The terrestrial biosphere sequesters 20-30% of global anthropogenic CO\({}_{2}\) emissions [11]; thus, it is an important task to accurately evaluate the regional and global ecosystem carbon balance, to better contribute to achieving the international double carbon goal. As an important basis, measuring net carbon exchange by forests is essential to obtain reliable carbon balance (e.g., source, sink, exchange) and validate/evaluate the model performances. As a second step, all kinds of models can be developed and then applied to 1) study processes and mechanisms of CO\({}_{2}\) flux, net ecosystem exchange (NEE), net ecosystem productivity (NEP) (NEE= -NEP), respiration (Re) and gross primary production (GPP), 2) simulate the variations of NEP, Re and GPP, 3) understand the responses of NEE, Re, GPP to their driving factors, as well as to climate change [12; 13; 14; 15]. For example, these models include empirical models, sophisticated models [16; 17; 18], and atmospheric inversions [19; 20]. The popular empirical models consider several processes (mostly in one direction) that are of limited in understanding the mechanisms. The complicated models can elucidate the specific processes in vegetation, soil, and atmosphere and obtain a better understanding of the processes and their mechanisms. The widely used process-based dynamic global vegetation models (DGVMs) are useful tools to calculate the natural land sink, and the DGVM multi-model mean estimate of the global land sink is in agreement with the global carbon budget residual land sink. Currently, novel and innovative machine learning algorithms offer powerful tools to estimate carbon balance (GPP, NEE, Re, etc. ), especially extreme gradient boosting (XGBoost), artificial neural networks (ANN), and GIS data-based studies. These artificial intelligence might meet the challenges in carbon balance. Until now, there are still large differences among models [14; 15; 18; 21; 22; 23; 24; 25; 26; 27; 28; 29]. Additionally, many hypotheses and assumed parameters were used in these complex models, which were limited by our current understanding of the carbon balance processes [21]. The Eddy covariance (EC) technique is reliable for obtaining CO\({}_{2}\) exchange between the atmosphere and ecosystem, and EC flux data are commonly used for validations [22; 23]. There are also large uncertainties in CO\({}_{2}\) flux observations and model predictions [22; 23; 24; 25; 26; 27; 28; 29]. On the basis of current progress and urgent needs, the internal processes and external drivers, as well as their relationships between solar energy, atmosphere, vegetation, and land, should be fully studied. Respiration is an important and integral part in regional and global carbon cycling [30; 31]. O\({}_{2}\) uptake and the subsequent metabolism of the reaction product in the photorespiratory process is the second highest metabolite flux in plants behind photosynthesis [32; 33]. New information about respiration and GPP to ensure a better partitioning between the components of the CO\({}_{2}\) atmospheric budget is necessary [34]. NEE or NEP is a vital indicator to evaluate ecosystem carbon exchange, whereas the accurate simulation of respiration is still a big challenge [31], as it is very small compared to NEE and GPP, and many factors/processes control daytime and nighttime respirations through different mechanisms. Three metabolic processes control carbon fluxes in vegetation, namely, photosynthesis, photorespiration, and respiration, which are significant and influenced by many factors (temperature, light, water, relative humidity, O\({}_{2}\) and CO\({}_{2}\) concentrations, etc.). Many photosynthesis models (e.g., photosynthesis-irradiance curves, non-rectangular hyperbolic, or rectangular hyperbolic models) were developed and commonly used in their simulations, but there are few photorespiration models [35; 36]. In addition, the Lloyd-Taylor equation was widely used to extrapolate the daytime respiration from nighttime respiration [37; 38].