# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
## 5 Discussion
Among the seven impact factors above, EVI and NDVI are indicators that measure vegetation growth and cover, and LULC reflects vegetation type. Therefore, surface vegetation characteristics are the most important category of impact factor in predicting GPP. This is consistent with the conclusion that GPP is closely related to vegetation growth status in other studies [73; 74; 75]. On the other hand, P, Tmax, PET, and ET reflect the regional climate and hydrothermal characteristics. Therefore, regional climate characteristics are also an important category of impact factor in GPP prediction. This is also consistent with the conclusions of previous studies [76; 77]. Based on the contribution ratio data obtained in this study and the experience of existing studies, it can be considered that EVI, NDVI, P, LULC, Tmax, PET, and ET are the key impact factors for GPP prediction. Selecting them as inputs for the machine learning model can produce satisfactory GPP prediction results. Sufficient and high-quality sample data is the key to machine learning model training [78; 79]. Taking the measured data of the flux station as the input of model training will be the most accurate, and the precision of model training will be high [80; 81]. On the other hand, using the measured data of the flux station as the verification of the prediction results will be the most accurate. However, in the Mongolian Plateau region, the harsh reality is that there is only one flux station in Xilingol, and the data is publicly available for a limited number of years. In this case, we have to rely on satellite remote sensing data. Inevitably, this has more errors than the measured data from the flux station [27, 82]. Nevertheless, the study of [PERSON] et al. shows that the correlation coefficient between MODIS GPP data and the measured data of the flux station is about 0.72, which means it is still an acceptable data source [41]. [PERSON] et al. validated the quality of MODIS GPP data in the grassland area of China, and the results show that the correlation between MODIS GPP data and ground-measured data is about 0.8, which is at a high level [83]. In the Mongolian Plateau region, because the flux station data is too small to be used for training, we only use it as part of the validation data, which is Figure 4i. Nevertheless, this data is very important for the evaluation of prediction accuracy. Our results show that the prediction accuracy is generally satisfactory (the time curve of the predicted GPP is close to the reference data from the flux station). In the future, studies can make use of the monitoring data provided by American Flux ([[https://ameriflux.lbl.gov/](https://ameriflux.lbl.gov/)]([https://ameriflux.lbl.gov/](https://ameriflux.lbl.gov/)), accessed on 15 April 2023) and European Flux ([[http://www.europe-fluxdata.eu/](http://www.europe-fluxdata.eu/)]([http://www.europe-fluxdata.eu/](http://www.europe-fluxdata.eu/)), accessed on 15 April 2023), which are distributed among flux sites. On the other hand, a limited amount of monitoring data can also be used to constrain and correct the remote sensing data to obtain a large number of accurate reference data. This will help to further improve the accuracy and reliability of machine learning models [84, 85]. Simulation and prediction have the same mechanism; they both need to train the model with historical data and then calculate the GPP value for the desired time. Among them, GPP prediction has greater practical value. For example, herders are more concerned about whether the grass will grow well next year to help them make breeding plans, or where the grass will grow well next week to help them determine grazing sites. However, GPP prediction first requires data on future impact factors. In the GPP prediction of the short-term future (such as one week later), surface factor data (such as NDVI and EVI) can be obtained by using reasonable prediction methods, and climate factor data (such as temperature and precipitation) can be obtained by using meteorological forecasting methods [86, 87].