Sea State from Sentinel-1 SAR

for Maritime Situation Awareness

09.10.2019

Andrey Pleskachevsky

Björn Tings

Sven Jacobsen

1. Introduction

2. New algorithm for S1 WV

3. Algorithm function

4. Ground truth and cross validation

5. Conclusion

German Aerospace Center (DLR)

Maritime Safety and Security Lab, Bremen

1. Introduction

2. New algorithm for S1 WV

3. Algorithm function

4. Ground truth and cross validation

5. Conclusion

1.1. Introduction

German Aerospace Center (DLR)

Maritime Safety and Security Lab, Bremen

(Team SAR Oceanography)

Remote Sensing Technology Institute

SAR Signal Processing

- Development of algorithms

- Prototype software for NRT

- Processor software for ground station NZ

- Processing data – algorithm improvements,

geophysics

German Aerospace Center (DLR)

National Ground Segment, Neustrelitz

German Remote Sensing Data Center

Ground station “Neustrelitz”

NRT services

Integrated Processor for MSA: Near Real Time services(NRT)

1.2. Concept: maritime situation awareness (MSA) for safe navigation

ice classification

oil detection

ship detection

sea state

surface wind

coastline

fast, full automatic

raster processing

layer processing

information from different

layers helps each over

and improve accuracy

coupled processor

DLR Maritime safety and security Lab Bremen

algorithms and processor development DLR Ground Station Neustrelitz (NZ)

NRT chain

NRT products

Map client, ftp, E-mail

FUSION

WITH DATA

FROM OTHER

SOURCES

+ measurements

+ forecast

+ ship AIS

SAR image

operationally:

-sea state

-wind

-ships

-icebergs

Screenshot of the NRT Service demonstrator at DLR Ground Station “Neustrelitz”

1.3. Sea State Processor for Maritime Situation Awareness

NRT services: SENTINEL-1 IW daily processing for German waters - waves, wind, ships

Raster: 6 km, Subscenes: 2.5kmx2.5km

Different product layers available on the GeoServer in NRT and

displayed on the Maritime Security Web-mapping Client.

DLR Ground Station

NRT chain

Arctic Sea, 05.01.2017

1.4. Support of a research cruise in Antarctic Seas

Processed in NRT

and send to research

vessel

“Akademik Treshnikov” on

Antarctic Circumnavigation

Expedition

route optimization International Antarctic

Circumnavigation 2017

1.5. Storm Tracking

Hurricane „Irma“ 2017 (S-1)

Hurricane Irma

NASA:

NOAA's GOES-East visible image

10.09.2017 01:08

KUBA

FLORIDA

2017-09-10 23:25 UTC

new techniques and

algorithms allow observation

and validation of forecast

models worldwide

ESA news:

Sentinel-1 sees through hurricanes

“… information about the sea state can

help to assess how destructive a

hurricane is and can predict its path

respectively time and location on which

it will make landfall ….”

estimation of sea state fields

from Sentinel-1 IW in NRT

75cm RMSE world wide

55cm North Sea, Baltic, etc.

1. Introduction

2. New algorithm for S1 WV

3. Algorithm function

4. Ground truth and cross validation

5. Last slide

Sentinel Wave Mode WV

20km × 20 km vignettes each 200km

wv1 incidence angle ~23°

wv2 incidence angle ~34°

► ~120 orbits/month collocated with NDBC buoy < 50km

~ 130.000 GB/month

~ 1.5 TB /year

Ground truth:

► model data:

temporal interpolated 3h outputs :

► CMEMS (1/12°)

► WW3 NOAA (0.5° >interpolated)

► measurements:

NDBC collocated stationary 41 buoys (Atlantic & Pacific)

2.1. Data general information

Scientific Points:

- non linear SAR imaging of moving waves

- different sea state systems – different SAR imaging

- accuracy of 50cm – 10.000 combinations of waves/dir/wind

- only ~50% of imagettes have no distorted wave-looking structures

► S1 WV overfly:

30 – 160 imagettes

2.5 – 15 GB

S1 data general information

WV SLC products

2.2. Algorithm general information

Total integrated parameters (1):

