###### Abstract
Quantifying the atmospheric CO\({}_{2}\) flux is necessary to determine the carbon sequestration potential of an ecosystem. Several CO\({}_{2}\) monitoring approaches have been used to quantify carbon sequestration in an ecosystem such as biomass and soil carbon inventory studies using different sensing methods, for example, in-situ CO\({}_{2}\) flux monitoring using eddy covariance flux tower, remote measurements through geostationary satellites, and ecosystem models [30]. However, in-situ CO\({}_{2}\) measurements are limited and only available at a point scale, which cannot be used to study spatiotemporal distributions of CO\({}_{2}\) at a larger scale [31; 32; 33]. Knowledge of quantifying the dynamic state of the terrestrial carbon cycle is important to understand the ecosystem response under a changing climate. However, it is difficult to measure and remains poorly quantified at a global scale [34]. An understanding of the relationship between regional carbon fluxes in the forms of gross primary productivity (GPP) and net ecosystem exchange (NEE) with environmental changes is necessary to understand the response of ecosystems to global climate changes [35; 36; 37; 38]. GPP, a critical parameter for carbon cycle and climate change, is the total amount of carbon fixed by plants through photosynthesis in an ecosystem [39; 40; 41; 42]. Accurate measurement or estimate of GPP is essential for the carbon cycle and climate change studies [43; 44]. Net Ecosystem Exchange between the ecosystem and atmosphere represents the balance between GPP and ecosystem respiration and plays a critical role in climate change science [45; 46]. NEE of the ecosystem is a measure of the balance between carbon uptake by vegetation GPP and carbon losses through total ecosystem respiration rate (Re), and loss due to disturbance [47]. The Re is the sum of plant autotrophic respiration (Ra) and soil heterotrophic respiration (Rh). Gross primary productivity and NEE of ecosystems can also be quantified using process-based biogeochemical models [48] and satellite remote sensing [49]. For example, [PERSON] et al. (2018) [50] used four Terrestrial Biosphere Models (TBMs): (i) Carnegie-Ames Stanford Approach (CASA), (ii) Simple Biosphere model version 3 (SiB3), (iii) Canadian Terrestrial Ecosystem Model (CTEM), and (iv) Joint UK Land Environment Simulator (JULES) to examine GPP, NEE, and Re. On the other hand, [PERSON] et al. (2017) [51] used the Boreal Ecosystem Productivity Simulator (BEPS) model to identify NEE for the bamboo forest. Remote sensing products such as vegetation type, NDVI, and other vegetation indices were used to estimate GPP [52]. One of the main advantages of satellite remote sensing method is that it estimates atmospheric CO\({}_{2}\) at the global scale; however, its coarse spatial resolution is a major disadvantage [53]. On the other hand, satellite data combined with in-situ measurements and modeled data can be the best approach to monitoring and evaluating carbon fluxes at various spatial and temporal scales [54; 51]. For example, [PERSON] et al. (2013) [55] used the carbon flux model integrated with remotely sensed Normalized Difference Vegetation Index (NDVI) and in-situ meteorology data to quantify GPP and NEE over a pan-boreal/Arctic domain and found that drought and temperature had larger impacts on GPP and NEE. Currently, there are various satellites in operation to estimate carbon fluxes globally [32]. For example, Soil Moisture Active Passive (SMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) are in operation to estimate NEE (SMAP)/NPP (MODIS) and GPP, respectively, at different spatial and temporal resolutions. Many studies have used MODIS GPP to study the carbon cycle associated with terrestrial ecosystems [56; 57; 58]. On the other hand, since SMAP was launched in 2015, only a few studies have used SMAP NEE and GPP to study the carbon cycle and terrestrial ecosystem [59; 60].