Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity

Abstract

Gross primary productivity (GPP) is the most important component of terrestrial carbon flux. Red-edge (680–780 nm) reflectance is sensitive to leaf chlorophyll content, which is directly correlated with photosynthesis as the pigment pool, and it has the potential to improve GPP estimation. The European Space Agency (ESA) Sentinel-2A and B satellites provide red-edge bands at 20-m spatial resolution on a five-day revisit period, which can be used for global estimation of GPP. Previous studies focused mostly on improving cropland GPP estimation using red-edge bands. In this study, we firstly evaluated the relationship between eight vegetation indices (VIs) retrieved from Sentinel-2 imagery in association with incident photosynthetic active radiation (PARin) and carbon flux tower GPP (GPPEC) across three forest and two grassland sites in Australia. We derived a time series of five red-edge VIs and three non-red-edge VIs over the CO2 flux tower footprints at 16-day time intervals and compared both temporal and spatial variations. The results showed that the relationship between the red-edge index (CIr, ρ 783 ρ 705 − 1 ) multiplied by PARin and GPPEC had the highest correlation (R2 = 0.77, root-mean-square error (RMSE) = 0.81 gC∙m−2∙day−1) at the two grassland sites. The CIr also showed consistency (rRMSE defined as RMSE/mean GPP, lower than 0.25) across forest and grassland sites. The high spatial resolution of the Sentinel-2 data provided more detailed information to adequately characterize the GPP variance at spatially heterogeneous areas. The high revisit period of Sentinel-2 exhibited temporal variance in GPP at the grassland sites; however, at forest sites, the flux-tower-based GPP variance could not be fully tracked by the limited satellite images. These results suggest that the high-spatial-resolution red-edge index from Sentinel-2 can improve large-scale spatio-temporal GPP assessments.

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

“…Based on the spectral bands and vegetation indices extracted from Sentinel-2 data, this study has shown that B5 (Red-Edge 1) was the most important variable when estimating the FSV using both the machine learning methods and MLR method, which had been confirmed in recent studies concerning forest prediction [102] and tree species classification [103]. In the gross primary productivity field (GPP), Lin et al found that the red-edge band was useful for estimating the GPP, and noted that the red-edge reflectance was sensitive to the leaf chlorophyll content [104]. In addition, the leaf chlorophyll content was an important forest variable.…”

Status of Phenological Research Using Sentinel-2 Data: A Review

“…The red-edge information from the MERIS based MTCI (operational from 2002-2012) was reported to have a robust relationship with gross primary productivity (GPP), but the data were limited by the coarse spatial resolutions of 300 m [131]. Lin et al (2019) [33] also highlighted the suitability of red-edge-based vegetation indices from Sentinel-2 over the conventional non-red-edge indices (i.e., NDVI, EVI and the NIR reflectance of terrestrial vegetation (NIR v )). In this study chlorophyll red-edge and chlorophyll green indices, both based on red-edge bands, showed high correspondence (coefficient of determination ranging from 0.69 to 0.89) with in situ estimates of grassland GPP.…”

“…Apart from using the original reflectance values, information from more than one spectral band is often combined to enhance the vegetation properties within a satellite pixel. A list of frequently cited vegetation indices and their algorithms can be found in [32][33][34]. LSP metrics are typically estimated as the day of the year corresponding to the start of season (SOS), end of season (EOS) and peak of season (POS), when particular values of thresholds (or rates of change) on the ascending and descending portions, and the peak values on the reflectance or vegetation index (VI) time series are respectively reached [30,35].…”

Estimation of Corn Leaf Area Index and Ground Cover With Vegetation Indices as a Result of Irrigation Dose

“…The CIg (Green Chlorophyll Index) was used to estimate the leaf chlorophyll content (Lin et al, 2019). Because the leaf chlorophyll content is directly correlated with the plant leaf area, this VI was used to estimate the LAI too.…”