Luca Pasolli 1,2 Claudia Notarnicola 2 Lorenzo Bruzzone 1 Giacomo Bertoldi 3 Georg Niedriest 3

1Remote Sensing LaboratoryDept. of Information Engineering and Computer Science

University of TrentoVia Sommarive, 14, I-38123 Povo, Trento, Italy

2Institute for Applied Remote Sensing3Institute for Alpine Environment

Eurac ResearchViale Druso, 1, I-39100 Bolzano, Italy

4Department of Computer, System and Production EngineeringTor Vergata University

Via del Politecnico, 1, I-00133 Rome Italy

Luca Pasolli1,2

Claudia Notarnicola2

Lorenzo Bruzzone1

Giacomo Bertoldi3

Georg Niedriest3

Ulrike Tappeiner3

Marc Zebisch2

Fabio Del Frate4

Gaia Vaglio Laurin4

Spatial and Temporal Mapping of Soil Moisture Content with Polarimetric RADARSAT2 SAR Imagery

in the Alpine Area

E-mail: [email protected]@eurac.edu

Web: http://rslab.disi.unitn.ithttp://www.eurac.edu

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Introduction

Aim of the Work

Estimation System Description

1

Analysis of Results

Study Area and Dataset

2

3

4

5

Outline

Conclusion6

IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

Introduction

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SOFIA: SOil and Forest Information retrieval by using RADARSAT2 images• ESA AO-SOAR 6820 • Supported in the framework of the IRKIS project (Civil Protection Department, Province of

Bolzano)

Main Innovative Aspects:• Fully-polarimetric RADARSAT2 satellite SAR data• Mountain landscape (Alpine area)• Advanced estimation methods

Objectives:• Estimation of soil moisture content on bare and vegetated areas (alpine meadows and

pastures)• Estimation of vegetation biomass (forest)• Investigation on the influence of soil and vegetation parameters in connection to natural

hazard in Alpine regions.

• Estimation of soil moisture content on bare and vegetated areas (alpine meadows and pastures)


Introduction

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Soil moisture estimation supports various application domains:• drought monitoring • flood and landslide prediction • climate change analysis

Challenges: • non-linearity of the relationship between microwave signals and soil moisture• sensitivity of microwave signals on different target properties (moisture content,

roughness, vegetation, land use)• influence of topography on the microwave signal acquired by the sensor

In a previous study (Pasolli et al., 2010) RADARSAT2 SAR images have shown to be promising for the retrieval of soil moisture in Alpine areas:

• by integrating the information coming from ancillary data• by exploiting an advanced retrieval algorithm based on the Support Vector Regression (SVR)

method


L. Pasolli, C. Notarnicola, L. Bruzzone, G. Bertoldi, S. Della Chiesa, V. Hell, G. Niedrist, U. Tappeiner, M. Zebisch, F. Del Frate, G.V. Laurin, “Estimagion of Soil Moisture in an Alpine catchment with RADARSAT2 images”, Applied and Environmental Soil Science, in press

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To Further Investigate the Retrieval of Soil Moisture

from RADARSAT2 SAR Images in Alpine Areas

1.By exploiting the fully-polarimetric capability of RADARSAT2 in combination with standard and advanced feature extraction/selection methods

2.By extending the analysis in time and space with the available images


Aim of the Work

Study Area

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Well known and monitored area

Well representative in terms of• Topography• Land use • Soil moisture content conditions

Mazia Valley, Alto Adige, Italy


Dataset

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Meadow Pasture

June July June July

Min Diel 6.7 3.8 6.4 3.2

Max Diel 21.8 27 16.2 25.63

Average Diel 16.5 14.2 11.2 7.7

2.Field measurements:• 77 soil dielectric constant measurements on

meadows (blue) and pasture (red) acquired contemporary to satellite overpasses (3rd June and 21st July) RADARSAT2, 21° July 2010 (R=HH, G=HV, B=VV)

1.Satellite SAR images:• 4 RADATSAT2 quad-pol standard mode

images (3rd June, 21st July, 14th August, 5th October 2010)

• DEM geocoded, filtered (Frost 7x7)• Final pixel size 20 m

3.Ancillary data:

• NDVI map extracted from MODIS Terra images (pixel size 250 m)

• Land use map (meadows, pasture);

• DEM (pixel size 2.5 m)


Estimation System

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Data Pre-processing

Feature Extraction & Selection

Retrieval Algorithm

Polarimetric RADARSAT2 SAR image

Estimated Soil Moisture Content Map

Ancillary Data


Estimation System: Retrieval Algorithm

9

Data Pre-processing


Retrieval Algorithm



Ancillary Data

Aim: to define the mapping between the input features and the target biophysical variable• Support Vector Regression (SVR) technique trained on

Field Reference Samples• Multi-objective Model Selection Approach

Ground TruthFeatures from

Remotely Sensed Image

SVR Learning

SVREstimation

PerformanceEvaluation

Model Selection

Reference Samples

Training SetValidation Set

SVR Parameters Config.

