## 4 Discussion
### Advantages and Limitations of Present Research and Future Work  
We modelled the carbon fluxes at three different forest ecosystems using four different modeling techniques. The ability of these techniques has been demonstrated in terms of dealing with the nonlinear processes that control the daily carbon fluxes. In general, the best predictive results for the three carbon fluxes were provided at the evergreen needle-leaf forest (CA-Obs), followed by the deciduous broadleaf forest (CA-Oas), whereas the worst performance was obtained at the mixed forest (CA-Gro), maybe due to the difficulty for our proposed models in seizing the useful knowledge resulting from the influences of more complex and instable underlying surface (Table 6). Moreover, this performance difference among the three sites may be due to the climate extreme events. For instance, at CA-Gro site, compared with the 4-year means over the training period, the annual average values of \(R_{n}\), \(T_{s}\) and \(T_{a}\) and in the testing period were higher by about 0.2 mol m\({}^{-2}\), 0.2 \({}^{\circ}\)C and 1.6 \({}^{\circ}\)C, respectively. These three variables have been found to be the most important driving factors dominating the variation of carbon fluxes (Tables 4, 7 and 8). On the other hand, for each estimated flux at all the three forest ecosystems, our applied models generally produced satisfactory results (Tables 5 and 6), especially for GPP and \(R\) fluxes. In addition, more remarkable difference among the three fluxes was that the predictive results of both GPP and \(R\) were substantially superior to those of NEE, which is in accordance with the previous studies [22; 65]. As a matter of fact, NEE, as a balance between GPP and \(R\) fluxes, is controlled by the interplay of assimilation and respiration processes between the biosphere and the atmosphere, and this interplay highly influences the diurnal, seasonal and inter-annual variations in NEE involving amplitude and phase [66]. Consequently, accurately estimating NEE remains a great challenge for the researchers to date. Furthermore, it is a generally acknowledged fact that pursuing excessively the convenience and simplification of the used models may cause the under-fitting for the trained models. In our study, a possible reason for the lower explanation of NEE from the applied models seems to be the missing of some key driving variables for model design, such as biomass pools and management activities. Therefore, in the follow-up work, the sensitivity analysis of a variety of variables in relation to carbon fluxes (particularly for NEE), as well as the addition of effective driving variables as model inputs should be further investigated to improve the predictive performance of our proposed models for the carbon flux forecasting. On the other hand, NEE measurements have been extensively used for estimating carbon budgets at different spatial and temporal scales. Therefore, data gaps in NEE measured by EC technique must be precisely filled [19; 67]. However, no standard gap-filling approach for CO\({}_{2}\) flux time series data with the EC method has been widely accepted [15; 68]. Consequently, it is of importance to identify optimal filling approaches for accurately determining carbon budgets. According to the present estimates, our proposed methods could be used as useful alternatives to address the problem of gap-filling in EC measurements in the future. In practical terms, our study presented the performance difference among the utilized models and also found that the instability of the data-driven models in terms of predictive accuracy for each case indeed existed (Tables 5, 9 and 10). To obtain more stable model as well as better predictive accuracy, a novel technique, namely ensemble learning, has gained extensive attention in recent years [69; 70]. The purpose of ensemble learning is to combine the positive properties of a diverse set of learners generated from either the same or different machine learning methods.