement.

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“…Typically, SAR images with VV and VH polarizations are used to monitor oodwater, and their applicability for mapping ood inundation was investigated (Agnihotri et al, 2019). Currently, operational satellites, namely RADARSAT-2, TerraSAR, ALOS-2, and Sentinel-1, provide SAR data with global uniformity (Benzougagh et al, 2022). Sentinel-1, TerraSAR, RADARSAT-2, and ALOS are SAR satellites that operate in different frequency bands.…”

Section: Introductionmentioning

confidence: 99%

Near real-time flood inundation and hazard mapping of Baitarani River Basin using Google Earth Engine and SAR imagery

Atchyuth,

Swain,

Das

2023

Environ Monit Assess

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Flood Inundation mapping and satellite imagery monitoring are critical and effective responses during ood events. Mapping of a ood using optical data is limited due to the unavailability of cloud-free images. Because of its capacity to penetrate clouds and operate in all kinds of weather, synthetic aperture radar is preferred for water inundation mapping. Flood mapping in Eastern India's Baitarani River Basin for 2018, 2019, 2020, 2021, and 2022 was performed in this study using Sentinel-1 imagery and Google Earth Engine with Otsu's algorithm. Different machine-learning algorithms were used to map the LULC of the study region. Dual polarizations VH and VV and their combinations VV×VH, VV + VH, VH-VV, VV-VH, VV/VH, and VH/VV were examined to identify non-water and water bodies. The Normalized Difference Water Index (NDWI) map derived from Sentinel-2 data validated the surface water inundation with 80% accuracy. The total inundated areas were identi ed as 440.

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Flood Mapping Using Multi-temporal Sentinel-1 SAR Images: A Case Study—Inaouene Watershed from Northeast of Morocco

Cited by 21 publications

References 20 publications

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

Analysis of Effects of Spatial Distributed Soil Properties and Soil Moisture Behavior on Hourly Streamflow Estimate through the Integration of SWAT and LSM

Near real-time flood inundation and hazard mapping of Baitarani River Basin using Google Earth Engine and SAR imagery

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