## 1 Introduction  
Gross primary production (GPP) represents the amount of organic matter and energy through photosynthesis per unit time and area in territorial ecosystem [1]. The accumulation of GPP in ecosystems is a process through which atmosphere carbon dioxide is fixed by plant to form organic carbon [2]. GPP is a direct basis for reflecting the productivity of territorial ecosystem and carbon reserves [3] and also the key factor to realize global carbon balance [4]. The data from the Food and Agriculture Organization (FAO) show that the global cropland area in 2020 was 1576 million hectares, accounting for about 12.09%of the world's total land area [5]. This proportion is expected to increase in the future to accommodate the food demand with an increasing population. Compared with other natural ecosystems, the cropland accounts for 9.4% of the global total GPP [6], of which maize GPP is the largest proportion (14.9% of global cropland GPP) [7]. Therefore, the accurate estimation of daily maize GPP plays a significant role in evaluating the global carbon cycle. Several GPP estimation models have been developed and can be divided into three categories: vegetation index (VI)-based models, process-based models, and light use efficiency (LUE) models. VI-based models employ a purely statistical approach to estimate GPP. For example, [PERSON] et al. [8] utilized variables such as land surface temperature (LST) and enhanced vegetation index (EVI). Nonetheless, models based on statistical relationships between variables and GPP may not be optimally adaptable for estimation in varying conditions, as the function developed for one site may not be applicable to another site [9]. Process-based models comprehensively consider the integration of soil, vegetation, and the atmosphere to dynamically simulate the physiological process of a plant [10]. They are distinguished by their profound recognition of the mechanism of vegetation growth and estimate GPP mechanistically. However, due to the scarcity and quality of available vegetative parameters and the intricate nature of the model process, it is difficult to generalize process-based models. LUE models use the maximum of LUE (LUEmax) [11] for GPP calculation and consider the effect of environmental conditions such as water, temperature, and phenology on vegetation photosynthesis [12; 13; 14]. However, LUE models greatly rely on environmental factors. Water pressure variables like vapor pressure deficit (VPD) cannot adequately characterize the effects of water availability on vegetation production [15]. In addition, VIs (e.g., the normalized difference vegetation index (NDVI) [16]) as a proxy for the fraction of photosynthetically active radiation (FPAR) will also produce errors in the estimation of GPP [15]. In summary, the previously mentioned methods have their own limitations and face challenges in GPP estimation. Therefore, there is a crucial need to identify an efficient method for estimating GPP. Crop growth is affected by growing environments (e.g., air temperature, soil properties, and field management) and controlled by plant phenology [17; 18]. The heterogeneity of time and space suggests that these factors interact and collectively affect crop production. Changing factors affecting crop productivity often involve nonlinear processes [19; 20]. Additionally, traditional methods fall short in supporting the development of modern agriculture, which requires abundant data and robust algorithms [21]. Consequently, machine learning (ML) has gained popularity. The ML method disregards the intricate process of crop physics; instead, simple inputs and outputs assist in creating effective relationships and reconstructing knowledge frameworks [22]. It can effectively model complex processes using extensive field data [23]. Several popular ML methods such as decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM) have demonstrated effectiveness in estimating ecosystem productivity [24; 25; 26; 27].