Estimation Perform. (MSE, R2)

K-Fold Cross Validation

Multi-Objective Model Selection

Features from Ancillary Data

Training Phase

Sub-Sample Generator

Estimation Operational PhaseInput Features

(Image + Ancillary)SVR

EstimatorOutput

SMC Value


Estimation System: Features Extraction and Selection

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Data Pre-processing


Retrieval Algorithm



Ancillary Data

Aim: to extract and select from the remotely sensed data the most relevant information for the estimation problem considered

Features Extraction• Standard Intensity&Phase SAR processing

• Polarimetric backscattering coefficients• Polarimetric Combinations: Span (HH+HV+2HV), Polarization

Ratio (HH/VV) and Linear Depolarization Ratio (HV/VV)• Polarimetric phase difference (PPD) and interferometric

coherence

• Polarimetric Decompositions• H/A/α decomposition

• General purpose feature extraction techniques• Independent Component Analysis (ICA)

Features Selection• Sequential Forward Selection (SFS) strategy with

performance evaluation on a subset of reference samples


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Experiment 1: Assessment of the Estimation System with the proposed Feature Extraction & Selection strategies

• 60 reference samples for training/tuning the estimation system according to a 5-fold cross validation procedure

• Retrieval Algorithm Settings:• SVR with Gaussian RBF kernel function• Hyper-parameters ranges: 10-3 < γ < 103 , 10-3< C < 103 , 10-3 < ε < 10• Multi-objectives model selection according to RMSE and R2 quality metrics

• Performance assessment on 17 independent test reference samples according to:• Root Mean Squared Error (RMSE)• Determination coefficient (R2)• Slope and Intercept of the linear tendency line between estimated and measured target values

Experimental Setup

Experiment 2: Assessment of Spatially and Temporally Distributed Soil Moisture Estimates in the Alpine Area

• Exploitiation of the estimation system configuration identified in Experiment 1• Generation of soil moisture content maps associated with RADARSAT 2 SAR images time

series acquired during summer 2010• Qualitative and quantitative assessment with prior knowledge on the area and field station

measurements


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Results: Experiment 1


Selected Features RMSE R2 Slope Intercept

Reference

HH 2.79 0.79 0.77 2.13

HH HV/VV features ICA1 ICA4 features α A features

HH feature

Intensity & Phase Features

HH HV/VV 2.55 0.82 0.8 2.37

ICA Features

ICA1 ICA4 2.66 0.81 0.86 1.53

Cloude Decomposition Features

α A 3.1 0.73 0.76 3.09

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Estimated Soil Moisture Content Map, June 2010

Estimated dielectric constant Map, October 2010

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Estimated dielectric constant Map, August 2010

Estimated dielectric constant map, July 2010

Estimated Dielectric constant Map, June 2010

Conclusion

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The potential of fully-polarimetric RADARSAT 2 SAR images in combination with an advanced retrieval algorithm has been investigated for the mapping in space and time of soil moisture in the Alpine area

1.Polarimetric features are effective for improving the retrieval of soil moisture in the challenging Alpine environment

• Generally, they allow one to reduce the ambiguity in the data and increase the accuracy of the estimation

• The HH HV/VV configuration has shown to be the most suitable in this specific operative conditions

2.The achieved results suggest the potential of the proposed estimation system in combination with RADARSAT 2 SAR data for the retrieval of soil moisture in Alpine areas

• Good capability to reproduce the spatial patterns of the desired target parameter• Good agreement with the measured temporal trends of soil moisture

Future work• Investigation of the proposed estimation system in combination with higher geometrical resolution

polarimetric SAR data• Integration of data from different sensors (e.g., L-Band SAR images)


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Thank you for the Attention!!

Questions?

[email protected]@eurac.edu


Luca Pasolli 1,2 Claudia Notarnicola 2 Lorenzo Bruzzone 1 Giacomo Bertoldi 3 Georg Niedriest 3

Documents

retrieval of soil moisture

alpine areas

estimagion of soil moisture

influence of soil

alpine catchment

alpine regions

radarsat2 sar images

vegetated areas alpine