Journal Pre-proofs Research papers Towards high resolution flood monitoring: An integrated methodology using passive microwave brightness temperatures and Sentinel synthetic aperture radar imagery Ziyue Zeng, Yanjun Gan, Albert J. Kettner, Qing Yang, Chao Zeng, G. Robert Brakenridge, Yang Hong PII: DOI: Reference:
S0022-1694(19)31112-6 https://doi.org/10.1016/j.jhydrol.2019.124377 HYDROL 124377
To appear in:
Journal of Hydrology
Received Date: Revised Date: Accepted Date:
8 May 2019 14 November 2019 18 November 2019
Please cite this article as: Zeng, Z., Gan, Y., Kettner, A.J., Yang, Q., Zeng, C., Brakenridge, G.R., Hong, Y., Towards high resolution flood monitoring: An integrated methodology using passive microwave brightness temperatures and Sentinel synthetic aperture radar imagery, Journal of Hydrology (2019), doi: https://doi.org/10.1016/j.jhydrol. 2019.124377
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Towards high resolution flood monitoring: An integrated methodology
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using passive microwave brightness temperatures and Sentinel synthetic
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aperture radar imagery
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Ziyue Zenga, Yanjun Ganb, Albert J. Kettnerc, Qing Yangd,e, Chao Zengf, G. Robert Brakenridgec, Yang Hongg
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aWater
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China
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bState
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China
Resources Department, Changjiang River Scientific Research Institute, Wuhan, Hubei,
Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing,
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cDartmouth
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and Alpine Research, University of Colorado, Boulder, CO, USA
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dCollege
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eCivil
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fSchool
of Resource and Environmental Science, Wuhan University, Wuhan, Hubei, China
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gSchool
of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK,
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USA
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Corresponding Author 1: Yang Hong
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Corresponding Author 2: Albert J. Kettner
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*Email:
[email protected]
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**Email:
[email protected]
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Phone: +86 18601113856; +1 303-735-5486
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Abstract: Monitoring changes in flood extent is critical for flood control and mitigation
Flood Observatory, Community Surface Dynamic Modelling System, Institute of Arctic
of Civil Engineering and Architecture, Guangxi University, Nanning, Guangxi, China
and Environmental Engineering, University of Connecticut, Storrs, CT, USA
1
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purposes in areas where flooding affects many people and dense infrastructure and
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properties. Remote sensing can be an effective technique to detect changes in surface
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water extent and its dynamics. Compared to optical remote sensing, microwave
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information is suitable for working in any weather condition without severe cloud
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interference. Usually passive microwave data has a high temporal but a rather coarse
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spatial resolution, whereas for active microwave data this is reversed and only with
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ideal satellite constellation observations it can reach high sampling rates. To overcome
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these spatial and temporal restrictions, we proposed an integrated methodology to
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combine the passive and active microwave remote sensing and thus provide flood
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information more frequently at a high resolution. In this paper, a demonstration of the
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methodology is presented for a flood event occurring in Wuhan, Hubei Province of
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China in July 2016. The major inundation occurred along the Jushui River, part of the
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Middle Yangtze River basin. Using the brightness temperature data from a special data
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set (MEaSUREs), a daily passive microwave signal at the resolution of 3.125 km is
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used as an indicator to monitor flood occurrence and obtain the flood duration over time.
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Synthetic Aperture Radar (SAR) imagery (12-day revisit, 10-m spatial resolution) from
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Sentinel-1 was processed to estimate high resolution flood extents within the time span
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of the flood based on a threshold-based method together with the High Above Nearest
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Drainage (HAND) index post-processor. Surface water fraction data generated from
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SAR images presents a strong correlation with the passive microwave signal (R2=0.84)
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when using a quadratic polynomial fit. The average bias between surface water fraction
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computed by the passive microwave signals and SAR observations for water pixels 2
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within the Jushui River basin for the validation dates is 0.34%, indicating that this
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relationship can be applied to interpolate surface water fraction for each pixel along the
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river and other permanent water bodies for days when SAR observations are not
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available. This integrated method takes advantage of both passive and active
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microwave remote sensing and enriches temporally sparse flood data. Given the global
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coverage of the datasets used in this study, it can be utilized to estimate flood status for
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other flood-prone areas and thus contribute towards societal flood preparedness and
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response.
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Key terms: Flood monitoring, microwave remote sensing, MEaSUREs CETB,
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Sentinel-1 SAR
3
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1. Introduction
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1.1 Background
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Monitoring flood inundation extent is critical as flooding is one of the most destructive
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and devastating natural disasters and is responsible yearly for thousands of fatalities,
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many more displacements and loss of properties and infrastructure (Hirabayashi et al.,
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2013; Jonkman, 2005). Timely mapping to determine impacted regions over the course
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of a flood event can be fundamental in providing timely decision support data to
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emergency responders, especially for warning or supporting evacuations in urban areas
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(Klemas, 2015). Additionally, a long-time record of inundation maps provides
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important information for many other applications, including flood hazard assessment,
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public health management, biodiversity conservation, water use and urban planning
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(Eppink et al., 2004; Gessner, 2014; Moel et al., 2009; Viala, 2008). However, flood
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extent is highly dynamic and the terrain of flood-prone areas is often complex, requiring
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high resolution data, which makes flood extent estimation a challenge (Brivio et al.,
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2002).
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Current flood monitoring methods can be divided into two main categories:
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hydrological/hydraulic modelling and satellite observation (Bates, 2004; Bates et al.,
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1997; Khan et al., 2011). Flood monitoring based on hydrological/hydraulic models can
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provide real-time guidance for flood control and mitigation if the model is well
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calibrated and running operationally using high resolution data. However, information
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on model inputs such as observed discharge and accurate river morphology are often
4
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inadequate for many areas, which restricts the applicability of the modelling method
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(Gleason et al., 2014; Pechlivanidis et al., 2011). Weather data over a large region (e.g.,
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the meteorological forcing inputs provided by coarse-resolution satellite estimates or
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point-scale ground observations) can also limit the accuracy of hydrological/hydraulic
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modelling (Tang et al., 2018). With rapid development of remote sensing techniques, a
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number of earth observation satellites are monitoring some of the ongoing processes on
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the Earth’s surface at multiple spatial and temporal scales, providing an alternative way
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to estimate flood dynamics over time. Studies have shown that surface water
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distribution across the globe can be estimated using moderate resolution imagery, such
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as obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS, ~250 m,
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twice a day) (Brakenridge and Anderson, 2006; Carroll et al., 2009) and Landsat (~30
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m, once every 8 days) (Frazier and Page, 2000; Pekel et al., 2016). However, water
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bodies mapping based on these optical remote sensing images can be complicated even
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in good weather conditions. For example, influenced by the factors including
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variabilities in the optical properties, seasonal patterns of phytoplankton and suspended
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matter, optical remote sensing algorithms have to be different for different water bodies
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or even the same water body in different regions (Arst, 2003; Haas et al., 2009). Another
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restriction is that flood monitoring for a certain region at a high spatial resolution with
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a frequent return period (e.g. daily or near daily repeats) can hardly be achieved by only
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optical satellite observations due to cloud cover.
