Improved Accuracy of the Asymmetric Second-Order Vegetation Isoline Equation over the RED-NIR Reflectance Space.

Munenori Miura,Kenta Obata,Hiroki Yoshioka,Kenta Taniguchi

doi:10.3390/s17030450

Abstract

The relationship between two reflectances of different bands is often encountered in cross calibration and parameter retrievals from remotely-sensed data. The asymmetric-order vegetation isoline is one such relationship, derived previously, where truncation error was reduced from the first-order approximated isoline by including a second-order term. This study introduces a technique for optimizing the magnitude of the second-order term and further improving the isoline equation’s accuracy while maintaining the simplicity of the derived formulation. A single constant factor was introduced into the formulation to adjust the second-order term. This factor was optimized by simulating canopy radiative transfer. Numerical experiments revealed that the errors in the optimized asymmetric isoline were reduced in magnitude to nearly 1/25 of the errors obtained from the first-order vegetation isoline equation, and to nearly one-fifth of the error obtained from the non-optimized asymmetric isoline equation. The errors in the optimized asymmetric isoline were compared with the magnitudes of the signal-to-noise ratio (SNR) estimates reported for four specific sensors aboard four Earth observation satellites. These results indicated that the error in the asymmetric isoline could be reduced to the level of the SNR by adjusting a single factor.

Highlights

Estimation of biophysical parameters from remotely sensed reflectance requires calibration [1], inter-comparison of reflectance spectra [2] and derived data products [3]
The variables used in a series of numerical simulations were computed using the canopy radiative transfer code, PROSAIL [25], which consists of the leaf optical properties model (PROSPECT) [32] and the canopy reflectance model (SAIL) [33]
The results section focuses on the use of a Spherical model to represent the leaf angle distribution (LAD), with the exception of the simulations presented in Section 4.6, which employs five LAD models to examine the effects of the LAD on our simulations

Summary

Introduction

Estimation of biophysical parameters from remotely sensed reflectance requires calibration [1], inter-comparison of reflectance spectra [2] and derived data products [3]. Numerous investigations have reported the development and improvement of biophysical parameter retrieval algorithms, many of these algorithms involve simple algebraic band manipulations known as spectral vegetation indices (VIs) [6,7]. A key component of VI model development is the relationship between two reflectances of different bands obtained under fixed biophysical parameter conditions. This relationship produces a reflectance spectrum trajectory in a reflectance subspace attributed to a fixed biophysical parameter value; this relationship is known as a vegetation isoline. The simplest form of the vegetation isoline equation was derived by truncating the soil–canopy interaction terms at the first-order (single interaction) [26]. The first-order isoline equation may be written (with the truncation term e1 ) as:

Methods

Results

Conclusion