A Broadband Green-Red Vegetation Index for Monitoring Gross Primary Production Phenology

Abstract

The chlorophyll/carotenoid index (CCI) is increasingly used for remotely tracking the phenology of photosynthesis. However, CCI is restricted to few satellites incorporating the 531 nm band. This study reveals that the Moderate Resolution Imaging Spectroradiometer (MODIS) broadband green reflectance (band 4) is significantly correlated with this xanthophyll-sensitive narrowband (band 11) (R2=0.98,p<0.001), and consequently, the broadband green-red vegetation index GRVI—computed with MODIS band 1 and band 4—is significantly correlated with CCI—computed with MODIS band 1 and band 11 (R2=0.97,p<0.001). GRVI and CCI performed similarly in extracting phenological metrics of the dates of the start and end of the season (EOS) when evaluated with gross primary production (GPP) measurements from eddy covariance towers. For EOS extraction of evergreen needleleaf forest, GRVI even overperformed solar-induced chlorophyll fluorescence which is seen as a direct proxy of plant photosynthesis. This study opens the door for GPP and photosynthetic phenology monitoring from a wide set of sensors with broadbands in the green and red spectral regions.

Combination of Vegetation Indices and SIF Can Better Track Phenological Metrics and Gross Primary Production

“…For example, Yin et al. (2022) showed NDVI can better track SOS than SIF for 21 grassland ecosystems, and GRVI can better track EOS than SIF for 37 evergreen needleleaf forest (ENF) ecosystems, using VIs from MCD19A1 data set (daily and 1 km spatiotemporal resolution) and SIF from GOSIF data set (8‐day and 0.05° spatiotemporal resolution). Similarly, J. Zhang et al.…”

“…Recent studies demonstrated that the green‐red vegetation index (GRVI) is suitable for capturing phenological variations (Yin et al., 2020, 2022). Unlike other VIs that only characterize vegetation structure, GRVI has a high correlation with leaf pigment pools (Yin et al., 2022), which is crucial in determining the maximum leaf photosynthetic capacity and consequently has an impact on photosynthesis (Nagai et al., 2012, 2014). Finally, solar‐induced chlorophyll fluorescence (SIF), as a by‐product of photosynthesis, is the re‐emission of a fraction of solar radiation absorbed by chlorophyll molecules (usually around 1%) and has recently been detected by remote sensing technology (Guanter et al., 2021; Joiner et al., 2013; Y. Zhang, Joiner, Alemohammad, Zhou, & Gentine, 2018).…”

“…However, the presently available SIF data sets exhibit low temporal frequency and spatial resolution (A. Chen et al., 2022; Mohammed et al., 2019), thereby constraining their practical utility in tracking phenology. Several studies have demonstrated that SIF does not always perform better than VIs compared with in situ GPP‐derived phenology at the site level (Yin et al., 2022; Y. Zhang et al., 2020). For example, Yin et al.…”

“…Chen et al, 2022;Mohammed et al, 2019), thereby constraining their practical utility in tracking phenology. Several studies have demonstrated that SIF does not always perform better than VIs compared with in situ GPP-derived phenology at the site level (Yin et al, 2022;. For example, Yin et al (2022) showed NDVI can better track SOS than SIF for 21 grassland ecosystems, and GRVI can better track EOS than SIF for 37 evergreen needleleaf forest (ENF) ecosystems, using VIs from MCD19A1 data set (daily and 1 km spatiotemporal resolution) and SIF from GOSIF data set (8-day and 0.05° spatiotemporal resolution).…”

“…Based on NDVI, EVI introduces a blue band to reduce the atmospheric impact (Huete et al, 2002), and NIRv introduces a near-infrared band to reduce the impact of soil background (Badgley et al, 2017(Badgley et al, , 2019. Recent studies demonstrated that the green-red vegetation index (GRVI) is suitable for capturing phenological variations (Yin et al, 2020(Yin et al, , 2022. Unlike other VIs that only characterize vegetation structure, GRVI has a high correlation with leaf pigment pools (Yin et al, 2022), which is crucial in determining the maximum leaf photosynthetic capacity and consequently has an impact on photosynthesis (Nagai et al, 2012(Nagai et al, , 2014.…”