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A Simple Method of Predicting Autumn Leaf Coloring Date Using Machine Learning with Spring Leaf Unfolding Date

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Abstract

Predicting plant phenology is considered the foundational for the forecast of ecosystem function and dynamics from species level to global level. However, the exact prediction of plant phenology remains limited because of the challenges associated with adding exact environmental and physiological cues to numerical models. In this study, we developed a simple data-based prediction model for leaf coloring dates of temperate deciduous trees by applying machine learning to datasets obtained from the newly established South Korean national-scale phenology network (NPN). Ground observations of spring leaf unfolding dates for 2009–2018 obtained from NPN together with data on the environmental drivers of leaf coloring (summer mean temperature, altitude) were utilized for the model. The model can be evaluated to have simulated the characteristics of observed leaf coloring dates relatively accurate, with only a two-day difference between the average observed and predicted leaf coloring dates. In addition, the model yielded an RMSE value of approximately 7 days, which is within the acceptable error criteria when compared to the sampling frequency, despite the use of only three input variables. Data-based machine learning using existing spring leaf unfolding data as an input help us predict autumn phenology better, even without precise species-specific physiological knowledge on leaf coloring mechanisms. Consequently, a phenology network across the globe based on steady observations will be favorable datasets for a phenology prediction model that can be applied widely.

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Data Availability

The datasets for the temperature are freely available from https://data.kma.go.kr/. The leaf unfolding, coloring dates and specific information of each stations are available from the data developers upon request.

Code Availability

Python code: available and open to researchers.

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Acknowledgements

This work was supported by the National Research Foundation (NRF) of Korea and a grant funded by the Government of Korea (NRF-2019R1A2C3002868).

Funding

This work was supported by the National Research Foundation (NRF) of Korea and a grant funded by the Government of Korea (NRF-2019R1A2C3002868).

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Authors and Affiliations

  1. Department of Environmental Planning, Graduate School of Environmental Studies, Seoul National University, Seoul, Korea

    Sehyun Lee, Sujong Jeong, Chang-Eui Park & Jongho Kim

  2. Environmental Planning Institute, Seoul National University, Seoul, Korea

    Sujong Jeong & Chang-Eui Park

Authors
  1. Sehyun Lee
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  2. Sujong Jeong
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  3. Chang-Eui Park
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  4. Jongho Kim
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Contributions

Sehyun Lee and Sujong Jeong conceived the ideas and designed methodology; Sehyun Lee and Jongho Kim analysed the data; All authors led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

Corresponding author

Correspondence to Sujong Jeong.

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The authors declare that they have no conflicts of interest.

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Communicated by Seon Tae Kim.

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Lee, S., Jeong, S., Park, CE. et al. A Simple Method of Predicting Autumn Leaf Coloring Date Using Machine Learning with Spring Leaf Unfolding Date. Asia-Pacific J Atmos Sci 58, 219–226 (2022). https://doi.org/10.1007/s13143-021-00251-4

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  • DOI: https://doi.org/10.1007/s13143-021-00251-4

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