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

1,2, 1,*, 1,2, 3,4,5, 1 and 1,2
1
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China
4
Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Nanjing 210023, China
5
School of Geographical Sciences, Nanjing Normal University, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1303; https://doi.org/10.3390/rs11111303
Received: 21 April 2019 / Revised: 24 May 2019 / Accepted: 28 May 2019 / Published: 31 May 2019
(This article belongs to the Special Issue Terrestrial Carbon Cycle)
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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. View Full-Text
Keywords: Sentinel-2; red edge; canopy chlorophyll content; time-series data; photosynthesis; grassland; evergreen broadleaf forests Sentinel-2; red edge; canopy chlorophyll content; time-series data; photosynthesis; grassland; evergreen broadleaf forests
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Lin, S.; Li, J.; Liu, Q.; Li, L.; Zhao, J.; Yu, W. Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity. Remote Sens. 2019, 11, 1303.

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