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Dense cloud cover and flooding often goes hand in hand, hampering the use of optical
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satellite data to detect flood extent, at least for part of the time an area is inundated. 5
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During these unsuitable meteorological conditions, instead of detecting inundation by
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optical imagery, microwave remote sensing is advantageous due to its cloud penetrating
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properties (Woodhouse, 2017). The fundamental parameter measured by passive
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microwave radiometers, brightness temperature (TB), is a measurement of the radiance
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of the microwave radiation traveling upward from the Earth’s surface to the satellite
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(Alley and Jentoft, 1999; McFarland et al., 1990). These TB observations can be
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provided by satellite sensors such as the Advanced Microwave Scanning Radiometer-
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Earth Observing System (AMSR-E), the Scanning Multi-channel Microwave
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Radiometer (SMMR) and the Special Sensor Microwave Imager and the Special Sensor
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Microwave Imager Sounder (SSM/I-SSMIS). For the most common sensor channels,
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the low-frequency channels of 6.6 and 10.7 GHz (SSMR) or 6.9 and 10.7 GHz (AMSR-
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E) suffer from radio frequency interferences (Njoku et al., 2005). The 19 and 22 GHz
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(SSM/I-SSMIS) or 18.7 and 23.8 GHz (AMSR-E) channels are close to the water vapor
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spectral line, which could be susceptible to atmospheric contaminations (Skou, 1989).
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Therefore, 36.5 or 37 GHz is optimal for detecting surface water change. Previous
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studies have proven that TB data in the H-polarization at 36.5 or 37 GHz have the
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strongest differential response to water and land with a lesser sensitivity to soil moisture
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(e.g., Brakenridge et al., 2007; Van Dijk et al., 2016). Such remote sensing data can be
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used to detect changes in surface water areas and thus monitor river discharge changes:
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increased discharge is accompanied by both higher stage and a larger area of surface
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water (Brakenridge et al., 2005, 2012).
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Besides these relative low resolution measurements, microwave spaceborne Synthetic 6
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Aperture Radar (SAR) sensors can also provide valuable high resolution spatial
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observations and thus map flood extent (Henry et al., 2006; Oberstadler et al., 1997).
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Non-disturbed surface water acts like a mirror to the side-looking beamed radar,
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resulting in dramatically lower backscatter than most non-water land cover types. This
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makes water appear very dark in SAR images, which can thus discriminate water and
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non-water features (Brivio et al., 2002; Martinis and Voigt, 2009). To improve flood
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mapping accuracy based on SAR imagery is one of the research focuses. Different
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approaches have been proposed and presented in many successful flood mapping cases
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(e.g., Behnamian et al., 2017; Bolanos et al., 2016; Borghys et al., 2006; Li and Wang
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2015; Martinis et al., 2015; Schlaffer et al., 2015). Among them, because of the
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simplicity and expediency, threshold-based approaches have been extensively used,
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especially in detecting surface water operationally (Shen et al., 2019a, b; Twele et al.,
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2016). Selecting an appropriate threshold of the intensity SAR imagery to differentiate
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water from land is critical in these approaches. In recent techniques, the threshold value
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can be determined by combining the backscatter image thresholding with segmentation
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algorithms, which involve data or approaches such as active contours (e.g., Hahmann
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et al., 2010), object-oriented method (e.g., Martinis et al., 2015), texture information
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(e.g., Bolanos et al., 2016; Li and Wang 2015) and topography data (e.g., Bioresita et
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al., 2018). Relying on the ancillary data or approaches, these techniques often result in
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high accuracies mainly by reducing image speckle and false classification for flood
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mapping and in the meanwhile longer processing times.
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To date, techniques for flood extent monitoring based on both passive microwave and 7
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SAR imagery have led to many successful applications to flood detection (e.g.,
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Galantowicz and Picton, 2014; Martinis et al., 2015; Ormsby et al., 1985; Sippel et al.,
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1994). Flood monitoring requires frequent large coverage observations with a high
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spatial resolution. Generally, observations at an interval of 6 to 12 hours with a
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resolution of 20 m are recommended to successfully map the extent of flash flooding
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(Mouratidis and Sarti, 2013). The time constraint for regular floods can be less critical,
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but daily observations should be achieved (Yilmaz et al., 2010). Methods using passive
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microwave remote sensing can obtain signals at a high frequency (e.g., twice a day).
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However, the coarse resolution (e.g., 10 to 70 km) is often impractical for detailed flood
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mapping. In contrast, SAR sensors, such as TerraSAR-X, COSMO-SkyMed, ALOS-2
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and Sentinel-1, achieve much higher spatial resolutions (e.g., up to 1-10 m). Their
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repeat cycle is often 6 to 16 days depending on the latitudinal location on the earth. In
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some instances, with SAR constellations higher temporal sampling rate can be reached.
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For example, repeat time can be occasional 1-day and average 4-day for COSMO-
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SkyMed (Milillo et al., 2016) and revisit frequency is 3-day at equator and less than 1-
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day at high latitudes for Sentinel-1 (Torres et al., 2012). However, such temporal
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sampling rate can hardly meet the demands of high resolution flood mapping, especially
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of capturing the maximum extent of inundation. It is clear that, at present, neither SAR
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nor passive microwave imaging alone can provide adequate data. Therefore this paper
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explores the use of both together.
