## 5 Conclusions  
GPP plays key role in maintaining carbon balance in terrestrial ecosystems and climate change. It is essential to accurately quantify daily GPP. This study, taking maize as an example, based on five traditional inputs (NDVI, SSR, Tmean, Trin, and Tmax), we discussed the importance of NMP and SLN in improving the daily GPP estimation via four popular ML methods (RF, SVM, CNN, and ELM). The prediction results are assessed in detail and comprehensively compared using accuracy metrics (NSE, RMSE, Bias, CV, and URMSE). The advantages of introducing NMP and SLN into inputs have been demonstrated by all applied ML methods with the flux data in four sites. It is just that different ML methods have different sensitivities to SLN and NMP. The significance of SLN and NMP was also confirmed in the importance ranking of random forest. It is noted that considering the fixed relationship between the maximum allowable SLN and NMP, for the CNN and ELM methods, introducing NMP or SLN alone may obtain superior results than introducing them simultaneously. This study indicates that plant phenology and leaf-level photosynthetic factors have great value in improving GPP estimation via ML methods. But they have been commonly ignored by previous research. ML methods with the consideration of SLN or NMP are expected to improve the evaluation accuracy of global maize GPP. All in all, as organic matter accumulates via maize photosynthesis, GPP exhibits a direct correlation with photosynthesis rate. SLN and NMP, concurrently regulating photosynthesis, exert an influence on GPP synthesis. Integrating these dynamic physiological aspects of maize as input variables into machine learning models has notably improved the models' accuracy. This study provided new insights to improve GPP estimation via ML methods. C.H. : methodology, software, validation, formal analysis, data curation, writing--original draft preparation; S.H. : conceptualization, methodology, validation, formal analysis, data curation, investigation, data curation, writing--review and editing, supervision, project administration, funding acquisition; L.Z. : formal analysis, writing--review and editing; K.M. : writing--review and editing; Z.L. : project administration, writing--review and editing; K.W. : writing--review and editing. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the National Nature Science Foundation of China program (Grant No. 42207098), Yinshanbeilu Grassland Eco-hydroology National Observation and Research Station, China Institute of Water Resources and Hydropower Research (Grant No. YSS202302). **Data Availability Statement:** The data are available from the corresponding author on reasonable request. The data are not publicly available due to that there are still graduate students using it for research. **Conflicts of Interest:** The authors declare no conflicts of interest. * [PERSON] and [PERSON] (2001) [PERSON]; [PERSON] Effects of Spatial Detail of Soil Information on Watershed Modeling. _J. Hydrol._**2001**, _248_, 54-77. [CrossRef]
* [PERSON] et al. (2004) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production. _BioScience_**2004**, _54_, 547. [CrossRef]
* [PERSON] et al. (1997) [PERSON]; [PERSON]; [PERSON] The Photochemical Reflectance Index: An Optical Indicator of Photosynthetic Radiation Use Efficiency across Species, Functional Types, and Nutrient Levels. _Ocologia_**1997**, _112_, 492-501. [CrossRef] [PubMed]
* [PERSON] and [PERSON] (2012) [PERSON]; [PERSON] Gross Primary Production and Ecosystem Respiration of Irrigated and Rainfed Maize-Soybean Cropping Systems over 8 Years. _Agric. For. Meteorol._**2012**, _165_, 12-24. [CrossRef]
* [PERSON] et al. (2005) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Modeling Gross Primary Production of an Evergreen Needleleaf Forest Using Modis and Climate Data. _Ecol. Appl._**2005**, _15_, 954-969. [CrossRef]
* [PERSON] et al.