1 Remote Sensing Laboratory Dept. of Information Engineering and Computer Science University of Trento Via Sommarive, 14, I Povo, Trento, Italy 2 Institute for Applied Remote Sensing Eurac Research Viale Druso, 1, I Bolzano, Italy 1 Remote Sensing Laboratory Dept. of Information Engineering and Computer Science University of Trento Via Sommarive, 14, I Povo, Trento, Italy 2 Institute for Applied Remote Sensing Eurac Research Viale Druso, 1, I Bolzano, Italy Luca Pasolli 1,2 Lorenzo Bruzzone 1 Claudia Notarnicola 2 A Novel Hybrid Approach to the Estimation of Biophysical Parameters from Remotely Sensed Data A Novel Hybrid Approach to the Estimation of Biophysical Parameters from Remotely Sensed Data Web page:

2 Introduction and Motivation Aim of the Work Experimental Analysis 1 Discussion and Conclusion Proposed Hybrid Estimation Approach IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 Outline

3 ESTIMATION SYSTEM Prior Information Remotely Sensed Data Target Biophysical Parameter Estimates IMPORTANCE: Efficient and effective way for spatially and temporally mapping biophysical parameters at local, regional and global scale Support for many application domains: Natural resources management Climate change and environmentak risk assessment CHALLENGES: Complexity and non-linearity of the relationship (mapping) between remotely sensed data and output target parameter Limited availability of prior information Field reference samples IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 Investigated Topic: Estimation of Biophysical Parameters from Remotely Sensed Data Introduction and Motivation

4 Continuous Target Biophysical Variable Input Remotely Sensed Variables Mapping Function Empirical Model Development Reference Samples Regression Technique Parametric / Non-Parametric Regression Strength: Good accuracy in specific domains ideally no analytical simplifications implicit modelization of specific application issues Weakness: Limited robustness and generalization ability well representative reference samples required site and sensor dependency Theoretical Forward Model Inversion Modelization of the Physical Problem Theoretical Forward Model Inversion Technique Iterative Methods Look Up Tables Machine Learning Strength: Good robustness and generalization ability solid physical foundation ideally no reference samples required Weakness: Limited accuracy in specific domains simplifications due to analytical modelization no modelization of specific application issues The Estimation Problem implies the Definition of a Mapping Function: IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 Introduction and Motivation

5 To Develop a Novel Hybrid Approach to the Estimation of Biophysical Variables from Remote Sensing Data HYBRID ESTIMATION APPROACH THEORETICAL FORWARD MODEL Robustness and Generalization Ability REFERENCE SAMPLES Accuracy in specific domains IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 Aim of the Work The proposed approach aims at improving both the accuracy and the robustness of the estimates is based on the integration of theoretical forward model and available (few) reference sampes

6 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 General Estimation Problem Continuous Target Biophysical Variable Desired Mapping Function Deviation Function THEORETICAL FORWARD MODEL + INVERSION TECHNIQUE REFERENCE SAMPLES REFERENCE SAMPLES Input Remotely Sensed Variables Hybrid Estimation Function Proposed Approach: Problem Formulation

7 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, dimensional input space Example: Estimation Problem with two Input Variables (x 1,x 2 ) 1.Theoretical Forward Model + Inversion Technique 2.Available (few) Reference Samples Proposed Approach: Problem Formulation Goal: To associate a target parameter estimate ŷ to each position of the input space

8 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 Hypothesis: points close in the input space have similar values of δ(.) Idea: to exploit the deviation associated with the available Reference Samples 2-dimensional input space Proposed Approach: Characterization of δ(.)

8 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, dimensional input space Proposed Approach: Characterization of δ(.) Case I: Very Few Reference Samples Global Deviation Bias (GDB) Strategy δ(.) is approximated with a constant value in the whole input space Hypothesis: points close in the input space have similar values of δ(.) Idea: to exploit the deviation associated with the available Reference Samples

8 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 Proposed Approach: Characterization of δ(.) Case II: More Reference Samples Local Deviation Bias (LDB) Strategy δ(.) is assumed variable within the input space but locally constant For defining N(x) : 2-dimensional input space Fixed local neighborhood Fixed quantization of the input space according to and Hypothesis: points close in the input space have similar values of δ(.) Idea: to exploit the deviation associated with the available Reference Samples

