Improved Estimation of Surface Biophysical Parameters From Kerneldriven
Improved Estimation of Surface Biophysical Parameters From Kerneldriven BRDF models Upgrading workshop Mathias Disney UCL Geography
• Context of project • advent of EOS era - launch of Terra high repeat (e. g. 2 -3 days for MODIS) moderate resolution (100 s of m to km) angular sampling (e. g. POLDER) • large quantity of multi-angular observations • range of wavelengths, particularly visible/NIR • good spatial/temporal coverage
• Detailed characterisation of vegetated regions highly desirable • vegetal functioning at regional/global scales NDVI/LAI • carbon cycle (NDVI/NPP) • energy fluxes between surface atmosphere f. APAR (absorbing) albedo (reflecting) • hydrology, water cycle, soil • relation to climate etc.
• Model development over last 20 years • characterise surface directional reflectance (BRDF) • different classes of model empirical physical (RT/GO/hybrid) computer simulation (MCRT/radiosity) semi-empirical GO/RT (kernel-driven) • inversion of models optimisation, LUT inversion, linearising etc.
• Semi-empirical kernel-driven models (linear): • appropriate for moderate resolutions driven by requirement for useful ‘products’ rather than reflectance or abstract parameters. . . • scale linearly (for linear additive combination) • small number of parameters, easily inverted matrix inversion, order 3/4 say • angular integration interp. /extrap. of BRDF spectral albedo/broadband albedo
Angular variation of semi-empirical RT/GO kernels
• BUT. . . . • models treat physics very simply combine different components of surface scattering behaviour relation of model parameters to biophysical parameters? can any relations be treated simply? parameter coupling? • implications of model simplicity for derived products (e. g. albedo) ?
• This project aims to address: • assumptions behind kernel-driven models canopy be represented as linear combination of volumetric and GO components? can volumetric and GO components be separated by respective kernels? • information contained in inverted parameters separation of soil/vegetation components spectral information remaining, coupling • extension of kernel-driven concept from angular to spectral domain
• Experimental method • fieldwork - 3 D measurements of canopy structure leaf zen. /az. angles, lengths, widths, stems etc. • measure LAI, directional reflectance, % cover etc. • BPMS 3 D canopy simulate BRDF under assumptions made in kernel-driven models i. e. : single scattering only leaf bi-Lambertian, leaf = leaf , soil Lambertian direct illumination only
barley wheat sugar beet Aerial photograph of Barton Bendish farm, 6/8/97.
Simulated barley canopy (slightly dodgy ears!)
• Information obtained from simulations Proportions of sunlit/shaded leaf/soil (barley 18/4/97)
• Shapes of separate components of simulated canopy Volumetric component, (barley, 15/5/97)
• Shapes of separate components of simulated canopy GO component, (barley, 15/5/97)
• Analysis of components of canopy show: • components are separable, and predominantly independent of each other • volumetric component > GO component • volumetric - asymmetric upward bowl shape, corresponds to proportion of visible sunlit leaf • GO - downward bowl shape, symmetric about nadir, corresponds to proportion of visible sunlit soil
leaf = 1 and soil = 0 i. e. volume scattering. leaf = 0 and soil = 1 i. e. GO scattering.
• for BPMS simulations volumetric component GO component • so, if there is linear relation between components of simulated canopy and kvol and k. GO then: • regress components of simulated canopy ( , ) against kvol and k. GO
• Relationship between kvol and Barley canopy 15/5/97
• Relationship between k. GO and Barley canopy 15/5/97
• Demonstrates that: • volumetric and GO components of canopy can be described by respective kernels, but. . relationship between kvol and stronger than that between k. GO and disparity increases as canopy develops Ross. Thick generally better than Ross. Thin Li. Sparse generally better than Li. Dense r 2 kvol against high when LAI is high, and viceversa for k. GO and
• Conclusions • canopy can be split into volumetric and GO components • there is a linear relationship between kvol and , k. GO and • breaks down as canopy departs from assumptions of kernel-driven models (implications? ) • Next question: • do kernels act independently? i. e. • is volumetric component of canopy described adequately by kvol alone, and GO component described by kvol alone? Coupling?
