Fitting remote sensing data with linear bidirectional reflectance models

Jeffrey L. Privette; Eric F. Vermote

doi:10.1117/12.228620

18 December 1995 Fitting remote sensing data with linear bidirectional reflectance models

Jeffrey L. Privette, Eric F. Vermote

Proceedings Volume 2586, Global Process Monitoring and Remote Sensing of the Ocean and Sea Ice; (1995) https://doi.org/10.1117/12.228620
Event: Satellite Remote Sensing II, 1995, Paris, France

Abstract

Kernel-driven linear bidirectional reflectance models are gaining increasing attention for their potential use in operational processing of global remote sensing data. Nevertheless, the ability of these models to simulate actual reflectance anisotropy has not been completely explored with remote sensing data. To assess the suitability of linear models for the MODIS atmospheric correction system, we inverted a series of models with AVHRR and MODIS airborne simulator (MAS) data. For comparison, we also fit 2-stream turbid medium models to the respective data sets. Although the more complex models produced more accurate fits, the linear models were acceptably accurate and considerably faster. We conclude that linear models perform with sufficient speed and accuracy for atmospheric correction algorithms.

Citation Download Citation

Jeffrey L. Privette and Eric F. Vermote "Fitting remote sensing data with linear bidirectional reflectance models", Proc. SPIE 2586, Global Process Monitoring and Remote Sensing of the Ocean and Sea Ice, (18 December 1995); https://doi.org/10.1117/12.228620

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