Publications

Zhang, FJ; Zhang, ZJ; Long, Y; Zhang, L (2021). Integration of Sentinel-3 OLCI Land Products and MERRA2 Meteorology Data into Light Use Efficiency and Vegetation Index-Driven Models for Modeling Gross Primary Production. REMOTE SENSING, 13(5), 1015.

Abstract
Accurately and reliably estimating total terrestrial gross primary production (GPP) on a large scale is of great significance for monitoring the carbon cycle process. The Sentinel-3 satellite provides the OLCI FAPAR and OTCI products, which possess a higher spatial and temporal resolution than MODIS products. However, few studies have focused on using LUE models and VI-driven models based on the Sentinel-3 satellites to estimate GPP on a large scale. The purpose of this study is to evaluate the performance of Sentinel-3 OLCI FAPAR and OTCI products combined with meteorology reanalysis data in estimating GPP at site and regional scale. Firstly, we integrated OLCI FAPAR and meteorology reanalysis data into the MODIS GPP algorithm and eddy covariance light use efficiency (EC-LUE) model (GPP(MODIS-GPP) and GPP(EC-LUE), respectively). Then, we combined OTCI and meteorology reanalysis data with the greenness and radiation (GR) model and vegetation index (VI) model (GPP(GR) and GPP(VI), respectively). Lastly, GPP(MODIS-GPP), GPP(EC-LUE), GPP(GR), and GPP(VI) were evaluated against the eddy covariance flux data (GPP(EC)) at the site scale and MODIS GPP products (GPP(MOD17)) at the regional scale. The results showed that, at the site scale, GPP(MODIS-GPP) and GPP(EC-LUE) agreed well with GPP(EC) for the US-Ton site, with R-2 = 0.73 and 0.74, respectively. The performance of GPP(GR) and GPP(VI) varied across different biome types. Strong correlations were obtained across deciduous broadleaf forests, mixed forests, grasslands, and croplands. At the same time, there are overestimations and underestimations in croplands, evergreen needleleaf forests and deciduous broadleaf forests. At the regional scale, the annual mean and maximum daily GPP(MODIS-GPP) and GPP(EC-LUE) agreed well with GPP(MOD17) in 2017 and 2018, with R-2 > 0.75. Overall, the above findings demonstrate the feasibility of using Sentinel-3 OLCI FAPAR and OTCI products combined with meteorology reanalysis data through LUE and VI-driven models to estimate GPP, and fill in the gaps for the large-scale evaluation of GPP via Sentinel-3 satellites.

DOI:
10.3390/rs13051015

ISSN: