Flood Mapping using Multi-temporal Open Access Synthetic ... · Gagandeep Singh* and Ashish Pandey...

Roorkee Water Conclave 2020

Organized by Indian Institute of Technology Roorkee and National Institute of Hydrology,

Roorkee during February 26-28, 2020

Flood Mapping using Multi-temporal Open Access Synthetic Aperture Radar Data in

Google Earth Engine

Gagandeep Singh* and Ashish Pandey

Indian Institute of Technology Roorkee, India

Abstract: Floods being one of the most frequent natural hazards have been causing havoc and

destruction in all parts of the world. Every year hundreds of thousands of people get adversely affected

by floods in addition to massive biodiversity devastation. Since it's impossible to avoid floods and the

after-effects are evident, continuous efforts are in progress to assess and quantify the damages caused

by the flood events. With the rapid developments in satellite remote sensing, it has been possible to

observe the earth surfaces in the flood-prone areas to monitor and map the flood extent and quantify the

damages. The European Space Agency’s (ESA) Copernicus is one of the most ambitious Earth

Observation (EO) programme having operational satellite constellations providing continuous, accurate,

and easily accessible satellite data for the entire globe.

This study demonstrates the use of Google Earth Engine (GEE) and Dual polarized (VV and VH)

Sentinel-1 Synthetic Aperture Radar (SAR) data for mapping flooded areas in the Indian state of Punjab.

Various districts of Punjab experienced heavy rains in August 2019, followed by the release of excess

water from the Bhakra dam resulting in excess discharge in the Sutlej River and its tributaries. As a

result, over 300 villages in over a dozen districts of Punjab were inundated, causing extensive damages

to crops, especially paddy, and homes in low-lying areas. A change detection and thresholding

methodology has been adopted in Google Earth Engine (Javascript based) Platform to determine the

extent of flooding using multiple Sentinel‐ 1 SAR images captured before and after the floods of August

2019 in Punjab.

Keywords: Floods; Earth Observation, Synthetic Aperture Radar (SAR); Sentinel-1; Google

Earth Engine (GEE)

1. Introduction

Floods are one of the most severe catastrophic natural calamities which cause unprecedented

destruction all around the world. According to the Global Assessment Report (2019) on

Disaster Risk Reduction, between the years 1997 to 2017, floods have affected 76 million

people. Floods can be described as the presence of water on dry land. The causes of that

flooding can be excessive precipitation, snowmelt that occurs in a very short time interval, a

dam break, a storm surge, inadequate water management practices, etc.

India is second in absolute terms of people killed by floods, but relatively several other

countries have more casualties per million inhabitants by floods than India. India being an

agriculture based economy, its economic growth has always been under the influence of the

weather, especially extreme weather events(Vishnu et al. 2019). Besides heavy agricultural

losses, such extreme events also result in huge losses of life, property, and unrest in the

economic activities.

Punjab is a state in northwestern India. It covers an area of 50,362 km2 i.e., 1.53% of India's

total geographical area. Continuous and heavy rainfall in August 2019 caused widespread

destruction in several districts of Punjab along the banks of the Sutlej and Beas rivers. Districts

of Amritsar, Fatehgarh Sahib, Ferozepur, Gurdaspur, Jalandhar, Kapurthala, Ludhiana, Moga,

Mohali, Patiala, Roopnagar and Sangrur were among the most severely affected.

Satellite data is very useful for quick damage extent assessment. A critical element during an

ongoing flood event is flood inundation assessment which forms a very important component

to formulate damage relief plans, damage assessment, estimation and distribution of




compensations and selecting appropriate planning and land use in flood-affected area (Ran

and Nedovic-Budic 2016; Vishnu et al. 2019). Satellite remote sensing data products are

exceptional resources in flood mapping as they offer impeccable advantages of synoptic views

and reviews. Optical remote sensing data products are not very useful to monitor flood-affected

areas in case of an ongoing event due to the presence of cloud cover, which creates hindrance

for data retrieval in visible and near-infrared regions. Microwave remote sensing data products

have an advantage of all-weather, day-night coverage with cloud penetration capability

(Amitrano et al. 2018; Plank et al. 2017; S. Martinis 2017; Twele et al. 2016; Uddin et al.

2019). Therefore, active radar sensors operating in the microwave band are the most preferred

choice for flood inundation mapping. Hence sentinel-1 dataset was used in this study to map

flood-affected areas in Punjab during and following the August 2019 event.

