## 5 Discussion
Secondly, the precision of in situ measurements also impacts model performance, although the Eddy Covariance (EC) method is widely used for measuring water and carbon fluxes [76]. Previous studies have proven that EC suffers a typical error of ET estimation of about 5-20% [77; 78]. Furthermore, discrepancies arise from the mismatch between the spatial resolution of satellite observations and the flux tower footprint [21]. Additionally, it is crucial to note that estimating ET and NEE often necessitates temporal up-scaling, unlike status-describing indicators like leaf area or canopy coverage. Converting instantaneous satellite observation data to daily-scale ET or NEE is essential. When it comes to physical models, the upscaling process based on energy ratios incurs errors ranging from approximately 16.27 to 26.33% [4; 79]. Although empirical models bypass this process, treating it as a \"black box\", this does not signify the elimination of errors. From Figure 8 and Table 5, it is evident that the proposed model still faces the challenge of uneven spatial accuracy. Apart from the varying observation sample sizes across the six sites, this issue may also stem from regional indicators that have not been taken into account. From the perspective of model input variables, vegetation indices that describe vegetation growth remain the most effective indicators for estimating ET (Evapotranspiration) and NEE (Net Ecosystem Exchange). The physiological activities of plants, such as transpiration and photosynthesis, are largely determined by their growth status. Multiple studies have shown the potential of using surface temperature to estimate air temperature or net radiation [80; 81; 82; 83]. However, surface water conditions and solar radiation energy primarily serve as supplementary variables to vegetation growth, enhancing the accuracy of remote sensing models in estimating ET and NEE. Additionally, it is important to note that there are potential correlations among the input variables, allowing for some degree of substitution between them. For example, solar radiation exhibits seasonal characteristics and thus has a certain correlation with the Day of Year (DOY). The albedo of vegetation is highly correlated with vegetation growth and water status. Therefore, even without radiation-related variables as model inputs, the accuracy of ET and NEE remains relatively high. In general, the combination of multiple variables can effectively improve the accuracy of the model, as well as enhance its stability [45]. The combination of remote sensing and machine learning has gained increasing popularity in recent times, offering a streamlined and cost-effective approach for monitoring land surface indicators. Furthermore, the perception of machine learning algorithms as inscrutable \"black boxes\" has diminished [69]. In the context of ET and NEE estimation, we propose three potential avenues for future research: (1) developing down-scaling techniques to acquire more precise meteorological data, or devising algorithms to retrieve meteorological indicators from satellite observations; (2) exploring more dependable in situ measurement methods that align with the spatial resolution of satellite observations, such as integrating unmanned aerial vehicle observations with the EC system; (3) creating a more robust up-scaling method, independent of specific NEE or ET estimation algorithms, to accurately translate instantaneous satellite data to a daily scale.