8 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 Proposed Approach: Characterization of δ(.) 2-dimensional input space Hypothesis: points close in the input space have similar values of δ(.) Idea: to exploit the deviation associated with the available Reference Samples Case II: More Reference Samples Local Deviation Bias (LDB) Strategy δ(.) is assumed variable within the input space but locally constant For defining N(x) : Fixed local neighborhood Adaptive local neighborhood K-Nearest Neighborhood according to

9 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 Training Phase REFERENCE SAMPLES REFERENCE SAMPLES Characterization of δ(.) + Operational Estimation Phase Proposed Approach: Implementation

10 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 Experimental Analysis: Context and Dataset Application Domain: Soil Moisture Estimation from Microwave Remotely Sensed Data Challenging and complex estimation problem High spatial and temporal variability of the target parameter Sensitivity of the microwave signal to many different target properties Limited availability of reference samples Study Area: bare agricultural fields near Matera, Italy Medium/dry soil moisture conditions High variability of roughness conditions due to plowing practice Dataset: 17 reference samples Backscattering measurements with a field scatterometer C-Band (5.3 GHz) Dual-polarization (HH and VV) Multi-angle (23° - 40°) Field measurements of soil parameters Soil moisture/dielectric constant (5 < ε < 15) Soil roughness (1.3 < σ < 2.5 cm)

11 Estimation of the Soil Moisture Content performed according to 1.Theoretical Forward Model Inversion Integral Equation Model (IEM) Inversion perfomed by means of the Support Vector Regression technique with Gaussian RBF kernel function according to [1] 2.Correction of the deviation term according to the proposed approach in two operative scenarios: Experiment 1: Very few reference samples available Global Deviation Bias (GDB) strategy Experiment 2: More reference samples available Local Deviation Bias (LDB) strategy with fixel local neighborhood Experimental Analysis: Setup Estimation Performance Assessment Comparison with theoretical Forward Model inversion without deviation term correction Cross Validation procedure Evaluation of quantitative quality metrics Root Mean Squared Error (RMSE) Correlation Coefficient (R) Slope and Intercept of the linear tendency line between estimated and measured target values IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 [1]L. Pasolli, C. Notarnicola and L. Bruzzone, “Estimating Soil Moisture with the Support Vector Regression Technique,” IEEE Geoscience and Remote Sensing Letters, in press

Proposed Hybrid Estimation Approach (GDB Strategy) Standard Theoretical Forward Model Inversion 12 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 Results: Experiment 1 HP: Very Few Reference Samples 2-dimensional Input Space Influence of the # of Reference Samples Available # Reference SamplesRMSERSlopeIntercept 8 (2 folds CV) (5 folds CV) (leave one out LOO CV)

Proposed Hybrid Estimation Approach (LDB Strategy with fixed local neighborhood) Standard Theoretical Forward Model Inversion 13 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 Results: Experiment 2 HP: Few Reference Samples 2-dimensional Input Space

14 Discussion The experimental results presented are in agreement with those obtained with other datasets in different operative conditions active (scatterometer) and passive (radiometer) C-band microwave data over bare areas P-band SAR data over vegetated areas IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 The potential and effectiveness of the method is shown especially when challenging operative conditions are addressed High level and variability of soil roughness Presence of vegetation More advanced and complex strategies can be defined for the characterization of the deviation function δ(.) Machine Learning (ML) methods

15 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 A novel hybrid approach to the estimation of biophysical parameters has been presented It is based on the inversion of a theoretical forward model for performing the estimation It exploits available (few) referene samples to correct approximations intrinsic in the forward model formulaiton The proposed approach is promising and effective to address the estimation of biophysical parameters from remote sensing data It allows one to increase the estimation accuracy It is capable to handle the variability of the deviation δ(.) in the input space domain It is general, simple, easy to implement and fast during the processing Future Activities Development of novel adaptive strategies for the characterization of δ(.) Investigation of the proposed appraoch in other challenging application domains Conclusion

IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 A special Thank to Dr. Claudia Notarnicola and Prof. Lorenzo Bruzzone Thank you for the Attention!! Questions?

16 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 Vancouver, Canada – July, 2011 Results: Experiment P-Band SAR Study Area: Vegetated Agricultural Fields (SMEX O2 Experiment) Dataset: 35 reference samples Airborne SAR data (AirSAR) L-Band (0.44 GHz) Dual-polarization (HH and VV) Acquisition angle 40° Field measurements of soil parameters Soil moisture/dielectric constant (5 < ε < 16) Soil roughness (1.3 < σ < 2.5 cm) Standard Theoretical Forward Model Inversion Proposed Hybrid Approach (LDB)Proposed Hybrid Approach (GDB)