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1 Remote Sensing Laboratory Dept. of Information Engineering and Computer Science University of Trento Via Sommarive, 14, I-38123 Povo, Trento, Italy 1 Remote Sensing Laboratory Dept. of Information Engineering and Computer Science University of Trento Via Sommarive, 14, I-38123 Povo, Trento, Italy 2 Institute for Applied Remote Sensing 3 Institute for Alpine Environment Eurac Research Viale Druso, 1, I-39100 Bolzano, Italy 4 Department of Computer, System and Production Engineering Tor Vergata University Via del Politecnico, 1, I-00133 Rome Italy Luca Pasolli 1,2 Claudia Notarnicola 2 Lorenzo Bruzzone 1 Giacomo Bertoldi 3 Georg Niedriest 3 Ulrike Tappeiner 3 Marc Zebisch 2 Fabio Del Frate 4 Gaia Vaglio Laurin 4 Spatial and Temporal Mapping of Soil Moisture Content with Polarimetric RADARSAT2 SAR Imagery in the Alpine Area 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

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2 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

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

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Introduction 4 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 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada 24-29 July, 2011 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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5 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 Aim of the Work

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Study Area 6 Well known and monitored area Well representative in terms of Topography Land use Soil moisture content conditions Mazia Valley, Alto Adige, Italy IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada 24-29 July, 2011

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Dataset 7 MeadowPasture JuneJulyJuneJuly Min Diel6.73.86.43.2 Max Diel21.82716.225.63 Average Diel16.514.211.27.7 2.Field measurements: 77 soil dielectric constant measurements on meadows (blue) and pasture (red) acquired contemporary to satellite overpasses (3 rd June and 21 st July) RADARSAT2, 21 July 2010 (R=HH, G=HV, B=VV) 1.Satellite SAR images: 4 RADATSAT2 quad-pol standard mode images (3 rd June, 21 st July, 14 th August, 5 th 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) IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada 24-29 July, 2011

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Estimation System 8 Data Pre-processing Feature Extraction & Selection Retrieval Algorithm Polarimetric RADARSAT2 SAR image Estimated Soil Moisture Content Map Ancillary Data IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada 24-29 July, 2011

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Estimation System: Retrieval Algorithm 9 Data Pre-processing Feature Extraction & Selection Retrieval Algorithm Polarimetric RADARSAT2 SAR image Estimated Soil Moisture Content Map 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 Truth Features from Remotely Sensed Image SVR Learning SVR Estimation Performance Evaluation Model Selection Reference Samples Training Set Validation Set SVR Parameters Config. Estimation Perform. (MSE, R 2 ) K-Fold Cross Validation Multi-Objective Model Selection Features from Ancillary Data Training Phase Sub-Sample Generator Estimation Operational Phase Input Features (Image + Ancillary) SVR Estimator Output SMC Value IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada 24-29 July, 2011

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Estimation System: Features Extraction and Selection 10 Data Pre-processing Feature Extraction & Selection Retrieval Algorithm Polarimetric RADARSAT2 SAR image Estimated Soil Moisture Content Map 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/ 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 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada 24-29 July, 2011

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11 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 < < 10 3, 10 -3 < C < 10 3, 10 -3 < < 10 Multi-objectives model selection according to RMSE and R 2 quality metrics Performance assessment on 17 independent test reference samples according to: Root Mean Squared Error (RMSE) Determination coefficient (R 2 ) 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 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada 24-29 July, 2011

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11 Results: Experiment 1 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada 24-29 July, 2011 Selected FeaturesRMSER2R2 SlopeIntercept Reference HH2.790.790.772.13 HH HV/VV featuresICA1 ICA4 features A featuresHH feature Intensity & Phase Features HH HV/VV2.550.820.82.37 ICA Features ICA1 ICA42.660.810.861.53 Cloude Decomposition Features A 3.10.730.763.09

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11 Results: Experiment 2 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada 24-29 July, 2011 Estimated Soil Moisture Content Map, June 2010

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

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Conclusion 15 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

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16 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