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.
Highlights
Gross primary productivity (GPP) products were developed based on moderate-resolution imaging spectroradiometer (MODIS) data at spatial resolutions of 500 to 1000 m, such as MODIS GPP (MOD17 [2,3]), the breathing earth system simulator (BESS [4]), the vegetation photosynthesis model (VPM [5,6]), and the eddy covariance light-use efficiency (EC-LUE [7]) model, which explained up to 70% of the eight-day average GPP variance when correlated with FLUXNET 2015 carbon flux datasets at the site level [8]
This study evaluated how much GPP variation can be explained by spatio-temporally continuous Sentinel-2 satellite-based red-edge data in grassland and evergreen forests
We found that GPP based on CIr × photosynthetic active radiation (PARin) had the highest correlation with GPP from eddy covariance (GPPEC) across grassland sites, and it showed low bias with GPPEC at each forest site
Summary
GPP products were developed based on moderate-resolution imaging spectroradiometer (MODIS) data at spatial resolutions of 500 to 1000 m, such as MODIS GPP (MOD17 [2,3]), the breathing earth system simulator (BESS [4]), the vegetation photosynthesis model (VPM [5,6]), and the eddy covariance light-use efficiency (EC-LUE [7]) model, which explained up to 70% of the eight-day average GPP variance when correlated with FLUXNET 2015 carbon flux datasets at the site level [8]. Because red-edge reflectance strongly corresponds to vegetation chlorophyll content, researchers used it to construct VIs to estimate canopy chlorophyll contents and improve GPP estimations [39,40]. Previous studies used vegetation red-edge reflectance VIs based on remote sensing as proxy information to improve GPP estimation. Some authors built the red-edge chlorophyll index (CI red edge, CIr) and green chlorophyll index (CI green, CIg) [39,43], and correlated these indices with canopy chlorophyll content and linked them to GPP at cropland sites. The VIs derived from high-spatial-resolution remote-sensing reflectance can reduce carbon flux estimation uncertainties because of spatial heterogeneity [50,51]. Sentinel-2-based vegetation red-edge-based VIs are a useful dataset for high-spatial-resolution mid-term GPP estimation
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