Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Li, Yuan; Grimaldi, Stefania; Walker, Jeffrey P.; Pauwels, Valentijn R. N.

doi:10.3390/rs8060456

Cited by 77 publications

(54 citation statements)

References 219 publications

(358 reference statements)

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“…And despite various efforts to reduce their socio-economic and financial impacts, these initiatives remain insufficient given the constant increase in the frequency of these phenomena due to climate changes affecting our planet. In this context, remote sensing data has repeatedly shown its interest and usefulness during the various phases of flood management process [1][2][3] by providing an overview of the situation on the ground without direct contact with the flooded area and by allowing the decision makers to follow the water extent during the disaster. Hence the interest of developing new techniques to precisely delimit the extent of flooded areas based on high-resolution satellite images made increasingly available.…”

Section: Introductionmentioning

confidence: 99%

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

et al. 2018

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Nowadays, satellite images are considered as one of the most relevant sources of information in the context of major disasters management. Their availability in extreme weather conditions and their ability to cover wide geographic areas make them an indispensable tool toward an effective disaster response. Among the various available sensors, Synthetic Aperture Radar (SAR) is distinguished in the context of flood management by its ability to penetrate cloud cover and its robustness to unfavourable weather conditions. This work aims at developing a new technique for flooded areas extraction from high resolution time-series SAR images. The proposed approach is mainly based on three steps: first, homogeneous regions characterizing water surfaces are extracted from each SAR image using a local texture descriptor. Then, mathematical morphology is applied to filter tiny artifacts and small homogeneous areas present in the image. And finally, spatial and radiometric information embedded in each pixel are extracted and are fused with the same pixel information but from another image to decide if the current pixel belongs to a flooded region. In order to assess the performance of the proposed algorithm, our methodology was applied to time-series images acquired before and during three different flooding events: (1) Richelieu River and lake Champlain floods, Quebec, Canada in 2011; (2) Evros River floods, Greece in 2014 and (3) Western and southwestern of Iran floods in 2016. Experiments show that our approach gives very promising results compared to existing techniques.

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Section: Introductionmentioning

confidence: 99%

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

et al. 2018

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“…Extensive validation studies, either through direct comparison against in-situ measurements, or intercomparison among different remotely sensed products, have been conducted to quantify the accuracy of these soil moisture products [19][20][21][22][23]. In addition, the applications of these products have also been widely explored in many fields like agriculture, hydrology, and climate sciences [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Soil Moisture Estimation from Remote Sensing

Peng

Loew

2017

Water

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Monitoring soil moisture dynamics from local to global scales is essential for a wide range of applications. The field of remote sensing of soil moisture has expanded greatly and the first dedicated soil moisture satellite missions (SMOS, SMAP) were launched, and new missions, such as SENTINEL-1 provide long-term perspectives for land surface monitoring. This special issue aims to summarize the recent advances in soil moisture estimation from remote sensing, including recent advances in retrieval algorithms, validation, and applications of satellite-based soil moisture products. Contributions in this special issue exploit the estimation of soil moisture from both microwave remote sensing data and thermal infrared information. The validation of satellite soil moisture products can be very challenging, due to the different spatial scales of in situ measurements and satellite data. Some papers present validation studies to quantify soil moisture uncertainties. On the other hand, soil moisture downscaling schemes and new methods for soil moisture retrieval from GPS are also addressed by some contributions. Soil moisture data are used in fields like agriculture, hydrology, and climate sciences. Several studies explore the use of soil moisture data for hydrological application such as runoff prediction.

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“…The products after assimilating results in, for instance, significantly improved cyclone prediction, reflecting mostly in the cyclone's track, the associated frontal structure and the associated precipitation along the front [3]. Rainfall monitoring is also important to assess possible anthropogenic impacts on global climate [4][5][6], to monitor hydrometeorological natural disasters, such as flood and flash flood events [7][8][9][10][11], and to improve precipitation estimates in Earth System Models (ESMs) [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Decorrelation of Satellite Precipitation Estimates in Space and Time

et al. 2018

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Precise estimates of precipitation are required for many environmental tasks, including water resources management, improvement of numerical model outputs, nowcasting and evaluation of anthropogenic impacts on global climate. Nonetheless, the availability of such estimates is hindered by technical limitations. Rain gauge and ground radar measurements are limited to land, and the retrieval of quantitative precipitation estimates from satellite has several problems including the indirectness of infrared-based geostationary estimates, and the low orbit of those microwave instruments capable of providing a more precise measurement but suffering from poor temporal sampling. To overcome such problems, data fusion methods have been devised to take advantage of synergisms between available data, but these methods also present issues and limitations. Future improvements in satellite technology are likely to follow two strategies. One is to develop geostationary millimeter-submillimeter wave soundings, and the other is to deploy a constellation of improved polar microwave sensors. Here, we compare both strategies using a simulated precipitation field. Our results show that spatial correlation and RMSE would be little affected at the monthly scale in the constellation, but that the precise location of the maximum of precipitation could be compromised; depending on the application, this may be an issue.

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Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Cited by 77 publications

References 219 publications

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

Recent Advances in Soil Moisture Estimation from Remote Sensing

Decorrelation of Satellite Precipitation Estimates in Space and Time

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