Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2010, Vol. 7, No. 4, pp. 42-50
Using multyparticle Kalman filter and PROSAIL vegetation model
for remote hyperspectral sensing data processing
B.M. Balter
1, V.V. Egorov
1, A.P. Kalinin
2, I.P. Rodionova
3, M.V. Stalnaya
11 Space Research Institute PAS, 117997 Moscow, 84/32 Profsoyuznaya str
2 A. Ishlinsky Institite for Problems in Mechanics RAS, 119526 Moscow, 101 Vernadskogo prosp
3 N. Semenov Institute of Chemical Physics RAS, 119991 Moscow, 4 Kosigina str
Using multyparticle Kalman filter for procedure of parameterization and adaptation PROSAIL vegetation model for
remote hyperspectral sensing data processing of cereal crops seeds and estimation of leaf area index (LAI) is investigated.
PROSAIL model which allows to calculate emitted seeds radiation in 0.4-2.5 μ range is function of 18 parameters
(dimensions, leaf orientation, soil structure, intensity of illumination and so on). It was shown that multyparticle
Kalman filter can do without that function derivatives (to operate in nonlinear vector mode). In such computation
up to 4 fitting model parameters are used. As the results of such computations averaged prediction accuracy
of cereal crops seeds spectra amounts to 2% and absolute LAI precision estimation is equal approximately 0.2.
Keywords: multyparticle Kalman filter, hyperspectrometer, remote sensing, cereal crops seeds, scattering, resolution, leaf area index
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