1Remote Sensing Laboratory
Dept. of Information Engineering and Computer Science
University of Trento
Via Sommarive, 14, I-38123 Povo, Trento, Italy
2Institute
for Applied Remote Sensing
for Alpine Environment
Eurac Research
Viale Druso, 1, I-39100 Bolzano, Italy
3Institute
4Department
of Computer, System and
Production Engineering
Tor Vergata University
Via del Politecnico, 1, I-00133 Rome Italy
Spatial and Temporal Mapping of Soil Moisture Content
with Polarimetric RADARSAT2 SAR Imagery
in the Alpine Area
E-mail:
luca.pasolli@disi.unitn.it
luca.pasolli@eurac.edu
Web:
http://rslab.disi.unitn.it
http://www.eurac.edu
Luca Pasolli1,2
Claudia Notarnicola2
Lorenzo Bruzzone1
Giacomo Bertoldi3
Georg Niedriest3
Ulrike Tappeiner3
Marc Zebisch2
Fabio Del Frate4
Gaia Vaglio Laurin4
Outline
1
Introduction
2
Aim of the Work
3
Study Area and Dataset
4
Estimation System Description
5
Analysis of Results
6
Conclusion
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
2
Introduction
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.
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
3
Introduction
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
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
4
Aim of the Work
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
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
5
Study Area
Mazia Valley, Alto Adige, Italy
Well known and monitored area
Well representative in terms of
• Topography
• Land use
• Soil moisture content conditions
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
6
Dataset
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
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)
3.Ancillary data:
Meadow
• DEM (pixel size 2.5 m)
• NDVI map extracted from MODIS Terra
images (pixel size 250 m)
• Land use map (meadows, pasture);
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
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
7
Estimation System
Polarimetric RADARSAT2 SAR image
Data
Pre-processing
Feature
Extraction & Selection
Ancillary Data
Retrieval Algorithm
Estimated Soil Moisture Content Map
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
8
Estimation System: Retrieval Algorithm
Polarimetric
RADARSAT2
SAR image
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
Data
Pre-processing
Training Phase
Features from
Ancillary Data
Features from
Remotely Sensed Image
Ground Truth
Reference
Feature
Samples
Extraction & Selection
Sub-Sample
K-Fold Cross Validation
Validation Set
Training Set
Generator
Ancillary Data
SVR
Learning
Retrieval Algorithm
Performance
Evaluation
SVR
Estimation
SVR Parameters Config.
Multi-Objective
Model
Model Selection
Selection
Estimation Perform. (MSE, R2)
Estimation Operational Phase
Input Features
Estimated Soil Moisture
(Image
Content
+ Ancillary)
Map
SVR
Estimator
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
Output
SMC Value
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Estimation System: Features Extraction and Selection
Polarimetric RADARSAT2 SAR image
Aim: to extract and select from the remotely
sensed data the most relevant information for the
estimation problem considered
Features Extraction
Data
Pre-processing
Feature
Extraction & Selection
Ancillary Data
• 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
Retrieval Algorithm
• H/A/α decomposition
• General purpose feature extraction techniques
• Independent Component Analysis (ICA)
Estimated Soil Moisture Content Map
Features Selection
• Sequential Forward Selection (SFS) strategy with
performance evaluation on a subset of reference samples
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
10
Experimental Setup
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
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
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
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Results: Experiment 1
Selected Features
RMSE
R2
Slope
Intercept
0.77
2.13
0.8
2.37
0.86
1.53
Reference
HH
2.79
0.79
Intensity & Phase Features
HH HV/VV
2.55
0.82
ICA Features
ICA1 ICA4
2.66
0.81
Cloude Decomposition Features
αA
3.1
HH HV/VV features
0.73
0.76
3.09
ICA1 ICA4 features
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
HH feature
α A features
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Results: Experiment 2
Estimated Soil Moisture Content Map, June 2010
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
11
Results: Experiment 2
Estimated dielectric constant map, July 2010
Estimated dielectric constant Map, August 2010
Estimated Dielectric constant Map, June 2010
Estimated dielectric constant Map, October 2010
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
14
Conclusion
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)
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
15
Thank you for the Attention!!
Questions?
luca.pasolli@disi.unitn.it
luca.pasolli@eurac.edu
IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011
Vancouver, Canada – 24-29 July, 2011
16