Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine

Rapid and robust monitoring of flood events using Sentinel 1 and Landsat data on the Google Earth Engine Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage www el[.]

Remote Sensing of Environment 240 (2020) 111664 Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine T Ben DeVriesa,b, , Chengquan Huangb, John Armstonb, Wenli Huangb,c, John W Jonesd, Megan W Lange ⁎ a Department of Geography, Environment and Geomatics, University of Guelph, Guelph, ON, Canada Department of Geographical Sciences, University of Maryland, College Park, MD, USA c School of Resource and Environmental Science, Wuhan University, Wuhan, China d U.S Geological Survey, Hydrologic Remote Sensing Branch, Reston, VA, USA e U.S Fish and Wildlife Service, National Wetlands Inventory, Falls Church, VA, USA b ARTICLE INFO ABSTRACT Edited by: Jing M Chen Synthetic aperture radar (SAR) sensors represent an indispensable data source for flood disaster planners and responders, given their ability to image the Earth's surface nearly independently of weather conditions and time of day The decision by the European Space Agency (ESA) Copernicus program to open data from its Sentinel-1 SAR satellites to the public marks the first time global, operational SAR data have been made freely available Combined with the emergence of cloud computing platforms like the Google Earth Engine (GEE), this development presents a tremendous opportunity to the disaster response community, for whom rapid access to analysis-ready data is needed to inform effective flood disaster response interventions and management plans Here, we present an algorithm that exploits all available Sentinel-1 SAR images in combination with historical Landsat and other auxiliary data sources hosted on the GEE to rapidly map surface inundation during flood events Our algorithm relies on multi-temporal SAR statistics to identify unexpected floods in near real-time Additionally, historical Landsat-based surface water class probabilities are used to distinguish unexpected floods from permanent or seasonally occurring surface water We assessed our algorithm over three recent flood events using coincident very high- spatial resolution imagery and operational flood maps Using very high resolution optical imagery, we estimated an area-normalized accuracy of 89.8 ± 2.8% (95% c.i.) over Houston, Texas following Hurricane Harvey in late August 2017, representing an improvement of between 1.6% and 9.8% over flood maps derived from a simple backscatter threshold Additionally, comparison of our results with SARderived Copernicus Emergency Management Service (EMS) maps following devastating floods in Thessaly, Greece and Eastern Madagascar in January and March 2018, respectively, yielded overall agreement rates of 98.5% in both cases Importantly, our algorithm was able to ingest hundreds of SAR and optical images served on the GEE to produce flood maps over affected areas within minutes, circumventing the need for time-consuming data download and pre-processing steps The flexibility of our algorithm will allow for the rapid processing of future open-access SAR data, including data from future Sentinel-1 missions Keywords: Sentinel-1 SAR Flood disasters Cloud computing Introduction Floods are among the costliest of natural disasters and inflict loss of life and property on millions of people worldwide While climate change is increasing flood risk in already vulnerable areas (Hirabayashi et al., 2013), commonly used flood risk models have been shown to significantly under-predict flood risk (Wing et al., 2018), underscoring the need for observation-driven flood monitoring methods Satellitebased Earth Observation (EO) data provide synoptic, repeated views of ⁎ potentially flooded regions, and are increasingly used in operational disaster monitoring systems (Voigt et al., 2016) Two recent developments in the EO sector have the potential to significantly improve the efficacy of flood monitoring systems across the globe First, the opening of data from operational EO satellites, such as Landsat, has enabled land change monitoring at relatively high spatial and temporal resolutions (Wulder et al., 2012) The European Sentinel-1A and e1B satellites comprise the first ever global, operational synthetic aperture radar (SAR) mission whose data are open to Corresponding author at: Department of Geography, Environment and Geomatics, University of Guelph, Guelph, ON, Canada E-mail address: bdv@uoguelph.ca (B DeVries) https://doi.org/10.1016/j.rse.2020.111664 Received October 2018; Received in revised form August 2019; Accepted 14 January 2020 Available online 31 January 2020 0034-4257/ © 2020 Elsevier Inc All rights reserved Remote Sensing of Environment 240 (2020) 111664 B DeVries, et al the global public Launched by the European Space Agency (ESA) through its Copernicus program in March 2014 and 2016, respectively, the Sentinel-1 satellites provide nominal 6-day repeat imagery over Europe and nominal 12-day repeat imagery over the rest of Earth's terrestrial surfaces, enabling regular monitoring of change processes (Torres et al., 2012) Given the limitations of optical data for high-revisit monitoring of floods, expanded access to operational SAR EO data is an important development for disaster monitoring However, despite the continued availability of SAR data through the Sentinel-1 satellites and the free, open-source pre-processing software distributed by ESA, SAR pre-processing still remains a technically challenging and computationally intensive task Second, the advent of cloud computing architectures is leading to a shift in the way in which EO data are processed, allowing users and developers to access entire EO data archives and circumventing the need to download and locally store huge volumes of data By leveraging the advantages of high performance computing (HPC) and “data cube” architecture, EO cloud computing services allow for accelerated access to large volumes of EO data, reducing the burden of data pre-processing and formatting traditionally borne by users (Lewis et al., 2016) The Google Earth Engine (GEE), a novel computing platform introduced by Google, Inc., has enabled the development of global-scale data products using satellite image time series, such as that from the Landsat archive (Gorelick et al., 2017) The GEE has been used to conduct global and regional scale investigations of land surface dynamics, including forest cover (Hansen et al., 2013; Johansen et al., 2015), surface water (Donchyts et al., 2016; Pekel et al., 2016; Tang et al., 2016), populated areas (Patela et al., 2015), cropland and soils (Padarian et al., 2015; Xiong et al., 2017) and other applications (Dong et al., 2016; Joshi et al., 2016; Lee et al., 2016) Among its large store of geospatial datasets, the GEE houses a complete and continually updated archive of Sentinel-1 Ground Range Detected (GRD) data The provision of analysis-ready SAR data on the GEE represents a significant step forward in applied SAR remote sensing, as the complexity of SAR preprocessing has previously presented a barrier to its adoption Additionally, the presence of other data sources, such as the entire global Landsat archive, allow for relatively easy integration of diverse EO data sources The