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

1. Spatial and Temporal Mapping of Soil Moisture Content with Polarimetric RADARSAT2 SAR Imagery in the Alpine Area Luca Pasolli1,2 Claudia Notarnicola2 Lorenzo Bruzzone1 Giacomo Bertoldi3 Georg Niedriest3 Ulrike Tappeiner3 Marc Zebisch2 Fabio Del Frate4 Gaia Vaglio Laurin4 E-mail: luca.pasolli@disi.unitn.it luca.pasolli@eurac.edu Web: http://rslab.disi.unitn.it http://www.eurac.edu

2. Outline Introduction 1 Aim of the Work 2 Study Area and Dataset 3 4 Estimation System Description Analysis of Results 5 6 Conclusion IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

3. 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-polarimetricRADARSAT2 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) IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

4. Introduction • Soil moisture estimation supports various application domains: • drought monitoring • flood and landslide prediction • climate change analysis • Challenges: • non-linearityof the relationship between microwave signals and soil moisture • sensitivityof 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, “EstimagionofSoilMoisture in an Alpine catchmentwith RADARSAT2 images”, Applied and EnvironmentalSoil Science, in press IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

5. Aimof the Work ToFurther Investigate the RetrievalofSoilMoisture from RADARSAT2 SAR Images in Alpine Areas Byexploiting the fully-polarimetriccapabilityof RADARSAT2 in combinationwith standard and advancedfeatureextraction/selectionmethods Byextending the analysisin time and spacewith the availableimages IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

6. 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

7. Dataset • Satellite SAR images: • 4 RADATSAT2 quad-pol standard mode images (3rd June, 21stJuly, 14th August, 5th October 2010) • DEM geocoded, filtered (Frost 7x7) • Final pixel size 20 m • 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) • Ancillary data: • DEM (pixel size 2.5 m) • NDVI map extracted from MODIS Terra images (pixel size 250 m) • Land use map (meadows, pasture); IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

8. Estimation System Polarimetric RADARSAT2 SAR image Data Pre-processing Feature Extraction & Selection Ancillary Data RetrievalAlgorithm EstimatedSoilMoistureContentMap IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

9. Estimation System: RetrievalAlgorithm • 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 Polarimetric RADARSAT2 SAR image Featuresfrom RemotelySensedImage Featuresfrom Ancillary Data Ground Truth ReferenceSamples Training Phase Data Pre-processing Validation Set Training Set K-Fold Cross Validation Performance Evaluation SVR Learning SVR Estimation Feature Extraction & Selection SVR ParametersConfig. Sub-Sample Generator Ancillary Data ModelSelection Multi-ObjectiveModelSelection RetrievalAlgorithm EstimationPerform. (MSE, R2) • Estimation Operational Phase SVR Estimator Input Features (Image + Ancillary) Output SMC Value EstimatedSoilMoistureContentMap IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

10. Estimation System: FeaturesExtraction and Selection Aim: to extract and select from the remotely sensed data the most relevant information for the estimation problem considered Polarimetric RADARSAT2 SAR image • 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/αdecomposition • Generalpurposefeatureextractiontechniques • IndependentComponentAnalysis (ICA) Data Pre-processing Feature Extraction & Selection Ancillary Data RetrievalAlgorithm • FeaturesSelection • SequentialForwardSelection (SFS) strategywith performance evaluation on a subset ofreferencesamples EstimatedSoilMoistureContentMap IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

11. Experimental Setup • Experiment 1: Assessmentof the Estimation System with the proposedFeatureExtraction & Selectionstrategies • 60 referencesamplesfor training/tuning the estimation system accordingto a 5-fold cross validation procedure • RetrievalAlgorithmSettings: • SVR withGaussian RBF kernelfunction • Hyper-parametersranges: 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: AssessmentofSpatially and TemporallyDistributedSoilMoistureEstimates in the Alpine Area • Exploitiationof the estimation system configurationidentified in Experiment 1 • Generation ofsoilmoisturecontentmapsassociatedwith RADARSAT 2 SAR imagestimeseriesacquiredduringsummer 2010 • Qualitative and quantitative assessmentwithpriorknowledge on the area and field station measurements IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

12. Results: Experiment 1 HH feature HH HV/VV features ICA1 ICA4 features α Afeatures IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

13. Results: Experiment 2 EstimatedSoilMoistureContentMap, June 2010 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

14. Results: Experiment 2 Estimated dielectric constant Map, October 2010 Estimated dielectric constant Map, August 2010 Estimated dielectric constant map, July 2010 Estimated Dielectric constant Map, June 2010 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – 24-29 July, 2011

15. 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 • 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 • 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

16. Thankyoufor 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