Share Email Print
cover

Proceedings Paper

A radiosity-based model to compute the radiation transfer of soil surface
Author(s): Feng Zhao; Yuguang Li
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

A good understanding of interactions of electromagnetic radiation with soil surface is important for a further improvement of remote sensing methods. In this paper, a radiosity-based analytical model for soil Directional Reflectance Factor's (DRF) distributions was developed and evaluated. The model was specifically dedicated to the study of radiation transfer for the soil surface under tillage practices. The soil was abstracted as two dimensional U-shaped or V-shaped geometric structures with periodic macroscopic variations. The roughness of the simulated surfaces was expressed as a ratio of the height to the width for the U and V-shaped structures. The assumption was made that the shadowing of soil surface, simulated by U or V-shaped grooves, has a greater influence on the soil reflectance distribution than the scattering properties of basic soil particles of silt and clay. Another assumption was that the soil is a perfectly diffuse reflector at a microscopic level, which is a prerequisite for the application of the radiosity method. This radiosity-based analytical model was evaluated by a forward Monte Carlo ray-tracing model under the same structural scenes and identical spectral parameters. The statistics of these two models' BRF fitting results for several soil structures under the same conditions showed the good agreements. By using the model, the physical mechanism of the soil bidirectional reflectance pattern was revealed.

Paper Details

Date Published: 7 October 2011
PDF: 7 pages
Proc. SPIE 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 81742G (7 October 2011); doi: 10.1117/12.898131
Show Author Affiliations
Feng Zhao, Beijing Univ. of Aeronautics and Astronautics (China)
Yuguang Li, Beijing Univ. of Aeronautics and Astronautics (China)


Published in SPIE Proceedings Vol. 8174:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

© SPIE. Terms of Use
Back to Top