際際滷

際際滷Share a Scribd company logo

On Estimation of Surface Soil Moisture.ppt

Download as PPT, PDF
Prashanth Janardhan
Prashanth Janardhan

On Estimation of Surface Soil Moisture

1 of 16
Download to read offline
On Estimation of Soil Moisture with SAR Jiancheng Shi ICESS University of California, Santa Barbara
Importance of Water Circle
Basic Consideration (1) Common idea of the current algorithm Inverse - two equations two unknowns. It can be re-ranged to one equation for one unknown. Disadvantages: Requires both formula all in good accuracy Error in the estimated one unknown the other ) , , ( ) ( 2 1 r r pp s or s f f
Basic Consideration (1) - continue ) log( 36 . 3 ) log( 09 . 3 ) log( ) log( 78 . 4 ) log( 79 . 3 19 . 2 ) ) ( log( ) log( 57 . 2 ) log( 09 . 2 03 . 2 ) log( 2 hh vv h hh vv r hh vv R W ks S ks in (a) in (b) in (c) Different weight sensitive to different surface parameter Independent direct estimation of soil moisture and RMS height (a) ks (b) Sr (c) Rh
Basic Consideration (2) IEM -- Power expansion and nonlinear relationships ! ) 0 , 2 ( | | 2 exp 2 1 2 2 2 2 2 n k W I s s k k x n n n pp n z o pp Higher order inverse formula improve accuracy Example: estimate surface RMS height 28 . 0 ) , ( ) 2 ( RMSE f hh vv 36 . 0 ) , ( ) 1 ( RMSE f hh vv s s s s
Basic Consideration (3) Polorization Magnitude Roughness function SP PO GO Tradition Backscattering Models 2 2 2 ) sin ( exp ) ( ) ( kl kl ks 2 2 sin cos sin cos r r r r ) 1 ( ) 1 ( r r ) 2 tan exp( 2 1 2 m m n kl n n kl kl kl n n 4 ) ( exp ! ) cos ( ) sin ( exp ) ( 2 1 2 2 2 2 2 2 sin cos sin 1 sin ) 1 ( r r r r Inverse model for different roughness region improve accuracy
Validation Using Michigan's Scatterometer Data Correlation: mv - 0.75, rms height - 0.96 RMSE: mv - 4.1%, rms height - 0.34cm mv S RMSE for S Measured parameters Estimated incidence
Characteristics of Backscattering Model (4) ) ( ) ( pp sv v pp v v pp t f f ) ( ) 1 ( ) ( 2 pp s v pp pp s v f L f First-order backscattering model Surface parameters surface dielectric and roughness properties Vegetation parameters dielectric properties, scatter number densities, shapes, size, size distribution, & orientation 2 ) ( ) ( ) ( pp pp sv pp s pp v v L f Fraction of vegetation cover Direct volume backscattering (1) Direct surface backscattering (4 & 3) Surface & volume interaction (2) Double pass extinction
