Water Body Change Detection Based on Sentinel-1 and HJ-1A/B

Satellites Data

Wei Zheng

and Jiali Shao

National Satellite Meteorological Center, China Meteorological Administration, Beijing 10008 , China

Email: zhengw@cma.gov.cn

Keywords: Water body, Sentinel-1, HJ-1A/B, Poyang Lake

Abstract: In the operational water body monitoring work by satellite data, Sentinel-1 and HJ-1A/B satellites data are

the proper data source which can be download freely and quickly. The method of water body change

detection by using Sentinel-1 and HJ-1A/B Satellite data is presented in order to make full use of these data

to obtain water body information. The fusion of Sentinel-1 and HJ-1A/B with high resolution can monitor

the water body in all-weather condition. The method is applied in Poyang Lake of China and shows

promising results. Comprehensive and effective utilization of multi-source satellite data could provide

reliable water body information for water resource management, flood warning, real-time monitoring of

flood development, rapid and accurate assessment of flood losses.

1 INTRODUCTION

Earth surface water body is the one of most

important water resources. Water body change

detection using the satellite can provide useful

information for flood disasters monitoring and water

resource management (Zheng et al., 2017; Zheng et

al., 2016). Synthetic Aperture Radar (SAR) and

optical sensors with high spatial resolution are very

useful for obtaining detailed water body information

(Abileah and Vignudelli, 2011). SAR data with both

the two features of high spatial resolution and cloud

penetration are very attractive to water body

monitoring (Delmeire, 1997; Liao et al., 2004; Lv et

al., 2005; Juval et al., 2016), and the high spatial

resolution optical data provide the ideal data source

for water detection under the clear sky (Tholey et al.,

1997; Profeti and Macintosh, 1997). Comprehensive

using the SAR and optical remote sensing data both

can enhance the all-weather satellite observing

capability and data processing efficiency.

2 DATA

In the operational satellite monitoring work, easy to

obtain the satellite data is required. Sentinel-1 and

HJ-1A/B satellites data all can be download freely

and quickly by user register in official website. So,

in this paper, the two satellite data mainly are used

to monitor the water body.

Sentinel-1 is a space mission funded by the

European Union and carried out by the ESA within

the Copernicus Program, consisting of a

constellation of two satellites, Sentinel-1A and

Sentinel-1B. he first satellite, Sentinel-1A, launched

on 3 April 2014, and Sentinel-1B was launched on

25 April 2016. The payload of Sentinel-1 is a

Synthetic Aperture Radar in C band that provides

continuous imagery (day, night and all weather). In

this paper, Sentinel-1 is applied to attain surface

water information in time for it is not influenced by

cloudy and rainy, all-weather observation. The C-

SAR instrument supports operation in dual

polarisation implemented through one transmit chain

and two parallel receive chains for H and V

polarisation. Sentinel-1 operates in four exclusive

acquisition modes showed in Table 1.

HJ-1A and HJ-1B, launched on 6 September

2008, are as two optical satellites of environment

and disaster monitoring and forecasting small

satellite constellation. The two satellites are

equipped with 3 remote sensors such as wide-

coverage CCD scanner, infrared multispectral

scanner and hyper-spectral imager, comprising a

more complete earth observation remote sensing

series characterized by high and medium space

resolution, high time resolution, high spectrum

resolution and wide coverage. CCD scanner observe

484

Zheng, W. and Shao, J.

Water Body Change Detection Based on Sentinel-1 and HJ-1A/B Satellites Data.

In Proceedings of the International Workshop on Environment and Geoscience (IWEG 2018), pages 484-488

ISBN: 978-989-758-342-1

in parallel to complete scanning and imaging for

earth with swath width of 700 km, ground pixel

resolution of 30m and 4 spectrum bands(table.2).

HJ-1A and HJ-1B have the same orbit with phase

position difference of 180°. The revisit period of two

CCD cameras is only 2 days after networking.

Table 1: Characters of SENTINEL-1A/B.

Operation

mode

Spatial

resolution

Swath

width

Polarisation

Stripmap(SM) 5×5 m

80 km

HH or VV

VV+VH or

HH+HV

Scan SAR-

Interferometirc

wide-

swath(IW)

5×20 m

250 km

HH or VV

VV+VH or

HH+HV

Extra-Wide

swath (EW)

20×40 m

400 km

HH or VV

VV+VH or

HH+HV

Wave mode

(WV)

20×5 m

20×20km

every

100km

HH or VV

Table 2: Channel parameters of HJ-1A/ B CCD.

Channel

number

Spectral

ranger

(µm)

Spatial

resolu-

tion(m)

Swath

width(km)

Repeti-tion

cycle(days)

0.43-

0.52

360(single)

700(double)

4(single)

2(double)

0.52-

0.60

0.63-

0.69

0.76-

0.90

3 METHOD

3.1 Water Body Identifying Based on

HJ-1A/B

HJ-1A/B data have 30-m spatial resolution. For the

CCD sensor, red channel 1(0.63~0.69μm) and near

infrared channel (0.76~0.90μm) are taken as the

main channels for water detection due to the specific

spectral properties in these two channels. Red

channel has low reflectance over vegetation and bare

land but relatively higher reflectance over water

surface. Opposite to red channel, near infrared

channel has much higher reflectance over vegetation

and land but much lower reflectance over water

surface. In addition, considering atmospheric

absorption and cloud contamination, it is difficult to

use single channels to distinguish water from land.

Therefore, the ratio between red channel and near

infrared channel are more effective variables instead

of single channel reflectance to separate water from

vegetation, bare land, and cloud shadow. A

threshold method or decision–tree method can be

used to detect earth surface water by using the ratio

(Zheng, 2008; Zheng et al., 2013):

inf

red

(1)

Where R

inf

and R

red

are the reflectance of near

infrared channel and red channel respectively, R

the thresholds of the ratio.