ability of SAR sensors to detect floods relies in large part on the distinct scattering mechanisms exhibited at open water surfaces SAR sensors transmit microwave energy to the Earth's surface at off-nadir angles, leading to specular reflection – the near-complete reflection of transmitted energy away from the sensor – from smooth, open water surfaces This reflection results in very low backscatter, or signal received by the sensor Many SAR-based flood detection algorithms exploit this scattering mechanism by applying backscatter thresholds to SAR images to classify water pixels (Chini et al., 2017; Matgen et al., 2011; Pulvirenti et al., 2011; Twele et al., 2016), often in concert with object-based detection methods (Giustarini et al., 2013; Martinis et al., 2009, 2015; Mason et al., 2014) Capillary waves, often present across large water surfaces in the presence of high winds, give rise to Bragg scattering, presenting a challenge for simple threshold methods, especially when vertically polarized transmitted and received energy is used (Brisco, 2015) Change detection approaches using multi-temporal SAR imagery are often used to identify flooded pixels compared to baseline conditions (Amitrano et al., 2018; Badji and Dautrebande, 1997; Cian et al., 2018; Clement et al., 2017; Hostache et al., 2012; Long et al., 2014; Lu et al., 2015; Schlaffer et al., 2017) Interferometric or coherence-based change detection methods exploit both the amplitude (intensity) and phase components of the received microwave energy, allowing for a more detailed characterization of the complex SAR backscatter signatures that typically result from wind-affected open water surfaces or flooding among standing vegetation or built-up areas (Geudtner et al., 1996; Plank et al., 2017; Refice et al., 2014) While SAR images can provide reasonably reliable estimates of large areal flood extents, challenges arising from limited spatial and temporal resolution or complexities in SAR backscatter signatures have led to the development of approaches involving the integration of SAR observations into models or other types of data streams For example, assimilation of SAR data into hydraulic models has been shown to improve flood extent estimates on a near real-time basis (Giustarini et al., 2011) and in the presence of built-up areas (Mason et al., 2014) SAR data are also frequently used in combination with other remotely sensed observations to enhance flood inundation predictions Topographic data derived from digital elevation models (DEMs) are routinely used to constrain SAR-based flood estimates to areas likely to experience flooding (Brakenridge et al., 1994, 1998; Huang et al., 2017) A number of studies have also demonstrated the potential of combining SAR data with optical data or other a priori surface water datasets to allow for multi-scale flood assessment (Martinis et al., 2013), automated training data selection (Huang et al., 2018; Westerhoff et al., 2013) or an increase in observation density during flooding events (Chaouch et al., 2012) Despite the range of methods developed for monitoring floods with SAR data, few studies have employed dense time series stacks of SAR or optical imagery towards this goal, due to challenges in accessing and processing such large volumes of data Open-access data policies like those of the NASA-USGS and Copernicus programs, and cloud-computing platforms like the GEE are now poised to facilitate the exploration of methods that exploit entire archives of multiple satellite missions, with the aim of improving observation-based flood monitoring systems In this paper, we describe a new method for monitoring floods using dense time series of Sentinel-1 SAR and Landsat data on the GEE Our algorithm uses temporal SAR Z-scores, computed against a baseline during which no flooding is assumed to have occurred In describing our algorithm, we pose the following questions: What influence does flooding have on Z-scores derived from vertical transmit vertical receive (VV) and vertical transmit horizontal receive (VH) SAR backscatter time series? How accurate are flood predictions using Sentinel-1 derived Zscores, and how well they compare with existing operational flood maps? We defined floodwater in this study as new water appearing above the ground surface or vegetation canopy (if present) in comparison to a historical reference period The algorithm described in the following section was accordingly designed to detect these instances of “unexpected” floods Site and event descriptions In this study, we demonstrated our algorithm over a range of flood events across the globe in the past two years and focused our evaluation of the method over three of these events: (1) Hurricane Harvey in August 2017 over Houston, Texas; (2) Floods in March 2018 in Thessaly, Central Greece; and (3) Cyclone Ava in January 2018 over the east coast of Madagascar We selected these sites to demonstrate the utility of Sentinel-1 time series data over a range of acquisition strategies and consequent data densities (Fig 1) Operational 12-day revisit imagery were acquired over Eastern Madagascar, while the 6-day revisit imagery programmed for Europe were available over Thessaly, Greece Houston, Texas represents a special case where imagery acquired under an additional observation mode (described in Section 3.1) were available Each of these sites and events are briefly described below and shown in Fig 2.1 Houston, Texas: Hurricane Harvey The 2017 hurricane season along the southeastern coast of the United States, including Puerto Rico, is being recognized as the most costly in the country's history, with thousands of deaths during the aftermath of the storms and over $250 billion in damages (Blake, 2018; Remote Sensing of Environment 240 (2020) 111664 B DeVries, et al Fig Overview of validation sites described in this study, with colours representing the number of Sentinel-1 observations available on the GEE for all of 2017 and validation regions shown as hatched polygons Left: Houston, Texas Centre: Thessaly, Greece Right: Eastern Madagascar Note that different value ranges are used for each site Halverson, 2018) Hurricane Harvey, which made landfall on the coast of Texas on 2017-08-23, brought with it unprecedented levels of flooding to the city of Houston and surrounding areas and resulted in an estimated 70 deaths (Jonkman et al., 2018) This paper focusses on the area to the west of Houston, which is dominated by cropland interspersed with forests, woody wetlands and coastal emergent wetlands constellations operated under the ESA Copernicus program Data from the Sentinel-1 satellites are operationally acquired in four imaging modes: interferometric wide-swath (IW), strip map (SM) extra wideswath (EW) and wave (WV) mode, each with different acquisition configurations An overview of these imaging modes and their associated specifications is given in Torres et al (2012), and a brief summary of the two modes used in this study (IW and SM) is given here Since all SAR sensors are side-viewing instruments, the imaged land surface is illuminated over a range of view and incidence angles Even though methods exist to correct for the effects of incidence angle and terrain (Small, 2011), radiometric variations due to local incidence angle may still persist after pre-processing