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

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

Importance of Water Circle

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

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

Electromagnetic Spectrum

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

SIR-A Observation

Synthetic Aperture Radar (SAR)

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 PIs

JPL/NASA AIRSAR C-band InSAR

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

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

Stoke Matrix and Backscattering Coefficient  Stoke Matrix  Bckscattering Coefficient

Dependence of SAR Measurements on Polarization

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

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

Phase Difference of Surface Backscattering

Coefficient of Variation Image An example of polarization property measurements

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

Radar Data Presentation

Spaceborne Imaging Geometry

SAR Data Processing Radar Equation or Airborne Spaceborne

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

Geometric Distortion in Radar Image

Comparison between ERS-1 and JERS-1 SAR Image

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

Terrain Correction Factor Spaceborne: Airborne:

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

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

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

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

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

Commonly Used PDF

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.

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

An Example of Multi-Look Image Data