Predicting reproductive phenology of wind-pollinated trees via PlanetScope time series
Airborne pollen triggers allergic reactions, which can have public health consequences. Accurate airborne pollen concentration modeling and prediction rely on understanding plant reproductive phenology, particularly the timing of flowering and pollen release. Flowering and pollen phenology data are...
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Elsevier
2025-06-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000112 |
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| author | Yiluan Song Daniel S.W. Katz Zhe Zhu Claudie Beaulieu Kai Zhu |
| author_facet | Yiluan Song Daniel S.W. Katz Zhe Zhu Claudie Beaulieu Kai Zhu |
| author_sort | Yiluan Song |
| collection | DOAJ |
| description | Airborne pollen triggers allergic reactions, which can have public health consequences. Accurate airborne pollen concentration modeling and prediction rely on understanding plant reproductive phenology, particularly the timing of flowering and pollen release. Flowering and pollen phenology data are often collected through ground observations and air sampling, but such in-situ data collection efforts are expensive and spatially sparse. In contrast to in-situ data collection, satellite-based estimates of plant phenology could potentially enable large-scale data collection, but it is challenging to detect the reproductive phenology of wind-pollinated flowers from space. Here, we infer the reproductive phenology of wind-pollinated plants on the individual tree level and city level using PlanetScope time series with a spatial resolution of 3 m and a daily revisit cycle. We complemented PlanetScope data by in-situ flower and pollen observations at the two scales, leveraging the correlation between vegetative and reproductive phenology. On the individual tree level, we extracted PlanetScope-derived green-up time and validated its correlation to flowering time using flower observations in a national-scale observatory network. Scaling up to the city level, we developed a novel approach to characterize pollen phenology from PlanetScope-derived vegetative phenology, by optimizing two tuning parameters: the threshold of green-up or green-down and the time lag between green-up/down and flowering. We applied this method to seven cities in the US and 14 key wind-pollinated tree genera, calibrated by measurements of airborne pollen concentrations. Our method characterized pollen phenology accurately, not only in-sample (Spearman correlation: 0.751, nRMSE: 13.5 % for Quercus spp.) but also out-of-sample (Spearman correlation: 0.691, nRMSE: 14.5 % for Quercus spp.). Using the calibrated model, we further mapped the pollen phenology landscape within cities, showing intra-urban heterogeneity. Using high spatiotemporal resolution remote sensing, our novel approach enables us to infer the flowering and pollen phenology of wind-pollinated plant taxa on a large scale and a fine resolution, including areas with limited prior in-situ flower and pollen observations. The use of PlanetScope time series therefore holds promise for developing process-based pollen models and targeted public health strategies to mitigate the impact of allergenic pollen exposure. |
| format | Article |
| id | doaj-art-190cd3ba36294eb88d9220fabb1da6c8 |
| institution | OA Journals |
| issn | 2666-0172 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | Science of Remote Sensing |
| spelling | doaj-art-190cd3ba36294eb88d9220fabb1da6c82025-08-20T02:36:16ZengElsevierScience of Remote Sensing2666-01722025-06-011110020510.1016/j.srs.2025.100205Predicting reproductive phenology of wind-pollinated trees via PlanetScope time seriesYiluan Song0Daniel S.W. Katz1Zhe Zhu2Claudie Beaulieu3Kai Zhu4School for Environment and Sustainability and Institute for Global Change Biology, University of Michigan, Ann Arbor, MI, USA; Michigan Institute for Data and AI in Society, University of Michigan, Ann Arbor, MI, USA; Department of Environmental Studies, University of California, Santa Cruz, CA, USA; Corresponding author. School for Environment and Sustainability and Institute for Global Change Biology, University of Michigan, Ann Arbor, MI, USA.School of Integrative Plant Science, Cornell University, Ithaca, NY, USADepartment of Natural Resources and the Environment, University of Connecticut, Storrs, CT, USADepartment of Ocean Sciences, University of California, Santa Cruz, CA, USASchool for Environment and Sustainability and Institute for Global Change Biology, University of Michigan, Ann Arbor, MI, USA; Michigan Institute for Data and AI in Society, University of Michigan, Ann Arbor, MI, USA; Department of Environmental Studies, University of California, Santa Cruz, CA, USA; Corresponding author. School for Environment and Sustainability and Institute for Global Change Biology, University of Michigan, Ann Arbor, MI, USA.Airborne pollen triggers allergic reactions, which can have public health consequences. Accurate airborne pollen concentration modeling and prediction rely on understanding plant reproductive phenology, particularly the timing of flowering and pollen release. Flowering and pollen phenology data are often collected through ground observations and air sampling, but such in-situ data collection efforts are expensive and spatially sparse. In contrast to in-situ data collection, satellite-based estimates of plant phenology could potentially enable large-scale data collection, but it is challenging to detect the reproductive phenology of wind-pollinated flowers from space. Here, we infer the reproductive phenology of wind-pollinated plants on the individual tree level and city level using PlanetScope time series with a spatial resolution of 3 m and a daily revisit cycle. We complemented PlanetScope data by in-situ flower and pollen observations at the two scales, leveraging the correlation between vegetative and reproductive phenology. On the individual tree level, we extracted PlanetScope-derived green-up time and validated its correlation to flowering time using flower observations in a national-scale observatory network. Scaling up to the city level, we developed a novel approach to characterize pollen phenology from PlanetScope-derived vegetative phenology, by optimizing two tuning parameters: the threshold of green-up or green-down and the time lag between green-up/down and flowering. We applied this method to seven cities in the US and 14 key wind-pollinated tree genera, calibrated by measurements of airborne pollen concentrations. Our method characterized pollen phenology accurately, not only in-sample (Spearman correlation: 0.751, nRMSE: 13.5 % for Quercus spp.) but also out-of-sample (Spearman correlation: 0.691, nRMSE: 14.5 % for Quercus spp.). Using the calibrated model, we further mapped the pollen phenology landscape within cities, showing intra-urban heterogeneity. Using high spatiotemporal resolution remote sensing, our novel approach enables us to infer the flowering and pollen phenology of wind-pollinated plant taxa on a large scale and a fine resolution, including areas with limited prior in-situ flower and pollen observations. The use of PlanetScope time series therefore holds promise for developing process-based pollen models and targeted public health strategies to mitigate the impact of allergenic pollen exposure.http://www.sciencedirect.com/science/article/pii/S2666017225000112Airborne pollenFlowering phenologyTime series analysisLeaf-out phenology |
| spellingShingle | Yiluan Song Daniel S.W. Katz Zhe Zhu Claudie Beaulieu Kai Zhu Predicting reproductive phenology of wind-pollinated trees via PlanetScope time series Science of Remote Sensing Airborne pollen Flowering phenology Time series analysis Leaf-out phenology |
| title | Predicting reproductive phenology of wind-pollinated trees via PlanetScope time series |
| title_full | Predicting reproductive phenology of wind-pollinated trees via PlanetScope time series |
| title_fullStr | Predicting reproductive phenology of wind-pollinated trees via PlanetScope time series |
| title_full_unstemmed | Predicting reproductive phenology of wind-pollinated trees via PlanetScope time series |
| title_short | Predicting reproductive phenology of wind-pollinated trees via PlanetScope time series |
| title_sort | predicting reproductive phenology of wind pollinated trees via planetscope time series |
| topic | Airborne pollen Flowering phenology Time series analysis Leaf-out phenology |
| url | http://www.sciencedirect.com/science/article/pii/S2666017225000112 |
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