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Utilizing TanDEM-X for Crop Assessment via PolInSAR Techniques

This study explores using TanDEM-X for agricultural crop parameter retrieval via single-pass PolInSAR techniques. The research aims to provide crucial information for improving farming practices by timely and localized crop data assessment. The methodology involves developing direct models and analyzing interferometric coherences to retrieve parameters such as vegetation height and structural features related to crop conditions. The study includes examples and baseline requirements for effective data analysis. Planned acquisitions will cover various crop types and farming practices to enhance agricultural data assessments.

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Utilizing TanDEM-X for Crop Assessment via PolInSAR Techniques

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  1. On the potential of TanDEM-X for the retrieval of agricultural crop parameters by single-pass PolInSAR Juan M. Lopez-Sanchez and J. David Ballester-Berman Signals, Systems and Telecommunications Group University of Alicante, Spain

  2. Motivation • Final application: information for helping farming practices • Requirement: Timely and local information about crop phenology, condition, and other indicators • TanDEM-X: proof-of-concept • High spatial resolution (1-3 m) • Short revist time (11 days) • Single-pass interferometry • Parameters of interest to be retrieved with single-pass PolInSAR: • Vegetation height: correlated to phenology in many crops (especially during the vegetative phase) • Structural parameters related to phenology and crop condition: • Extinction • Vertical profiles (using PCT) • Other physical features (target decomposition): randomness, orientation of leaves and branches, water content, etc.

  3. Methodology • A number of direct models have been developed in the last years: • General: Homogeneous volume over ground • Model: Analytical expression of the complex interferometric coherence, as a function of polarization channel 2 Oriented Random Alternate-tx (monostatic) 1 Single-tx (bistatic) VOLUME Direct Double-bounce GROUND Both

  4. Methodology RVoG: alternate-tx, and single-tx with direct ground RVoG: single-tx with double-bounce from ground • Examples • All coherences are aligned • Line depends on scene • and interferometer

  5. Methodology • Examples OVoG: single-tx with double-bounce from ground OVoG: alternate-tx, and single-tx with direct ground Coherences are no longer aligned

  6. Methodology OVoG with both ground contributions • Examples RVoG with both ground contributions Alternate-tx Single-tx Alternate-tx Single-tx

  7. Methodology • Baseline requirements • If too small: no decorrelation, hence all coherences in a small cluster (i.e. insensitive) • If too large: extreme volume decorrelation, hence low coherence and presence of phase noise • Important: kz * hv (or kv = kz*hv/2) • Ideal case: kv = 1 [Cloude 2009] • Typical example for crops: hv = 1 m • With the mentioned criterion: kz = 2, i.e. hamb = 3.14 m • TanDEM-X: Bn ~ 3 km (bistatic) or 1.5 km (alternate-tx) • Normal mode: Bn ~ 250-300 m, kz ~ 0.2-0.4 • Some sensitivity is expected…

  8. Available data set: Wallerfing (Germany) • Date: April 12, 2011 (no ground truth, but scarce agriculture is expected) • Mode: Bistatic • Polarizations: HH VV • Incidence angle (scene center): 32.66 degrees • Height of ambiguity: 133.4 m • Perpendicular baseline: 76.56 m • InSAR sensitivity: • Vertical wavenumber kz = 0.0471 • For agricultural crops with hv = 1 m, kv = 0.0236 << 1

  9. Wallerfing: RGB composite HH-VV VV HH

  10. Wallerfing: coherence maps HH+VV HH HH-VV VV

  11. Wallerfing (sample): RGB composite Sample extracted from the image HH-VV VV HH

  12. Wallerfing (sample): coherence maps HH+VV HH HH-VV VV

  13. Wallerfing (sample): backscatter HH VV

  14. Wallerfing (sample): SNR effect • Low backscattering levels are expected from agriculture fields • Data sample: bare fields or with scarce vegetation: below -10 dB • NESZ in these TSX/TDX images (from annotated info): - 21 to -24 dB • Decorrelation due to SNR:

  15. Wallerfing (sample): SNR effect • If NESZ = -22 dB is assumed, decorrelation due to SNR can be estimated from backscattering levels: Example: Typical values for HH and VV over rice fields with TSX

  16. Wallerfing (sample): SNR effect • Application to these data: HH, and similar for VV Estimated from SNR Measured

  17. Wallerfing (sample): SNR effect • Application to these data: HH+VV Estimated from SNR Measured

  18. Wallerfing (sample): SNR effect • Application to these data: HH-VV Estimated from SNR Measured

  19. Wallerfing (sample): coherence set Set of 6 coherences: HH, VV HH+VV, HH-VV Optimum (1st and 2nd)

  20. Wallerfing (sample): interf. phases HH+VV HH HH-VV VV

  21. Wallerfing (sample): diff. interf. phases Phase HH – Phase VV Phase HH+VV – Phase HH-VV Height HH – Height VV Height HH+VV – Height HH-VV

  22. Wallerfing (sample): PolSAR Average alpha Entropy alpha1

  23. Planned acquisitions • Generic: various types of crops • Barrax (Albacete), SE Spain • Types: wheat, barley, maize, etc. • Farming practices information and optical images available • Measurements of LAI, vegetation height, phenology, soil moisture • Roseworthy farm (Adelaide), S Australia • Types: wheat, barley, legumes, peas, beans, canola. • Measurements of vegetation height, phenology, etc. • Thematic: • Rice fields in Sevilla, SW Spain • Weekly measurements of phenology, height, condition changes • Extra data: sowing & harvest date, plantation density, yield

  24. Expected results • Better results are expected for the planned acquisitions (Jun-Aug 2011): • Baselines: 240 – 300 m • Many acquisitions in alternate bistatic mode • Regarding the application: Expected limitations: • Noise • Reduced swath: small spatial coverage • Potential solution: combination of passes (asc, desc, etc.)

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