Towards high resolution flood monitoring: An integrated methodology using passive microwave brightness temperatures and Sentinel synthetic aperture radar imagery

Towards high resolution flood monitoring: An integrated methodology using passive microwave brightness temperatures and Sentinel synthetic aperture radar imagery

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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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© 2019 Published by Elsevier B.V.

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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

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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

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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

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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

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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]

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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

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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

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factors can be mostly eliminated (Brakenridge et al., 2012; De Groeve, 2010). 14

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Specifically, the M value is the TB value of the M located on the river, including the

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river channel and parts of the floodplain along it. The C value is the brightest (95th

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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

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(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;

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𝑇B,water is the TB of water; 𝑇B,M is the TB of the M; 𝑇M is the physical temperature of

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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.

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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

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approximately equal (marked as 𝜀land) (De Groeve, 2010), shown by Eq. (4)-(5): (4)

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𝜀land,M

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𝑇M ≈ 𝑇C

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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

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(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

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applied generate the time series of daily M/C signals, because it can fill between any

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missing days without over-smoothing the M/C time series.

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3.2 Preprocessing for SAR images

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Level-1 GRD data was preprocessed using the ESA’s Sentinel-1 toolbox (S1TBX)

310

embedded

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(https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-1). After removing the thermal

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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,

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2004). Additionally, a terrain correction was conducted to compensate for topography-

317

induced distortion and then scenes were reprojected to the WGS84 geographic

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projection. After these steps, as usually expressed, the digital number (DN) values of

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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 =

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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

References

628

Alley, R., & Jentoft-Nilsen, M., 1999. Algorithm theoretical basis document for:

629 630 631

brightness temperature. Arst, H., 2003. Optical properties and remote sensing of multicomponental water bodies. Springer Science & Business Media. pp:135-137.

632

Bates, P. D., Horritt, M. S., Smith, C. N., & Mason, D., 1997. Integrating remote

633

sensing observations of flood hydrology and hydraulic modelling. Hydrol.

634

Process. 11(14), 1777-1795.

635 636

Bates, P. D., 2004. Remote sensing and flood inundation modelling. Hydrol. Process. 18(13), 2593-2597.

637

Behnamian, A., Banks, S., White, L., Brisco, B., Milard, K., Pasher, J., ... & Battaglia,

638

M., 2017. Semi-automated surface water detection with synthetic aperture radar

639

data: a wetland case study. Remote Sens. 9(12), 1209. 32

640

Bioresita, F., Puissant, A., Stumpf, A., & Malet, J. P., 2018. A Method for Automatic

641

and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery. Remote Sens.

642

10(2), 217.

643 644

Bolanos, S., Stiff, D., Brisco, B., & Pietroniro, A., 2016. Operational surface water detection and monitoring using Radarsat 2. Remote Sens. 8(4), 285.

645

Borghys, D., Yvinec, Y., Perneel, C., Pizurica, A., & Philips, W., 2006. Supervised

646

feature-based classification of multi-channel SAR images. Pattern Recognit

647

Lett. 27(4), 252-258.

648

Brakenridge, R., & Anderson, E., 2006. MODIS-based flood detection, mapping and

649

measurement: the potential for operational hydrological applications.

650

In Transboundary floods: reducing risks through flood management (pp. 1-12).

651

Springer, Dordrecht.

652

Brakenridge, G. R., Nghiem, S. V., Anderson, E., and Chien, S, 2005. Space-based

653

measurement

of

river

runoff,

654

doi:10.1029/2005EO190001.

Eos

Trans.

AGU,

86,

185-188.

655

Brakenridge, G. R., Nghiem, S. V., Anderson, E., and R. Mic, 2007. Orbital microwave

656

measurement of river discharge and ice status. Water Resour. Res.. 43, W04405,

657

doi:10.1029/2006WR005238.

