Quantifying the Robustness of Vegetation Indices through Global Sensitivity Analysis of Homogeneous and Forest Leaf-Canopy Radiative Transfer Models

Abstract

Vegetation indices (VIs) are widely used in optical remote sensing to estimate biophysical variables of vegetated surfaces. With the advent of spectroscopy technology, spectral bands can be combined in numerous ways to extract the desired information. This resulted in a plethora of proposed indices, designed for a diversity of applications and research purposes. However, it is not always clear whether they are sensitive to the variable of interest while at the same time, responding insensitive to confounding factors. Hence, to be able to quantify the robustness of VIs, a systematic evaluation is needed, thereby introducing a widest possible variety of biochemical and structural heterogeneity. Such exercise can be achieved with coupled leaf and canopy radiative transfer models (RTMs), whereby input variables can virtually simulate any vegetation scenario. With the intention of evaluating multiple VIs in an efficient way, this led us to the development of a global sensitivity analysis (GSA) toolbox dedicated to the analysis of VIs on their sensitivity towards RTM input variables. We identified VIs that are designed to be sensitive towards leaf chlorophyll content (LCC), leaf water content (LWC) and leaf area index (LAI) for common sensors of terrestrial Earth observation satellites: Landsat 8, MODIS, Sentinel-2, Sentinel-3 and the upcoming imaging spectrometer mission EnMAP. The coupled RTMs PROSAIL and PROINFORM were used for simulations of homogeneous and forest canopies respectively. GSA total sensitivity results suggest that LCC-sensitive indices respond most robust: for the great majority of scenarios, chlorophyll a + b content (Cab) drives between 75% and 82% of the indices’ variability. LWC-sensitive indices were most affected by confounding variables such as Cab and LAI, although the equivalent water thickness (Cw) can drive between 25% and 50% of the indices’ variability. Conversely, the majority of LAI-sensitive indices are not only sensitive to LAI but rather to a mixture of structural and biochemical variables.

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

“…Different combinations of leaf constituents, canopy structure, soil reflectivity, and sun-sensor geometry are given by a pre-defined look-up table (LUT) as Table 1. In general, Table 1 is adapted from previously used LUTs [3,5,6,10], but small Cab values (0-10 ug/cm 2 ) are excluded in order to avoid potential biased sensitivity resulting from unrealistic scenarios. Because the size of the LUT grows dramatically with increasing LUT variables, we use the full random sampling method provided by ARTMO GUI, which samples the LUT uniformly to generate a random subset with each variable ranging within given boundaries.…”

“…In this study, the range of Cab was set from 10 to 80 ug/cm 2 on the basis of the fact that crop leaves have relatively high chlorophyll concentration. Therefore, the sensitivity of VIs to Cab is relatively weak compared to that in a recently published paper [6]. The boundaries of input parameters and the corresponding influences on GSA results should be further studied in future works.…”

“…Recently, a comprehensive global sensitivity analysis (GSA) for Cab-, LAI-and Cw-sensitive 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]. 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. For example, for agricultural drought monitoring, an ideal VI could be related to both vegetation greenness and canopy water content (CWC) [7].…”

“…Typically, GSA can be based on RTM simulations and a variance-based method [3,6,10,11], but some limitations exist for a variance-based GSA for VIs. A variance-based GSA uses certain mathematical techniques to decompose the variance of the output to different inputs [11,12].…”

“…Appropriate radiative transfer models for forest canopies exist, such as DART [39] and INFORM [40], but are not studied in our research, which focuses on homogeneous canopies. For example, a recent paper [6] shows large differences between the sensitivity of PROSAIL (for agricultural crops) and that of INFORM (for forests) to various canopy traits, demonstrating that our study needs to be extended to also cover those vegetation types. This seems even more important when considering the relatively large footprint of sensors such as OLCI and MODIS, as those sensors usually mix the signal stemming from several cover types.…”