Development of land surface reflectance models based on multiscale simulation

Adam A. Goodenough; Scott D. Brown

doi:10.1117/12.2177262

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21 May 2015 Development of land surface reflectance models based on multiscale simulation

Adam A. Goodenough, Scott D. Brown

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Proceedings Volume 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI; 94720D (2015) https://doi.org/10.1117/12.2177262
Event: SPIE Defense + Security, 2015, Baltimore, Maryland, United States

Abstract

Modeling and simulation of Earth imaging sensors with large spatial coverage necessitates an understanding of how photons interact with individual land surface processes at an aggregate level. For example, the leaf angle distribution of a deciduous forest canopy has a significant impact on the path of a single photon as it is scattered among the leaves and, consequently, a significant impact on the observed bidirectional reflectance distribution function (BRDF) of the canopy as a whole. In particular, simulation of imagery of heterogeneous scenes for many multispectral/hyperspectral applications requires detailed modeling of regions of the spectrum where many orders of scattering are required due to both high reflectance and transmittance. Radiative transfer modeling based on ray tracing, hybrid Monte Carlo techniques and detailed geometric and optical models of land cover means that it is possible to build effective, aggregate optical models with parameters such as species, spatial distribution, and underlying terrain variation. This paper examines the capability of the Digital Image and Remote Sensing Image Generation (DIRSIG) model to generate BRDF data representing land surfaces at large scale from modeling at a much smaller scale. We describe robust methods for generating optical property models effectively in DIRSIG and present new tools for facilitating the process. The methods and results for forest canopies are described relative to the RAdiation transfer Model Intercomparison (RAMI) benchmark scenes, which also forms the basis for an evaluation of the approach. Additional applications and examples are presented, representing different types of land cover.

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Adam A. Goodenough and Scott D. Brown "Development of land surface reflectance models based on multiscale simulation", Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, 94720D (21 May 2015); https://doi.org/10.1117/12.2177262

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