1. On Estimation of Soil Moisture & Snow Properties with SAR Jiancheng Shi Institute for Computational Earth System Science University of California, Santa Barbara

2. Personal Background Research Interest: 1) Hydrology, 2) EM properties of snow and soil, 3) Retrieval snow and soil properties using remote sensing data Past Experiences – Active Microwave Remote Sensing: SIR-C/X-SAR & AIRSAR Snow Properties: extent, wetness, snow water equivalence, grain size Soil Moisture: dielectric and roughness properties –Optical Remote Sensing: Hyper- & multi-spectral Image data Snow properties: snow fraction mapping, grain size and net radiation

3. Importance of Water Circle

4. Class Syllables Morning of May 29 –Introduction active microwave remote sensing Afternoon of May 29 –Surface scattering modeling and observations Morning of May 30 –Inverse estimation of soil moisture Afternoon of May 30 –Classification techniques and snow mapping Morning of May 31 –Modeling and observation snow cover Afternoon of May 31 –Inverse estimation of snow properties

5. Today’s Outline Introduction active microwave remote sensing –Why active microwave remote sensing? –Basic characteristics of SAR –Radar Polarization –Geometric properties of SAR image and terrain correction –Image speckle

6. Electromagnetic Spectrum

7. Why Synthetic Aperture Radar? Advantages: All weather free All day free High resolution Penetration  thickness information Very sensitive to Moisture Disadvantages: Expensive Large data volume More difficult in image analyses

8. SIR-A Observation

9. Synthetic Aperture Radar (SAR)

10. Space Shuttle Image Radar C & X Band SAR German, Italy, and US L, C band fully polarimetric & X- band VV Two 10 days missions in 1994 72 PIs

11. JPL/NASA AIRSAR C-band InSAR

12. Radar Equation Where P t = Transmitted power = Radar wavelength R = Distance to scattering area G t (  )= Transmit antenna gain at angle  G r (  )= Receive antenna gain at angle   = Radar look angle  0 = Normalized radar cross-section for area A A is the area on the ground responsible for the scattering, and, Where,  r and  a are the slant range and azimuth pixel spacing, respectively. With R s and R t the magnitude of the spacecraft and target position vectors relative to the center of the earth

13. Basic Definitions of Polarimetric Measurement  Scattering Matrix  Stoke Vector Horizontal Polarization:  =0;  =0, Vertical Polarization:  =90;  =0

14. Stoke Matrix and Backscattering Coefficient  Stoke Matrix  Bckscattering Coefficient

15. Dependence of SAR Measurements on Polarization

16. Polarization Dependence of Surface Backscattering Rock Surface Top 50 degree Bottom 30 degree

17. Other presentations  Scattering Matrix  Covariance Matrix Fractional polarization, coefficient of variation, degree of polarization, relative phase difference, depolarization factors, ….

18. Phase Difference of Surface Backscattering

19. Coefficient of Variation Image An example of polarization property measurements

20. Terrain Effect on Radar Image SIR-C’s C-Band Multi-Polarization superimposed on a DEM, on 04/12/94 at Mammoth Mt., California

21. Radar Data Presentation

22. Spaceborne Imaging Geometry

23. SAR Data Processing Radar Equation or Airborne Spaceborne

24. Terrain Effect on Radar Image 1.Effect on radiometric property’s estimation 2.Effect on antenna gain estimation 3.Geometric distortion on image coordinates

25. Geometric Distortion in Radar Image

26. Comparison between ERS-1 and JERS-1 SAR Image

27. Impact of Surface Topography on Gain Estimation Error Due to Pixel Size Antenna Gain

28. Terrain Correction Factor Spaceborne: Airborne:

29. An Example of Spaceborne Data Correction DEM Cosine Incidence Calibration Factor C-band

30. An Example of Airborne Data Calibration Geometric distortion DEMCosine Incidence Antenna Angle

31. Intensity Type of Measurements backscattering coefficient for any given polarization each element of Stokes matrix or cross product of scattering matrix polarized and unpolarized components of scattered power Intensity type measurements requires topographic information for radiometric calibration Interferometric Type of Measurements Interferometric phase and coherence SAR Measurement Properties

32. Polarization Type of Measurements Polarization impurity: degree of polarization … Specific polarization feature: a given polarization / total power polarization ratios: depolarization factors …. Polarization property measurements are independent of the SAR data presentation Polarization property measurements from spaceborne SAR can be obtained without requiring topographic information SAR Measurement Properties

33. Concept of Speckle Speckle  coherent imaging systems. It, to first order, is shaped by the system, and not the scene Properties of speckle a large number of scatterers no dominate scatter statistically independent phases of scatterers are uniformly distributed over 0  2π These properties  the exponential pdf

34. Commonly Used PDF

35. Speckle Reduction To reduce the image variance produced by spackle Criteria: 1)Obtain max. resolution 2)Preserve radiometric quantity 3)Preserve edges and the sharpness of point targets An example: sigma filter (Lee, 1986) Assumption: homogeneous area T is total # of pixels matched the condition. 1<=q<=2.

36. Today’s Outline Introduction active microwave remote sensing –Why active microwave remote sensing? –Basic characteristics of SAR –Radar Polarization –Geometric properties of SAR image and terrain correction –Image speckle

37. An Example of Multi-Look Image Data