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1.2 Framework and objectives
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In this paper, we proposed an integrated methodology to combine both passive and 8
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active microwave remote sensing algorithms to overcome spatial and temporal
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restrictions for high resolution flood monitoring. Specifically, this integrated
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methodology is based on the TB data provided by the Making Earth System data records
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for Use in Research for Earth Science (MEaSUREs) multi-platform Calibrated
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Brightness Temperature Earth System Data Record (ESDR) (CETB) (Brodzik et al.,
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2016) and SAR images from Sentinel-1 satellites (Torres et al., 2012). The main
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objective of this study is to determine the feasibility of using passive microwave remote
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sensing data to estimate flood extent combined with SAR-based flood mapping
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information. This paper was organized as follows. First, the study area and the test event
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was introduced, followed by a description of the data used for analysis and validation.
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Then, the proposed methodology was described and applied in these three steps: (1)
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with a high temporal sampling rate TB data were used to derive a daily flood occurrence
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signal (i.e., M/C signal, hereinafter) using an existing method proposed by Brakenridge
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et al. (2007), which was subsequently analyzed to determine the flood time span; (2)
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according to this time span, SAR imagery from Sentinel-1 were selected to produce
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flood maps at an interval of ~12 days with 10 m spatial resolution based on a widely
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used water/land classification algorithm. Accuracy assessment was performed based on
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a high-resolution GeoEye-1 image and the flood inundation result from another reliable
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source; (3) further investigation was conducted to explore how the M/C signal
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correlated with the surface water fraction derived from SAR-based flood mapping
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results. Their relation curve could then be used together with the daily M/C signal to
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provide the dynamic surface water fraction information at a high spatial and temporal 9
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resolution (3.125 km × 3.125 km/daily). In the end, the significances and limitations of
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this integrated methodology were discussed and summary and conclusions were drawn.
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2. Study area, flood event and data coverage
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2.1 Study area and flood event
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In Southern and Central China, driven by the strong El Niño of 2015-2016, precipitation
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started earlier and was more pronounced in the summer of 2016 than usual, thus
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resulting in severe flooding (Tang et al., 2017). The Xinzhou district in Wuhan city of
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Hubei Province, located along the Yangtze River (30.68°N, 114.84°E), was one of the
195
most affected areas. Precipitation of record-breaking magnitude caused a levee failure
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on July 1st, 2016 on one of the tributaries of the Yangtze River, the Jushui River. Figure
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1 shows the topography of the study area (a subset of a Sentinel-1A SAR scene) from
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the Shuttle Radar Topography Mission (SRTM) 1 arc-second Digital Elevation Model
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(DEM) (Farr et al., 2007) with a superimposed flood occurrence map from the Global
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Surface Water Dataset (Pekel et al., 2016) derived from Landsat imagery, which is
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provided by the European Commission’s Joint Research Center (JRC). Permanent
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water surfaces are defined here as areas that experience 80-100% occurrence of surface
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water over a 32-year period, 1984 to 2015.
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[insert Figure 1 here]
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Figure 1. Overview of the study area location for the 2016 flood event in Wuhan, Hubei
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Province of China (within the middle Yangtze River basin), showing the topography (Source:
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Shuttle Radar Topography Mission (SRTM) 1 arc-second Digital Elevation Model) overlaid by a 10
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flood occurrence map (Source: European Commission Joint Research Center/Google; Rectangle:
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the coverage of a GeoEye-1 image as reference data (see Section 2.2)).
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2.2 Data
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(1) Passive microwave data
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Passive microwave data used in this study is from the MEaSUREs CETB product
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(https://nsidc.org/data/nsidc-0630), released by the National Snow and Ice Data Center
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(NSIDC). It provides global brightness temperature time series generated from data
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records of the SMMR, SSM/I-SSMIS and AMSR-E sensors from 1978 to mid-2017 in
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the Equal-Area Scalable Earth Grid 2.0 (EASE-Grid 2.0) projection (Brodzik et al.,
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2012). These data have been produced at smoothed 25 km resolution and spatially
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enhanced resolutions up to 3.125 km using imaging reconstruction algorithms
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developed at Brigham Young University (Long and Stroeve, 2011). The resolution
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enhancement for the data is channel-dependent (Long, 2015). For this study, the
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microwave domain of 37 GHz in the H-polarization from the SSMIS on the Defense
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Meteorological Satellite Program (DMSP) was chosen to provide the TB data. It has the
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highest spatial resolution (3.125 km) with the temporal resolution of twice a day.
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(2) SAR imagery
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Sentinel-1 is a constellation of two satellites, including Sentinel-1A launched in April
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2014 and Sentinel-1B launched in April 2016 by the European Space Agency (ESA).
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It provides C-band data in two polarization configurations (VH and VV) with a
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temporal single pass return period of 12 days at the equator. The data are acquired in 11
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four modes: Stripmap (SM), Interferometric Wide Swath (IW), Extra Wide Swath (EW)
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and Wave (WW). To detect surface water changes related to a flood event, we used
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Level-1 Ground Range Detected (GRD) products in IW mode of VH polarization.
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Because SAR images from Sentinel-1B are not available for the flood event, only
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specifications of the SAR scenes from Sentinel-1A are provided in Table 1 (6 scenes
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of pre-storm and during-flood) and Table 2 (one scene of after-flood).
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Table 1 Parameters of Sentinel-1A SAR scenes Sentinel 1-A SAR scenes
Observation
Storm duration area
Acquisition obit
Acquisition time Incidence angle (°) May 6th, 2016
30.68~46.10
May 18th, 2016
30.68~45.96 June 30th-July 7th
Wuhan NO. 40
May 30th, 2016
30.67~46.10 and July 12th-July
(Middle (Ascending)
June 11th, 2016
30.67~45.96 15th, 2016
Yangtze River) July 5th, 2016
30.67~45.96
July 17th, 2016
30.67~45.96
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(3) Reference data
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GeoEye-1 is a high-resolution commercial Earth observation satellite operated by
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DigitalGlobe. Launched on September 6th, 2008, it collects images at a spatial
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resolution of 0.46 m (panchromatic) and 1.84 m (multispectral, 4 bands). The satellite
240
uses a low earth sun-synchronous orbit with a mean altitude of 681 km and its swath
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width is 15.2 km. We selected one GeoEye-1 scene acquired on August 10th, 2016 from 12
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the DigitalGlobe GBDX platform (https://platform.digitalglobe.com/gbdx/) as around
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July only on that day both GeoEye-1 and Sentinel-1 had observations for the study area.
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Table 2 shows the parameters of the available GeoEye-1 imagery for validation and its
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corresponding Sentinel-1A scene. The coverage of this GeoEye-1 image is presented
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in Figure 1.
