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Incorporating climate data with machine learning can improve rice phenology estimation
Crop phenology provides essential information for crop management and production. Satellite-based methods are commonly used for phenology estimation but still struggle to capture interannual variations of phenological events. The importance of climate variation in crop phenology has been well acknowledged, but the potential of incorporating climate data to improve phenology estimation remains unclear. Here, we developed a hybrid model by incorporating the growth-specific climate predictors and satellite-derived phenology using random forest approach. Results showed that our hybrid model successfully reduced errors by over 60% compared to traditional satellite-based methods. The inclusion of climate data provided additional contributions beyond what was offered by satellite data, resulting in a 13% average improvement in R ^2 . Among climate predictors, temperature-related indicators contributed the most to accuracy enhancement. Additionally, CSIF outperformed LAI in the hybrid model in terms of absolute error, due to its finer temporal resolution. Our hybrid model highlights the importance of considering the diverse climatic information to further improve crop phenology estimation, rather than relying solely on satellite data. We expect our proposed model can offer new insights into improving crop phenology estimation and understanding the effects of climate variations on crop phenology.
Incorporating climate data with machine learning can improve rice phenology estimation
Crop phenology provides essential information for crop management and production. Satellite-based methods are commonly used for phenology estimation but still struggle to capture interannual variations of phenological events. The importance of climate variation in crop phenology has been well acknowledged, but the potential of incorporating climate data to improve phenology estimation remains unclear. Here, we developed a hybrid model by incorporating the growth-specific climate predictors and satellite-derived phenology using random forest approach. Results showed that our hybrid model successfully reduced errors by over 60% compared to traditional satellite-based methods. The inclusion of climate data provided additional contributions beyond what was offered by satellite data, resulting in a 13% average improvement in R ^2 . Among climate predictors, temperature-related indicators contributed the most to accuracy enhancement. Additionally, CSIF outperformed LAI in the hybrid model in terms of absolute error, due to its finer temporal resolution. Our hybrid model highlights the importance of considering the diverse climatic information to further improve crop phenology estimation, rather than relying solely on satellite data. We expect our proposed model can offer new insights into improving crop phenology estimation and understanding the effects of climate variations on crop phenology.
Incorporating climate data with machine learning can improve rice phenology estimation
Yiqing Liu (author) / Weihang Liu (author) / Tao Ye (author) / Shuo Chen (author) / Xuehong Chen (author) / Zitong Li (author) / Ning Zhan (author) / Ran Sun (author)
2025
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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