IW imagery are acquired at incidence angles between 31° and 46° and SM imagery may be acquired at incidence angles ranging from 20° to 47° (Torres et al., 2012) While IW images are consistently acquired across swaths spanning these angles, SM imagery span narrower swaths, and the precise range varies from site to site Each of the two Sentinel-1 satellites orbits the globe once every 12 days, allowing for a joint potential 6-day repeat frequency over the equator and a 3-day revisit frequency when both ascending and descending orbits are considered Operational IW imagery are acquired at nominal 6-day intervals over Europe and nominal 12day intervals over the rest of the Earth's surface, with higher repeat frequencies at higher latitudes and areas where targeted acquisitions are planned The GEE hosts Sentinel-1 Ground Range Detected (GRD) data acquired in EW, IW and SM modes, pre-processed using tools available through the ESA Sentinels Application Platform (SNAP) software package First, restituted orbit files were applied to the GRD images, resulting in geometric accuracies within 10 cm (Prats-Iraola et al., 2015) GRD and thermal noise removal, which masks artificially low backscatter pixels found at the edge of the image swath, was then carried out for all imagery acquired after 2018-01-12 (Ali et al., 2018) We applied additional extreme value thresholds to VV and VH backscatter (-35 dB and -40 dB, respectively) for images included in the historical baseline period (described in the following section), to further reduce the impact of these border regions Radiometric calibration was then carried out to produce the unitless backscatter intensity (σint) (Sabel et al., 2012) The images were then terrain geo-coded using a digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) or the ASTER Global DEM for latitudes above 60° North or South Finally, backscatter intensity was converted to backscatter coefficient (σ0) measured in decibels (dB) according to Eq (1): 2.2 Thessaly, Greece: 2018 spring floods The Thessaly region in Central Greece is traversed by the Pinios River, which is the third longest river in Greece and drains an area of 10,700 km2 northwards into the Aegean Sea (Migiros et al., 2011) Between 1880 and 2010, 16 to 25 major flood events were recorded in the Thessaly region, one of which in 1907 was the most severe flood recorded in Greece during this period (Diakakis et al., 2012) Intense rainfall between the 21st and 26th of February, 2018, caused catastrophic flooding in the region, particularly throughout the agricultural plains between the cities of Larissa and Trikala, with mostly cropland and several villages in the region being heavily impacted (Davies, 2018) 2.3 Eastern Madagascar: Cyclone Ava The southwest Indian Ocean is a hotspot for tropical cyclones, which are the most significant natural hazard for Madagascar (Ganzhorn, 1995) Historical records suggest that the number of tropical cyclones making landfall in Madagascar has increased from the 19th century to more recent years (Nash et al., 2015), with some analyses suggesting a link to increases in sea surface temperature (Mavume et al., 2009) Cyclone Ava made landfall on the northeast coast of Madagascar on the 3rd of January, 2018 and affected an estimated 123,000 people over the following five days as it moved southwards along the eastern coast (ReliefWeb, 2018) The affected regions described in this study are dominated by an agricultural mosaic land cover, comprised of a patchwork of forests, woodlands and croplands Data and methods 3.1 Sentinel-1 data Sentinel-1A, launched in April 2014, and Sentinel-1B, launched in April 2016, are the first among a series of Earth imaging satellite Remote Sensing of Environment 240 (2020) 111664 B DeVries, et al Fig Time series VV backscatter (σ0VV), VV backscatter anomaly (∆σ0VV) and VV Z-score (ZVV) for a single pixel located southwest of Houston, Texas Data markers correspond to the acquisition modes (IW or SM) and orbital directions (ascending: ASC, or descending: DSC) available for this pixel Zero anomaly and Z-scores are shown as a dotted grey line in the middle and bottom panels and an example flooding Z-score threshold of −2.5 is shown as a broken green line in the bottom panel (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) = 10log10 int (1) baseline was first defined using a priori knowledge of surface water dynamics at each respective site For example, inspection of several SAR pixel time series suggested that no floods had occurred between 201706-01 and 2017-08-15 in Houston, so these dates were chosen as the bounds of the historical baseline for that site All Sentinel-1 images acquired between those dates were then used to compute the temporal mean backscatter coefficient ( o ) (Fig 3B) and standard deviation backscatter coefficient (std(σo)) (Fig 3C) These statistics were computed separately for polarization modes (VV and VH), as well as for subsets of the baseline image stack based on orbital direction (ascending and descending) and acquisition mode (IW and SM) The All imagery were projected to WGS84 latitude/longitude projection (EPSG:4326) during terrain correction and export of image products 3.2 Temporal SAR statistics Our approach to monitoring floods using temporal anomaly measures consisted of several stages, demonstrated in Fig and Fig First, a temporal subset of all SAR backscatter data was selected for each pixel based on a historical baseline for each flood event This Fig Demonstration of the summary statistics computed for a flood event in Kale, Myanmar on 2017-09-19 A: VV backscatter coefficient image from 2017-09-19 B: Mean VV backscatter coefficient for the baseline period between 2017-04-01 and 2017-09-01 C: Standard deviation VV backscatter coefficient for the same baseline period D: VV Z-score corresponding to the image in (A) Remote Sensing of Environment 240 (2020) 111664 B DeVries, et al backscatter anomaly (∆σ0) and Z-score (Z) were then computed for each observation acquired at time t under polarization mode p, sensor acquisition mode m, and orbital direction d according to Eqs (2) and (3), respectively: p, m, d (t ) Z p, m, d (t ) = = p, m, d (t ) p, m, d p, m, d (t ) std ( 0p, m, d ) et al., 2016) We computed the historical probability of open water from both the JRC and DSWE datasets as the proportion of all unmasked pixels classified as open water We additionally computed the probability of partial water from the DSWE-classified images These class probabilities were used to classify water pixels based on their historical occurrence, as described in the following section (2) 3.4 Flood classification (3) Our classification scheme is shown in Fig First, we identified pixels with permanent open water (POW), defined as having a probability of open water > 95% in either the JRC or DSWE products We then applied Z-score thresholds on both the VV and VH Z-score images If both the VV and VH Z-scores were lower than these thresholds for a given pixel, that pixel was assigned a high-confidence flood label If only one of the two polarizations resulted in a Z-score lower than the thresholds, the pixel was assigned a moderate-confidence flood label Although VV backscatter is generally used alone to detect floodwater, we included both polarizations to provide an additional