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Spatial and Temporal Mapping of Soil Moisture Content <br />with Polarimetric RADARSAT2 SAR Imagery <br />in the Alpine Ar...
2<br />Outline<br />Introduction<br />1<br />Aim of the Work<br />2<br />Study Area and Dataset<br />3<br />Estimation Sys...
Introduction<br />3<br />SOFIA: SOil and Forest Information retrieval by using RADARSAT2 images<br /><ul><li>ESA AO-SOAR 6...
Supported in the framework of the IRKIS project (Civil Protection Department, Province of Bolzano)</li></ul>Main Innovativ...
Mountain landscape (Alpine area)
Advanced estimation methods</li></ul>Objectives:<br /><ul><li>Estimation of soil moisture content on bare and vegetated ar...
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) </li></ul>IEEE International...
Introduction<br />4<br />Soil moisture estimation supports various application domains:<br /><ul><li>drought monitoring
flood and landslide prediction
climate change analysis</li></ul>Challenges: <br /><ul><li>non-linearityof the relationship between microwave signals and ...
sensitivityof microwave signals on different target properties (moisture content, roughness, vegetation, land use)
influence of topography on the microwave signal acquired by the sensor</li></ul>In a previous study (Pasolli et al., 2010)...
by exploiting an advanced retrieval algorithm based on the Support Vector Regression (SVR) method</li></ul>L. Pasolli, C. ...
5<br />Aimof the Work<br />ToFurther Investigate the RetrievalofSoilMoisture<br />from RADARSAT2 SAR Images in Alpine Area...
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  1. 1. Spatial and Temporal Mapping of Soil Moisture Content <br />with Polarimetric RADARSAT2 SAR Imagery <br />in the Alpine Area<br />Luca Pasolli1,2<br />Claudia Notarnicola2<br />Lorenzo Bruzzone1<br />Giacomo Bertoldi3<br />Georg Niedriest3<br />Ulrike Tappeiner3<br />Marc Zebisch2<br />Fabio Del Frate4<br />Gaia Vaglio Laurin4<br />E-mail: luca.pasolli@disi.unitn.it<br /> luca.pasolli@eurac.edu<br />Web: http://rslab.disi.unitn.it<br /> http://www.eurac.edu<br />
  2. 2. 2<br />Outline<br />Introduction<br />1<br />Aim of the Work<br />2<br />Study Area and Dataset<br />3<br />Estimation System Description<br />4<br />Analysis of Results<br />5<br />Conclusion<br />6<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  3. 3. Introduction<br />3<br />SOFIA: SOil and Forest Information retrieval by using RADARSAT2 images<br /><ul><li>ESA AO-SOAR 6820
  4. 4. Supported in the framework of the IRKIS project (Civil Protection Department, Province of Bolzano)</li></ul>Main Innovative Aspects:<br /><ul><li>Fully-polarimetricRADARSAT2 satellite SAR data
  5. 5. Mountain landscape (Alpine area)
  6. 6. Advanced estimation methods</li></ul>Objectives:<br /><ul><li>Estimation of soil moisture content on bare and vegetated areas (alpine meadows and pastures)
  7. 7. Estimation of vegetation biomass (forest)
  8. 8. Investigation on the influence of soil and vegetation parameters in connection to natural hazard in Alpine regions.
  9. 9. Estimation of soil moisture content on bare and vegetated areas (alpine meadows and pastures) </li></ul>IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  10. 10. Introduction<br />4<br />Soil moisture estimation supports various application domains:<br /><ul><li>drought monitoring
  11. 11. flood and landslide prediction
  12. 12. climate change analysis</li></ul>Challenges: <br /><ul><li>non-linearityof the relationship between microwave signals and soil moisture
  13. 13. sensitivityof microwave signals on different target properties (moisture content, roughness, vegetation, land use)
  14. 14. influence of topography on the microwave signal acquired by the sensor</li></ul>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:<br /><ul><li>by integrating the information coming from ancillary data
  15. 15. by exploiting an advanced retrieval algorithm based on the Support Vector Regression (SVR) method</li></ul>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<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  16. 16. 5<br />Aimof the Work<br />ToFurther Investigate the RetrievalofSoilMoisture<br />from RADARSAT2 SAR Images in Alpine Areas<br />Byexploiting the fully-polarimetriccapabilityof RADARSAT2 in combinationwith standard and advancedfeatureextraction/selectionmethods<br />Byextending the analysisin time and spacewith the availableimages<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  17. 17. Study Area<br />6<br />Mazia Valley, Alto Adige, Italy<br />Well known and monitored area<br />Well representative in terms of<br /><ul><li>Topography
  18. 18. Land use
  19. 19. Soil moisture content conditions</li></ul>IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  20. 20. Dataset<br />7<br />Satellite SAR images:<br /><ul><li>4 RADATSAT2 quad-pol standard mode images (3rd June, 21stJuly, 14th August, 5th October 2010)
  21. 21. DEM geocoded, filtered (Frost 7x7)
  22. 22. Final pixel size 20 m</li></ul>Field measurements:<br /><ul><li>77 soil dielectric constant measurements on meadows (blue) and pasture (red) acquired contemporary to satellite overpasses (3rd June and 21st July)</li></ul>RADARSAT2, 21° July 2010 (R=HH, G=HV, B=VV)<br />Ancillary data:<br /><ul><li>DEM (pixel size 2.5 m)
  23. 23. NDVI map extracted from MODIS Terra images (pixel size 250 m)