658

Brakenridge, G. R., Cohen, S., Kettner, A. J., De Groeve, T., Nghiem, S. V., Syvitski,

659

J. P., & Fekete, B. M., 2012. Calibration of satellite measurements of river

660

discharge using a global hydrology model. J. Hydrol. 475, 123-136.

661

Brivio, P. A., Colombo, R., Maggi, M., and Tomasoni, R, 2002. Integration of remote 33

662

sensing data and GIS for accurate mapping of flooded areas. Int. J. Remote Sens.

663

23, 429–441.

664

Brodzik, M.J., Billingsley, B., Haran, T., Raup, B. and Savoie, M.H., 2012. EASE-Grid

665

2.0: Incremental but significant improvements for Earth-gridded data sets.

666

ISPRS Int. Geo-Inf. 1(1), pp.32-45.

667

Brodzik, M. J., D. G. Long, M. A. Hardman, A. Paget, & R. Armstrong. 2016, Updated

668

2018. MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily

669

EASE-Grid 2.0 Brightness Temperature ESDR, Version 1. Boulder, Colorado

670

USA. NASA National Snow and Ice Data Center Distributed Active Archive

671

Center. doi:

672

0630.001.

https://doi.org/10.5067/MEASURES/CRYOSPHERE/NSIDC-

673

Carroll, M. L., Townshend, J. R., DiMiceli, C. M., Noojipady, P., & Sohlberg, R. A,

674

2009. A new global raster water mask at 250 m resolution. Int. J. Digit.

675

Earth. 2(4), 291-308.

676 677

De Groeve, T., 2010. Flood monitoring and mapping using passive microwave remote sensing in Namibia. Geomat. Nat. Hazard Risk. 1, 19-35.

678

De Groeve, T., Brakenridge, G. R., & Paris, S, 2015. Global flood detection system

679

data product specifications. JRC Technical Report. http://www. gdacs.

680

org/flooddetection/Download/Technical_Note_GFDS_Data_Products_v1. pdf.

681

Dobson, M.C., Pierce, L.E. and Ulaby, F.T., 1996. Knowledge-based land-cover

682

classification using ERS-1/JERS-1 SAR composites. IEEE Trans. Geosci.

683

Remote Sens. 34(1), pp.83-99. 34

684

Eppink, F.V., Van Den Bergh, J.C., Rietveld, P., 2004. Modelling biodiversity and land

685

use: Urban growth, agriculture and nature in a wetland area. Ecol. Econ. 51,

686

201-216.

687 688 689 690

Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., ... & Seal, D. 2007. The shuttle radar topography mission. Rev. Geophys. 45(2). Frazier, P. S., & Page, K. J., 2000. Water body detection and delineation with Landsat TM data. Photogramm. Eng. Remote Sens. 66(12), 1461-1468.

691

Galantowicz, J. F., & Picton, J., 2014. Flood extent depiction by physical downscaling

692

of flooded fraction estimates from microwave remote sensing. In Geoscience

693

and Remote Sensing Symposium (IGARSS), 2014 IEEE International (pp.

694

3854-3857). IEEE.

695

Gessner, M.O., Hinkelmann, R., Nützmann, G., Jekel, M., Singer, G., Lewandowski,

696

J., Nehls, T., Barjenbruch, M., 2014. Urban water interfaces. J. Hydrol. 514,

697

226-232.

698

Gleason, C. J., Smith, L. C., & Lee, J., 2014. Retrieval of river discharge solely from

699

satellite imagery and at-many-stations hydraulic geometry: Sensitivity to river

700

form and optimization parameters. Water Resour. Res. 50(12), 9604-9619.

701

Guo, Q., Pu, R., Li, J., & Cheng, J., 2017. A weighted normalized difference water

702

index for water extraction using Landsat imagery. Int. J. Remote Sens. 38(19),

703

5430-5445.

704

Haas, E. M., Bartholomé, E., & Combal, B., 2009. Time series analysis of optical

705

remote sensing data for the mapping of temporary surface water bodies in sub35

706

Saharan western Africa. J. Hydrol. 370(1-4), 52-63.

