Abstract
Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPPEC). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R2 = 0.13–0.88, p < 0.001; OCO-2 SIF: R2 = 0.11–0.99, p < 0.001; MODIS GPP: R2 = 0.15–0.79, p < 0.001). MODIS GPP demonstrates a high correlation with GPPEC in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gCm−2day−1 for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPPEC for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP.
Highlights
Gross primary production (GPP) is the total amount of carbon fixed by vegetation through photosynthesis, contributing the largest global carbon flux and driving ecosystem functions [1,2]
We evaluated the performance of the three remotely sensed products MYD17A2H, GOSIF, and orbiting carbon observatory-2 (OCO-2) sun-induced chlorophyll fluorescence (SIF) when estimating GPP
We modeled and compared remotely sensed product-derived GPP estimates with GPPEC data from 23 flux towers and eight vegetation types
Summary
Gross primary production (GPP) is the total amount of carbon fixed by vegetation through photosynthesis, contributing the largest global carbon flux and driving ecosystem functions [1,2]. Many studies have investigated performances of estimating GPP using SIF products derived from greenhouse gases observing satellite (GOSAT) and global ozone monitoring experiment-2 (GOME-2) [27]. The footprint of OCO-2 is very sparse and cannot match the flux tower well, which generates huge challenges in estimating the global GPP using OCO-2 SIF [19,23]. To solve this problem, Li and Xiao have developed a new SIF product based on OCO-2 SIF and meteorological data, named ‘GOSIF’, which has a time resolution of eight days and a spatial resolution of 0.05◦ [18]
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