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.

Introduction: CHRIS/PROBA

• Platform characteristics

• Angular sampling• Spectral sampling

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

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

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 1 P 2 R 1 R 2 R 3 R 4

1 2 0.010 0.300 0.500 0.30

2 4 0.020 0.350 0.550 0.280

3 6 0.025 0.380 0.550 0.250

4 8 0.030 0.400 0.600 0.300

5 10 0.040 0.500 0.75 0.350

R 1 R 2 R 3 R 4

0.023 0.390 0.540 0.295

P 1 P 2 RMSE

1 2 0.099469

2 4 0.043977

3 6 0.047212

4 8 0.061433

5 10 0.243955

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

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

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

Results: HyMap

Chlorophyll concentration

LAI

Interpolation Median

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

Results: CHRIS MVA Composite

March 27th 2003 June 13th 2003

Results: CHRIS MVA Composite

R = -55 nominal vzaG = 55 nominal vzaB = 0 nominal vza

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