Monitoring China's Coastal Zones and Adjacent

Seas under Global Change by Satellite Data (10470)

Dr. Tingwei Cui（崔廷伟）

First Institute of Oceanography, SOA, Qingdao, China

Dr. Juergen Fischer

Institut fuer Weltraumwissenschaften, Freie Universitaet Berlin, Germany

Outline

General introduction

Objectives, Research Content, Expected outcome, Team, Data

Recent results

Global Evaluation of Satellite Ocean Color Products from Europe,

America and China: Toward a 15-yr merged global dataset

Monitoring coastline change in Guangxi province since 1973 by Landsat

and HJ-1 CCD images

Coastal wetlands classification by PROBA CHRIS image in Yellow

River Delta

Coastal shallow water depth inversion model of ZY-3 image

Future work

Motivation: China’s coastal zone is experiencing dramatic changes due to significant socio-economical effects and the intense human activities, and the resource and environment condition here is also susceptible to the global change.

Part I: General introduction

To disclose the spatio-temporal variation of land use/cover changes and wetlands along China coast as well as the causes and implications with respect to global change;

------continued work based on Dragon 2 (ID:5292)

To develop new methods to detect submerged macroalgae, river plume by synergistic use of optical, infrared and microwave satellite data;

To validate geographical products of satellite data along China coast to assess the uncertainties of these geophysical products and to offer useful suggestions for higher-order applications.

Objectives

Satellite monitoring and evaluation of land use/cover

changes (LUCC) along China coast

Satellite monitoring and evaluation of wetland along China

coast

Macroalgae bloom monitoring by synergistic use of satellite

data

River plume monitoring by satellite-derived suspended

sediment and sea surface salinity

Validation of satellite data along China coast

Research Content

Chinese Partners- 10

– Dr. Tingwei CUI (PI) ─ First Institute of Oceanography (FIO), State Oceanic Administration (SOA)

– Dr. Yi Ma ─ FIO, SOA, China – Dr. Guangbo Ren ─ FIO, SOA, China – Dr. Ping Qin ─ Ocean University of China (OUC), China – Jianhua Zhu ─ National Ocean Technology Center (NOTC), SOA, China – Huping Ye-NOTC, SOA, China – Bing Mu-OUC, China – Shaoqi Ni ─ FIO, SOA, China – Xiaomin Li ─ FIO, SOA, China – Xiaoqing-Cai─ FIO, SOA, China

European Partners- 3

– Dr. Juergen FISCHER(PI) ─ Institute for Space Sciences, Freie Universitat Berlin, Germany;

– Dr. David DOXARAN ─Laboratoire d'Océanographie de Villefranche, France; – Dr. Lena Kritten ─ Institute for Space Sciences, Freie Universitat Berlin,

Germany

Team

Expected outcome

•New method of monitoring macroalgae bloom by synergistic use of optical, infrared and microwave satellite data

•New method of monitoring river plume by satellite derived suspended sediment as well as synthetic sea surface salinity

•Annual changes of land use/land cover and wetlands along China coast as well as the causes and implications

•Macroalgae bloom dynamics in the Yellow Sea and East China Sea and annual variability

•River plume dynamics in the Yellow River, Yangtze River and Pearl River and the mechanism

•Uncertainties of MERIS, OLCI products along China coast

Satellite data

ESA ◦ MERIS, SAR, AATSR, OLCI (Sentinel-3), SPOT,……

CHINESE ◦ HJ, FY, ZY, HY,……

TPM ◦ ALOS, GOCI, TM,……

a). Analyze spectral characteristics of LUCC types

based on field work; HJ-1 CCD and MERIS images

b). Establish LUCC classification system

consider LUCC type changes in different geographical regions along China coast

c). Extract LUCC types information and generate thematic maps

develop semi-automatic information extraction method

d). Analyze driving factors of LUCC changes

policy, human activity, climate changes, sea level rise….

develop a model to make the forecast

Land use/cover Changes(LUCC)

a). Analyze HJ-1 CCD and MERIS imaging capabilities of the coastal

wetland

focusing on typical coastal wetland types: reed, spartina alterniflora,

mangrove, tamarix, sea grass,……

b). Extract coastal wetlands information and generate thematic maps

Human-Computer Interaction method, semi-automatic method

c). Analyze wetland landscape structure distribution and dynamics

as well as the driving factors

d). Evaluate wetland ecological quality

health and service function assessment

Wetland

a).Evaluate the effects of environmental conditions and satellite data processing methods on the detection of free floating macroalgae bloom