line of evidence for flooding For example, Bragg scattering of VV energy over rough water surfaces may mask the presence of water, in which case a negative VH Z-score would still provide evidence for flooding We tested a combination of Z-scores for each polarization over each study site to assess their effects on classification accuracy Finally, all pixels not classified as POW but with a historical inundation probability > 25%, where inundation was defined as having either an open water or partial water DSWE class label, were assigned a class label modifier describing the likelihood of prior inundation In summary, the class labels employed in this study can be represented as a 2-D matrix (Fig 4), in which SAR-based flood confidence forms one axis and Landsat-based historical inundation information forms the other 3.3 Landsat data and historical inundation The temporal SAR statistics described above provide an objective baseline from which to measure backscatter anomalies that can be related to “new” floods within a given time period However, it is also important to determine whether these floods may occur as part of a regular intra-annual surface water regime (e.g., seasonal wetlands and other water bodies) or are unexpected and potentially catastrophic To aid in contextualizing the SAR anomaly measures, we used historical Landsat data available on the GEE to map previously permanently or seasonally inundated pixels using two different data products or algorithms We relied on Landsat due to the fact that no SAR data acquired before 2016 are available on the GEE First, we accessed the Landsatbased Monthly Water History dataset produced by the Joint Research Centre (JRC) of the European Commission (Pekel et al., 2016) and computed water occurrence probability from all available images on the GEE Second, we applied the Dynamic Surface Water Extent (DSWE) algorithm described in Jones (2015) to all available Landsat-5 TM and Landsat-7 ETM+ images acquired between 2000 and 2015 on the GEE to expand the range of water body types included in our algorithm DSWE employs a series of thresholds and decision rules to identify dry land, open water and partial water (surface water mixed with vegetation and/or soil targets), from each individual cloud-masked Landsat image Landsat-8 OLI data were excluded from this step of the analysis due to spectral and radiometric adjustments on the OLI sensor that could affect the performance of the original DSWE thresholds (Vermote 3.5 Validation We validated our flood predictions over the study sites described in Section using two different datasets First, we performed an accuracy assessment on randomly drawn samples of flooded and non-flooded Fig Classification tree and colour key indicating floodwater classes included in this study The open water probability, P(ow), and total inundation probability, P (in), were derived from historical Landsat data Permanent open water (POW) was assigned to all pixels with a P(ow) > 95% Other flood classes were derived from VV and VH Z-scores (ZVV and ZVH, respectively) with an applied threshold (thdVV and thdVH, respectively) Remote Sensing of Environment 240 (2020) 111664 B DeVries, et al pixels over the Houston site, using 3-m PlanetScope imagery (Planet Team, 2017) acquired on 2017-08-29 as reference data We stratified flood maps produced on the GEE from Sentinel-1 data acquired on the same date by SAR flood classes Since our objective with the accuracy assessment was to validate the SAR-based flood labels and not the previously published Landsat-based algorithms, we excluded all pixels with historical inundation class labels We rejected any samples that included mixed water/non-water pixels when overlaid onto Planet data, ensuring that only pure water or land pixels were included in the validation After removing seven samples where visual interpretation was not possible or extreme Z-score values were found, a total of 493 samples taken from predicted 2017-08-30 flood and non-flood classes over Houston, Texas remained We labeled each remaining sampled pixel as “flooded” or “not flooded” based on visual interpretation of true colour RGB composites of PlanetScope imagery at the same location From these interpreted samples, we computed the overall accuracy, user's accuracy and producer's accuracy using both a count-based confusion matrix as well as an area-normalized proportion matrix, which adjusts confusion matrix counts based on the relative area represented by each stratum (Stehman et al., 2003) Using the PlanetScope samples, we also computed accuracies for flood maps derived using a series of VV backscatter thresholds ranging from −24 to −15 dB for the purpose of comparison with the accuracies of the Zscore derived maps Second, we assessed the similarity between our predicted flood maps and operational flood monitoring data, using vector data from the Copernicus Emergency Mapping Service (EMS) produced in response to the flood events in Greece and Madagascar We first rasterized and resampled the EMS vector data using nearest neighbor resampling to match our 10-m resolution flood maps We then overlaid the rasterized EMS vector data onto our predicted flood rasters and counted all instances of agreement and disagreements among the predicted flood and non-flood classes As with the validation of the Houston flood predictions, we excluded all pixels where prior inundation had been detected with Landsat data, limiting the comparison to the SAR-based predictions Results 4.1 Houston, Texas Z-score distributions among the PlanetScope samples taken from the Houston site for both VV and VH polarizations were approximately normal (Fig 5A), although Z-score distributions from samples in the flood class (n = 152) were slightly skewed towards low values VV and VH Z-scores from non-flood samples (n = 341) had means of −0.76 ± 1.6 (s.d.) and − 0.047 ± 1.5 (s.d.) respectively, and medians of −0.69 and 0.15, respectively VV and VH Z-scores from flood samples had means of −3.2 ± 1.8 (s.d.) and − 3.5 ± 2.5 (s.d.) respectively, and medians of −3.0 and − 3.2, respectively VV and VH Z-scores across all samples were moderately correlated with each other (R2 = 0.47, p < 0.001) (Fig 5B) We estimated count-based and area-normalized overall accuracies for various combinations of VV and VH Z-score thresholds (Fig 6) Maximum count-based overall accuracies were achieved when the VV Z-score threshold was set to −3.0 and the VH Z-score threshold ranged from −1.5 to −3.0 Using Z-score thresholds of −3.0 for both polarizations (Table 2), the overall count-based accuracy was estimated at 0.854 and the overall area-normalized accuracy at 0.868 ± 0.0309 (95% confidence interval) Count-based user's and producer's accuracies were estimated at 0.803 and 0.697, respectively Area-normalized user's and producer's accuracies were estimated at 0.803 ± 0.0719 (95% c.i.) and 0.330 ± 0.0783 (95% c.i.), respectively Maximum area-normalized overall accuracy was achieved when the VV and VH Z-score thresholds were set to −2.0 and − 2.5, respectively In this case, the overall count-based accuracy was estimated at 0.805 and the overall area-normalized accuracy at 0.898 ± 0.0278 (95% confidence interval) (Table 3) Count-based user's and producer's accuracies were estimated at 0.635 and 0.868, respectively