707

Hahmann, T., & Wessel, B., 2010, June. Surface water body detection in high-

708

resolution TerraSAR-X data using active contour models. In 8th European

709

Conference on Synthetic Aperture Radar (pp. 1-4). VDE.

710 711

Henry, J.B., Chastanet, P., Fellah, K., Desnos, Y.L., 2006. Envisat multi-polarized ASAR data for flood mapping. Int. J. Remote Sens. 27, 1921-1929.

712

Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe,

713

S., ... & Kanae, S., 2013. Global flood risk under climate change. Nat. Clim.

714

Chang. 3, 816-821.

715

Huang, Q., Long, D., Du, M., Zeng, C., Qiao, G., Li, X., ... & Hong, Y. 2018a.

716

Discharge estimation in high-mountain regions with improved methods using

717

multisource remote sensing: A case study of the Upper Brahmaputra River.

718

Remote Sens. Environ., 219, 115-134.

719

Huang, Q., Long, D., Du, M., Zeng, C., Li, X., Hou, A., & Hong, Y. 2018b. An

720

improved approach to monitoring Brahmaputra River water levels using

721

retracked altimetry data. Remote Sens. Environ., 211, 112-128.

722 723 724 725

Jensen, J.R., 2004. Introductory digital image processing: A remote sensing perspective. Upper Saddle River, NJ: Prentice Hall, 3rd Ed., 526 p. Jonkman, S. N., 2005. Global perspectives on loss of human life caused by floods. Nat. Hazards. 34, 151-175.

726

Khan, S. I., Hong, Y., Wang, J., Yilmaz, K. K., Gourley, J. J., Adler, R. F., ... & Irwin,

727

D., 2011. Satellite remote sensing and hydrologic modeling for flood inundation 36

728

mapping in Lake Victoria basin: Implications for hydrologic prediction in

729

ungauged basins. IEEE Trans. Geosci. Remote Sens. 49(1), 85-95.

730 731 732 733 734 735 736

Klemas, V., 2015. Remote sensing of floods and flood-prone areas: An overview. J. Coast. Res. 31, 1005-1013. Lee, J. S., 1980. Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. Pattern Anal. Mach. Intell. (2), 165-168. Li, J., and Wang, S., 2015. An automatic method for mapping inland surface waterbodies with Radarsat-2 imagery. Int. J. Remote Sens. 36(5), 1367-1384. Long, D. G., 2015. Selection of Reconstruction Parameters. MEaSUREs Project White

737

Paper.

NSIDC.

Boulder,

CO.

Available

online:

738

http://nsidc.org/pmesdr/files/2015/04/Long_0150316_Resolution_Enhanceme

739

nt_Tradeoffs.v3.3.pdf.

740

Long, D. G. and J. Stroeve. 2011. Enhanced-Resolution SSM/I and AMSR-E Daily

741

Polar Brightness Temperatures. Boulder, Colorado USA: NASA DAAC at the

742

National Snow and Ice Data Center.

743

Martinis, S., Kuenzer, C., Wendleder, A., Huth, J., Twele, A., Roth, A., & Dech, S.,

744

2015. Comparing four operational SAR-based water and flood detection

745

approaches. Int. J. Remote Sens. 36(13), 3519-3543.

746

Martinis, S., Twele, A., & Voigt, S., 2009. Towards operational near real-time flood

747

detection using a split-based automatic thresholding procedure on high

748

resolution TerraSAR-X data. Nat. Hazards Earth Syst. Sci. 9(2), 303-314.

749

McFarland, M. J., Miller, R. L., & Neale, C. M., 1990. Land surface temperature 37

750

derived from the SSM/I passive microwave brightness temperatures. IEEE

751

Trans. Geosci. Remote Sens. 28(5), 839-845.

752

McFeeters, S., 1996. The use of the Normalized Difference Water Index (NDWI) in the

753

delineation of open water features. Int. J. Remote Sens. 17, 1425-1432.