  24. 24. Land use map (meadows, pasture);</li></ul>IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  25. 25. Estimation System<br />8<br />Polarimetric RADARSAT2 SAR image<br />Data <br />Pre-processing<br />Feature<br />Extraction & Selection<br />Ancillary Data<br />RetrievalAlgorithm<br />EstimatedSoilMoistureContentMap<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  26. 26. Estimation System: RetrievalAlgorithm<br />9<br />Aim: to define the mapping between the input features and the target biophysical variable<br /><ul><li>Support Vector Regression (SVR) technique trained on Field Reference Samples
  27. 27. Multi-objective Model Selection Approach</li></ul>Polarimetric RADARSAT2 SAR image<br />Featuresfrom<br />RemotelySensedImage<br />Featuresfrom<br />Ancillary Data<br />Ground Truth<br />ReferenceSamples<br />Training Phase<br />Data <br />Pre-processing<br />Validation Set<br />Training Set<br />K-Fold Cross Validation<br />Performance<br />Evaluation<br />SVR Learning<br />SVR<br />Estimation<br />Feature<br />Extraction & Selection<br />SVR ParametersConfig.<br />Sub-Sample Generator<br />Ancillary Data<br />ModelSelection<br />Multi-ObjectiveModelSelection<br />RetrievalAlgorithm<br />EstimationPerform. (MSE, R2)<br />Estimation Operational Phase<br />SVR<br />Estimator<br />Input Features<br />(Image + Ancillary)<br />Output <br />SMC Value<br />EstimatedSoilMoistureContentMap<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  28. 28. Estimation System: FeaturesExtraction and Selection<br />10<br />Aim: to extract and select from the remotely sensed data the most relevant information for the estimation problem considered<br />Polarimetric RADARSAT2 SAR image<br />Features Extraction<br /><ul><li>Standard Intensity&Phase SAR processing
  29. 29. Polarimetric backscattering coefficients
  30. 30. Polarimetric Combinations: Span (HH+HV+2HV), Polarization Ratio (HH/VV) and Linear Depolarization Ratio (HV/VV)
  31. 31. Polarimetric phase difference (PPD) and interferometric coherence
  32. 32. Polarimetric Decompositions
  33. 33. H/A/αdecomposition
  34. 34. Generalpurposefeatureextractiontechniques
  35. 35. IndependentComponentAnalysis (ICA)</li></ul>Data <br />Pre-processing<br />Feature<br />Extraction & Selection<br />Ancillary Data<br />RetrievalAlgorithm<br />FeaturesSelection<br /><ul><li>SequentialForwardSelection (SFS) strategywith performance evaluation on a subset ofreferencesamples</li></ul>EstimatedSoilMoistureContentMap<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  36. 36. 11<br />Experimental Setup<br />Experiment 1: Assessmentof the Estimation System with the proposedFeatureExtraction & Selectionstrategies<br /><ul><li>60 referencesamplesfor training/tuning the estimation system accordingto a 5-fold cross validation procedure
  37. 37. RetrievalAlgorithmSettings:
  38. 38. SVR withGaussian RBF kernelfunction
  39. 39. Hyper-parametersranges: 10-3 < γ < 103 , 10-3< C < 103 , 10-3 < ε < 10
  40. 40. Multi-objectives model selection according to RMSE and R2 quality metrics
  41. 41. Performance assessment on 17 independent test reference samples according to:
  42. 42. Root Mean Squared Error (RMSE)
  43. 43. Determination coefficient (R2)
  44. 44. Slope and Intercept of the linear tendency line between estimated and measured target values</li></ul>Experiment 2: AssessmentofSpatially and TemporallyDistributedSoilMoistureEstimates in the Alpine Area<br /><ul><li>Exploitiationof the estimation system configurationidentified in Experiment 1
  45. 45. Generation ofsoilmoisturecontentmapsassociatedwith RADARSAT 2 SAR imagestimeseriesacquiredduringsummer 2010
  46. 46. Qualitative and quantitative assessmentwithpriorknowledge on the area and field station measurements</li></ul>IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  47. 47. 11<br />Results: Experiment 1<br />HH feature<br />HH HV/VV features<br />ICA1 ICA4 features<br />α Afeatures<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  48. 48. 11<br />Results: Experiment 2<br />EstimatedSoilMoistureContentMap, June 2010<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  49. 49. 14<br />Results: Experiment 2<br />Estimated dielectric constant Map, October 2010<br />Estimated dielectric constant Map, August 2010<br />Estimated dielectric constant map, July 2010<br />Estimated Dielectric constant Map, June 2010<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  50. 50. Conclusion<br />15<br />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<br />Polarimetric features are effective for improving the retrieval of soil moisture in the challenging Alpine environment<br /><ul><li>Generally, they allow one to reduce the ambiguity in the data and increase the accuracy of the estimation
  51. 51. The HH HV/VV configuration has shown to be the most suitable in this specific operative conditions</li></ul>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<br /><ul><li>Good capability to reproduce the spatial patterns of the desired target parameter
  52. 52. Good agreement with the measured temporal trends of soil moisture</li></ul>Future work<br /><ul><li>Investigation of the proposed estimation system in combination with higher geometrical resolution polarimetric SAR data
  53. 53. Integration of data from different sensors (e.g., L-Band SAR images)</li></ul>IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
  54. 54. 16<br />Thankyoufor the Attention!!<br />Questions?<br />luca.pasolli@disi.unitn.it<br />luca.pasolli@eurac.edu<br />IEEE International Geoscience and Remote Sensing Symposium IGARSS 2011 <br />Vancouver, Canada – 24-29 July, 2011<br />
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