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Table 2 Sentinel-1A SAR scene and the corresponding reference GeoEye-1 image
Validation area
Acquisition time
GeoEye-1 coverage (see Figure 1)
Acquisition Satellite sensor obit
Incident Solar azimuth angle/offangle (°) nadir angle (°)
Sentinel-1A
Ascending
30.67~45.96
-
GeoEye-1
Descending
13.61
126.06
Aug 10th, 2016
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In addition, the SRTM 1 arc-second DEM and the river network shapefile from the
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Global River Network (GRIN) database (Schneider et al., 2017) was used to generate
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the Height Above the Nearest Drainage (HAND) index (Rennó et al., 2008) to reduce
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falsely classified water pixels derived from the SAR images.
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3. Methods
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We developed a workflow of the new data integration methodology to detect inundation
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extent over time, as shown in Figure 2. It includes four major steps: (1) generating daily
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time series of the passive microwave signal (i.e., M/C signal, see Section 3.1) from TB
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data of the MEaSUREs CETB product; (2) classifying water/land based on Sentinel-
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1A SAR imagery and assessing the accuracy; (3) developing the relation curve between 13
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M/C signal and surface water fraction derived from SAR-based water maps from step
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(2); and (4) generating the daily surface water fraction estimated by M/C signal using
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the relationship developed in step (3). In step (2), the accuracy assessment of water/land
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classification from SAR imagery is conducted separately based on the water map
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derived from the GeoEye-1 image and flood inundation mapped by a recently
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developed NRT system named RAdar-Produced Inundation Diary (RAPID) (Shen et
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al., 2019a). [insert Figure 2 here]
265
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Figure 2. A schematic overview of the developed workflow of the integrated methodology to
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detect inundation extent over time.
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3.1 Processing steps for passive microwave data
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There is a significant radiation dissimilarity of water and land and therefore the TB data
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can be used to detect changes in surface water area. However, TB measurements are
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often influenced by factors that could vary in time and space such as physical
272
temperature, atmosphere attenuation and differences in emissivity and the relative
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contribution of these factors to the TB value cannot be measured. Studies have shown
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that if the contribution of these factors is assumed to be constant over a certain region
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when comparing a “wet pixel” (measurement pixel, hereafter M) partly covering a river
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channel for a potential inundation location with the nearby “dry pixel” (calibration pixel,
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hereafter C) only covering land, the potential noise caused by the aforementioned
278
factors can be mostly eliminated (Brakenridge et al., 2012; De Groeve, 2010). 14
279
Specifically, the M value is the TB value of the M located on the river, including the
280
river channel and parts of the floodplain along it. The C value is the brightest (95th
281
percentile, to avoid selecting outliers) value of the pixels within a 9×9 pixel array
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centered on the M. The M and C values can be expressed via Eq. (1)-(3):
283
284
285
(1)
𝑇B = (1 ― 𝑤)𝑇B,land +𝑤𝑇B,water
(2)
M = 𝑇B,M = 𝑇M((1 ― 𝑤)𝜀land,M +𝑤𝜀water)
(3)
C = 𝑇B,C = 𝑇C𝜀land,C
286
where 𝑤 stands for the surface water fraction of the pixel; 𝑇B,land is the TB of land;
287
𝑇B,water is the TB of water; 𝑇B,M is the TB of the M; 𝑇M is the physical temperature of
288
the M; 𝜀land,M is the emissivity of the land part of the M; 𝑇B,C is the TB of the C; 𝑇C
289
is the physical temperature of the C; 𝜀land,C is the emissivity of the land part of the C
290
and 𝜀water is the emissivity of water.
291
For nearby pixels within a certain area, their physical temperatures can be considered
292
as the same and the emissivity of the land portion for each pixel can be assumed to be
293
approximately equal (marked as 𝜀land) (De Groeve, 2010), shown by Eq. (4)-(5): (4)
294
𝜀land,M
295
𝑇M ≈ 𝑇C
296
Then the ratio, M/C is simplified as a function of w, as shown in Eq. (6):
297 298
M C
≈
𝜀land,C
=
𝜀land
(5)
𝑇B,M
= 𝑇B,C =
𝑇M((1 ― 𝑤)𝜀land + 𝑤𝜀water) 𝑇c𝜀land
𝜀water
≈ 1-𝑤 + 𝑤 𝜀land = 𝑓(𝑤)
(6)
M/C has been proven to have a strong correlation with observed discharge in some 15
299
regional studies (e.g., Brakenridge et al., 2005, 2012; De Groeve et al., 2015). It would
300
be more correlated to the surface water fraction than the discharge. For the flood
301
occurrence indicator, a low ratio of M to C value means high coverage of the pixel by
302
water. In this study, we used the average value of ascending and descending orbits
303
(night and day, around 6:35 pm and 6:35 am for DMSP-F17 (17th Flight), respectively)
304
to best match with the Sentinel-1A observation (around 10:20 am for scenes in Table 1
305
and 2). To reduce noise and remove data gaps, a 4-day forward running mean was
306
applied generate the time series of daily M/C signals, because it can fill between any
307
missing days without over-smoothing the M/C time series.
308
3.2 Preprocessing for SAR images
309
Level-1 GRD data was preprocessed using the ESA’s Sentinel-1 toolbox (S1TBX)
310
embedded
311
(https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-1). After removing the thermal
312
noise, SAR images were calibrated to directly relate pixel values to radar backscatter,
313
which then was filtered using a 7×7 window Lee filter (Lee, 1980). This filter can
314
remove the speckle noise by minimizing either the mean square error or the weighted
315
least square estimation for the SAR imagery without eliminating fine details (Jensen,
316
2004). Additionally, a terrain correction was conducted to compensate for topography-
317
induced distortion and then scenes were reprojected to the WGS84 geographic
318
projection. After these steps, as usually expressed, the digital number (DN) values of
319
each SAR scene were converted into backscatter coefficients in decibel (dB) scale as
320
σ0 at a resolution of 10 m. Incidence angles range from 1° to 60.78°, which can cause
in
the
Sentinel
Application
16
Platform
(SNAP)
321
a backscatter energy reduction from near to far range. To limit the σ0 variations, a
322
widely used cosine-based normalization method was applied (Mladenova et al., 2013):
323
𝜎0ref =
324
where 𝜎0ref stands for the normalized backscattering coefficient; 𝜎0𝜃 is the measured
325
backscattering coefficient; 𝜃 is the incidence angle and 𝜃ref is the reference angle,
326
which is set to be 30° in this study.