Area-normalized user's and producer's accuracies were estimated at 0.635 ± 0.0680 (95% c.i.) and 0.525 ± 0.0829 (95% c.i.), respectively We conducted a similar analysis using flood maps derived by applying VV-backscatter thresholds We achieved a maximum overall accuracy of 0.849 ± 0.016 (95% c.i.), with a user's accuracy of 0.787 ± 0.075 (95% c.i.) and a producer's accuracy of 0.399 ± 0.081 (95% c.i.) using a VV threshold of −16 dB 3.6 Application programming interfaces and software packages We developed and executed our flood mapping algorithm using the GEE javascript and python application programming interfaces (API) The javascript API, also known as the GEE “playground”, features several frames, including a coding, documentation and object query frame, as well as a mapping/visualization frame (Gorelick et al., 2017) In addition to mapping and querying pixels on the fly, the javascript API also allows for the export of results to cloud storage for further offline analysis The GEE addresses of all datasets used in this study are listed in Table All validation work was done using the R programming language (R Development Core Team, 2008) and the ‘raster’ and ‘rgdal’ packages (Bivand et al., 2017; Hijmans, 2016) The EMS comparison work was carried out in python using the ‘osgeo/gdal,’ ‘numpy’ and ‘rasterio’ packages (GDAL/OGR contributors, 2018; Gillies et al., 2013; Oliphant, 2006) 4.2 Thessaly, Greece Comparison statistics of our predicted flood maps and those of the Copernicus EMS data for Thessaly, Greece are shown in Table Overall agreement rates of 0.966, 0.981 and 0.985 were estimated using Z-score thresholds of −2, −2.5 and − 3, respectively Agreement within the predicted flood class (analogous to user's accuracy) ranged from 0.432 for a Z-score threshold of −2 to 0.793 for a Z-score threshold of −3 Agreement within the predicted non-flood class (analogous to producer's accuracy) ranged from 0.629 for a Z-score threshold of −2 to 0.776 for a Z-score threshold of −3 Both PlanetScope (Fig 7, top) and Sentinel-1 backscatter and Z-score time series (Fig 7, bottom) over a large polygon omitted by our algorithm failed to confirm that they were Table Datasets and corresponding GEE asset addresses used in this study Dataset GEE asset address Purpose Sentinel-1 GRD Landsat-5 TM Surface Reflectance Landsat-7 ETM+ Surface Reflectance JRC Monthly Water History Shuttle Radar Topography Mission (SRTM) 1-arcsecond DEM COPNERICUS/S1_GRD LANDSAT/LT05/C01/T1_SR LANDSAT/LE07/C01/T1_SR JRC/GSW1_0/MonthlyHistory USGS/SRTMGL1_003 SAR-based anomalies and Z-scores Historical surface water regimes Historical surface water regimes Permanent water bodies Slope masking Remote Sensing of Environment 240 (2020) 111664 B DeVries, et al Fig A: Violin plot of VV and VH Z-score distributions for reference samples taken over the Houston, Texas region on 2017-08-30 B: Scatterplot of VV and VH Zscores for the same reference samples (F = “flooded”; NF = “not flooded”) in fact flooded, suggesting that this disagreement is not due to any error on the part of our algorithm Given the consistently low VV and VH backscatter through time for a pixel chosen within this polygon (Fig 7, bottom), it is likely that this is a smooth, non-inundated land surface feature that was erroneously mapped as water in the EMS vector dataset and therefore not a true omission error Table Confusion matrix from comparison between GEE-predicted (using Z-score thresholds of −3.0 for both polarizations) and interpreted samples from PlanetScope imagery over Houston, Texas following Hurricane Harvey Actual number of samples and respective normalized area fractions per category are shown (NF = “not flooded; F = “flooded”) Reference Predicted 4.3 Eastern Madagascar Comparison statistics of our predicted flood maps and those of the Copernicus EMS data for Eastern Madagascar are shown in Table Overall agreement rates of 0.979, 0.984 and 0.985 were estimated using Z-score thresholds of −2, −2.5 and − 3, respectively Agreement within the predicted flood class ranged from 0.533 for a Z-score threshold of −2 to 0.707 for a Z-score threshold of −3 Agreement within the predicted non-flood class ranged from 0.840 for a Z-score threshold of −2 to 0.905 for a Z-score threshold of −3 Although overall agreement rates were very close to that of Thessaly, Greece, # Samples NF NF 315 F 26 Σ 341 F 46 106 152 Σ 361 132 493 Normalized NF 0.809 0.0143 0.824 area fraction F 0.118 0.0582 0.176 Σ 0.928 0.0724 agreement within the predicted flood class was significantly lower, especially among the northernmost sites (Error! Reference source not found.) Visual inspection of our flood maps over Eastern Madagascar revealed that these “extraneous” flood pixels were usually located adjacent to EMS-mapped polygons An example near the coastal town of Fig Count-based (A) and area-normalized (B) overall accuracies estimated using different combinations of VV and VH Z-score thresholds Red shades indicate higher accuracies and blue shades indicate lower accuracies (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Remote Sensing of Environment 240 (2020) 111664 B DeVries, et al Table Confusion matrix from comparison between GEE-predicted (using Z-score thresholds of −2.0 and −2.5 for VV and VH polarizations, respectively) and interpreted samples from PlanetScope imagery over Houston, Texas following Hurricane Harvey Actual number of samples and respective normalized area fractions per category are shown (NF = “not flooded; F = “flooded”) Table Agreement statistics between GEE flood prediction maps and Copernicus EMS vector data layers for Eastern Madagascar using three Z-score thresholds applied to both polarizations Overall agreement Agreement within GEE flood class Agreement within GEE non-flood class Reference Predicted # Samples NF NF 265 F 76 Σ 341 F 20 132 152 Σ 285 208 493 Normalized NF 0.828 0.0398 0.868 area fraction F 0.0625 0.0692 0.132 Σ 0.891 0.109 Z < −3 Z < −2.5 Z < −2 0.985 0.707 0.840 0.984 0.636 0.879 0.979 0.533 0.905 Discussion 5.1 SAR backscatter anomaly trends Table Agreement statistics between GEE flood prediction maps and Copernicus EMS vector data layers for Thessaly, Greece using three Z-score thresholds applied to both polarizations Overall agreement Agreement within GEE flood class Agreement within GEE non-flood class Z < −3 Z < −2.5 Z < −2 0.985 0.793 0.629 0.981 0.642 0.711 0.966 0.432 0.776 The first objective of this study was to better understand how SAR backscatter Z-scores are affected by floods Assuming that per-pixel SAR backscatter values are normally distributed over time during the baseline period, the mean VV and VH Z-score values of −3.2 and − 3.5, respectively, for all Houston flood samples correspond to standard normal probabilities < 0.001, showing that floods elicit strong deviations from expected baseline backscatter values These deviations, demonstrated in Fig and Fig 5, are due to transitions from volumetric or surface scattering mechanisms typical of rough land surfaces (e.g., rough soils or vegetation canopies) to specular reflection typical of smooth open water surfaces Since water bodies present during the baseline period would already have low mean backscatter values, they Nosy Varika shown in Fig reveals that these pixels follow valleybottom tributaries leading