atmospheric condition, observation geometry, thin cloud, haze,…

atmospheric correction, NDVI method

b).Comparison of detection capabilities of free floating macroalgae bloom by different satellite data

optical, SAR and infrared images

focus on MERIS chlorophyll fluorescence measurements

c).Develop methods to detect submerged macroalgae by concurrent optical, SAR and infrared data

d).Analyze the dynamics from just the beginning to the collapse of the macroalgae bloom in the Yellow Sea

time series of satellite images, detect the bloom as early as possible

Macroalgae bloom

a). Develop regional algorithms to retrieve suspended sediment concentration

in situ observation; calibration and validation of the regional SPM algorithms

for ENVISAT MERIS and HJ-1 CCD

b).Develop regional algorithms to retrieve sea surface salinity(SSS) of the river

plume

in situ observation of SSS and ocean optical properties

calibrate and validate empirical and semi-analytical models to retrieve SSS by

spectral remote sensing reflectance (Rrs)

c).Analyze river plume dynamics by time series of satellite images

in terms of river discharge, hydrodynamic condition and wind.

River plume

a). Inter-comparison and cross-calibration of satellite radiance data

OLCI (Sentinel-3), VIIRS(Suomi NPP), MERSI (FY-3)

Based on L1b data

b). Validation of satellite data product along China coast

OLCI (Sentinel-3), MERIS (ENVISAT), MERSI (FY-3), VIIRS(Suomi NPP)

The Bohai Sea, Yellow Sea, East China Sea, South China Sea

Validation

Global Evaluation of Satellite Ocean Color Products from Europe,

America and China: Toward a 15-yr merged global dataset

Monitoring coastline change in Guangxi province since 1973 by

Landsat and HJ-1 CCD images

Coastal wetlands classification by PROBA CHRIS image in

Yellow River Delta

Coastal shallow water depth inversion model of ZY-3 image

Part II: Recent results

Global Evaluation of Satellite Ocean

Color Products from Europe, America

and China: Toward a 15-yr merged

global dataset

Background

A 15-yr long global ocean color dataset is to be produced

by merging major satellite data from Europe (MERIS),

America (SeaWiFS and MODIS) and China (MERSI,

COCTS).

The first step is to evaluate the accuracy of these satellite

data in the global scale and make a detailed comparison.

Major global ocean color sensors

SeaWiFS/Orbview-2

Bands Signal-to-Noise Purpose

402～422nm 499 CDOM

433～453nm 674 Chlorophyll

480～500nm 667 Pigment, Kd490

500～520nm 640 Chlorophyll

545～565nm 596 Pigment, optical property, SPM

660～680nm 442 Atmosphere correction, chlorophyll

745～785nm 455 Atmosphere correction, chlorophyll

845～885nm 467 Atmosphere correction, chlorophyll

August 1997~December 2010

8 bands from 412 to 865 nm

spatial resolution: 1 km

swath width: 1502km

MODIS/Terra (Aqua)

Bands Signal-to-Noise

405～420nm 880

438～448nm 838

483～493nm 802

526～536nm 754

546～556nm 750

662～672nm 910

673～683nm 1087

743～753nm 586

862 – 877nm 516

1999~ (Terra); 2002~(Aqua)

totally 36 bands, including 9 ocean color bands from

405 to 877 nm

spatial resolution: 1 km

swath width: 2330 km

MERIS/ENVISAT April 2002~ March 2012.

15 bands from 390 to 1040 nm

spatial resolution: 1.2 km/0.3 km (RR/FR)

swath width: 1150km

Central wavelength/nm Band width/nm Design objective 412.5 10 Yellow substance and detrital pigments

442.5 10 Chlorophyll absorption maximum

490 10 Chlorophyll other pigments

510 10 Suspended sediment, red tides

560 10 Chlorophyll absorption minmum

620 10 Suspended sediment

665 10 Chlorophyll absorption & fluo. reference

681.25 7.5 Chlorophyll fluorescence peak

708.75 10 Fluo. Reference, atmosphere corrections

753.75 7.5 Vegetation , cloud

760.625 3.75 O2-branch absorption band

778.75 15 Atmosphere corrections

865 20 Vegetation, water vapor reference

886 10 Atmosphere corrections

900 10 Water vapor, land

MERSI/FY-3 series

The second generation of China’s polar-orbit meteorological satellites.

FY-3A, FY-3B, FY-3C was launched in May 2008, Nov. 2010 and Sep.

2013, respectively.

Totally 20 bands, including 9 ocean color bands

Swath width: 2900 km.

