Observing floods from space: Experience gained from COSMO-SkyMed observations

Pierdicca, Nazzareno; Pulvirenti, Luca; Chini, Marco; Guerriero, L.; Candela, Leonardo

doi:10.1016/j.actaastro.2012.10.034

Cited by 110 publications

(75 citation statements)

References 33 publications

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“…Ref. [7] showed the potential of the COSMO-SkyMed data for flood detection by showing case studies in several locations all over the globe (e.g., Tarano River overflow, Italy, April 2009; Pakistan inundation, July-September 2010; Thailand flood, October 2010; and Australia flood, January 2011). COSMO-SkyMed instruments provided very high resolution X-band SAR images, but covered limited areas (the highest spatial resolution is ∼1 m for an observation area of 10 km × 10 km).…”

Section: Introductionmentioning

confidence: 99%

Surface Water Monitoring within Cambodia and the Vietnamese Mekong Delta over a Year, with Sentinel-1 SAR Observations

Pham-Duc

Prigent

Aires

2017

Water

116

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This study presents a methodology to detect and monitor surface water with Sentinel-1 Synthetic Aperture Radar (SAR) data within Cambodia and the Vietnamese Mekong Delta. It is based on a neural network classification trained on Landsat-8 optical data. Sensitivity tests are carried out to optimize the performance of the classification and assess the retrieval accuracy. Predicted SAR surface water maps are compared to reference Landsat-8 surface water maps, showing a true positive water detection of ∼90% at 30 m spatial resolution. Predicted SAR surface water maps are also compared to floodability maps derived from high spatial resolution topography data. Results show high consistency between the two independent maps with 98% of SAR-derived surface water located in areas with a high probability of inundation. Finally, all available Sentinel-1 SAR observations over the Mekong Delta in 2015 are processed and the derived surface water maps are compared to corresponding MODIS/Terra-derived surface water maps at 500 m spatial resolution. Temporal correlation between these two products is very high (99%) with very close water surface extents during the dry season when cloud contamination is low. This study highlights the applicability of the Sentinel-1 SAR data for surface water monitoring, especially in a tropical region where cloud cover can be very high during the rainy seasons.

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Section: Introductionmentioning

confidence: 99%

Surface Water Monitoring within Cambodia and the Vietnamese Mekong Delta over a Year, with Sentinel-1 SAR Observations

Pham-Duc

Prigent

Aires

2017

Water

116

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“…The above effects are also reviewed by Pierdicca et al [14], who employed Italian COSMO SkyMed data for semi-automated flood detection in numerous case study sites located in Italy, Pakistan, Albania, Thailand, and Australia. These authors also underline the impact of microwave frequency on water detectability.…”

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confidence: 99%

“…Here the chance for a water-related return signal (if the stand is not too dense) is definitely higher than for X-or C-band data. Pierdicca et al [14] also state that detailed local knowledge of an area (land cover, terrain, typical flood patterns) definitely helps to improve and interpret the flood mapping result and addresses the trade-off between semi-automated methods involving more human interaction (maybe even manual threshold selection) yielding very high accuracy versus fully automated rapid mapping approaches.…”

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confidence: 99%

Flood Mapping and Flood Dynamics of the Mekong Delta: ENVISAT-ASAR-WSM Based Time Series Analyses

et al. 2013

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Satellite remote sensing is a valuable tool for monitoring flooding. Microwave sensors are especially appropriate instruments, as they allow the differentiation of inundated from non-inundated areas, regardless of levels of solar illumination or frequency of cloud cover in regions experiencing substantial rainy seasons. In the current study we present the longest synthetic aperture radar-based time series of flood and inundation information derived for the Mekong Delta that has been analyzed for this region so far. We employed overall 60 Envisat ASAR Wide Swath Mode data sets at a spatial resolution of 150 meters acquired during the years 2007-2011 to facilitate a thorough understanding of the flood regime in the Mekong Delta. The Mekong Delta in southern Vietnam comprises 13 provinces and is home to 18 million inhabitants. Extreme dry seasons from late December to May and wet seasons from June to December characterize people's rural life. In this study, we show which areas of the delta are frequently affected by floods and which regions remain dry all year round. Furthermore, we present which areas are flooded at which frequency and elucidate the patterns of flood progression over the course of the rainy season. In this context, we also examine the impact of dykes on floodwater emergence and assess the relationship between retrieved flood occurrence patterns and land use. In addition, the advantages and shortcomings of ENVISAT ASAR-WSM based flood mapping are discussed. The results contribute to a comprehensive understanding of Mekong Delta flood

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“…SAR sensors are particularly useful to produce flood maps, owing to their all weather and day night operation capability [8]- [10]. In particular, the latest generation of SAR sensors, such as TerraSAR-X and the COSMO-SkyMed constellation, supplies a large amount of daily acquired radar images, characterized by high spatial resolution, that can be particularly useful in flood detection problems [3], [11], [12].…”

Section: Literature Surveymentioning

confidence: 99%

“…For instance, the contrast between dry soil and flooded one can be significantly reduced in the presence of wind roughening the water surface. Also, vertical structures on the ground, such as trees or man-made objects, could determine a double bounce mechanism of the radar signal, which is enhanced when the ground surface is flooded with respect to dry conditions: this can make the radar return from vegetated or urban flooded areas higher than that under nonflooded conditions [12]. In general, the exploitation of a set of images (as a multi-temporal stack) seems to provide more satisfactory results in the presence of these critical situations [8], [13].…”

Section: Introductionmentioning

confidence: 99%