From a radar perspective, flooding can be defined as an occurrence of temporary or permanent

water surface either underneath a tall or short vegetation cover regardless of whether it is forest

or agriculture or just open water. Flood maps can help monitor inundation extent and dynamics

for disaster assessment and management. The radar backscatter mechanism that is primarily

relevant in terms of flood inundation (Hess et al. 1990) is a key aspect to be discussed at this

juncture. The specular scattering occurs in the case of a smooth water surface wherein the

signal is scattered away from the satellite sensor, which results in the appearance of open water

as very dark in the satellite image (Lillesand et al. 2015). Rough surface scattering occurs when

there is some level of roughness in the water surface due to the presence of short floating

vegetation, wind, or heavy rainfall resulting in the signal getting scattered in different directions

but mostly away from the satellite sensor. Such areas appear dark but not as dark as completely

smooth water surface. The rougher the surface, the larger the signal scattered back to the

satellite and brighter that pixel will appear on the image. Double bounce scattering occurs when

two smooth surfaces create a right angle and deflect the incoming radiation causing most of

the radiation returning to the sensor (Lillesand et al. 2015). These areas appear very bright in

the image. This type of scattering is commonly observed in case of flooded vegetation, which

acts as a vertical surface to the horizontal water surface. It is a characteristic of urban areas.

Another important concept related to microwaves is polarization. Polarization is the plane of

propagation of the electric field of the signal, which can be either in the horizontal or vertical

plane (Lillesand et al. 2015). Irrespective of wavelength, radar signals can be transmitted and

or received in different modes of polarization, and there are four combinations of both

transmitted and received polarizations. These are: HH-horizontally transmitted, horizontally

received; HV- horizontally transmitted, vertically received; VH- vertically transmitted,

horizontally received; VV- vertically transmitted, vertically received. Penetration depth of

radar signal is influenced by polarization.

Looking to the aforementioned, the main objective of this study is to demonstrate the potential

use of Sentinel-1 SAR images in the cloud-based platform of Google Earth Engine for flood

inundation mapping. The operational methodology used in this study has been used to quantify

the areal extent of the flooded area.

II. Materials and Methodology/Study Area and Methods

Study Area

Fig.1 shows the study area which comprises of 12 districts namely Amritsar, Fatehgarh Sahib,

Ferozepur, Gurdaspur, Jalandhar, Kapurthala, Ludhiana, Moga, Mohali, Patiala, Roopnagar




and Sangrur in the State of Punjab which lies between 29º N to 32º30’ N Latitude and the 73º

E to 77º E Longitude. The total area of the selected districts is 28386.73 km2. The study area

is a part of the Indo-Gangetic alluvial plain. The area is drained by two perennial rivers Sutlej

and Beas (Chopra and Sharma 1993).

Fig. 1: Location map of the study area

Analysis of SRTM DEM for the flood-affected districts in the state shows that the elevation in

the area varies from 143 m to 777 m above MSL. Out of a total area of 28386.74 km2, 40.03

% area lies in the elevation range of 143 m to 230 m. 44.02 % of the total area lies in the

elevation range of 230 m to 260 m. 14.15 % of the total area falls under an elevation range of

260 to 330 m and a mere 1.8 % area lies in the high elevation range of 330 to 777 m. This gives

a decent overview of the terrain and signifies that more than 80 % of the area has minimal

changes in the elevation. Apart from 1.8 % area which can be classified under high elevation

range, the entire terrain is plain.

Datasets used

In this study, Sentinel-1 dataset has been used to obtain the flood inundation areas in various

districts of Punjab. A total of 32 images of dual-polarized (VV and VH) Sentinel-1 SAR

datasets acquired from 13 March 2019 to 13 June 2019 are utilized before the flood event

analysis and 14 images acquired from 21 August 2019 to 16 September 2019 are used for this

study. In this study, dual-polarized (VV and VH) of 5 × 20 m resolution (10-m pixel spacing)

Level-1 Ground Range Detected (GRD) Sentinel-1 SAR datasets acquired in Interferometric

Wide Swath (IW) mode are used. Additionally, SRTM DEM of 30-m spatial resolution was

used for extraction of elevation zones within the state.

The sentinel-1 data is a C-band synthetic aperture radar data. GEE has the entire Sentinel-1

database. It is being provided by the European Space Agency using two satellites Sentinel-1A

and 1B, which individually offers global coverage of 12 days. Besides, global coverage of 6




days over the equator is obtained using data from both satellites. There are four different modes

in which the satellite sensors acquire Sentinel-1 data. These are: Extra Wide Swath Mode-

which is being exclusively used for monitoring oceans and coasts; Strip Mode- which is

operated by special order only and is intended for special needs; Wave Mode- it is used for the

routine collection for the ocean; Interferometric Wide Swath Mode- which is used for routine

collection for land (this mode is exclusively used for flood mapping applications).

SAR data processing platform

In this study, the Sentinel-1 SAR datasets were processed using Google Earth Engine, which

is a cloud-based geospatial processing platform and is being used widely to analyze the Earth’s

surface. GEE provides a huge collection of time-series satellite data products and geospatial

datasets all for free and hosted on the cloud. GEE also provides a Javascript-based code editor wherein

codes were developed to datasets retrieval, processing and flood inundation mapping.

