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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,

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Presentation on theme: "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,"— Presentation transcript:

1 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.

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 P 1P 2R 1R 2R 3R R 1R 2R 3R P 1P 2RMSE

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 Chlorophyll concentration LAI InterpolationMedian Original image data 8 x 8 LUT of LAI and chlorophyll concentration used (based on a regular grid) – significant quantisation noticeable in the median result

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