into EMS-mapped polygons, suggesting that these are in fact valid flooded pixels not mapped in the EMS dataset Fig Top: True colour RGB composite PlanetScope imagery over a portion of the Thessaly flooded site acquired on 2018-02-17 (left) and 2018-02-27 (centre and right) Predicted flood classes (see Fig for colour table) and Copernicus EMS vector (hatched overlay) are shown on the top-right Bottom: VV and VH σ0 backscatter coefficient (top panel) and corresponding Z-scores (bottom panel) for the potential EMS commission error shown as a yellow ‘X’ in the top-right image The date for the classified map in the top-right panel is indicated as a black arrow (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Remote Sensing of Environment 240 (2020) 111664 B DeVries, et al Fig Agreements and disagreements resulting from overlay of GEE-predicted floods and EMS vector data for the area surrounding Nosy Varika, Madagascar Original EMS polygons are shown as a hatched overlay would be expected to have Z-scores near zero, allowing for the automatic discrimination between existing surface water bodies and new inundation (i.e., flooding) based on these high-magnitude negative Zscores The distribution of Z-scores shows a high degree of separability between flood and non-flood samples taken over Houston during Hurricane Harvey (Fig 5) However, we observed subtle differences in the VV and VH Z-score distributions among non-flood samples VH Zscore values were normally distributed around a mean of zero, which is not surprising, since no significant changes were expected in these samples (Fig 5B) However, the mean Z-score for VV non-flood samples was slightly below zero, and a possible second mode greater than zero was observed, suggesting other land surface changes in response to the hurricane, resulting in both negative and positive anomalies For example, high winds brought on by the hurricane could damage or alter vegetation canopies (e.g., orientation), resulting in decreased backscatter (Ramsey et al., 2015) Additionally, elevated soil and vegetation moisture and enhanced double-bounce scattering during and following the hurricane would give rise to an increase in enhanced dielectric scattering off of these surfaces, resulting in increased backscatter following the storm (Lucas et al., 2010; Merot et al., 1994; Schmugge, 1980) In fact, the time series trajectory shown in Fig shows a slight increase in Z-scores following the hurricane, suggesting that the elevated backscatter following the hurricane is persistent Finally, windinduced Bragg scattering over open surface water during hurricanes would also give rise to positive Z-scores over open water bodies (Zhang et al., 2014), but since our sampling strategy excluded open water bodies (based on Landsat historical classifications), these anomalies are not included in this analysis While the trends described here warrant further investigation and could help to improve methods for post-hurricane impact assessments, they had no impact on our classification scheme, which targets high-magnitude negative Z-scores for flood identification accuracies, as well as the rates of commission or omission errors The count-based overall accuracies shown in Fig 6A suggest an optimal VV Z-score threshold of −3.0, with a broader range of optimal VH Z-score thresholds This finding is consistent with the Z-score distributions shown in Fig 5A, where VH Z-scores are distributed much more tightly around a mean of zero, resulting in reduced confusion between flood and non-flood classes and greater flexibility with respect to threshold selection Similarly, we achieved maximum overall agreement using Zscore thresholds of −3 for the Greece and Madagascar sites Consistent with this observation, increasing the Z-score thresholds to −2 in the Greece and Madagascar cases reduced the agreement within the GEEpredicted flood class (analogous to user's accuracy) to a larger degree than the increase in agreement within the GEE-predicted non-flood class (analogous to producer's accuracy) We found that the Z-score derived maps out-performed maps derived from simple VV-backscatter thresholds, with estimated differences in overall accuracies ranging between 1.6% to 9.8% While the user's accuracy of the VV-backscatter threshold maps was found to be marginally higher than that of the Z-score based maps, the large spread of the confidence intervals indicates that this difference is highly uncertain On the other hand, the Z-score based maps had a significantly higher producer's accuracy than that of the VV-backscatter thresholded maps We therefore conclude that the Z-score method improves upon simple VV-backscatter thresholds by reducing the omission errors of the flood map, which is important for rapid-response products The improvements that we observed in the Z-score derived maps are likely due to the fact that SAR backscatter is impacted by variations in incidence angle across the sensor's field of view (Westerhoff et al., 2013), making selection of a static VV-backscatter threshold difficult In our algorithm, we computed baseline statistics by grouping Sentinel-1 imagery by acquisition modes and orbital direction This method of computing baseline statistics ensures that Z-scores are influenced by historical and current conditions at the pixel level, and not by spatial variability caused by viewing geometry Our validation has a number of limitations worth noting here First, area normalization, in which weights are assigned to samples based on their respective inclusion probabilities, is recommended for accuracy assessments and area estimations in land cover and land use change studies where validation samples have been drawn from stratified random samples (Olofsson et al., 2014; Stehman et al., 2003) However, the fact that the size of our “non-flood” stratum in our sampling design greatly exceeded that of the “flood” stratum resulted in a large disparity 5.2 Flood classification accuracy Our second objective was to determine the accuracy of our classified flood maps and their comparability with operational flood map products We estimated a maximum area-normalized accuracy of 89.8 ± 2.8% (95% c.i.) for the Houston case, and maximum comparability estimates of 98.5% for both Greece and Madagascar In all cases, the choice of the threshold had notable impacts on the overall Remote Sensing of Environment 240 (2020) 111664 B DeVries, et al contextualization of Sentinel-1 Z-scores contemporary with the flood events, allowing for further discrimination between floods and historically permanent or seasonally inundated areas Since SAR Z-scores are based on statistics computed from observations within a limited baseline temporal window, historical Landsat-based inundation probabilities provide an important constraint for our classification algorithm For example, seasonally inundated wetlands like the coastal wetlands shown in Fig may experience flooding above and beyond what has historically occurred Knowledge on where floods are occurring in previously non-flooded areas is an important component to flood monitoring systems, as these floods have the potential to be catastrophic and likely require greater attention in disaster response and rehabilitation plans Finally, our algorithm is based on pixel-based statistics derived from SAR and