Vegetation isoline equations with first- and second-order interaction terms for modeling a canopy-soil system of layers in the red and near-infrared reflectance space

Munenori Miura,Hiroki Yoshioka,Kenta Obata

doi:10.1117/1.jrs.9.095987

Abstract

A previously proposed vegetation isoline equation suffers from errors if the soil background of a canopy layer is bright. These errors arise from the truncation of the second- and higher-order terms that represent photon interactions between the canopy and the soil. An isoline equation that includes a second-order interaction term is introduced. The equation was initially derived by explicitly including a second-order interaction term in both the red and near-infrared (NIR) reflectance spectra (symmetric approximation). We also examined an alternative model in which the interaction term was included only in the NIR band (asymmetric approximation). In this model, the derived isolines tend to shift upward (overcorrection effects). Numerical experiments revealed that the errors in the isoline obtained by the asymmetric approximation were reduced in magnitude to nearly one-fifth of the errors in the previously proposed method. Its accuracy was higher than that of the symmetric approximation, despite the fact that the asymmetric approximation included fewer terms than the symmetric approximation. The improved model accuracy resulted from the overcorrection effects, which compensated for the truncation error. With the simplicity and improved accuracy, the current isoline equations provide a good alternative to the previous approach.

Highlights

Biophysical parameter retrieval from remotely sensed reflectance spectra is a fundamental goal in the field of land remote sensing
Qin et al.[1] categorized the available retrieval algorithms into four groups based on the approaches taken: (1) techniques that relied on a spectral vegetation index (VI) and its correlation with biophysical parameters, such as the leaf area index (LAI);[2,3,4,5,6,7] (2) algorithms that used lookup tables;[8,9] (3) neural networks;[10,11,12] and (4) direct inversions of numerical models [e.g., models of radiative transfer (RT)] using optimization methods.[1,9,13,14,15]
This study describes the formal steps, which used to derive the improved version of the vegetation isoline equation and validate its accuracy by conducting numerical experiments based on a coupled leaf and canopy RT model, PROSAIL.[39]

Summary

Introduction

Biophysical parameter retrieval from remotely sensed reflectance spectra is a fundamental goal in the field of land remote sensing. Qin et al.[1] categorized the available retrieval algorithms into four groups based on the approaches taken: (1) techniques that relied on a spectral vegetation index (VI) and its correlation with biophysical parameters, such as the leaf area index (LAI);[2,3,4,5,6,7] (2) algorithms that used lookup tables;[8,9] (3) neural networks;[10,11,12] and (4) direct inversions of numerical models [e.g., models of radiative transfer (RT)] using optimization methods.[1,9,13,14,15] These approaches present advantages and disadvantages over other approaches in terms of accuracy, computational costs, complexity, and applicability. The isoline concept has been used as an analytical tool for investigating the influence of the soil on the retrieved parameters.[22,23,24,25,26,27,28] From this standpoint, vegetation isoline equations provide a model for Journal of Applied Remote Sensing

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Conclusion