Satellite Flood Assessment and Forecasts from SMAP and Landsat

Jinyang Du; John Kimball; Justin Sheffield; Ming Pan; Colby Fisher; Hylke Beck; Eric Wood

doi:10.1109/IGARSS39084.2020.9323552

Satellite Flood Assessment and Forecasts from SMAP and Landsat

Jinyang Du, John Kimball, Justin Sheffield, Ming Pan, Colby Fisher, Hylke Beck, Eric Wood

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

The capability of synergistic satellite flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. In this study, the Soil Moisture Active Passive (SMAP) fractional water (FW) data sets were used for flood mapping over southeast Africa during the Cyclone Idai event. We then developed a machine-learning approach with the support of Google Earth Engine (GEE) for 24-hour flood forecasting and 30-m inundation mapping using observations from SMAP and Landsat coupled with rainfall forecasts from Global Forecast System (GFS) 384-Hour Predicted Atmosphere Data. The forecast results for the Idai event captured the flood dynamics at 30-m resolution and showed inundation patterns consistent with independent satellite Synthetic Aperture Radar (SAR) observations. The approach provides new capacity for flood monitoring and forecasts from synergistic satellite observations and is particularly valuable for data sparse regions.

Original language	English (US)
Title of host publication	International Geoscience and Remote Sensing Symposium (IGARSS)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3334-3337
Number of pages	4
ISBN (Print)	9781728163741
DOIs	https://doi.org/10.1109/IGARSS39084.2020.9323552
State	Published - Sep 26 2020
Externally published	Yes

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Generated from Scopus record by KAUST IRTS on 2023-02-14

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10.1109/IGARSS39084.2020.9323552

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title = "Satellite Flood Assessment and Forecasts from SMAP and Landsat",

abstract = "The capability of synergistic satellite flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. In this study, the Soil Moisture Active Passive (SMAP) fractional water (FW) data sets were used for flood mapping over southeast Africa during the Cyclone Idai event. We then developed a machine-learning approach with the support of Google Earth Engine (GEE) for 24-hour flood forecasting and 30-m inundation mapping using observations from SMAP and Landsat coupled with rainfall forecasts from Global Forecast System (GFS) 384-Hour Predicted Atmosphere Data. The forecast results for the Idai event captured the flood dynamics at 30-m resolution and showed inundation patterns consistent with independent satellite Synthetic Aperture Radar (SAR) observations. The approach provides new capacity for flood monitoring and forecasts from synergistic satellite observations and is particularly valuable for data sparse regions.",

author = "Jinyang Du and John Kimball and Justin Sheffield and Ming Pan and Colby Fisher and Hylke Beck and Eric Wood",

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year = "2020",

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day = "26",

doi = "10.1109/IGARSS39084.2020.9323552",

language = "English (US)",

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T1 - Satellite Flood Assessment and Forecasts from SMAP and Landsat

AU - Du, Jinyang

AU - Kimball, John

AU - Sheffield, Justin

AU - Pan, Ming

AU - Fisher, Colby

AU - Beck, Hylke

AU - Wood, Eric

N1 - Generated from Scopus record by KAUST IRTS on 2023-02-14

PY - 2020/9/26

Y1 - 2020/9/26

N2 - The capability of synergistic satellite flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. In this study, the Soil Moisture Active Passive (SMAP) fractional water (FW) data sets were used for flood mapping over southeast Africa during the Cyclone Idai event. We then developed a machine-learning approach with the support of Google Earth Engine (GEE) for 24-hour flood forecasting and 30-m inundation mapping using observations from SMAP and Landsat coupled with rainfall forecasts from Global Forecast System (GFS) 384-Hour Predicted Atmosphere Data. The forecast results for the Idai event captured the flood dynamics at 30-m resolution and showed inundation patterns consistent with independent satellite Synthetic Aperture Radar (SAR) observations. The approach provides new capacity for flood monitoring and forecasts from synergistic satellite observations and is particularly valuable for data sparse regions.

AB - The capability of synergistic satellite flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. In this study, the Soil Moisture Active Passive (SMAP) fractional water (FW) data sets were used for flood mapping over southeast Africa during the Cyclone Idai event. We then developed a machine-learning approach with the support of Google Earth Engine (GEE) for 24-hour flood forecasting and 30-m inundation mapping using observations from SMAP and Landsat coupled with rainfall forecasts from Global Forecast System (GFS) 384-Hour Predicted Atmosphere Data. The forecast results for the Idai event captured the flood dynamics at 30-m resolution and showed inundation patterns consistent with independent satellite Synthetic Aperture Radar (SAR) observations. The approach provides new capacity for flood monitoring and forecasts from synergistic satellite observations and is particularly valuable for data sparse regions.

UR - https://ieeexplore.ieee.org/document/9323552/

UR - http://www.scopus.com/inward/record.url?scp=85102007901&partnerID=8YFLogxK

U2 - 10.1109/IGARSS39084.2020.9323552

DO - 10.1109/IGARSS39084.2020.9323552

M3 - Conference contribution

SN - 9781728163741

SP - 3334

EP - 3337

BT - International Geoscience and Remote Sensing Symposium (IGARSS)

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Satellite Flood Assessment and Forecasts from SMAP and Landsat

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