MODIS MERIS

SeaWiFS MERSI

8.43% 9.68%

6.25% 11.75%

Evaluation of Rrs products Satellite v.s. In-situ (SeaBASS)

-150° E -100° E -50° E 0° E 50° E 100° E 150° E

-50° N

0° N

50° N

MODIS

MERIS

SeaWiFS

Match-ups: ±3 hours, 3×3pixels

SeaWiFS

412nm 443nm 490nm

530nm 555nm 670nm

MODIS

412nm 443nm 488nm

531nm 547nm 667nm

MERIS

413nm 443nm 490nm

510nm 560nm 665nm

N R2 RMSE（sr-1） APD( %)

MODIS

Rrs412 1691 0.76 0.0015 30.05

Rrs443 2109 0.85 0.0011 16.73

Rrs488 1809 0.91 0.0011 14.97

Rrs531 713 0.92 0.0012 13.87

Rrs547 890 0.93 0.0012 13.42

Rrs667 345 0.92 0.0002 35.25

MERIS

Rrs413 974 0.67 0.0020 41.97

Rrs443 882 0.79 0.0014 23.49

Rrs490 1090 0.86 0.0014 18.17

Rrs510 306 0.85 0.0011 14.41

Rrs560 205 0.91 0.0011 16.22

Rrs665 234 0.92 0.0004 44.40

SeaWiFS

Rrs412 1204 0.77 0.0018 28.31

Rrs443 1273 0.78 0.0014 18.76

Rrs490 1456 0.81 0.0013 15.53

Rrs510 769 0.85 0.0010 14.71

Rrs555 1157 0.85 0.0013 15.19

Rrs670 525 0.84 0.0006 32.15

Satellite VS. Satellite MERSI/FY-3 v.s. MODIS/AQUA

MERSI daily products in 2011 from FY-3A and FY-3B were

compared with concurrent MODIS.

Close correlation and significant bias (>52%) were found.

443nm 490nm 565nm

FY-3B

FY-3A R=0.487 R=0.370 R=0.557

R=0.806 R=0.580 R=0.547

Satellite Rrs from SeaWiFS, MODIS and MERIS show comparable accuracy.

◦ Rrs uncertainties have the same spectral feature for, e.g. higher (28%~45%) in blue

and red bands, and lower in green bands (13%~23%).

◦ Performance of MODIS and SeaWiFS is slightly better than MERIS, especially for the

blue (412nm) and red band (670nm).

MERSI/FY-3 Rrs data needs to be improved before being merged.

Summary

Evaluation of CHL products

In situ data:

from Ocean Biology Processing Group (OBPG)

period: 2000~2013

47944 observation data (8:00-16:00) were selected

Satellite v. s. In-situ

Mach-ups R2* APD-median（%） RMSE*

MERIS 655 0.81 38.80 0.33

MODIS 688 0.82 38.25 0.32

SeaWiFS 1654 0.85 36.59 0.29

FY-3A CHL 172 0.27 129.49 0.70

FY-3B CHL 111 0.43 69.15 0.38

* in log10 scale

MERIS

MODIS

SeaWiFS MERSI FY-3A MERSI FY-3B

Satellite v. s. Satellite

t

m ji

mji

jimean

STDCV

1 ,

,,

,

Based on the daily satellite chlorophyll in 2010, the consistency of

MERIS, MODIS and SeaWiFS products were analyzed with the

variation of coefficient CV.

MERIS VS. SeaWiFS

MERIS VS. MODIS

MODIS VS. SeaWiFS

For most regions, CV of MERIS, MODIS and

SeaWiFS chlorophyll a content products is less

than 0.2.

For the clear water of sub-tropic where the

chlorophyll content is low all the year round, CV

is higher.

MERSI/FY-3 VS. MODIS/AQUA

R* RMSE* Bias* APD-median FY-3A vs. MODIS 0.84 0.48 0.33 43.5% FY-3B vs. MODIS 0.84 0.49 0.31 42.0% * in log10 scale

FY-3A FY-3B

Satellite Chl from SeaWiFS, MODIS and MERIS show comparable accuracy

(35%~40%).

MERSI/FY-3 Chl data needs to be improved before being merged.

Summary

Improvement of MERSI/FY-3 CHL

products

Step 1: Evaluate the accuracy of MERSI-derived ocean

color indices (e.g. Rrs(443)/Rrs(565), Rrs(490)/Rrs(565), CI)

based on the MODIS counterparts.

Step 2: Modify the MERSI-derived indices based on their

regression relationships with MODIS counterparts.

Step 3: Retrieve chlorophyll concentration using the

modified MERSI-derived.

