A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning

Siheng Wang,Zhen Li,Liangyun Liu,Lifu Zhang,Changping Huang,Dong Yang

doi:10.3390/rs11212547

Abstract

Remote sensing (RS) provides operational monitoring of terrestrial vegetation. For optical RS, vegetation information is generally derived from surface reflectance (ρ). More generally, vegetation indices (VIs) are built on the basis of ρ as proxies for vegetation traits. At canopy level, ρ can be affected by a variety of factors, including leaf constituents, canopy structure, background reflectivity, and sun-sensor geometry. Consequently, VIs are mixtures of different information. In this study, a global sensitivity analysis (GSA) is made for several commonly used satellite-derived VIs in order to better understand the application of these VIs at large scales. The sensitivities of VIs to different parameters are analyzed on the basis of PROSPECT-SAIL (PROSAIL) radiative transfer model simulations, which apply for homogeneous canopies, and random forest (RF) learning. Specifically, combined factors such as canopy chlorophyll content (CCC) and canopy water content (CWC) are introduced in the RF-based GSA. We find that for most VIs, the leaf area index is the most influential factor, while the broad-band sensor-derived enhanced VI (EVI) exhibits a strong sensitivity to CCC, and the universal normalized VI (UNVI) is sensitive to CWC. The potential and uncertainty for the application of all the considered VIs are analyzed according to the GSA results. The results can help to improve the use of VIs in different contexts, and the RF-based GSA method can be further applied in more sophisticated situations.

Highlights

Satellite remote sensing (RS) uniquely provides dynamic monitoring of terrestrial vegetation at a variety of spatial-temporal scales
A comprehensive global sensitivity analysis (GSA) for Cab, Leaf are index (LAI)- and Cw-sensitive vegetation indices (VIs) has been made on the basis of radiative transfer model (RTM) simulations to evaluate the robustness of VIs with respect to corresponding sensitive parameters [6]
Because the size of the look-up table (LUT) grows dramatically with increasing LUT variables, we use the full random sampling method provided by automated radiative transfer models operator (ARTMO) graphic user interface (GUI), which samples the LUT uniformly to generate a random subset with each variable ranging within given boundaries

Summary

Introduction

Satellite remote sensing (RS) uniquely provides dynamic monitoring of terrestrial vegetation at a variety of spatial-temporal scales. The applications of VIs can be roughly categorized into two classes: i) estimating bio-physical parameters and ii) being integrated into ecological models as proxies for vegetation traits. While results in [6] provide a good guidance for applying VIs in parameter retrieval, for the second class of applications, i.e., using VIs to represent vegetation traits at large scales, the sensitivity of VIs to a single parameter is not always concerned, but rather VIs are sometimes expected to be sensitive to combined parameters. A GSA of VIs considering combined parameters such as CWC and CCC that have clear physical meaning is of primary importance for understanding the potential and uncertainty of applying VIs at large scales

Objectives

Methods

Results