3.2 Water Body Identifying Based on

Sentinel-1

Under rainy weather conditions, Sentinel-1 data is

quite ideal water body monitoring because it not

only has high spatial resolution, but also can

penetrate cloud. The software SNAP is used to

process the Sentinel-1 data, Firstly, the level-1

Sentinel-1 data was processed for the radiometric

calibration, and the backscatter coefficient could be

calculated; Secondly, Sentinel-1 data was processed

for topographic correction. Furthermore, SAR

images are subjected to an inherent granular noise

called speckle, degrading the quality of the image

and making water extraction more difficult. The

adaptive Gamma filter produces speckle removed

images with relatively low processing time (Martinis

et al., 2009). This method was ultimately selected

for speckle removal, as it was effective for all of the

available SAR resolutions, angles, and sensor modes

(Long et al., 2014). Sentinel-1 data was removed

speckle based on the filter method. Last, water has

lower backscatter signature than other surface

features in SAR imagery, so it can be identified

based on the threshold method as follows:

_SAR T flood

σσ

(2)

Where R

inf

and R

red

are the reflectance of near

infrared channel and red channel respectively, R

the thresholds of the ratio.

3.3 Water Change Detection with

Multi-Source Data

Comparison of two different time water body can

analyze the water change information. Although

SAR has many advantages for being used to detect

the water body at all-weather condition, the

procedure of processing the SAR data is much

harder than optical remote sensing data, and it also

Water Body Change Detection Based on Sentinel-1 and HJ-1A/B Satellites Data

485

can’t determine water change area if only using

radar image. HJ-A/B data can be used to obtain

background water body. Therefore, water change

area can be confirmed by combining the Sentinel-1

image at cloudy and rainy day during flood season

and HJ-A/B image before flood season as follows:

OPT

cha SAR

W=W WI

(3)

Where W

cha

is the water extent extracted by

Sentinel-1 image during flood season, W

OPT

is the

water extent extracted by HJ-A/B image before the

flood season.

4 RESULT AND ANALYSES

The method described in section 3 is applied to

monitor the water body change in Poyang Lake of

China. Poyang Lake, located in Jiangxi Province, is

the largest freshwater lake in China(Figure 1). The

lake is fed by the Gan, Xin, and Xiu rivers, which

connect to the Yangtze through a channel. Water

body change of Poyang Lake can indicate the

climate change and effect of human activity. Using

satellite data to monitor the water body of this lake

is valuable and meaningful (Zeng et al., 2017;

Andreoli et al., 2007). Poyang Lake was hit by

severe heavy rains in early June of 2017. The rise of

water level in lakes caused water body enlarging.

Because of the interruption of precipitation, effective

optical remote sensing data can’t be obtained,

Sentinel-1 data in flood season and HJ-1A/B data

before the rainfall are used to research the lake area

change. The Poyang lake is observed at least every 6

days from Sentinel1A/B and at least 2 days from

HJSentinel-1 data on June 24 of 2017 and HJ-1B

data on May 25 of 2017 were acquired. This

Sentinel-1 data is imaged by IW model and VH

polarisation. Based on the method in section 3,

Poyang Lake water area production on June 24

shows that the water area of Poyang Lake is about

3307 km

(Figure 2), this result was compared to

the Poyang Lake water area production by HJ-1B

data on May 25. The comparing result indicated that

the lake area increase by about 43% in flood season

(Figure 3). In order to validate the monitoring result,

the field investigation of Poyang Lake was carried

out. The typical water samples were selected to

investigate base on the water changing thematic map

(Figure 4). The GPS data and photos of the scene

account for the satisfying effect of water body

monitoring result, which indicated locally the good

accordance between field measurement and

extracted water. The water areas with a significant

increase are mainly located in the middle and east of

Poyang Lake. Based on these water monitoring and

assessing production, the water change information

can be showed obviously.

Figure 1: Location sketch map of Poyang Lake.

Figure 2: Sentinel-1 monitoring image on June 24, 2017.

IWEG 2018 - International Workshop on Environment and Geoscience

486

Figure 3: HJ-1B monitoring image on May 25, 2017.

Figure 4: Water changing thematic map.

5 DISCUSSION AND

CONCLUSIONS

The method of water change mapping by

synergizing different types data was developed and

tested. Sentinel-1 data in flood season and HJ-A/B

data before the flood season are employed. The

result shows the comprehensive and effective use of

multi-source satellite data can give reliable earth

surface water body change information. The method

also is proper to other satellite data such as Chinese

GF-3 and GF-1 data.

The optical remote sensing data, such as

Landsat/TM and Sentinel2 A/B data which have the

short wave infrared (SWIR) band is often applied to

detect the water body, for high turbid waters, the

SWIR channel is preferred. So, the choice of the best

spectral band(s) is required according the

characteristic of satellite data before detecting the

water body information. Furthermore, increasing the

satellite data revisiting time is also especially

important for short-time events as floods.

Geostationary satellite data, such as FengYun-4 and

Himawari-8 data with high spatial and temporal

resolution also can be used to monitor the water

body dynamically.

With the fast development of earth observation

technology, more types of satellite data can be

available easily. The method of water body mapping

based on multi-source satellite data will be improved

continually.

ACKNOWLEDGMENTS

The work was supported by National Nature Science

Found of China under grant 41571425 “Research on

flood and waterlogging quantitative retrieval and

super-resolution mapping based on passive

microwave remote sensing data” and National

Nature Science Found of China “Water and wetness

fraction by multi-source data and its application in

flood monitoring and warning in Huaihe river basin”

(40901231). We are grateful to the editor and the

reviewers for their helpful and constructive

comments and suggestions.

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