754

Milillo, P., Riel, B., Minchew, B., Yun, S. H., Simons, M., & Lundgren, P., 2016. On

755

the synergistic use of SAR constellations’ data exploitation for earth science

756

and natural hazard response. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.

757

9(3), 1095-1100.

758

Mladenova, I. E., Jackson, T. J., Bindlish, R., & Hensley, S., 2013. Incidence angle

759

normalization of radar backscatter data. IEEE Trans. Geosci. Remote

760

Sens. 51(3), 1791-1804.

761 762

Moel, H.D., Alphen, J.V. & Aerts, J.C.J.H., 2009. Flood maps in Europe-methods, availability and use. Nat. Hazards Earth Syst. Sci. 9: 289-301.

763

Mouratidis, A., & Sarti, F., 2013. Flash-flood monitoring and damage assessment with

764

SAR data: Issues and future challenges for Earth Observation from Space

765

sustained by case studies from the Balkans and Eastern Europe. In Earth

766

Observation of Global Changes (EOGC) (pp. 125-136).

767

Njoku, E. G., Ashcroft, P., Chan, T. K., & Li, L., 2005.Global survey and statistics of

768

radio-frequency interference in AMSR-E land observations, IEEE Trans.

769

Geosci. Remote Sens. 43, 938-947.

770

Oberstadler. R., Hnsch H., Huth D., 1997. Assessment of the mapping capabilities of

771

ERS-1 SAR data for flood mapping: A case study in Germany. Hydrol. Process. 38

772 773 774 775 776

11(10), 1415-1425. Ormsby, J. P., Blanchard, B. J., & Blanchard, A. J., 1985. Detection of lowland flooding using active microwave systems. Otsu, N., 1979. A threshold selection method from gray-level histograms, IEEE Trans. Syst. Man Cybern. 9, 62-66.

777

Pechlivanidis, I. G., Jackson, B. M., McIntyre, N. R., & Wheater, H. S., 2011.

778

Catchment scale hydrological modelling: a review of model types, calibration

779

approaches and uncertainty analysis methods in the context of recent

780

developments in technology and applications. Glob. Nest. J. 13(3), 193-214.

781 782 783

Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S., 2016. High-resolution mapping of global surface water and its long-term changes. Nature. 540(7633), 418. Rennó, C. D., Nobre, A. D., Cuartas, L. A., Soares, J. V., Hodnett, M. G., Tomasella,

784

J., & Waterloo, M. J., 2008. HAND, a new terrain descriptor using SRTM-DEM:

785

Mapping terra-firme rainforest environments in Amazonia. Remote Sens.

786

Environ. 112(9), 3469-3481.

787

Schlaffer, S., Matgen, P., Hollaus, M., & Wagner, W., 2015. Flood detection from

788

multi-temporal SAR data using harmonic analysis and change detection. Int. J.

789

Appl. Earth Obs. Geoinf. 38, 15-24.

790

Schneider, A., Jost, A., Coulon, C., Silvestre, M., Théry, S., & Ducharne, A., 2017.

791

Global-scale river network extraction based on high-resolution topography and

792

constrained by lithology, climate, slope, and observed drainage density.

793

Geophys. Res. Lett. 44(6), 2773-2781. 39

794 795

Schumann, G., & Bates, P. D. 2018. The need for a high-accuracy, open-access global DEM. Front. Earth Sci. 6, 225.

796

Schumann, G., Di Baldassarre, G., Alsdorf, D., & Bates, P. D., 2010. Near real-time

797

flood wave approximation on large rivers from space: Application to the River

798

Po, Italy. Water Resour. Res. 46(5).

799

Schumann, G., Hostache, R., Puech, C., Hoffmann, L., Matgen, P., Pappenberger, F.,

800

Pfister, L., 2007. High-resolution 3-D flood information from radar imagery for

801

flood hazard management. IEEE Trans. Geosci. Remote Sens. 45, 1715-1725.