Radar Target Decomposition Covariance (or correlation) matrix 0 0 0 0 1 * c T Decomposition based on eigenvalues and eigenvectors ' 3 3 1 ' 2 2 1 ' 1 1 1 k k k k k k T where, are the eigenvalues of the covariance matrix, k are the eigenvectors, and k means the adjoint (complex conjugate transposed ) of k. * hh hh S S c * * hh hh vv hh S S S S * * 2 hh hh hv hv S S S S * * hh hh vv vv S S S S and
Radar Target Decomposition Technique Total Power: single, double, multi VV: single, double, multi HH Correlation or covariance matrix -> Eigen values & vectors T T T * 3 3 3 * 2 2 2 * 1 1 1 K K K K K K T VV, HH, VH
Relationships in scattering components between decomposition and backscattering model 1. First component in decomposition (single scattering) direct volume, surface & its passes vegetation 2. Second component (double-bounce scattering) Surface & volume interaction terms 3. Third component defuse or multi-scattering terms
Properties of Double Scattering Component under Time Series Measurements 1. Variation in Time Scale surface roughness vegetation growth surface soil moisture 2. In backscattering Model 3. Ratio of two measurements independent of vegetation properties depends only on the reflectivity ratio ) ( ) ( ) ( 2 ) ( 2 pp pp s pp pp sv dL R n pp m pp n pp m pp R R 2 2
Comparison with Field Measurements VV, HH, VH Two Corn Fields Dielectric Constant Date n hh n vv m hh m vv R R R R n hh n vv m hh m vv 2 2 2 2 n hh n vv m hh m vv 2 2 2 2 Normalized VV & HH cross product of double scattering components for any n < m Corresponding reflectivity ratio n hh n vv m hh m vv R R R R Correlation=0.93, RMSE=0.42 dB
Estimate Absolute Surface Reflectance A) B) C) 2 2 | | | | m vv n vv v nm A 2 2 | | | | m hh n hh h nm A m hh n hh m vv n vv c nm A | | | | | | | | ) ( c nm v nm A f A ) ( c nm h nm A f A 2 2 2 2 2 | | | | 1 | | | | | | m hh n hh n hh m hh n hh h nm m hh n hh n hh A 1 | | | | | | 2 2 2 h nm v nm h nm v nm m hh n hh A A A A f 2 2 | | | | A) ) log( ) log( c nm v nm A A B) C) estimation
Current Evaluations Validity range of the second component measurements Effect of radar calibration and system noise What type and vegetation condition? How to obtain vegetation and surface roughness information What we can do with the first component measurements? What to do with sparse vegetated surface?
Summary Time series measurements with second decomposed components (double reflection) A promising (direct and simple technique) to estimate the relative change in dielectric constant for certain type of the vegetated surfaces A great possibility to derive soil moisture algorithm for the vegetated surface Advantages of this technique Do not require any information on vegetation Can be applied to partially covered vegetation surface