327
3.3 Flood mapping based on SAR images
328
(1) Threshold-based algorithm for water/land classification
329
Threshold-based methods are commonly used in unsupervised classification because of
330
its simplicity and flexibility. Because backscatter of the cross-polarized channels (HV,
331
VH) is less affected by wind than that of the co-polarized channels (HH, VV) (Henry
332
et al., 2006; Schumann et al., 2007), the threshold was applied to generate an initial
333
water/land classification based on the normalized backscattering coefficient of the VH
334
polarization. The threshold value for each SAR scene was obtained using the Otsu’s
335
method (Otsu, 1979), which is an automatic grey-level histogram shape-based
336
binarization algorithm widely used for SAR-based classification. This method separates
337
water and land pixels for images having a bimodal histogram and is often adopted in
338
SAR-based flood mapping (e.g., Huang et al., 2018a; Li and Wang 2015; Schlaffer et
339
al., 2015; Schumann et al., 2010).
340
(2) Post-processing for water/land classification
𝜎0𝜃cos2 (𝜃ref)
(7)
cos2 (𝜃)
17
341
Instead of using a simple slope or elevation filter to remove potential misclassifications,
342
results of the threshold-based SAR flood algorithm were post-processed by the HAND
343
index to eliminate the false positive water classification. It can be regarded as a
344
normalized DEM terrain descriptor to show the terrain’s ability of having standing
345
surface water as some riverbank pixels with high slope can also be flooded. This index
346
is defined as the height difference between a certain DEM pixel and its nearest drainage
347
network pixel. Large height difference represents high draining ability. Based on the
348
GRIN river network and SRTM 1 arc-second DEM data, flow direction and flow
349
accumulation were derived to determine the drainage pixels and height difference was
350
then calculated. Figure 3 shows the HAND index for the study area. We assume that
351
regions with a HAND index above 15 m are not prone to flooding. This criterion was
352
then used to remove the pixels which were falsely classified as water due to the shadow
353
effect caused by the steep terrain in the SAR images, therefore to improve the
354
classification accuracy of the threshold-based algorithm in a hydrologically plausible
355
way. [insert Figure 3 here]
356
357
Figure 3. HAND (Height Above Nearest Drainage) index for the study area.
358
(3) Flood inundation mapping
359
After the above steps, the surface water extent was estimated for each SAR images.
360
Therefore, the flood caused inundation can be derived from the water/land classification
361
results from a pair of SAR images (pre-storm and during-flood). If a pixel is classified 18
362
as “water” for the during-flood scene and as “land” for the pre-storm scene, it is defined
363
as an inundation pixel.
364
3.4 Water classification based on GeoEye-1 data
365
The high-resolution GeoEye-1 image was used to provide reference water map. There
366
are generally two types of index formulation models for water/non-water classification
367
for near-infrared and RGB bands: the coefficient model such as the High Resolution
368
Water Index (HRWI) (Yao et al., 2015) and the ratio model such as the Normalized
369
Difference Water Index (NDWI) (McFeeters, 1996). These two typical water indices
370
can be calculated by the following equations, respectively.
371
HRWI = 6 × G ― R ― 6.5 × NIR + 0.2
372
NDWI = G + NIR
373
where, G is the reflectance of the green band; R is the reflectance of the red band and
374
NIR is the reflectance of the near infrared band.
375
After choosing a proper threshold for each water index, the NDWI-based and HRWI-
376
based water classification maps (~2 m) from the GeoEye-1 image can be derived. To
377
assess the classification results of both water indices, a point set containing 1,500 points
378
for each class was extracted based on GeoEye-1 imagery by visual interpretation. The
379
one with higher accuracy can be used to evaluate the SAR-based water/land
380
classification results.
(8)
G ― NIR
(9)
19
381
4 Results
382
4.1 M/C indicator for flood occurrence
383
Considering the pixel size of M is 3.125 km and the major inundation area for the flood
384
event was along this river after a levee failure occurred, sites for the M were selected
385
on the Jushui River. To make the approach easy to apply, it is recommended that if
386
chosen properly just one site is needed to develop the relation curve between M/C and
387
the observed discharge by previous studies (e.g., Brakenridge et al., 2007, 2012; De
388
Groeve et al., 2015). In this study, after much trial and error, two typical sites were
389
chosen to generate the M/C time series given their representative geomorphologic
390
features. Figure 4(a) shows the site locations (background: Google map) and Figure 4(b)
391
is the boxplot of the 4-day running mean of the M/C signal for each site from March
392
2008 to April 2016. Site 1 covers parts of an active floodplain, which inundates when
393
stage height rises, while Site 2 does not. As shown in Figure 4(c), for both two sites the
394
M/C signal presents a seasonal fluctuation, which is usually lower during the wet season
395
when discharge is higher (corresponding to a decrease in the M value) and during the
396
winter period when the surface is covered by snow (corresponding to an increase in the
397
C value). Floods happen very seldom between December to April in this area and thus
398
only low values from May to November are considered as signals of flood occurrence.
399
For this flood event, M/C signal stays in low value almost throughout July (0.943 for
400
Site 1 and 0.970 for Site 2 as the lowest), below the 10th percentile of its long-term
401
record (see Figure 4(b)). Before and after that period, the value of it remains at a higher
402
level. It can be an indicator of flooding on a daily basis. Therefore the flood time span 20
403
was estimated as July 2016, which is consistent with the results of some relevant studies
404
(e.g.,
405
Flood-Damage-and-Community-based-Risk-Mapping-in-Flood-Diversion-Area-Case-
406
Study-in-Wuhan-Hubei-Province.pdf). Moreover, Site 1 shows a lower mean M/C
407
signal with a wider range when comparing to Site 2, as the floodplain in the river at Site
408
1 can lead to higher M values in dry conditions and lower values when inundated. Such
409
information can be used to choose the optimal location for the M, assuring the M/C
410
signal is sensitive enough to flood events.
411
http://iccr-drr.bnu.edu.cn/wp-content/uploads/2016/12/Rapid-Assessment-of-
[insert Figure 4 here]
412
Figure 4. (a) The measurement pixel (M) locations for the Jushui River, (b) Boxplot of 4-day
413
running mean of M/C signal for Site 1 and Site 2 (from March 2008 to April 2016) and (c) 4-day
414
running mean of M/C signal for Site 1 and 2 for 2016.
