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Comparative evaluation of inversion approaches of the radiative transfer model for estimation of crop biophysical parameters

Mridha, Nilimesh and Sahoo, Rabi N and Sehgal, Vinay K and Krishna, Gopal and Pargal, Sourabh and Pradhan, Sanatan and Gupta, Vinod K and Kumar, Dasika Nagesh (2015) Comparative evaluation of inversion approaches of the radiative transfer model for estimation of crop biophysical parameters. In: INTERNATIONAL AGROPHYSICS, 29 (2). pp. 201-212.

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Official URL: http://dx.doi.org/ 10.1515/intag-2015-0019


The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for para-meters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the DE GRUYTER OPEN LTD, BOGUMILA ZUGA 32A ST, 01-811 WARSAW, POLAND
Keywords: genetic algorithm; neural network; PROSAIL; leaf area index
Department/Centre: Division of Mechanical Sciences > Civil Engineering
Date Deposited: 15 Jun 2015 07:23
Last Modified: 15 Jun 2015 07:23
URI: http://eprints.iisc.ac.in/id/eprint/51656

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