Field-scale rice yield prediction from Sentinel-2 monthly image composites using machine learning algorithms

• Published in: Ecological informatics Vol. 69; p. 101618
• Main Authors: Son, Nguyen-Thanh, Chen, Chi-Farn, Cheng, Youg-Sin, Toscano, Piero, Chen, Cheng-Ru, Chen, Shu-Ling, Tseng, Kuo-Hsin, Syu, Chien-Hui, Guo, Horng-Yuh, Zhang, Yi-Ting
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-07-2022
• Subjects: Machine learning; Rice; Sentinel-2; Taiwan; Yield prediction; Sentinel-2; Machine learning; Taiwan; Rice; Yield prediction
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2022.101618

Machine learning (ML) along with high volume of satellite images offers an alternative to agronomists in crop yield predictions for decision support systems. This research exploited the possibility of using monthly image composites from Sentinel-2 imageries for rice crop yield predictions one month before the harvesting period at the field level using ML techniques in Taiwan. Three ML models, including random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), were designed to address the research question of yield predictions in four consecutive growing seasons from 2019 to 2020 using field survey data. The research findings of yield modeling and predictions showed that SVM slightly outperformed RF and ANN. The results of model validation, obtained from SVM models using the data from transplanting to ripening, showed that the root mean square percentage error (RMSPE) and the mean absolute percentage error (MAPE) values were 5.5% and 4.5% for the 2019 second crop, and 4.7% and 3.5% for the 2020 first crop, respectively. The results of yield predictions (obtained from SVM) for the 2019 second crop and the 2020 first crop evaluated against the government statistics indicated a close agreement between these two datasets, with the RMSPE and MAPE values generally smaller than 11.2% and 9.2%. The SVM model configuration parameters used for rice crop yield predictions indicated satisfactory results. The comparison results between the predicted yields and the official statistics showed slight underestimations, with RMSPE and MAPE values of 9.4% and 7.1% for the 2019 first crop (hindcast), and 11.0% and 9.4% for the 2020 second crop (forecast), respectively. This study has successfully proven the validity of our methods for yield modeling and prediction from monthly composites from Sentinel-2 imageries using ML algorithms. The research findings from this research work could useful for agronomists to timely formulate action plans to address national food security issues.
•This study performed rice crop yield predictions using Sentinel-2 image composites.•Data were processed for 2019–2020 crop seasons using machine learning algorithms.•SVM slightly outperformed RF and ANN for rice yield predictions at the field level.•Methods can be used for rice yield prediction one month before harvesting in Taiwan.