A unified vegetation index for quantifying the terrestrial biosphere

Abstract

Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO2 and mitigating global climate change.

Aboveground Biomass Estimation in Short Rotation Forest Plantations in Northern Greece Using ESA’s Sentinel Medium-High Resolution Multispectral and Radar Imaging Missions

“…NDVI shows stronger correlations to AGB in July and at the beginning of the fall season in September. This weaker relationship in the peak season can be expected due to the saturation effect of dense canopies [55] and the non-linear relationship between NDVI and green biomass [59]. Similar to NDVI, MCARI and GNDVI (including information from the green part of the spectrum) were not among the top-ranked indices in terms of correlation with AGB.…”

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

“…The correlations of kNDVI were higher in nearly all cases (e.g., Spearman correlation, distance correlation), thus confirming the advantage of kNDVI over other indices. Using the kNDVI index in future studies on vegetation will increase the significance of geomonitoring and terrestrial biosphere studies [85].…”

Land Degradation and Development Processes and Their Response to Climate Change and Human Activity in China from 1982 to 2015

“…NDVI reflects the vegetation conditions of terrestrial ecosystems based on the normalized difference between red and near-infrared red (NIR) radiation of healthy vegetation. However, there are two main flaws when using NDVI to represent the biomass and production of terrestrial ecosystems [8]. First, the relationships between NDVI, biomass and productivity are not linear, and may become saturated in high vegetation cover areas.…”

“…In addition, NDVI reflects the greenness instead of the photosynthesis of vegetation. This means that NDVI may not be very sensitive to a substantial increase or decline in vegetation, and therefore does not necessarily reflect the actual biomass and productivity dynamics in particular regions [8]. These issues result in the uncertainty of NDVI in evaluating the dynamics of LDD.…”

“…To improve the reliability of NDVI in monitoring LDD processes, some studies have attempted to construct a new index by integrating NDVI with transformation parameters. For instance, multiplying NDVI by NIR, Badgley et al [9] proposed the NIRv and Camps-Valls et al [8] built a kernel NDVI (kNDVI) using the theory of kernel methods based on machine learning. Both new indices showed higher correlations with gross primary productivity (GPP) and sun-induced chlorophyll fluorescence (SIF) than the original NDVI at specific temporal scales [8,9].…”