exploitation of radarsat-2 dual and quad- pol images and

Exploitation of Radarsat-2 dual and quad-pol images and modelled compact

polarimetry parameters for surface soil moisture estimation in mountainous areas.

F. Greifeneder1, C. Notarnicola1, J. Stamenkovic2, S. Paloscia3, M. Dabboor4, F. Charbonneau5

1Institute for Applied Remote Sensing, EURAC Research, Bolzano, Italy.2Signal Processing Laboratory, EPFL, Lausanne, Switzerland.

3Institute for Applied Physics, CNR, Florence, Italy.4Science and Technology Branch, Environment Canada, Torronto, Canada.

5Canadian Centre for Mapping and Earth Observation, Ottawa, Canada.

Institute for Applied Remote Sensing

Felix Greifeneder - [email protected]

Motivation

• 2018 the Radarsat-2 follow up mission will be launched

• Radarsat Constellation Mission (RCM)• 3 satellites• New mode: Compact Polarimetry (CP)• Main advantage: increased swath width –

shorter revisit times

• What are the capabilities for soil moisture estimation?

• Based on a support vector regression approach (Pasolli et al., 2010)

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Study Area

Mazia Valley, South Tyrol, Italy

Total area: ~100km²Altitude: 920 – 3738m a.s.l.Main land-cover types:

- Meadows- Pastures

Mean annual precipitation: 550mm

Area is constantly monitored by 16 fixed hydrological stations.

Further in-situ measurements are acquired during field campaigns

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Study Area

Meadows

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Study Area

Pastures

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Radarsat-2 FP

RADARSAT-2 Fine Quad-Pol mode

Polarization: HH, VV, VH, HV

Spatial Resolution: 11m x 9m

Acquisition Date: 15th of July, 2014

46 polarimetric decomposition parameters:

- Sinclair decomposition

- Pauli decomposition

- Freeman decomposition

- Yamaguchi decomposition

- H-Alpha decomposition

- Touzi decomposition

- Van Zyl decomposition

RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates Ltd. (2014) – All Rights Reserved. RADARSAT is an official trademark of the Canadian Space Agency.

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RCM CP Simulations

Simulated Radarsat Constellation Mission Acquisition in Strip-Map mode (Charbonneau et al., 2010)

Polarization: RR, RL, RV, RH

Spatial Resolution: 16m x 16m

Acquisition: Simulations, based on Radarsat-2 acquisition from 15th of July 2014

18 simulated polarimetric decomposition parameters:

- Circular cross polarization1

- Degree of polarization2

- Conformity coefficient3

- correlation coefficient RV – RH

- circular polarization ratio1

- m-chi decomposition4

- m-delta decomposition1

- Alpha-s parameteter5

- Shanon entropy

- Stokes vectos1

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Reference Data (training targets)

• ~140 in-situ SMC measurements (2014/07/14)

High Resolution Digital Terrain Model

• Spatial resolution: 5x5 m

Further Ancillary Data

• SAR Local Incidence Angle (LIA)

• High resolution land use / land cover map derived from ortho-photos, ground surveys and visual interpretation (25x25 m)

• Normalized Difference Vegetation Index (NDVI) maps extracted from MODIS Terra (250x250 m)

Ancillary Data

Aspect[deg]

Elevation [m] Slope [deg]

Land-Cover

NDVI SAR-LIA

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• Model relationship between measures SMC and input features

• Linear case: 𝑓𝑓 𝑥𝑥 = 𝑤𝑤1𝑥𝑥 + 𝑏𝑏

• Minimize: 12𝑤𝑤2 + 𝐶𝐶 ∑𝑖𝑖(𝜉𝜉𝑖𝑖 + 𝜉𝜉𝑖𝑖∗)

• „Kernel-trick” is used to map non-linear input to a higher dimensional feature space

Machine Learning - Support Vector Regression (SVR)

Method - SVR



SMC estimation

SAR data

SAR Processing Filtering

Rad/TerCorr.

