# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
## 1 Introduction  
Gross primary productivity (GPP), the amount of carbon dioxide that plants fix into organic matter through photosynthesis, is the largest component of the global terrestrial carbon flux [1, 2]. It plays a key role in the global carbon balance by regulating atmospheric carbon concentrations, offseting a portion of anthropogenic carbon dioxide emissions [3, 4]. GPP prediction is of great value for studying ecosystem carbon cycles and formulating sustainable policies [5, 6, 7]. As the largest terrestrial ecosystem, grassland covers approximately 54% of the world's total land area. Grassland GPP plays an important role in the global carbon budget [8, 9]. Unlike other terrestrial ecosystems (forest, farmland), grassland is more sensitive to climate factors such as temperature and precipitation, and the spatial distribution pattern and evolutionary law of grassland GPP are more complex [10, 11]. Timely, accurate, and large-scalepredictions of grassland GPP are still difficult [12]. Currently, the commonly used methods for estimating grassland GPP include ground monitoring and model simulation [13; 14]. Ground monitoring methods require the establishment of a ground monitoring system to calculate grassland GPP by measuring energy and material fluxes between vegetation and the atmosphere [15; 16; 17; 18]. Such methods can provide accurate GPP data, but the cost of monitoring is high, and it is difficult to apply on a large scale. The model simulation method establishes the relationship between environmental background data and GPP, which has unique advantages at a large scale and high spatial resolution [19; 20; 21; 22]. It includes the biogeochemical model (BGCM), light use efficiency model (LUEM), and machine learning model (MLM). Among them, BGCM estimates GPP by simulating vegetation physiological processes [23; 24]. BGCM requires a lot of knowledge about the vegetation growth process, and the establishment of the model is accompanied by a lot of mechanism simplification [25; 26]. Therefore, BGCM has high professional requirements, limited applicable scenarios, and is difficult to improve. LUEM simulates the GPP by measuring the effective solar radiation absorbed by vegetation [27; 28]. When all factors (temperature, water, nutrients, etc.) are at their optimum, vegetation reaches the theoretical maximum light energy use rate. In practical application, it is necessary to establish the limiting relationship between environmental factors and vegetation light energy use. LUEM is affected by the variation of the maximum light energy utilization rate, the diversity of geographical conditions, and the complexity of vegetation composition, and has the problems of large uncertainty and low interpretability [29; 30; 31]. MLM learns the driver and response mechanism of GPP and various environmental factors from data, establishes rules, and performs simulation and prediction. MLM no longer requires expert knowledge and avoids the resulting bias [32; 33; 34]. In the past decade, with the development of big data and artificial intelligence, the evaluation and prediction of GPP based on MLM have become a research hotspot [35; 36; 37]. In particular, it is necessary to assess the performance of different machine learning models in GPP prediction [38; 39]. Currently, scholars have conducted related research [40; 41; 42]. [PERSON] et al. [40] compared the performance of support vector machine (SVM), random forest (RF), artificial neural network (ANN), and deep neural network (DNN) in Korean forest GPP prediction using flux monitoring data as a reference. The results showed that DNN performed significantly better than other models. [PERSON] et al. used machine learning (RF, SVM, XGBoost) combined with remote sensing data, meteorological data, and topographic data to predict the GPP of Australia, and compared the accuracy of different models [41]. The results show that the random forest method has the best effect. [PERSON] et al.