Site-based NAI observations provide the most accurate way to record NAIs in a limited range within the core location of the site [2; 9; 10]. However, scarce monitoring sites have limited its regional application. Thus, more and more studies have tried to build up models to predict their spatio-temporal dynamics [5; 6]. At the site scale, data-driven empirical models were the first attempts to predict NAI dynamics in the past decades [11; 12; 13]. Among the numerous influencing factors, climatic factors (such as air temperature, air moisture, radiation, wind speed, etc.) and air quality (such as PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), NO\({}_{2}\), etc.) are the most investigated [2; 8; 9]. In these studies, multi-parameter regression models were used to model the temporal dynamics of NAIs [10; 14]. However, considering the limited site monitoring, the spatial distribution of NAIs is hard to predict [15]. At the regional scale, satellite-based bio-geophysical parameters such as the Normalized Difference Vegetation Index (NDVI), Gross Primary Productivity (GPP), Digital Elevation Model (DEM), air humidity, etc., were used to approach NAI regional distribution [16]. In these studies, simple regression models and semi-variogram models were adopted to predict the NAI spatial distribution with instantaneous NAI measurements [7; 15; 17]. In some other studies, these instantaneous data were also expanded via a geostatistical scheme, such as the variogram and Kriging method, to provide regional-scale NAIs according to the geostationary spatial variation [7; 15; 18]. Even though these site and regional scale models provide substantial reference for ecosystem services assessment [17; 18], some drawbacks still needed to be solved. Firstly, the data used to build NAI inversion models need to be refined [15]. Presently, studies with large amounts of long-term NAI monitoring data are still lacking. Even though a vast number of instantaneous point measurements might make up for the deficiency of site numbers, the lack of synchronization and temporal extensibility of these data perturb the feasibility of NAI modeling [11; 15; 17]. In addition, with more and more knowledge on NAI formation, plant functional traits such as transpiration, GPP, vegetation coverage, etc., have been progressively introduced into the NAI inversion [7; 9; 11]. However, these traits are alleged to indirectly interrupt NAI dynamics, which leads to weak NAI predictions [14]. Recently, photoelectric progress was argued to directly participate in NAI formations [10; 14; 17]. For example, NAIs were considered to be highly correlated to solar-induced chlorophyll fluorescence intensity (SIF), especially under higher PAR levels when vegetation photosynthesis was fully activated [10]. However, the spatial significance of the photosynthetic contribution to the regional NAI dynamics has been less assessed. Secondly, the widely adopted simple empirical model fails to include multi-factor interactions [19]. The drawbacks of the statistical models mentioned above encourage the use of advanced data-driven machine-learning models, such as Random Forest and ANN models, which have special characteristics to simulate the unknown relationships among different parameters [7; 13; 20; 21]. Machine-learning models can numerically formulate NAIs nonlinearly, solely based on historical data without requiring knowledge of the underlying physical processes. The continuous advancement of machine-learning methods over the last two decades demonstrates their suitability for object variables with an acceptable rate of outperforming conventional approaches [22]. In this study, the Random Forest (RF) machine-learning model was adopted to generate an NAI model based on site-based NAIs and observation multi-parameters. Furthermore, with the development of space technology, satellite-based models have been recorded as a possible way to model specific variables at a larger spatial scale [7; 8; 23; 24]. Thus, the spatial information was also considered to realize the NAI inversion in this study.