415
4.2 Water map based on GeoEye-1 image
416
To classify water and land, both of the default threshold values for HRWI and NDWI
417
are around zero (Guo et al., 2017; Yao et al., 2015). After searching in the neighborhood
418
of zero, the thresholds for HRWI and NDWI were set to be 0 and 0.25, respectively. In
419
general, both water indices of HRWI and NDWI can differentiate water from land with
420
very good accuracies, while HRWI outperforms NDWI, especially in detecting small
421
water bodies. Figure 5(a) is the false color composite map of a typical subscene of the
422
GeoEye-1 image, consisting of three main land cover types (water, crop land and urban
423
area). NDWI shows many omission errors near the boundaries of small water bodies 21
424
and some water bodies were discontinuous (see Figure 5(b)), while HRWI can detect
425
most water bodies (see Figure 5(c)-(d)) with an accuracy of 97.47% for the entire
426
GeoEye-1 scene (see Table 3). Small water bodies can be reliably detected by HRWI.
427
Therefore, the result of water/non-water classification by HRWI for the GeoEye-1
428
image is used as reference data to evaluate the SAR-based classification results.
429
Table 3 Accuracy assessment for water/non-water classification based on the GeoEye-1 image
430
(Date: Aug 10th 2016) using a point sample set*
NDWI
HRWI
Water
95.87%
97.47%
Non-water
99.20%
99.96%
431
* 1,500 points selected for each class in the point sample set
432
[insert Figure 5 here]
433
Figure 5. A subscene of (a) GeoEye-1 color composite (R: NIR, G: RED, B: GREEN), (b)
434
NDWI-based water map, (c) HRWI-based water map and (d) Comparison of both water maps.
435
The GeoEye-1 image validation areas were chosen such that areas hampered by clouds
436
and shades were eliminated. The cloud mask was generated using thresholds from gray-
437
level histograms of the GeoEye-1 scene after atmospheric correction and the shade
438
mask was manually created. The classification map based on HRWI was then converted
439
to vector and small-polygon patches of areas less than 100 m2 or with a perimeter less
440
than 40 m were deleted. The total validation area is 732.71 km2, containing 158.80 km2
441
of water and 573.91 km2 of non-water. 22
442
4.3 Results of the water/land classification based on SAR images
443
Otsu’s threshold for water/land segmentation was applied for each SAR image to
444
provide timely information on flood dynamics. The flood extent dynamically extracted
445
by this threshold-based method inundated the largest area on July 5th and showed
446
insignificant recession till July 17th, 2016 (see Figure 6). Notably, the Zhangdu Lake,
447
towards the middle of each subset of the SAR images, shows a decrease in size from
448
May to July even during flooding. The lake is where one of the largest wetland parks
449
in Wuhan City is located. During summer, hydrophytes in the lake grow and prosper
450
and their canopy cover cannot be penetrated by C-band radar microwaves, subsequently
451
causing the abnormality in flood dynamic changes. Generally, the threshold-based
452
method underestimates the extent of small water bodies such as small ponds, rice
453
paddies and narrow channels (see Figure 7(a)-(b)). There are two reasons for the
454
underestimation: (1) the resolution of 10 m of SAR data cannot meet the demand of
455
detecting narrow-edge-shaped water bodies, and (2) the speckle filtering process often
456
results in a mixing of land and water pixels, resulting in a reduction of differences
457
between water and land pixels and subsequently limit the ability of differentiating them.
458
Centers of water bodies are rarely misclassified by the threshold-based method, but it
459
shows less capability of detecting boundaries of lakes and rivers (see Figure 7(c)-(d)).
460
This can be explained by the existence of surface water roughness like waves and noises
461
caused by the fading effect in SAR images (Dobson et al., 1996).
462
[insert Figure 6 here]
23
463
Figure 6. Water classification maps from May to July 2016 based on SAR imagery generated by
464
the threshold-based method.
[insert Figure 7 here]
465
466
Figure 7. Water bodies detected using the threshold-based method in four subscenes (channel,
467
pond, lake and river) of SAR imagery (Date: 2016-08-10).
468
4.4 Accuracy assessments for water/land classification derived from SAR imagery
469
(1) Accuracy assessment for water/land classification results based on GeoEye-1
470
water map
471
The accuracy assessment (see Table 4) indicates that the threshold-based method can
472
detect water bodies very well with a total accuracy (OA) score of 89.83% and a Kappa
473
coefficient of 0.681. About 70% of water bodies were detected accurately and the error
474
of commission is 19.75%. It outperforms some typical supervised classifiers for water
475
mapping (see detailed comparisons of accuracy assessments for some commonly used
476
supervised classifiers and the threshold-based method classifier in Table S1). With a
477
classification accuracy not lower than but processing time much shorter than the other
478
supervised classifiers, the threshold-based method with the HAND index post processor
479
can provide reliable water/land classification for each date when SAR observation was
480
available.
481
Table 4 Accuracy assessment of detected water and land for the area of the GeoEye-1 image
482
(clouds and shades were eliminated) using the threshold-based method, in km2 (OA stands for
483
overall accuracy) 24
GeoEye-1 (as actual) Classifier
SAR
Water
Water
Land
108.56
26.73
OA
Threshold-based 89.83% (as predicted)
Land
47.80
Kappa
0.681
549.62
484
(2) Comparison of maximum flood inundation results generated by the threshold-
485
based method and the RAPID system
486
The RAPID system can process each Sentinel-1 SAR image to map the surface water
487
automatically with a high water/land classification accuracy via several steps including
488
the binary classification, morphological processing, compensation for underestimation
489
and threshold correction (Shen et al., 2019a). For this flood event, the inundation caused
490
was assumed as the maximum water extent by comparing flood maps of July 5th, 2016
491
(during-flood scene) and May 18th, 2016 (pre-storm scene). Figure 8 shows the
492
comparison of the maximum flood inundated water extent developed by the threshold-
493
based method (in red, total area: 302 km2) and the RAPID system (in black, total area:
494
310 km2). Both of the two results show that most of the flood water existed in regions
495
such as floodplains along the Jushui River or areas adjacent to other permanent water
496
bodies. Most inundation is related to the rising water level of the rivers, lakes, ponds
497
and regions like farmlands and residential areas, where depressed areas are widely
498
distributed and prone to hold water also after water levels start to decrease. The
499
threshold-based method shows a good agreement in inundation mapping with the
500
RAPID inundation result as the user’s accuracy and producer’s accuracy reach 74.4%
501
and 72.8%, respectively. Therefore, it can be considered as an effective way to estimate 25
502
flood dynamics with high confidence.