- SWH (Hs) significant wave height

- Tm1 first moment wave period

- Tm2 second moment wave period

- Tm-1 weighted mean wave period (Te)

Partial integrated parameters (2):

- SWHswell dominant swell significant wave height

- SWHwindsea windsea significant wave height

- Twindsea windsea wave period

Parameters estimated (two groups)

0.40m / 0.45m

0.40m / 0.45m

0.62s / 0.66s

wv1 / wv2 RMSE

0.34m / 0.38m

0.51s / 0.56s

0.46s / 0.51s

0.62s / 0.66s

- Tp peak wave period

- Tswell swell period

Algorithm for S1 WV: CWAVE_EX (Extended)

bias for all parameters ~0

1. Reading

Sea State Processor

2. Calibration

3. Land masking

5. Parameters (features) estimation:

primary and secondary

7. Model functions: TerraSAR-X, Sentinel-1 IW, Sentinel-1 WV

8. Results control

9. Results converting to products (interpolation, xml, nc, etc.)

4. Filtering

6. Filtering by using estimated parameters

2.3. Model validation for significant wave height Hs: ~ 35cm

wv1 wv2

2.4. Wave height validation with NDBC buoys: ~ 43 cm

SWH tuned with buoy RMSE=0.34 m Scattering Model/Buoy = 0.31 m (50km < collocations)

model / measurement

2.5. Model validation wave periods Tm1, Tm2, Tmean

RMSE=0.51 s

RMSE=0.55 s

wv1

wv2

Tm1 ~ 0.52 sec

RMSE=0.51 s

RMSE=0.52 s

wv1

wv2

Tm2 ~ 0.51 sec

RMSE=0.66 s

RMSE=0.62 s

wv1

wv2

Tmean ~ 0.64 sec

1. Introduction

2. New algorithm for S1 WV

3. Algorithm function

4. Ground truth and cross validation

5. Last slide

3.1. Empirical function parameters

Wind 2. Geophysical

Parameter type Parameters first order

NRCS, Norm.-variance, skewness, kurtosis,

+ 5 additional parameters (will be published later)

1. Subscene properties and statistics

3. GLCM (grey level co-occurrence matrix) GLCM-mean, variance, entropy, correlation, homogeneity, contrast,

dissimilarity, energy

5. Spectral-B 20 parameter by using orthonormal functions,

cutoff by ACF (autocorrelation function)

using spectral rings for different wavelengths 4. Spectral-A

Goda-parameter, Longuet-Higgins-parameter,

+ 5 additional parameters (will be published later)

Function: linear regression 𝑃𝑖 = 𝐴𝑛𝑆𝑛

𝑁

𝑛=0

Solution: quadratic minimization using SVD (singular value

decomposition) – optimal solution for a linear system

Parameters type 2

Wind, CMOD algorithm Geophysical

3.2. Empirical function parameters

NRCS, Norm.-variance, skewness, kurtosis Subscene properties and statistics

Parameters type 1

Additional 6 parameters will be published later

𝜎0 = 𝐷𝑁

𝐾𝑠2

2

𝑠𝑘𝑒𝑤 =1

𝑛 𝜎0𝑖 − 𝜎𝑜

3/𝑠3𝑛𝑖

𝑘𝑢𝑟𝑡 =1

𝑛 𝜎0𝑖 − 𝜎𝑜

4/𝑠4𝑛𝑖

with s=std(σo)

𝑐𝑣𝑎𝑟 = 𝑣𝑎𝑟𝐼 − 𝐼

𝐼 variance of normalized image intensity

NRCS

skewness - measure of the asymmetry of the

probability distribution about its mean.

kurtosis - measure of the steepness of the

probability distribution of a random variable.

