## 1 Introduction  
Terrestrial ecosystems play a vital role in sequestering the carbon dioxide in the atmosphere [1; 2]. In general, the amount of carbon sequestration in different terrestrial ecosystems varies on seasonal, annual, and inter-annual time scales [3]. To account for this, the responses of terrestrial carbon exchanges to global environmental change involving climate extremes such as windstorms, heat waves, frosts and droughts, and a variety of disturbances, mainly including land use changes, fires, nitrogen deposition, and CO\({}_{2}\) elevation, have recently attracted much attention [4; 5; 6]. These driversmay alter the composition, structure, and function of terrestrial ecosystems [7] and subsequently influence the magnitude and behavior of carbon cycles and their feedbacks to global change [8; 9]. The inability of sufficiently characterizing the ongoing evolution of carbon fluxes using different techniques has been a primary impediment in our understanding of the underlying mechanisms in terrestrial carbon exchange processes [10; 11; 12]. For this reason, it is crucial to accurately estimate the carbon budgets in terrestrial ecosystems ranging from flux towers to regional or global scales for diagnosing past and future climate change. To date, most prior studies that examine the roles of the aforementioned drivers and assess their induced uncertainties in the terrestrial carbon flux estimation have relied primarily upon the process-based models through describing complex biological and physical processes [13; 14]. In general, the precision and reliability of these models are severely hindered due to the underlying nonlinear and nonstationary characteristics of terrestrial ecosystems. In contrast to process-based models, [PERSON] and [PERSON] [15] suggested that mechanism-free models constitute the best alternative technology; such models depend upon constantly changing statistical characteristics of pooled data for short-term projections. [PERSON] et al. [16] demonstrated the superiority of empirical dynamic modeling over static equilibrium-based models with deterministic rules and parameters, due to the fact that the former is viewed as an equation-free mechanistic forecasting technology, with the capability of adjusting both non-equilibrium dynamics and nonlinearities in natural ecosystems. The potential and versatility of machine learning techniques has been widely acknowledged with respect to solving the complex nonlinear problems [17; 18] in the field of eco-informatics in the context of data-intensive ecological sciences [19]. Motivated by this and the recent availability of large amounts of accumulated data measured by the eddy covariance technique from flux tower networks across different vegetation types, the present research was undertaken to imitate the nonlinear relationships between carbon fluxes and environmental variables using machine learning approaches. Over the past decades, machine learning techniques have gained great attention for dealing with different nonlinear issues in the ecological, climatological, and environmental fields, such as above-ground biomass estimation [20], methane emission prediction [21], meteorological anomaly detection [22], and biophysical parameter retrieval from remote sensing data [23]. In fact, the carbon exchanges of terrestrial ecosystems are complex nonlinear processes, which are predominantly controlled by environmental variables (e.g., solar radiation, air temperature, wind speed, and relative humidity). For this reason, the use of machine learning techniques was taken into consideration to characterize the carbon exchange processes between land and the atmosphere. In recent years, several lines of evidence have illustrated that machine learning techniques are able to perform better than some physically based models in terms of estimating the terrestrial carbon fluxes at different spatial and temporal scales [14; 24; 25].