Methodology for inundation mapping

Methodology flowchart for inundation mapping is presented in Fig.2. The area of interest (AOI) was

selected in the GEE code editor platform by importing in the shapefiles of the flood-affected districts.

Once the shapefiles were loaded, the next step was to load the preprocessed sentinel-1 data from the

public data archive of the GEE. Various filters (instrument mode, transmitter/ receiver polarization,

orbit pass, resolution and AOI) were applied to create and load in the image data collection.




Fig. 2: Methodology flowchart

The first filtering for Sentinel-1 data collection was defined to load the SAR Ground Range

collection with Interferometric Wide (IW) instrument mode, VV & VH polarization and a

descending orbit pass for the previously defined AOI. After that, a second filter was defined to

select the above-filtered data for specific dates. Therefore, the above-obtained dataset

collection was further filtered by date for before and after the flood event. To collect the images

for ‘before the event’ the, ‘from’ and ‘to’ dates chosen were 13th March 2019 to 13th June 2019.

To collect the images for ‘after the event’ the, ‘from’ and ‘to’ dates chosen were 21 August

2019 to 16 September 2019.

The filtered image collections for before and after flood events were mosaicked respectively to

create a single image in both VV and VH polarization modes. A total of four images were ready

for post-processing. All the four images were then processed to remove speckle (noise

reduction). For this, a focal mean smoothing filter was applied with a radius of 50 pixels. The




resultant speckle free images obtained for VH polarization were further used to calculate the

difference between before and after the flood to find the inundated areas. The after flood

mosaicked image was divided by the before flood mosaicked image to obtain a new image with

flood inundated areas. To prominently highlight the flooded areas, a mask was created using a

difference threshold value of 1.25. Finally, the VH polarization threshold difference image was

exported to compute the areal extent of the flooded area in each district as well as in the entire

AOI.

3. Results & Discussion

The incessant rains in August 2019 caused severe floods in Punjab wherein 12 majorly affected

districts have been mapped for areal assessment of flood inundation. Google Earth Engine

(GEE) was used to conduct the entire satellite image processing, as explained in the

methodology. A total of 28386.72 km2 area of Punjab was selected as the area of interest for

the study.

Fig. 3: Flood inundation map of the area of interest

The final flood inundation map derived after processing the sentinel-1 SAR images is presented in Fig.

3. An area of 205.2 km2 was mapped as flooded in the analysis and has been represented in white color.

The non-flooded area of 28181.54 km2 is displayed in black and the river flowing through the study

area represented in blue color. Furthermore, a district-wise flooded and non-flooded area assessment

was carried out and has been presented in table 1.

S. No. District Flooded area

(km2)

Non-flooded

area (km2)

Total area

(km2)

1 Amritsar 3.59 2606.42 2610.01

2 Fatehgarh Sahib 7.52 1165.27 1172.79

3 Ferozepur 27.02 2471.33 2498.34

4 Gurdaspur 1.64 2574.46 2576.10




5 Jalandhar 45.89 2554.90 2600.78

6 Kapurthala 42.13 1614.42 1656.55

7 Ludhiana 35.66 3531.71 3567.37

8 Moga 25.18 2275.36 2300.54

9 Mohali 0.91 1087.22 1088.13

10 Patiala 6.40 3328.03 3334.43

11 Roopnagar 2.05 1378.81 1380.86

12 Sangrur 7.21 3593.61 3600.82

Total 205.2 28181.54 28386.72

Table 1: District-wise flooded and non-flooded area assessment

Out of the 12 districts Kapurthala, Ferozpur, Jalandhar, and Moga were the most affected ones.

Figs 4 and 5 show the flood inundation in each of the 12 districts.

Fig. 4: Flood inundation maps for Amritsar, Fatehgarh Sahib, Ferozpur, Gurdaspur, Jalandhar and

Kapurthala districts of Punjab.




Fig. 5: Flood inundation maps for Ludhiana, Moga, Mohali, Patiala, Roopnagar and Sangrur districts

of Punjab.

5. Conclusions

This study demonstrated the use of Sentinel-1 SAR data for near real-time flood inundation

mapping. During monsoon season, the availability of cloud-free optical satellite data products

is rare and occasional. SAR data offers a remarkable advantage of capturing data in all weather

conditions due to which it serves as the best data source to observe and map flood inundation

in near real-time.

Based on the results obtained, it can be concluded that the freely available Sentinel-1 SAR data

has immense potential for rapid flood mapping and monitoring. GEE can be effectively used

for planning disaster risk reduction, damage assessment, affected areas, and can be used well

along with land-use land cover information.

Acknowledgments

We acknowledge the European Space Agency (ESA) for providing Sentinel-1 SAR data. We

are also grateful to Google LLC for offering the Google Earth Engine platform.

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Flood Mapping using Multi-temporal Open Access Synthetic ... · Gagandeep Singh* and Ashish Pandey...

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