optical time series data, making it compatible with data cube computing architectures like the GEE (Lewis et al., 2017) Using GEE's web-based javascript application programming interface (API), we were able to generate time series flood maps for a number of recent flood events in several minutes (Fig 10), compared to a processing time of several days on a high performance computing cluster This performance improvement is not only due to the fact that all data are already downloaded and pre-processed on the GEE servers, but also because of the fact that pixel-based operations are easily parallelized, facilitating rapid data exploration and algorithm development (Gorelick et al., 2017) Additionally, the rapid deployment of time series algorithms allows users to query and interpret single pixel time series on the fly, which can be incorporated into interactive web-based tools as demonstrated in Fig 11 in weights assigned to samples drawn from the “non-flood” stratum As a consequence, the area-normalized producer's accuracies were typically very low, reflecting the lack of representativeness of these samples For this reason, we report both count-based and area-normalized accuracy statistics for the Houston, Texas validation Second, the high degree of comparability between GEE and EMS predictions is heavily influenced by disproportionately large areas of agreed non-flooding Despite very high overall agreement rates, large areas of disagreements reveal potential errors in the semi-automated EMS workflow, including large commission errors in the Thessaly, Greece case (Fig 7) In this example, low backscatter over a smooth non-flooded surface was likely erroneously labeled as flooded in the EMS dataset However, comparison of the backscatter and Z-score time series with PlanetScope imagery around the flood date revealed that this is a persistently non-flooded area While the purpose of this study was not to evaluate existing EMS map products, these results highlight the need for further improvement of algorithms and workflows used to generate operational disaster monitoring products 5.3 Strengths of the method The implementation of our algorithm on the GEE confers several advantages over conventional flood mapping and monitoring methods First, the temporal SAR backscatter Z-scores provide an objective measure of change for individual image pixels Since Z-scores are directly related to standard normal probability distribution functions, users can define thresholds based on a desired probability or confidence level In contrast, the decision on where to set a backscatter threshold can be complicated by variations in backscatter due to other factors like view angle (Huang et al., 2018; Westerhoff et al., 2013) or Bragg scattering (Zhang et al., 2014) It is important to note that the use of Zscores relies on the assumption that backscatter coefficient values are normally distributed through the baseline period, which may not necessarily hold true when observations taken under various sensor configurations are included in the baseline Specifically, the incidence angle for each observation is known to have an effect on backscatter off of inundated surfaces (Huang et al., 2018; Schlaffer et al., 2015; Westerhoff et al., 2013), which is likely the cause of the systematic difference between IW and SM backscatter values shown in Fig (top panel) We were able to remove most of this bias at the anomaly computation stage by computing baseline statistics separately for different acquisition modes and orbital directions (Fig 2, middle panel) When this bias is taken into consideration, the use of temporal Z-scores allows for rapid monitoring of floods while simultaneously correcting for systematic difference arising from varying sensor configurations Second, the use of historical Landsat allowed for the 5.4 Limitations and potential improvements Despite the strengths of our algorithm and the GEE platform in monitoring floods in near real-time, we encountered several limitations that warrant further study First, other land surface dynamics can give rise to temporal Z-scores on the order of that observed in our flood examples For example, the removal of forest or crop canopies is associated with a change in SAR backscatter signatures from those dominated by volumetric canopy scattering to those dominated by soil surface scattering (Shimada et al., 2014) While the latter backscatter signature is typically not identical to open surface water, such changes may be sufficiently large to generate large anomaly scores (Cian et al., 2018) In some cases, these dynamics may be a result of regular inundation patterns (e.g., flooded rice fields), which make selection of a non-flooded baseline with sufficient number of observations difficult In such cases, it will be possible to define baseline periods consisting of specific subsets of multiple years (e.g., only dry season imagery) as Fig Example of flooding along coastal wetlands south of Houston, TX on 2017-08-30 Predicted new (red) and recurring (purple) inundation (left panel) and coincident PlanetScope imagery (right panel) are shown for comparison The full colour key used in this map is shown in Fig (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 10 Remote Sensing of Environment 240 (2020) 111664 B DeVries, et al Fig 10 Time series flood maps for Hurricane Harvey near Houston (top row) and Hurricane Maria near San Juan, Puerto Rico (bottom row) PlanetScope (PS) true colour RGB composites for selected dates are shown at the end of each series The full colour key used in this map is shown in Fig of the globe, respectively, are therefore not sufficient to accurately track flood progression over time Improvements in temporal resolution require a higher density of flood observations during critical monitoring periods This need can be addressed in some cases with Sentinel-1 where additional acquisition modes or targeted acquisitions are available, allowing for the tracking of floodwater advance and retreat at near-daily temporal resolution (Fig 10) Incorporation of contemporary Landsat and Sentinel-2 data, in addition to the historical data already used in our algorithm, will increase the temporal resolution However, cloud cover during and after flood events would limit the extent to which these data can fill temporal gaps in most cases On the other hand, incorporation of data from other SAR sensors would have the potential to significantly increase the number of available observations during flood events Sentinel-1 data are currently the only open-access data available to the public and therefore the only SAR data served on the GEE, presenting a key limitation to our algorithm Future openaccess SAR missions like the National Aeronautics and Space Administration (NASA) - Indian Space Research Organization (ISRO) SAR (NISAR) mission, planned for launch in 2021, will provide an opportunity to explore the advantages of ingesting data from multiple SAR missions in our algorithm on the GEE Notably, NISAR will acquire imagery at a similar temporal resolution to Sentinel-1, but using longer wavelength energy, which will be especially useful for monitoring flooded vegetation Since our algorithm allows for adjustments of sensor- or configuration-specific bias by computing baseline statistics separately, we expect