503
[insert Figure 8 here]
504
Figure 8. (a) Comparison of the maximum flood inundation results provided by the threshold-
505
based method and the RAPID system (pre-storm: May 18th, 2016 and during-flood: July 5th, 2016),
506
and (b) and (c) are two zoom-in inundation maps.
507
4.5 Development and application of the relation curve
508
Surface water fraction for the M (3.125×3.125 km2) of Site 1 and 2 for each SAR image
509
in Table 1 and 2 was extracted and compared with the M/C signal for the corresponding
510
dates to explore the relationship between M/C signal and SAR-observed surface water
511
fraction. The relation curve presented in Figure 9 can be applied to combine the passive
512
and active microwave data to monitor daily surface water fraction. The M/C signal
513
shows a decreasing trend when surface water fraction increases. R2 reaches 0.84 when
514
using quadratic polynomial regression.
515
[insert Figure 9 here]
516
Figure 9. Surface water fraction (%) for the area of Site 1 and 2 (i.e., M of 3.125×3.125 km2) vs.
517
M/C signal value (Dates: May 6th, May 18th, May 30th, June 11st, July 5th, July 17th and August 10th
518
of 2016).
519
Therefore, interpolating the surface water fraction map for the periods between each
520
Sentinel-1 observation at the resolution of 3.125 km can help to monitor the flood
521
situation along a river when SAR images are not available. Bias between surface water
26
522
fraction computed by passive microwave M/C signal estimation and SAR observation
523
for May 6th, May 30th, June 11st, July 5th, July 17th and August 10th, 2016 for river and
524
lake pixels in the Jushui River basin (outlet: Site 1) ranges between -20% to 20% with
525
an average of 0.34% (see Figure 10(a)). Bias for May 18th, 2016 was not calculated
526
because there was no passive microwave observation for that day. More than 74% of
527
the water pixels show a bias within -10% to 10%, which indicates that surface water
528
fraction can be extracted to provide continuous information at a daily interval for flood
529
events using this relationship for the Jushui River basin. Figure 10(b) presents the
530
variation of bias for each validation date and the overall, of which July 5th shows the
531
largest range and June 11th the least. The flood peak was reached around July 4th (see
532
Figure 4(c)), which means the flood extent could be very dynamic and changing quickly
533
even within a day. Due to the mismatch of timescales of Sentinel-1A and DMSP-F17
534
observations, some bias might be introduced for surface water fraction estimation,
535
especially during the flood peak. Additionally, the downscaling algorithm can also
536
introduce uncertainties into the TB data and thus contributed to the bias.
537
[insert Figure 10 here]
538
Figure 10. (a) Distribution map and (b) Boxplot of bias between surface water fraction computed
539
by passive microwave M/C signal estimation and SAR observation for May 6th, May 30th, June 11st,
540
July 5th, July 17th and August 10th, 2016 (no passive microwave observation on May 18th) for river
541
and lake pixels in the Jushui River basin area (outlet: Site 1).
27
542
5 Discussion
543
Flood occurrence can be monitored daily using the aforementioned M/C signal. If the
544
M/C signal reaches its low value of the long-term record during the rainy season (below
545
the 10th percentile in this study), a high flood probability should be considered.
546
Accuracy assessment (see Table 3) indicates that HRWI increases the water
547
classification accuracy by 1.6% compared with NDWI, especially for detecting small
548
water bodies. This can be a reliable source of reference data for evaluating SAR-based
549
water classification results. To extract water from SAR data, the threshold-based
550
method based on the Otsu’s algorithm performs well with a promising overall accuracy
551
of 89.83% and a Kappa coefficient of 0.681. In addition, factors influencing the
552
accuracy of SAR-based water classification also include: (a) the limited resolution (10
553
m) comparing to commercial optical imagery (~2 m) and (b) uncertainties in the HAND
554
index mask derived from the SRTM DEM data.
555
In the proposed integrated methodology, it should be noted that the relation curve is
556
site specific as each site can be regarded as a control point of the river basin with its
557
unique geomorphology and hydrologic condition, which can affect the coefficients in
558
the regression function. Generally, this relationship should be inverse and can be
559
applied to upstream areas. Because the M is defined to be a river pixel, meaning that
560
only part of the pixel incorporates a river, the M/C signal can be used to estimate the
561
water and land proportion for areas where permanent water exists. Due to layover and
562
radar shadow, threshold-based algorithms for water/land classification generally
563
perform better in areas with relatively flat and open terrain (Schlaffer et al., 2015; 28
564
Schumann et al., 2011). The proposed methodology is consequently more capable of
565
monitoring floods for these areas if passive microwave data with frequent return periods
566
and SAR imagery at high spatial resolutions are available. Results also show that the
567
integrated methodology is capable of estimating inundation happening a few days after
568
the flood peak. However, surface water fraction generated by this approach just presents
569
water area percentage at pixel scale (3.125×3.125 km2) and water distribution in sub-
570
pixels cannot be calculated. It remains a challenge to estimate surface water fraction in
571
more detailed scales due to the limited spatial resolution of passive microwave
572
observation. Future studies can focus on developing passive microwave products with
573
finer spatial resolutions or combining microwave data with ground information such as
574
the open-access high-resolution DEM suggested by Schumann and Bates (2018) and
575
river water levels from satellite altimetry (e.g., Huang et al., 2018b) to explore water
576
distribution at the sub-pixel level. It can help our approach improve its accuracy of
577
flood monitoring results.
578
6 Summary and conclusions
579
To overcome spatial and temporal restrictions of microwave remote sensing
580
observation for flooding, we proposed an integrated methodology using passive
581
microwave remote sensing signal and SAR imagery to monitor and estimate flood
582
occurrence and its dynamics. This methodology supports daily monitoring for flood
583
occurrence at selected river locations based on the passive microwave M/C signal. It
584
can also achieve detailed observations of 10 m resolution for water extent using
585
Sentinel-1 SAR imagery at an interval of several days (depending on latitude with a 5 29
586
or 6-day revisit and 12-day same orbit revisit time at the equator). Furthermore, daily
587
surface water fraction at the resolution of 3.125 km can be obtained by combining
588
passive and active microwave observations.
