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An Initial Analysis of CHRIS-on-board-PROBA Data.

An Initial Analysis of CHRIS-on-board-PROBA Data. Graham Thackrah 1 , Philip Lewis 1 , Tristan Quaife 1 and Mike Barnsley 2 . 1 Department of Geography, University College London. 2 Department of Geography, University of Wales Swansea. Introduction: CHRIS/PROBA. Platform characteristics

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An Initial Analysis of CHRIS-on-board-PROBA Data.

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  1. An Initial Analysis of CHRIS-on-board-PROBA Data. Graham Thackrah1, Philip Lewis1, Tristan Quaife1 and Mike Barnsley2. 1Department of Geography, University College London. 2Department of Geography, University of Wales Swansea.

  2. Introduction: CHRIS/PROBA • Platform characteristics • Angular sampling • Spectral sampling

  3. Introduction: Study Site • Hill farm, Barton Bendish • MODIS core validation site • Extensive historical data collection • Commercial arable farm • Simple canopies appropriately modelled using CR models such as Kuusk • Flat topography • HyMap data from SHAC (BNSC/NRSC) 2000, CHRIS/PROBA data from 2003

  4. Introduction: Inversion • Canopy reflectance models: Kuusk, 3D scene model. • Assumptions mostly valid over our study site, i.e. homogenous canopies • Detailed plant canopy models exist for cereal crops • Choice of numeric inversion methods • High dimensional data (multiangle/multispectral) favour the faster numeric methods • Inversion of model over image data (single CHRIS scene is ½ million pixels) also highly favours fast methods

  5. Methods: Look-Up-Tables • LUTs provide fast means of model inversion • Flexible method capable of inverting many models • Relatively simple to implement • May require large amounts of disk storage

  6. Methods: Sparse Interpolated LUTs • LUT error surface generally smooth and well behaved in region of the minimum • Suitable for a local linear approximation over a small area of candidate LUT points • Various methods of selecting the candidate set of n points • Lowest n in terms of RMSE • All below a threshold t • Various methods of selecting a parameter set from a candidate set of minimum LUT points • Median and interpolation

  7. Methods: LUT Sampling • Linearised space • Desirable to approximately linearise model parameter space • Regular or random sampling • Regular sampling can lead to all the candidate minimum points lying along a reduced number of axes

  8. Results: Sparse Interpolated LUTs • Synthetic data used, random additive noise added • Interpolation method performs better than median • Advantage maintained even down to small LUT sizes – beneficial for inversion over image data

  9. Results: HyMap Interpolation Median Original image data Chlorophyll concentration 8 x 8 LUT of LAI and chlorophyll concentration used (based on a regular grid) – significant quantisation noticeable in the median result LAI

  10. Results: CHRIS MVA Composite March 27th 2003 June 13th 2003

  11. Results: CHRIS MVA Composite R = -55 nominal vza G = 55 nominal vza B = 0 nominal vza

  12. Conclusions • Sparse interpolated LUTs shown to perform well in inverting CR models over simulated data. • Interpolation outperforms median method for retrieving a candidate parameter set for a given observation • Sparse LUTs therefore seen as a practical method for inverting CR models over multispectral/multiangular data – even some success when applied to single view angle hyperspectral data • CHRIS/PROBA producing data and hope to have some inversions using real data for which we have contemporary ground measurements of the parameters of interest

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