that these new data can be seamlessly incorporated into our algorithm and existing flood monitoring systems Sentinel-1 and other open-access SAR archives grow over time, which will allow for the exclusion of regularly flooded pixels from the baseline, while maintaining a sufficient number of observations Selection of a baseline subset could further be supported through the use of ancillary data sources like historical hydrographs With our workflow on the GEE, these modifications would entail only a very minor alteration to the temporal subsetting step during the baseline statistic computation Second, we did not consider flooded vegetation in this analysis Double-bounce backscatter typical of flooded vegetation can be detected using single or dual polarized SAR backscatter, and therefore using positive backscatter changes as well (Cian et al., 2018) More robust methods for characterizing backscatter response to flooded vegetation (White et al., 2015), but they depend on complex SAR amplitude and phase data, which is incompatible with the GEE's image pyramiding scheme (Gorelick et al., 2017) On the other hand, the abundance of optical time series imagery and higher-level data products describing vegetation cover on the GEE offers the potential to further extend our algorithm to automatically flag potential flooding in vegetated areas (Sexton et al., 2013) Third, we did not consider flooding in heavily built-up areas in our analysis due to difficulties in characterizing backscatter signatures and a lack of robust reference data These areas are generally characterized by smooth impervious surfaces (roads and sidewalks) adjacent to large vertical features (buildings), generating high backscatter under both flooded and non-flooded conditions Since populated centres are highpriority targets for flood monitoring, addressing this challenge is particularly crucial Despite these challenges, temporal Z-scores have the potential to highlight areas of potential flooding in some urban areas due to an increase in backscatter in response to flooding An example of flooding conditions inducing a strong positive increase in VV-backscatter is shown in Fig 12, where residential sub-divisions beside and inside two flood-control reservoirs in western Houston experienced flooding Comparison with SkySat imagery (Murthy et al., 2014) acquired the following day confirmed that these positive Z-scores correspond with flooded pixels Further research is needed to fully develop this aspect of our algorithm Integrating these results with high-resolution topographic data and hydraulic models will help to expand flood predictions in urban areas (Mason et al., 2012) Finally, the temporal resolution of the flood maps produced by our algorithm were limited by the revisit period of the Sentinel-1 satellites Floods are highly dynamic phenomena, and daily or even sub-daily information is often required for effective disaster response Sentinel-1's 6-day and 12-day operational revisit intervals over Europe and the rest Conclusions In this study, we describe a method for monitoring floods in near real-time using contemporary SAR time series together with historical Landsat data on the GEE, allowing for the rapid discrimination between floods and previously inundated areas Temporal Z-scores were shown to offer a reliable and accurate alternative to backscatter thresholds for flood mapping and monitoring The GEE allows for rapid, on-the-fly computation of Z-scores for dense SAR time series, as well as integration of historical optical (Landsat) data for contextualization of Z-scores towards this goal This study highlights the benefits of cloud computing platforms like the GEE to allow for rapid, on-the-fly analysis of satellite data in near real-time, in support of disaster monitoring and management activities As similar publicly funded architectures emerge, such as the Geoscience Australia and Committee on Earth Observations 11 Fig 11 Screenshot of an interactive applications designed on the GEE Playground The main panel shows floods in Thessaly, Greece in March 2018 mapped on the fly, with example pixel time series queried from the map shown in the left panel (top: VV and VH backscatter coefficient time series; middle: VV Z-score time series; bottom: VH Z-score time series) B DeVries, et al Remote Sensing of Environment 240 (2020) 111664 12 Remote Sensing of Environment 240 (2020) 111664 B DeVries, et al Fig 12 VV backscatter (top left) and Z-scores (top right) for a Sentinel-1B image acquired over western Houston on 2017-08-30 High positive Z-scores resulting from elevated backscatter may indicate flooding in residential sub-divisions along the western edge of Barker's Reservoir (including example region A), the northern edge of Addick's Reservoir and downstream along the banks of Buffalo Bayou (including example region B) A 76-cm resolution SkySat image acquired on 2017-08-31 is shown for each of the example regions Flooded SkySat pixels appear dark in region A (clear water) and brown in region B (turbid water) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) (CEOS) data cube platforms (Lewis et al., 2016, 2017), the methods presented here have the potential to provide a link between these platforms and disaster decision support systems (DSS) Using our algorithm on the GEE, we were able to produce flood maps shown to be highly similar to existing operational flood maps for several recent flood disasters, suggesting that this approach is highly suitable to operational flood map production pipelines Further work is needed to integrate cloud computing and data cube platforms with DSS to allow for rapid updating of flood observations during disaster events, integrate flood predictions with other data sources and models, and disseminate maps, alerts and statistics to relevant stakeholders Acknowledgements and disclaimers This work was conducted with funding from the NASA Land Cover and Land Use Change (LCLUC) program (grant # NNH14ZDA001NLCLUC) We gratefully acknowledge the U.S Geological Survey (USGS), the European Space Agency (ESA) Copernicus program and the Google Earth Engine for providing optical and SAR data and processing capabilities free of charge The findings and conclusions in this article are those of the authors and not necessarily represent the views of the U.S Fish and Wildlife Service Use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S Government 13 Remote Sensing of Environment 240 (2020) 111664 B DeVries, et al Appendix A Supplementary data science.1244693 Hijmans, R.J., 2016 raster: Geographic Data Analysis and Modeling Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., Kim, H., Kanae, S., 2013 Global flood risk under climate change Nat Clim Chang 3, 816–821 https://doi.org/10.1038/nclimate1911 Hostache, R., Matgen, P., Wagner, W., 2012 Change detection approaches for flood extent mapping: how to select the most adequate reference image from online archives? 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the 5.4 Limitations and potential improvements Despite the strengths of our algorithm and the GEE platform in monitoring floods... instances of agreement and disagreements among the predicted flood and non -flood classes As with the validation of the Houston flood predictions, we excluded all pixels where prior inundation had