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11 December 2006 Retrieval of plant biophysical parameters through inversion of PROSAIL model
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Directional reflectance measurement has been found to be better and more reliable compared to the conventional statistical approach to retrieve plant biophysical parameters as it takes care of its anisotropic nature. Keeping this in view, a field experiment was conducted with the objectives set as (i) to relate canopy biophysical parameters and geometry to its bidirectional reflectance, (ii) to evaluate a canopy reflectance model to best represent the radiative transfer within the canopy for its inversion and (iii) to retrieve crop biophysical parameters through inversion of the model. Two varieties of the mustard crop (Brassica juncea L) were grown with two nitrogen treatments to generate a wide range of Leaf Area Index (LAI) and biomass. The reflectance data obtained at 5nm interval for a range of 400- 1100nm were integrated to IRS LISS -II sensor's four band values using Newton Cotes Integration technique. Biophysical parameters were estimated synchronizing with the bi-directional reflectance measurements. The radiative transfer model PROSAIL was used for its evaluation and to retrieve biophysical parameters mainly LAI and Average Leaf Angle (ALA) through its inversion. Look Up Table (LUT) of BRDF was prepared simulating through PROSAIL model varying only LAI (0.2 interval from 1.2 to 5.4 ) and ALA (5° interval from 40 to 55°) parameters and inversion was done using a merit function and numerical optimization technique given by Press et al., 1986. The derived LAI and ALA values from inversion were well matched with observed one with RMSE 0.521 and 5.57, respectively.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rahul Tripathi, R. N. Sahoo, V. K. Sehgal, R. K. Tomar, S. Pandey, D. Chakraborty, and N. Kalra "Retrieval of plant biophysical parameters through inversion of PROSAIL model", Proc. SPIE 6411, Agriculture and Hydrology Applications of Remote Sensing, 64110F (11 December 2006);

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