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

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.

Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations

“…However, these parameters may affect the CCC estimation models for different crops based on spectrum simulations. Previous studies have demonstrated that C w has little influence on the visible and red-edge bands commonly used for vegetation chlorophyll estimation [67], while N and C m can affect chlorophyll estimation [27,68,69]. The leaf parameters, such as N, should be adjusted according to the specific crop type if the proposed VI combination approach is applied to other crops.…”

Plant Traits Help Explain the Tight Relationship between Vegetation Indices and Gross Primary Production

“…The analysis on PROSAIL was run using the Global Sensitivity Analysis module v.1.08 of the ARTMO toolbox v.3.25 [54]. Previous sensitivity analysis on VIs have emphasized crop species, and parameter ranges has been either tailored for such ecosystems or arbitrarily selected [20,24]. The results of global sensitivity analyses depend heavily on the range of inputs specified, and we selected ranges that represent the variation across our study sites or from literature datasets that covered a diverse array of species and biomes (see Appendix A for details).…”

“…EVI and NIR v share NDVI's sensitivity to LAI, while adding sensitivity to differences in reflectance in the NIR [6,12,20,23]. Prior GSA studies have produced ambiguous relationships between plant traits, EVI and NIR v , suggesting a strong sensitivity to either mean canopy leaf angle or to chlorophyll and leaf mass per area [20,24]. NIR reflectance is related to scattering at air-water interfaces within leaves [25], and studies have shown that NIR reflectance is correlated with LAI and a suite of leaf traits including leaf mass per area, nitrogen content, water content, and canopy traits such as mean leaf inclination angle and canopy shape [26][27][28][29][30].…”

Hyperspectral response of agronomic variables to background optical variability: Results of a numerical experiment

“…Moreover, previous applications of LSA rarely included the spectral response in the shortwave infrared (SWIR) bands that are configured in popular satellite sensors (e.g., Landsat-8 OLI, Sentinel-2 MSI) and commonly used for LAI retrievals (Amin et al, 2021;Dong et al, 2020). By contrast, GSA quantifies simultaneously the contribution of various model input parameters to the reflected electromagnetic radiation (Gu et al, 2016;Mousivand et al, 2014;Wang et al, 2019;Xiao et al, 2014), and is therefore typically preferable over LSA. In previous studies, however, GSA has often ignored the sometimes high spatiotemporal variability in the background optical properties.…”