Evaluating the effectiveness of using vegetation indices based on red-edge reflectance from Sentinel-2 to estimate gross primary productivity

65Citations
Citations of this article
177Readers
Mendeley users who have this article in their library.

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 improveGPP 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, ρ783 / ρ705 - 1) multiplied by PARin and GPPEC had the highest correlation (R2 = 0.77, root-mean-square error (RMSE) = 0.81 gC·m-1·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.

Cite

CITATION STYLE

APA

Lin, S., Li, J., Liu, Q., Li, L., Zhao, J., & Yu, W. (2019). Evaluating the effectiveness of using vegetation indices based on red-edge reflectance from Sentinel-2 to estimate gross primary productivity. Remote Sensing, 11(11). https://doi.org/10.3390/rs11111303

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free