More Related Content

On Estimation of Surface Soil Moisture.ppt

  • 1. On Estimation of Soil Moisture with SAR Jiancheng Shi ICESS University of California, Santa Barbara
  • 3. Basic Consideration (1) Common idea of the current algorithm Inverse - two equations two unknowns. It can be re-ranged to one equation for one unknown. Disadvantages: Requires both formula all in good accuracy Error in the estimated one unknown the other ) , , ( ) ( 2 1 r r pp s or s f f
  • 4. Basic Consideration (1) - continue ) log( 36 . 3 ) log( 09 . 3 ) log( ) log( 78 . 4 ) log( 79 . 3 19 . 2 ) ) ( log( ) log( 57 . 2 ) log( 09 . 2 03 . 2 ) log( 2 hh vv h hh vv r hh vv R W ks S ks in (a) in (b) in (c) Different weight sensitive to different surface parameter Independent direct estimation of soil moisture and RMS height (a) ks (b) Sr (c) Rh
  • 5. Basic Consideration (2) IEM -- Power expansion and nonlinear relationships ! ) 0 , 2 ( | | 2 exp 2 1 2 2 2 2 2 n k W I s s k k x n n n pp n z o pp Higher order inverse formula improve accuracy Example: estimate surface RMS height 28 . 0 ) , ( ) 2 ( RMSE f hh vv 36 . 0 ) , ( ) 1 ( RMSE f hh vv s s s s
  • 6. Basic Consideration (3) Polorization Magnitude Roughness function SP PO GO Tradition Backscattering Models 2 2 2 ) sin ( exp ) ( ) ( kl kl ks 2 2 sin cos sin cos r r r r ) 1 ( ) 1 ( r r ) 2 tan exp( 2 1 2 m m n kl n n kl kl kl n n 4 ) ( exp ! ) cos ( ) sin ( exp ) ( 2 1 2 2 2 2 2 2 sin cos sin 1 sin ) 1 ( r r r r Inverse model for different roughness region improve accuracy
  • 7. Validation Using Michigan's Scatterometer Data Correlation: mv - 0.75, rms height - 0.96 RMSE: mv - 4.1%, rms height - 0.34cm mv S RMSE for S Measured parameters Estimated incidence
  • 8. Characteristics of Backscattering Model (4) ) ( ) ( pp sv v pp v v pp t f f ) ( ) 1 ( ) ( 2 pp s v pp pp s v f L f First-order backscattering model Surface parameters surface dielectric and roughness properties Vegetation parameters dielectric properties, scatter number densities, shapes, size, size distribution, & orientation 2 ) ( ) ( ) ( pp pp sv pp s pp v v L f Fraction of vegetation cover Direct volume backscattering (1) Direct surface backscattering (4 & 3) Surface & volume interaction (2) Double pass extinction
  • 9. Radar Target Decomposition Covariance (or correlation) matrix 0 0 0 0 1 * c T Decomposition based on eigenvalues and eigenvectors ' 3 3 1 ' 2 2 1 ' 1 1 1 k k k k k k T where, are the eigenvalues of the covariance matrix, k are the eigenvectors, and k means the adjoint (complex conjugate transposed ) of k. * hh hh S S c * * hh hh vv hh S S S S * * 2 hh hh hv hv S S S S * * hh hh vv vv S S S S and
  • 10. Radar Target Decomposition Technique Total Power: single, double, multi VV: single, double, multi HH Correlation or covariance matrix -> Eigen values & vectors T T T * 3 3 3 * 2 2 2 * 1 1 1 K K K K K K T VV, HH, VH
  • 11. Relationships in scattering components between decomposition and backscattering model 1. First component in decomposition (single scattering) direct volume, surface & its passes vegetation 2. Second component (double-bounce scattering) Surface & volume interaction terms 3. Third component defuse or multi-scattering terms
  • 12. Properties of Double Scattering Component under Time Series Measurements 1. Variation in Time Scale surface roughness vegetation growth surface soil moisture 2. In backscattering Model 3. Ratio of two measurements independent of vegetation properties depends only on the reflectivity ratio ) ( ) ( ) ( 2 ) ( 2 pp pp s pp pp sv dL R n pp m pp n pp m pp R R 2 2
  • 13. Comparison with Field Measurements VV, HH, VH Two Corn Fields Dielectric Constant Date n hh n vv m hh m vv R R R R n hh n vv m hh m vv 2 2 2 2 n hh n vv m hh m vv 2 2 2 2 Normalized VV & HH cross product of double scattering components for any n < m Corresponding reflectivity ratio n hh n vv m hh m vv R R R R Correlation=0.93, RMSE=0.42 dB
  • 14. Estimate Absolute Surface Reflectance A) B) C) 2 2 | | | | m vv n vv v nm A 2 2 | | | | m hh n hh h nm A m hh n hh m vv n vv c nm A | | | | | | | | ) ( c nm v nm A f A ) ( c nm h nm A f A 2 2 2 2 2 | | | | 1 | | | | | | m hh n hh n hh m hh n hh h nm m hh n hh n hh A 1 | | | | | | 2 2 2 h nm v nm h nm v nm m hh n hh A A A A f 2 2 | | | | A) ) log( ) log( c nm v nm A A B) C) estimation
  • 15. Current Evaluations Validity range of the second component measurements Effect of radar calibration and system noise What type and vegetation condition? How to obtain vegetation and surface roughness information What we can do with the first component measurements? What to do with sparse vegetated surface?
  • 16. Summary Time series measurements with second decomposed components (double reflection) A promising (direct and simple technique) to estimate the relative change in dielectric constant for certain type of the vegetated surfaces A great possibility to derive soil moisture algorithm for the vegetated surface Advantages of this technique Do not require any information on vegetation Can be applied to partially covered vegetation surface