GLCM - Gray Level Cooccurrence Matrix mean, variance, entropy, correl, homogeneity, contrast,

dissimilarity, energy

1. Gray level matrix 64 values for NRCS

2. Frequency matrix C(i,j):

combination of gray matrix values Y and X directions

3. Parameters

GLCM - a tabulation of how often different

combinations of pixel brightness values occur

in an image in certain directions offset by

certain distances

3.3. Empirical function parameters type 3: GLCM

spectra rings, Goda, Longuet-Higgins

Additional 5 parameters (wuill be published later) Spectral

normalized subscene image spectrum

FFT

► spectra rings (domains)

1 - 30 - 75 - 390 - 600 – 2000 >

E30 E75 E390 E600 E2000 E2500

wavelength, m

max

min

max

min

),(x

x

y

y

k

k

xyy

k

k

xIS dkdkkkISE

►whole integrated energy, no-noise int. energy

►energy integrated according to W. Rosenthal (scaled with 1/k2i)

►Longuet-Higgins and Goda parameters

►2 domains inside and outside cut-off

3.4. Empirical function parameters type 4: Spectral-A

Spectral 20 parameters by using orthonormal functions,

+ cutoff by ACF (Auto-Correlation-Function) Parameter Sij = Spectrum_Normalized * Orthonormal function ij

Image spectrum normalized

Orthonormal function ij = GegenbauerPolinomi*harmonic_functionj

orthonormal functions

3.1.4. Empirical function parameters type 5: Spectral-B

1. Introduction

2. New algorithm for S1 WV

3. Algorithm function

4. Ground truth and cross validation

5. Last slide

Hawaii (groop-2)

51002

51004

51202

51208

Alaska, Canada (groop-1)

46001

46004

46005

46035

46036

46066

46073

46075

46078

46085

46184

46208

46246

4.1. Collocation < 50 km NDBC stationary buoys: 3 groups

Atlantic (groop-3)

41001

41002

41041

41044

41046

41047

41049

44008

44011

44066

44095

44137

44139

44150

Collocation < 50km – < 65 km from imagette center

collocated

imagette

Listing S1 WV relative orbits collocated with NOAA NDBC buoys <50 km. Each orbit A or B ~2/3 times /month

Pacific (Canada, Aleutian islands, Hawaii) Atlantic

4.2. Collocated S1 WV orbits (collocation <50 km)

Ideal case:

~36 collocations vw1 → ~ 80 collocation/month → ~ 960 collocation/year

~37 collocations vw2 → ~ 80 collocation/month → ~ 960collocation/year

Reality:

~600 wv1 /year

~600 wv2 /year

collocation potential

Comparison WW3 and CMEMS (at position NDBC 46001)

Temporary: 3h output → 20min interpolated

Spatially: WW3 on 0.5deg →0.25deg

CMEMS

1/12°

WW3 NOAA

1/2°

2018-01-01 00:00

2018-01-01 00:00

Conditions for alghorithms training:

Latitude: -60° < LAT < 60°

Variance: wv1: 1.0 < nv < 2.0 (variance of normalized SAR image)

wv2: 1.0 < nv < 1.4

model collocations for buoy-collocated S1 WV orbits:

~90 orbits

~ 6000/6000 imagettes ww1/wv2 per month

4.3. Model data: two independent models CMEMS and WW3-NOAA

4.4. Cross validation SWH: two models and NDBC buoys

wv1 / wv2 RMSE (m)

VALIDATION DATA

TUNING DATA CMEMS WW3 BUOYS

CMEMS 0.33 / 0.38 0.35 / 0.40 0.42 / 0.44

WW3 0.34 / 0.39 0.34 / 0.39 0.44 / 0.46 WW3 & CMEMS 0.34 / 0.39 0.43 / 0.45

CMEMS

Tuning

Validation

WW3 Tuning

Validation

WW3 & CMEMS Tuning

Validation

BUOYS

Validation

CMEMS

Validation

WW3

Validation

Validation

Validation

1. Introduction

2. New algorithm for S1 WV

3. Algorithm function

4. Ground truth and cross validation

5. Last slide

Processing: 1 task 20 processors ~ 5-15 min for one orbit of 3 GB – 15 GB (first 2-6min reading)

Algorithm: CWAVE_EX

Data: S1 Wave Mode (WV)

Developed: DLR

Accuracy