Autom. Correg.

Feature Extraction

Automatic features ranking

SVR Learning

Ancillary Data

SMC Estimation

Filtering

SVR Estimation

K-Fo

ld C

ross

Val

idat

ion SVR – Model

Parameters

𝐼𝐼 𝑅𝑅: 𝑆𝑆 = �𝑟𝑟∈𝑅𝑅

�𝑠𝑠∈𝑆𝑆

𝑝𝑝 𝑟𝑟, 𝑠𝑠 log𝑝𝑝{𝑟𝑟, 𝑠𝑠}𝑝𝑝 𝑟𝑟 𝑝𝑝{𝑠𝑠}

Mutual Information Index (Peng et al., 2005)

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Results: feature analysis RS2

MEAN MI: 0.21

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MEAN MI: 0.21

Yamaguchi12




MEAN MI: 0.21

Yamaguchi H-α13



Results: feature analysis RCM CP

MEAN MI: 0.38

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Results: feature analysis RCM CP

MEAN MI: 0.38

m-X15



Results

Input parameter configurations tested

Radarsat-2 Dual/Quad-Pol + ancillary data

Radarsat-2 Quad-Pol + ancillary data +

decomposition features

Radarsat-2 Quad-Pol, no ancillary data

Radarsat-2 Quad-Pol + decomposition featues, no

ancillary data

RCM CP + ancillary data

RCM CP + ancillary data + decomposition features

RCM CP, no ancillary data

RCM CP + decomposition featues, no ancillary data

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Results: SVR training

Quad-Pol

(HH, VV, VH, HV, ancillary data)

Quad-Pol + polarimetricdecomposition

(HH, VV, VH, HV,

ancillary data, 11 decomposition features)

Dual-Pol

(VV, VH, ancillary data)

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Circular-Pol

(RH, RV, RL, RR, ancillary data)

Circular-Pol + polarimetric decomposition

(RH, RV, RL, RR,

ancillary data, 3 decomposition features)

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Quad-Pol without ancillary data

(HH, VV, VH, HV)

Quad-Pol without ancillary data, polarimetric decomposition

(HH, VV, VH, HV, 11 decomposition features)

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Circular-Pol without ancillary data

(RH, RV, RR, RL)

Circular-Pol without ancillary data, polarimetric decomposition

(RH, RV, RR, RL, 3 decomposition features)

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Conclusion

• It was demonstrated that the estimation algorithm can work efficiently

• Best SMC accuracy based on Radarsat-2 quad-pol: RMSE = 0.05

• CP backscatter bands: RMSE = 0.14

• There is no improvement in case of Radarsat-2 quad-pol + decomposition parameters

• Significant improvement between simulated RCM backscatter without and with decomposition parameters: RMSE = 0.06

• Ancillary data is essential for accurate estimation of SMC

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Thank you for your attentionCorresponding author: [email protected]

This study was supported by the project “HiResAlp”, financed by Provincia Autonoma di Bolzano, Alto Adige, Ripartizione Diritto allo Studio, Università e ricerca scientifica.

The RADARSAT-2 images were acquired in the framework of the joint COSMO-SkyMed-Radarsat2 AO project ID 2880-5225 entitled “SARweCAN”.

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RADARSAT-2 FP data

Simulated RCM CP data

Ancillary Data

Polarimetricdecomposition

Mutual Information (MI) –feature ranking/selection

𝐼𝐼 𝑅𝑅: 𝑆𝑆 = �𝑟𝑟∈𝑅𝑅

�𝑠𝑠∈𝑆𝑆

𝑝𝑝 𝑟𝑟, 𝑠𝑠 log𝑝𝑝{𝑟𝑟, 𝑠𝑠}𝑝𝑝 𝑟𝑟 𝑝𝑝{𝑠𝑠}

SMC estimation machine learning algorithm

In-Situ training data

Estimated SMC

Mutual Information Index (Peng et al., 2005)

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exploitation of radarsat-2 dual and quad- pol images and

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