## 5 Conclusions  
This study met its goal of proposing and validating a method for predicting cropland suitability using soybean (_Glycine max_ L.) as a representative crop, aiming to enhance the efficient utilization of natural resources while minimizing the reliance on fertilizers and pesticides. The main conclusions from the results of this study are:  
* The LAI and FAPAR provided coverage of the training and test data for cropland suitability prediction in continental Croatia for soybean higher than the 0.25% threshold for six out of the seven study years. This indicates that the proposed approach is viable for major crops, while cropland suitability prediction for minor crops would require higher spatial resolution. * Consequentially, with the potential requirement of higher spatial resolution, 300 m LAI and FAPAR data from PROBA-V/Sentinel-3 would not be viable. Previous studies suggested the use of phenology analysis for vegetation indices, such as the NDVI, from Sentinel-2. * RF consistently outperformed XGB, SVM, and their combination for the prediction of the peak LAI and FAPAR potential for soybean cultivation, indicating its superiority in dealing with restricted training and test datasets to other evaluated machine learning methods. RF resulted an in R\({}^{2}\) in the range of 0.250-0.590 for the prediction of the peak LAI and FAPAR on the entire agricultural land, indicating moderate prediction accuracy. * The K-means unsupervised classification algorithm fulfilled the need of aggregating a total of 14 annual peak LAI and FAPAR values to provide standardization of cropland suitability classes according to the FAO standard. Therefore, both specific objectives regarding developing, ensuring quality control of ground-truth datasets for training and testing machine learning models, as well as implementing cropland suitability predictions in alignment with FAO standards, and categorizing results into five classes through a fully objective methodology using machine learning while ensuring global applicability and accuracy evaluation were met. Conceptualization, D.R., M.G. and M.J.; methodology, D.R. ; software, D.R. ; validation, D.R., M.G. and M.J.; formal analysis, D.R. ; investigation, D.R. ; resources, D.R. ; data curation, D.R. ; writing--original draft preparation, D.R. ; writing--review and editing, D.R., M.G. and M.J.; visualization, D.R. ; supervision, M.G. and M.J.; project administration, D.R. ; funding acquisition, D.R. All authors have read and agreed to the published version of the manuscript. This research was funded by the European Space Agency (ESA) within the project titled \"Cropland suitability prediction method for land management planning based on Copernicus data for soybean--CropSuit\" under contract 4000143723/24/NL/MH/yd. Not applicable. The data presented in this study are available on request from the corresponding author. This research was supported by project \"Soybean cropland suitability prediction based on machine learning regression\" from the research team \"Technical and technological systems in agriculture, GIT, precision agriculture and environment protection\" of the Faculty of Agrobiotechnical Sciences Osijek. The authors declare no conflicts of interest. * (4) FAO. Chapter 1: The Nature and Principles of Land Evaluation. Available online: [[https://www.fao.org/3/x5310e/x5310e02.htm#chapter%201%20](https://www.fao.org/3/x5310e/x5310e02.htm#chapter%201%20) th%20 nature%20 and%20 principles%20 of%20 ol%20 evaluation]([https://www.fao.org/3/x5310e/x5310e02.htm#chapter%201%20](https://www.fao.org/3/x5310e/x5310e02.htm#chapter%201%20) th%20 nature%20 and%20 principles%20 of%20 ol%20 evaluation) (accessed on 13 July 2022). * (5) FAO. Chapter 3: Land Suitability Classifications.