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 acknow...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Environmental Research Letters |
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| Online Access: | https://doi.org/10.1088/1748-9326/ada56e |
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| author | Yiqing Liu Weihang Liu Tao Ye Shuo Chen Xuehong Chen Zitong Li Ning Zhan Ran Sun |
| author_facet | Yiqing Liu Weihang Liu Tao Ye Shuo Chen Xuehong Chen Zitong Li Ning Zhan Ran Sun |
| author_sort | Yiqing Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e9ab5abacdfc4922b29c4fef4d6399e7 |
| institution | Kabale University |
| issn | 1748-9326 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj-art-e9ab5abacdfc4922b29c4fef4d6399e72025-01-17T10:56:26ZengIOP PublishingEnvironmental Research Letters1748-93262025-01-0120202402010.1088/1748-9326/ada56eIncorporating climate data with machine learning can improve rice phenology estimationYiqing Liu0https://orcid.org/0009-0006-6440-8066Weihang Liu1https://orcid.org/0000-0002-0732-4898Tao Ye2https://orcid.org/0000-0002-5037-8410Shuo Chen3https://orcid.org/0009-0001-8125-0903Xuehong Chen4Zitong Li5Ning Zhan6https://orcid.org/0009-0005-0326-3994Ran Sun7State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University , Beijing 100875, People’s Republic of China; Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University , Beijing 100875, People’s Republic of China; Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education , Beijing 100875, People’s Republic of China; Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University , Beijing 100875, People’s Republic of China; Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University , Beijing 100875, People’s Republic of China; Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education , Beijing 100875, People’s Republic of China; Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University , Beijing 100875, People’s Republic of China; Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University , Beijing 100875, People’s Republic of China; Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education , Beijing 100875, People’s Republic of China; Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of ChinaAgricultural & Biological Engineering, Purdue University , West Lafayette, IN 47906, United States of AmericaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University , Beijing 100875, People’s Republic of China; Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University , Beijing 100875, People’s Republic of China; Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education , Beijing 100875, People’s Republic of China; Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University , Beijing 100875, People’s Republic of China; Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University , Beijing 100875, People’s Republic of China; Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education , Beijing 100875, People’s Republic of China; Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University , Beijing 100875, People’s Republic of China; Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University , Beijing 100875, People’s Republic of China; Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education , Beijing 100875, People’s Republic of China; Faculty of Geographical Science, Beijing Normal University , Beijing 100875, People’s Republic of ChinaCrop 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.https://doi.org/10.1088/1748-9326/ada56erice phenology estimationclimate datasatellite datamachine learninghybrid model |
| spellingShingle | Yiqing Liu Weihang Liu Tao Ye Shuo Chen Xuehong Chen Zitong Li Ning Zhan Ran Sun Incorporating climate data with machine learning can improve rice phenology estimation Environmental Research Letters rice phenology estimation climate data satellite data machine learning hybrid model |
| title | Incorporating climate data with machine learning can improve rice phenology estimation |
| title_full | Incorporating climate data with machine learning can improve rice phenology estimation |
| title_fullStr | Incorporating climate data with machine learning can improve rice phenology estimation |
| title_full_unstemmed | Incorporating climate data with machine learning can improve rice phenology estimation |
| title_short | Incorporating climate data with machine learning can improve rice phenology estimation |
| title_sort | incorporating climate data with machine learning can improve rice phenology estimation |
| topic | rice phenology estimation climate data satellite data machine learning hybrid model |
| url | https://doi.org/10.1088/1748-9326/ada56e |
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