# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 1 Introduction  
From the onset of the industrial era in 1750, the atmospheric concentration of carbon dioxide (CO\({}_{2}\)) has consistently increased [1]. Emphasizing the escalating adverse effects of climate change, China has declared its dedication to reaching a peak in carbon emissions by 2030, and attaining carbon neutrality by 2060 [2]. Natural mechanisms possess the capability to regulate atmospheric CO\({}_{2}\) levels, with various ecosystems like forests, wetlands, oceans, and grasslands playing a crucial role in absorbing and sustaining the balance of carbon dioxide [3]. The net ecosystem exchange serves as a metric to quantify the disparity between the organic carbon absorbed by plants in an ecosystem and the carbon emitted by them. A negative NEE indicates that the ecosystem functions as a carbon sink. This measure holds vital significance in assessing the carbon cycle within an ecosystem, and in determining its role as either a carbon source or sink [4]. Furthermore, the NEE serves as a criticalindicator to examine the influence of meteorological variations on the carbon balance of the ecosystem. Accurate and effective assessment of the net ecosystem exchange rate of CO2, also known as the carbon exchange rate, is of great help in understanding the ecosystem carbon cycle and improving the ability to cope with global climate change. Eddy covariance (EC) technology provides direct measurements of CO2 flux between land-atmosphere ecosystems and is considered one of the most representative methods for observing carbon flux [5]. The EC system offers a micrometeorological approach for monitoring the local or regional scale CO2 balance across key spatial domains [6]. This method allows for continuous flux data collection without disturbing the soil surface microenvironment at the study site. Eddy covariance technology has been widely applied in flux measurements across various ecosystems, including wetlands [7; 8], grasslands [9; 10], farmlands [11; 12], forests [13], and shrublands [14]. However, the observation range of eddy covariance technology is inherently limited, and the resulting data carry significant uncertainty, often necessitating data correction and revision. In addition, both static models based on ecological processes and dynamic models driven by data are commonly used in the study of ecosystem carbon budgets. Static models typically require complex parameterization, and simple mathematical formulas or rules may fail to accurately represent complex ecological processes [15]. Furthermore, process-based models may perform poorly when applied to new situations or datasets, limiting their general applicability. Machine learning methods such as random forests, decision trees, and support vector machines have been applied in NEE research. Utilizing the adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM) techniques, the long-term observations from eight forest ecosystem flux towers were investigated [16]. These were compared with the support vector machine (SVM) method, and the influence of internal parameters on the corresponding method was discussed. Subsequently, a random forest model was employed to conduct global-scale simulations based on data gathered from eddy covariance observations. The support vector regression algorithm (SVR) was applied by [17], utilizing data collected from 54 EC observation sites across Asia. Additionally, remote sensing data spanning the years 2000 to 2015 were integrated to facilitate data-driven estimations of gross primary productivity (GPP) and NEE. Utilizing the FLUXNET and AmeriFlux datasets, a study revealed that the association between NEE and environmental factors is stronger on daily and monthly scales than annually, with the leaf area index (LAI) and shortwave radiation (Rs) emerging as vital drivers of diurnal NEE variations across the majority of sites [4].