Automatic near real-time flood detection using Suomi-NPP/VIIRS data

Li, Sanmei; Sun, Donglian; Goldberg, Mitchell D.; Sjoberg, Bill; Santek, David; Hoffman, Jonathan; DeWeese, Mike; Restrepo, Pedro J.; Lindsey, S.; Holloway, Eric

doi:10.1016/j.rse.2017.09.032

Cited by 77 publications

(55 citation statements)

References 41 publications

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“…It provides a series of multispectral bands with spatial resolutions ranging from 375 to 1,000 m. Its capability in detecting surface water has been tested (Huang et al, ). It has also been used for flood detection (Plumb, ) and shown its potential for near‐real‐time flood monitoring due to its high temporal resolution (Li et al, ). More applications of Suomi NPP‐VIIRS in this field will surely emerge as time goes by and its time series grow.…”

Section: Detection Of Surface Water With Different Sensorsmentioning

confidence: 99%

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Huang

Chen

Zhang

et al. 2018

Reviews of Geophysics

469

281

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Observation of surface water is a functional requirement for studying ecological and hydrological processes. Recent advances in satellite-based optical remote sensors have promoted the field of sensing surface water to a new era. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field, including spatio-temporal scale issues, integration with in situ hydrological data and elevation data, obscuration caused by clouds and vegetation, and the growing need to map surface water at a global scale. Historically, sensors have exhibited a contradiction in resolutions. Techniques including pixel unmixing and reconstruction, and spatio-temporal fusion have been developed to alleviate this contradiction. Spatio-temporal dynamics of surface water have been modeled by combining remote sensing data with in situ river flow. Recent studies have also demonstrated that the river discharge can be estimated using only optical remote sensing imagery, providing valuable information for hydrological studies in ungauged areas. Another historical issue for optical sensors has been obscuration by clouds and vegetation. An effective approach of reducing this limitation is to combine with synthetic aperture radar data. Digital elevation model data have also been employed to eliminate cloud/terrain shadows. The development of big data and cloud computation techniques makes the increasing demand of monitoring global water dynamics at high resolutions easier to achieve. An integrated use of multisource data is the future direction for improved global and regional water monitoring.Plain Language Summary Observing surface water is essential for ecological and hydrological studies. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field. For example, it was found that pixel unmixing and reconstruction, and spatio-temporal fusion are two common and low-cost approaches to enhance surface water monitoring. Remote sensing data have been integrated with in situ river flow to model spatio-temporal dynamics of surface water. Recent studies have also proved that the river discharge can be estimated using only optical remote sensing imagery. This will be a breakthrough for hydrological studies in ungauged areas. Optical sensors are also easily obscured by clouds and vegetation. This limitation can be reduced by integrating optical data with synthetic aperture radar data and digital elevation model data. There is increasing demand of monitoring global water dynamics at high resolutions. It is now easy to achieve with the development of big data and cloud computation techniques. Enhanced global or regional water monitoring in the future requires integrated use of multiple sources of remote sensing data.

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Section: Detection Of Surface Water With Different Sensorsmentioning

confidence: 99%

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Huang

Chen

Zhang

et al. 2018

Reviews of Geophysics

469

281

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“…This is not routinely the case for remotely sensed flood maps that are delivered to end‐users during disasters, although validation should clearly be an implicit part of the mapping process. This said, some (semi‐)operational algorithms implement probability based or fractional flood mapping (e.g., Li et al, ; Matgen et al, ; Schumann et al, ; the “National Aeronautics and Space Administration NRT Global Flood Mapping” product ), and although this is no form of validation, it does, however, convey some uncertainty in the mapping process (Figure c,d).…”

Section: Are Forecast and Model Simulation Flood Maps Actually More Rmentioning

confidence: 99%

The need for scientific rigour and accountability in flood mapping to better support disaster response

Schumann

2019

Hydrological Processes

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“…The developed VIIRS flood mapping software is applied to process the VIIRS data and produce the VIIRS flood maps. A comprehensive introduction to this software package can be found in the reference published in 2017 [26].…”

Section: Viirs Flood Mappingmentioning

confidence: 99%

“…These new features make the VIIRS imagery very attractive for near real-time flood detection. Under the support of the NOAA JPSS program office, near real-time flood maps have been derived from the VIIRS [23][24][25][26]. The SNPP is thus able to provide a flood detection capability to support the National Weather Services (NWS), the U.S. Army Corps of Engineering (USACE), the Federal Emergency Management Agency (FEMA), the U.S. Geological Survey (USGS), and local agencies.…”

Section: Introductionmentioning

confidence: 99%

Contributions of Operational Satellites in Monitoring the Catastrophic Floodwaters Due to Hurricane Harvey

Goldberg¹,

Goodman

et al. 2018

Remote Sensing

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Hurricane Harvey made landfall as a Category-4 storm in the United States on 25 August 2017 in Texas, causing catastrophic flooding in the Houston metropolitan area and resulting in a total economic loss estimated to be about $125 billion. To monitor flooding in the areas affected by Harvey, we used data from sensors aboard the Suomi National Polar-Orbiting Partnership Satellite (SNPP) and the new Geostationary Operational Environmental Satellite (GOES)-16. The GOES-16 Advanced Baseline Imager (ABI) observations are available every 5 min at 1-km spatial resolution across the entire United States, allowing for the possibility of frequent cloud free views of the flooded areas; while the higher resolution 375-m imagery available twice per day from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the SNPP satellite can observe more details of the flooded regions. Combining the high spatial resolution from VIIRS with the frequent observations from ABI offers an improved capability for flood monitoring. The flood maps derived from the SNPP VIIRS and GOES-16 ABI observations were provided to the Federal Emergency Management Agency (FEMA) continuously during Hurricane Harvey. According to FEMA’s estimate on 3 September 2017, approximately 155,000 properties might have been affected by the floodwaters of Hurricane Harvey.

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Automatic near real-time flood detection using Suomi-NPP/VIIRS data

Cited by 77 publications

References 41 publications

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

The need for scientific rigour and accountability in flood mapping to better support disaster response

Contributions of Operational Satellites in Monitoring the Catastrophic Floodwaters Due to Hurricane Harvey

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