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.

Current observations show that the growth of the atmospheric CO 2 concentration is evidently lower than expected, suggesting that anthropogenic carbon emissions have been offset by some unclear carbon sinks. Oceans and terrestrial ecosystems are supposed to be responsible for the missing carbon sinks (Sabine et al., 2004;Takahashi et al., 2009). Scientists have utilized varieties of means to explore carbon fluxes of terrestrial ecosystems quantitatively in the recent decades, trying to narrow gaps between observations and estimates of models (Eldering et al., 2017;Watson et al., 2009). Comparing with progresses in estimating carbon fluxes of terrestrial ecosystems, we still have some barriers to a better understanding on carbon fluxes of oceans. We do know oceans offset anthropogenic carbon emissions but there are lacks of quantitative and accurate estimates of CO 2 fluxes over oceans. Therefore, we don't have insight knowledge on distributions and dynamics of ocean carbon uptakes. Some pieces of evidence suggest that there may be a trigger point beyond which CO 2 uptakes of oceans would rapidly decline or even shift to a net CO 2 emission (DeVries et al., 2019). Therefore, designing more measurement methods to estimate ocean CO 2 fluxes more reliably and efficiently is of great importance.The difference of CO 2 partial pressures in seawater (p(CO 2 ) sw ) and overlying air (p(CO 2 ) air ) would cause a net transfer of CO 2 flux between ocean and atmosphere (Wanninkhof, 2014). Experiments for measuring (p(CO 2 ) sw ) during ship tracks have been implemented in different regions of the global sea, which help to

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