R=0.869

RMSE=2.54

Bias=-2.03

APD=45%

R=0.809

RMSE=1.16

Bias=-0.9

APD=28%

R=0.933

RMSE=4.33*10-3

Bias=1.71*10-3

APD=144%

R=0.628

RMSE=4.78

Bias=-4.43

APD=63%

R=0.411

RMSE=1.94

Bias=-1.79

APD=42%

R=0.795

RMSE=5.62*10-3

Bias=3.44*0-3

APD=112%

Y=2.6311*X-1.2292 Y=1.7953*X-0.6672 Y=0.3109*X-0.0011

Y=2.4814*X+0.8442 Y=1.5584*X+0.4615 Y=0.2101X-0.0027

FY-3B

FY-3A

Evaluation and modification of MERSI-derived ocean color indices

Chlorophyll retrieval based on MERSI-derived indices

and the new algorithm (Hu et al., 2012)

R* RMSE* Bias* APD-median(%) FY-3A v. s. MODIS (CHL) 0.90 0.22 -0.085 10.21 FY-3B v. s. MODIS (CHL) 0.74 0.17 -0.0061 8.16

* In log10 scale.

MERSI

standard

After

Modification

FY-3A

FY-3A

FY-3B

FY-3B

MODIS

Comparison before and after modification

before

after

before

after

Merged by MODIS and

MERIS (2011/01/01), with

13.6% valid coverage

Merged by MODIS, MERIS

and FY-3 (2011/01/01), with

24.6% valid coverage

Benefit from merging FY-3 MERSI

Study area

- Important coastal zone for mangrove

protection in China

- Neighboring Vietnam

- Guangxi Beibu gulf economic zone

Satellite images

Acquisition date Imge type Number Spatial resolution (m)

1973-12 Landsat-1 MSS 2 60

1977-2/1978-12 Landsat-3 MSS 2 60

1990-12/1991-11 Landsat-5 TM 2 30

2000-9 Landsat-7 ETM+ 2 30

2013-9 HJ-1A 1 30

Coastline interpretation

•Bedrock coastline and its location

- The shape of coastline is

irregular

- The color of the rock tidal flat

is brighter and usually

without vegetation

•Sandy coastline and its location

- The tidal flat is narrow and

long

- The shape of coastline is

smooth

- The color of the tidal flat is

brighter and usually without

vegetation

•Muddy coastline and its location

- The tidal flat is wide

- The shape of coastline is

smooth and irregular

- The color of the tidal flat is

darker

- The coastline is located at the

seaside of the vegetation

•Artificial coastline and its location

- The shape of coastline is

straight

- The color of the land-use types

landside is brighter

- The coastline is located at the

seaside of the artificial

constructions

Coastline monitoring results since 1973

1973 1978 1990

2000 2013

The length and ratio of different coastline types in different times

since 1973 (km)

Bedrock Artificial Sandy Muddy Overall

1973 136.52 224.69 167.73 757.49

1376.61 9.92% 16.32% 12.18% 55.03%

1978 106.28 221.97 168.70 779.05

1360.60 7.81% 16.31% 12.40% 57.26%

1990 92.45 575.98 144.75 474.88

1357.26 6.81% 42.44% 10.66% 34.99%

2000 68.28 616.08 137.07 388.90

1367.38 4.99% 45.06% 10.02% 28.44%

2013 32.81 919.88 97.02 85.52

1378.78 2.38% 66.72% 7.04% 6.20%

The length and ratio of Artificial coastline increased from 1997 to 2013.

The other three kind of coastline decreased.

1973-1978 1978-1990

1990-2000 2000-2013

The changes in the Fangchenggang city

0

500

1000

1500

2000

2500

3000

3500

1973to1978 1978to1990 1990to2000 2000to2013

Are

a(h

a)

In each period, the coastline of Fangchenggang city moved seaward, generating a

changing area from 198.07hm2 to 2950.64 hm2.

The changes in the Qinzhou city

0

200

400

600

800

1000

1200

1400

1600

1800

1973to1978 1978to1990 1990to2000 2000to2013

Are

a(h

a)

In each period, the coastline of

Qinzhou city moved seaward,

generating a changing area from

167.5hm2 to 1569.65 hm2.

Summary

The coastline changes in Guangxi province since

1973 was extracted by Landsat and HJ-1 CCD

images.

The length and ratio of Artificial coastline increased

while the other three kind of coastline decreased.

The coastline of Fangchenggang and Qinzhou city

increased regularly.