• substitute volumetric and GO components of simulated canopy back into original expression for full kerneldriven model i. e. isotropic term purely volumetric GO term • plug avol, GO and bvol, GO values in, plus appropriate leaf and soil
Retrieved parameters (as a fn of ) for barley canopy 15/5/97
• Results • agreement between parameters inverted from simulated canopy, and those derived from regressed values of avol, GO and bvol, GO • ‘vegetation-like’ shape indicates spectral info. related to leaf in volumetric parameter (expected) and isotropic (not so expected) • for other dates, soil seen in GO parameters (expected) • -ve model parameters (meaning? )
• Conclusions • volumetric and GO components of canopy largely separable • canopy develops parameters increase departure from expectations • some of variation in canopy due to volumetric scattering is described by (k. GO) • some of variation in canopy due to GO scattering is described by (kvol) • problems separating components entirely - again, implications for parameter retrieval
• Principal component analysis of parameters 2 PCs of model parameters, barley canopy, 15/5/97
• PCA shows that PC 1 >> PC 2 i. e. always a dominant and secondary component (are separable) • BUT volumetric parameter dominant even in some low LAI cases kvol cannot be interpreted straightforwardly here not physically meaningful • -ve parameters arise due to poor model choice for a particular canopy not important if parameters used for classification for e. g. , but cannot be interpreted physically
• Conclusions • canopy can be separated into: volumetric component described by (and linearly related to) kvol GO component described by (and linearly related to) k. GO • breaks down when canopy departs from model assumptions (LAI, LAD, canopy type) • some portion of each component described by other kernel • parameters can contain spectral information (use for classification? ) • can take physically unrealisable values (constraint/auxiliary info. )
• Plan for completion • development/application of linear spectral kernels • Benefits? • spectral interpolant for (narrow-band) albedo to broad-band • combine samples from sensors with different spectral response e. g. POLDER + MSG • potentially separate vegetation and soil components of canopy - NPP/biomass etc. • potentially VERY useful!
• spectral kernels based on Price. . • Price’s method - soil represented by first 4 PCs of large data set of soil lab spectra • can same be done with veg and combined with soil to describe canopy? Assume. . sscatt soil sscatt veg term mscatt terms
Price’s soil basis functions (scaled)
• use lab spectra (LOPEX data) • wet/dry leaf, leaf bark/needles, pastilles (layered media - mscatt) • derive pcs from refl. /trans. spectra • OR sscatt albedo kernels - related to properties we require, physical constraints • 1 st basis function > 95% variance, 3 or more > 99% • these + 2 or 3 of Price’s soil terms can describe wide range of canopy behaviour
Examples of LOPEX spectra
‘Dry’ spectral kernels (scaled)
‘Dry’ spectral kernels (scaled) - continued. .
Effectiveness of fresh sscatt albedo kernels, reconstructing original , and albedo
• Results: • 3 -5 spectral kernels can describe > 99. 5% variance in data sets from which they are derived • visible cutoff at ~90% variance - RMSE rises rapidly after this • 90% threshold occurs at (reflectance) RMSE 0. 013 (refl. ) 0. 016 (trans) 0. 022 (sscatt albedo) • albedo kernels better at reconstructing + or fresh+dry, or albedo than or kernels alone • increase no. of kernels RMSE decreases
Effectiveness of sscatt albedo kernels in reconstructing leaf, leaf , albedo
• How to test/apply? • invert spectral kernels against multi-angular airborne data (ATM) • use params to recreate multi-spectral CASI data • can then apply spectral directional kernels simultaneously • require accurately co-registered data set, and good atmospheric correction (adapting MISR EOF algorithm) • If successful, spectral directional kernels could be powerful tool, partic. for albedo/NPP
- Slides: 39