Recurrent U-Net based dynamic paddy rice mapping in South Korea with enhanced data compatibility to support agricultural decision making

ABSTRACT

The integration of remote sensing and state-of-the-art deep learning models has enabled the generation of highly accurate semantic segmentation maps to serve the agricultural sector, for which continuous land monitoring is required. However, despite their wide presence in the research field, only a few such products are used in on-site decision-making processes. This is due to their incompatibility with existing datasets that are at the core of current operating processes. In this study, paddy rice mapping in South Korea was examined to determine whether it produces qualified products that can complement on-site surveys and simultaneously be compatible with existing domestic datasets. Cases of early predictions for timely rice supply control were examined using a recurrent U-Net architecture with diverse applications: chronological batch training (CBT), time-inversed padding (TIP), and super-resolution (SR). In addition, the paddy area was confirmed using diverse datasets by standardizing its spatial extent in the definition of each data manual and calibrating the levee error, which was considered a major source of incompatibility. The robustness of the recurrent U-Net in early predictions dramatically increased upon CBT and TIP, recording an F1 score of over 0.75 on July 10, when the on-site survey was performed; meanwhile, the best performance score was 0.81 at the end of the growing period. SR enhanced the spatial details of rice mapping near the levee area, which had an estimated width of 60 cm; however, the area was more similar to that in existing datasets when it was calibrated with the predicted probability of the levee ratio rather than SR. The calibration was scalable from the patch to city level, with the paddy area at both levels recording high R² for the farm map and statistics (0.99 for both the farm map and statistics at the city level, and 0.93 and 0.95, respectively, at the patch level). This study shows that remote-sensing-based paddy rice mapping can produce not only accurate but also timely and compatible predictions by integrating deep learning applications. The results show that the predictions are compatible with domestic datasets as much as they are with each other; therefore, remote-sensing approaches are expected to be more actively and practically integrated into agricultural decision-making processes.

KEYWORDS:

• Recurrent U-Net
• paddy rice mapping
• decision-making
• early prediction
• remote sensing
• semantic segmentation

Acknowledgments

This work was supported by the International Research and Development Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT [2021K1A3A1A78097879], and partially supported by the European Commission under contract H2020-CALLISTO [101004152].

Disclosure statement

No potential conflict of interest was reported by the authors.

Author contribution

H.W.J designed this study, analyzed and interpreted the data, and wrote the manuscript. E.P, V.S., A.K., and W.K.L contributed to part of the methodology and review. J.K, and S.L., conducted the data investigation. All authors provided comments and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.

Data availability statement

Paddy rice prediction maps from 2017 to 2021 and the labeled time-series Sentinel-1 patch dataset are available in a publicly accessible repository. If these datasets are used, please cite this publication. The “Paddy Rice Maps South Korea (2017 ~ 2021)” data are available at https://github.com/Agri-Hub/Callisto-Dataset-Collection.

Additional information

Funding

This work was supported by the International Research and Development Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT [2021K1A3A1A78097879], and partially supported by the European Commission under contract H2020-CALLISTO [101004152].