Study area

National Nature Reserve of the Yellow River Delta

Data sets

• Remote sensing data - PROBA CHRIS

Acquisition date 2012-06-01

Spatial resolution 17m

Spectral bands 400～1050nm

Bands

Central

wavelength

（nm）

Band width

（nm）

Band 1 411.3 10.9

Band 2 443.6 10.6

Band 3 491.8 11.7

Band 4 511.5 12.9

Band 5 532.0 11.6

Band 6 563.6 13.8

Band 7 576.1 11.0

Band 8 593.2 16.0

Band 9 625.3 13.7

Band 10 654.3 15.4

Band 11 672.9 10.9

Band 12 684.1 11.4

Band 13 692.7 5.8

Band 14 710.7 18.5

Band 15 760.4 14.2

Band 16 786.1 22.8

Band 17 878.6 27.6

Band 18 1026.7 44.1

• Field spectral data

ASD handheld 2：

- Spectral wavelength range:300-1100nm

- Spectral resolution:1nm

- Viewing angle:25°

Method

Hyperspectral feature

extraction based on

association mining

Hyperspectral remote

sensing image

Hyperspectral

feature sets of the

typical vegetation

Classification method

(decision tree)

field spectrum

feature analysis

field spectrum

data sets

Typical wetlands

classification on hyperspectral

remote sensing image

Classification results

Overall accuracy: 82.26% Kappa coefficient: 0.79

Comparison with SVM

Class

Decision tree SVM

Producer accuracy

(Percent)

User accuracy

(Percent)

Producer accuracy

(Percent)

User accuracy

(Percent)

Reeds 78.97 94.72 62.21 99.43

Nude beach 89.9 90.8 78.08 95.88

Clear water 83.74 93.5 85.84 94.31

Muddy water 79.92 87.01 87.23 74.11

Suaeda salsa 83.89 74.2 59.66 73.47

Tmarix chinensis 75.82 70.45 58.21 49.1

Spartina anglica 79.46 28.45 97.62 17.85

Bare land 46.01 26.15 74.3 9.39

Overall acuuracy 82.26% 70.90%

Kappa coefficient 0.79 0.66

Summary

Wetlands in Yellow River Delta were classified by

PROBA CHRIS image based on decision tree

classification method.

The classification results were validated by in-situ

data, which showed that Overall accuracy was

82.26% and the kappa coefficient was 0.79.

The decision tree method was better than SVM.

Yongxing island

Study area

The water around Yongxing island is very clean, which is applicable to water

depth inversion research.

Data sets

•Satellite remote sensing image-ZY-3(资源三号)

•Water depth data-Marine chart

Acquisition date 2012-12-19

Spatial resolution 2.1m

Spectral bands 4 bands:

485nm(Blue), 555nm(Green),

660nm(Red), 830nm(NIR)

Measure date 1971

Scale 10000

Method

Linear regression model (LRM)

-Single band -Dual bands

Water depth for regression (N=122) Water depth for validation (N=60)

Water depth inversion results

•Single band inversion result-validation table

Water

depth

region

Blue band Green band Red band

Absolute(m) relative Absolute(m) relative Absolute(m) relative

0 ~ 2 4.60 1162.6% 2.54 494.7% 3.14 816.1%

2 ~ 5 3.03 101.7% 2.98 98.5% 3.06 96.5%

5 ~ 10 2.40 29.6% 2.44 32.5% 1.79 23.9%

10 ~ 20 7.34 54.3% 4.84 35.2% 7.13 52.7%

> 20 4.37 60.0% 3.47 53.2% 4.10 56.0%

All 4.43 365.3% 3.22 175.4% 3.84 266.5%

Overall, the inversion results of green band was the best.

•Dual bands inversion result-validation table

Water

depth

region

Blue-Green bands Blue-Red band Green-Red band

Absolute

(m) relative

Absolute

(m) relative

Absolute

(m) relative

0 ~ 2 1.02 210.0% 3.31 808.3% 3.07 611.4%

2 ~ 5 2.31 70.1% 3.14 102.7% 2.94 97.2%

5 ~ 10 2.32 31.2% 2.02 26.6% 2.15 28.0%

10 ~ 20 3.55 25.1% 7.08 52.5% 4.16 30.0%

> 20 2.77 40.6% 4.19 58.6% 3.11 49.3%

All 2.28 87.5% 3.94 266.2% 3.10 205.0%

Overall, the inversion results of green band was the best.

Different coastal shallow water depth inversion

model was established and validated.

The inversion results of green band was the best.

Summary

Future work

Analyze driving factors of LUCC and wetlands changes.

Develop methods to detect submerged macroalgae by multi-

source satellite data.

Analyze river plume dynamics by time series of satellite

images.

Thanks for your attention!

cuitingwei@fio.org.cn