589
The main conclusions of this study can be summarized as follows:
590
(1) By selecting a suitable location of the measurement pixel (M), M/C signal can be
591
very sensitive to surface water change thus to determine the flood time span. For
592
the 2016 flood occurring in the Jushui River, M/C signal shows low values almost
593
for the entire month of July, which can be determined as the approximation of the
594
flood duration;
595
(2) By determining the threshold using the pattern of backscatter distribution, the
596
threshold-based method is effective and efficient for detailed flood mapping based
597
on SAR imagery. Based on the water extent map derived from the GeoEye-1 image
598
based on the HWRI algorithm, the accuracy assessment shows the threshold-based
599
method has a promising performance for detecting water bodies for the 2016 flood
600
event;
601
(3) Comparing passive microwave signals with SAR observations of Site 1 and 2, an
602
equation of M/C signal (Ratio) versus SAR-observed surface water fraction (SWF)
603
was developed as: SWF = 49674Ratio2 ― 96903Ratio + 47270, with an R2 of
604
0.84, which means M/C signal can be used to estimate surface water fraction and
605
its change, thus to achieve daily monitoring for flood events;
606
(4) This methodology results in bias of surface water fraction ranging from -20% to 20% 30
607
for water pixels of the validation dates (see Figure 10), showing a good ability in
608
estimating surface water fraction. Combining both passive and active remote
609
sensing observations, surface water fraction for permanent water pixels (e.g., river,
610
lake and pond) within the Jushui River basin can be generated based on M/C signals
611
for the days between each SAR observation. It can improve the temporal continuity
612
of microwave remote sensing observations and thus provide more complete
613
information on flood dynamics.
614
This study describes a reliable and efficient methodology to monitor floods, which
615
offers continuous information on daily flood dynamics. In general, based on high-
616
resolution microwave remote sensing data, this integrated methodology can be applied
617
to studies on flood monitoring or other hydrological applications in flood-prone regions
618
across the globe.
31
619
Acknowledgments
620
This study was financially supported by the National Key Research and Development
621
Program of China (Grant No. 71461010701) and the State Key Laboratory of Severe
622
Weather, Chinese Academy of Meteorological Sciences (Grant No. 2015LASW-A09).
623
The China Scholarship Council (CSC) supported the international traveling and
624
accommodations for the first author. We also thank the DigitalGlobe company for
625
providing the GeoEye-1 image without charge.
626
627
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Figure 1. Overview of the study area location for the 2016 flood event in Wuhan, Hubei
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Province of China (within the middle Yangtze River basin), showing the topography (Source:
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Shuttle Radar Topography Mission (SRTM) 1 arc-second Digital Elevation Model) overlaid by a
843
flood occurrence map (Source: European Commission Joint Research Center/Google; Rectangle:
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the coverage of a GeoEye-1 image as reference data (see Section 2.2)).
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Figure 2. A schematic overview of the developed workflow of the integrated methodology to
847
detect inundation extent over time.
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Figure 3. HAND (Height Above Nearest Drainage) index for the study area.
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Figure 4. (a) The measurement pixel (M) locations for the Jushui River, (b) Boxplot of 4-day
852
running mean of M/C signal for Site 1 and Site 2 (from March 2008 to April 2016) and (c) 4-day
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running mean of M/C signal for Site 1 and 2 for 2016.
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Figure 5. A subscene of (a) GeoEye-1 color composite (R: NIR, G: RED, B: GREEN), (b)
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NDWI-based water map, (c) HRWI-based water map and (d) Comparison of both water maps.
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Figure 6. Water classification maps from May to July 2016 based on SAR imagery generated by
859
the threshold-based method. 42
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Figure 7. Water bodies detected using the threshold-based method in four subscenes (channel,
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pond, lake and river) of SAR imagery (Date: 2016-08-10).
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Figure 8. (a) Comparison of the maximum flood inundation results provided by the threshold-
865
based method and the RAPID system (pre-storm: May 18th, 2016 and during-flood: July 5th, 2016),
866
and (b) and (c) are two zoom-in inundation maps.
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Figure 9. Surface water fraction (%) for the area of Site 1 and 2 (i.e., M of 3.125×3.125 km2) vs.
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M/C signal value (Dates: May 6th, May 18th, May 30th, June 11st, July 5th, July 17th and August 10th
870
of 2016).
871 872
Figure 10. (a) Distribution map and (b) Boxplot of bias between surface water fraction computed
873
by passive microwave M/C signal estimation and SAR observation for May 6th, May 30th, June 11st,
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July 5th, July 17th and August 10th, 2016 (no passive microwave observation on May 18th) for river
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and lake pixels in the Jushui River basin area (outlet: Site 1).
876
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Towards high resolution flood monitoring: An integrated methodology
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using passive microwave brightness temperatures and Sentinel synthetic
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aperture radar imagery
880 881
Ziyue Zenga, Yanjun Ganb, Albert J. Kettnerc, Qing Yangd,e, Chao Zengf, G. Robert Brakenridgec, Yang Hongg 43
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aWater
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China
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bState
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China
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cDartmouth
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and Alpine Research, University of Colorado, Boulder, CO, USA
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dCollege
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eCivil
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fSchool
of Resource and Environmental Science, Wuhan University, Wuhan, Hubei, China
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gSchool
of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK,
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USA
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Corresponding Author 1: Yang Hong
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Corresponding Author 2: Albert J. Kettner
Resources Department, Changjiang River Scientific Research Institute, Wuhan, Hubei,
Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing,
Flood Observatory, Community Surface Dynamic Modelling System, Institute of Arctic
of Civil Engineering and Architecture, Guangxi University, Nanning, Guangxi, China
and Environmental Engineering, University of Connecticut, Storrs, CT, USA
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CRediT author statement: Yang Hong: Conceptualization, Methodology. Albert J. Kettner: Methodology, Supervision, Writing- Reviewing and Editing. Ziyue Zeng: Writing- Original draft preparation, Data curation, Software, Visualization. Yanjun Gan: Visualization, Writing- Reviewing and Editing. Qing Yang: Software. Chao Zeng: Software. G. Robert Brakenridge: Writing- Reviewing and Editing.
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Declaration of interests
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☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Passive microwave signal can determine flood time span Observed surface water fraction presents a strong correlation with passive microwave signal Daily surface water fraction can be estimated using the proposed integrated method Spatial and temporal restrictions of remote sensing observations can be removed
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