802

Schumann, G., Neal, J. C., Mason, D. C., & Bates, P. D., 2011. The accuracy of

803

sequential aerial photography and SAR data for observing urban flood dynamics,

804

a case study of the UK summer 2007 floods. Remote Sens. Environ. 115(10),

805

2536-2546.

806

Shen, X., Anagnostou, E. N., Allen, G. H., Brakenridge, G. R., & Kettner, A. J., 2019a.

807

Near-real-time non-obstructed flood inundation mapping using synthetic

808

aperture radar. Remote Sens. Environ. 221, 302-315.

809 810

Shen, X., Wang, D., Mao, K., Anagnostou, E., & Hong, Y. 2019b. Inundation extent mapping by synthetic aperture radar: A review. Remote Sens. 11(7), 879.

811

Sippel, S. J., Hamilton, S. K., Melack, J. M., & Choudhury, B. J., 1994. Determination

812

of inundation area in the Amazon River floodplain using the SMMR 37 GHz

813

polarization difference. Remote Sens. Environ. 48(1), 70-76.

814 815

Skou, N., 1989. Microwave radiometer systems: Design and analysis. Artech House, Norwood, Mass. 40

816

Tang G., Long D., Hong Y., Gao J., Wan W., 2018. Documentation of multifactorial

817

relationships between precipitation and topography of the Tibetan Plateau using

818

spaceborne precipitation radars. Remote Sens. Environ. 208, 82-96.

819

Tang, G., Zeng, Z., Ma, M., Liu, R., Wen, Y., & Hong, Y., 2017. Can near-real-time

820

satellite precipitation products capture rainstorms and guide flood warning for

821

the 2016 summer in South China?. IEEE Geosci. Remote Sens. Lett. 14(8),

822

1208-1212.

823

Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., Potin, P., et

824

al., 2012. GMES Sentinel-1 mission. Remote Sens. Environ. 120, 9-24.

825

Twele, A., Cao, W., Plank, S., & Martinis, S., 2016. Sentinel-1-based flood mapping:

826

a fully automated processing chain. Int. J. Remote Sens. 37(13), 2990-3004.

827

Van Dijk, A. I., Brakenridge, G. R., Kettner, A. J., Beck, H. E., De Groeve, T., &

828

Schellekens, J., 2016. River gauging at global scale using optical and passive

829

microwave remote sensing. Water Resour. Res. 52(8), 6404-6418.

830 831

Viala, E., 2008. Water for food, water for life a comprehensive assessment of water management in agriculture. Irrig. Drain. Syst. 22, 127-129.

832

Woodhouse, I. H., 2017. Introduction to microwave remote sensing. CRC press.

833

Yao, F., Wang, C., Dong, D., Luo, J., Shen, Z, Yang, K., 2015. High-resolution

834

mapping of urban surface water using ZY-3 multi-spectral imagery. Remote

835

Sens. 7, 12336-12355.

836

Yilmaz, K. K., Adler, R. F., Tian, Y., Hong, Y., & Pierce, H. F., 2010. Evaluation of a

837

satellite-based global flood monitoring system. Int. J. Remote Sens. 31(14), 41

838

3763-3782.

839

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Figure 1. Overview of the study area location for the 2016 flood event in Wuhan, Hubei

841

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:

844

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

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running mean of M/C signal for Site 1 and Site 2 (from March 2008 to April 2016) and (c) 4-day

853

running mean of M/C signal for Site 1 and 2 for 2016.

854 855

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.

869

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

878

using passive microwave brightness temperatures and Sentinel synthetic

879

aperture radar imagery

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Ziyue Zenga, Yanjun Ganb, Albert J. Kettnerc, Qing Yangd,e, Chao Zengf, G. Robert Brakenridgec, Yang Hongg 43

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aWater

883

China

884

bState

885

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

891

gSchool

of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK,

892

USA

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Corresponding Author 1: Yang Hong

894

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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