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  • Presentation | B11C: Land Surface Phenology: Advances in Time Series Analysis and Phenological Detection, Modeling, and Prediction Through Emerging Technologies and Multiscale Observations I Oral
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  • B11C-06: Identify and track white flower and leaf phenology of deciduous broadleaf trees in spring with time series PlanetScope images
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  • Board 1652‚ 260
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Author(s):
Baihong Pan, University of Oklahoma Norman Campus (First Author, Presenting Author)
Xiangming Xiao, School of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma
Shanshan Luo, University of Oklahoma Norman Campus
Li Pan, University of Oklahoma
Yuan Yao, Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma
Chenchen Zhang, School of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma
Cheng Meng, University of Oklahoma Norman Campus
Yuanwei Qin, Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma


In spring, many deciduous broadleaf trees first grow flowers and then green leaves. These events mark the start of reproduction and growth. Trees that flower early help pollinators like bees, support biodiversity, and bring tourism benefits.


This study focuses on Callery Pear trees, which are common in many regions. We used ground photos and satellite data from spring 2024 to track when these trees bloom and when their green leaves appear. We analyzed images from three satellites: PlanetScope (3-meter spatial resolution, daily), Sentinel-2 (10-meter, every 5 days), and HLS (30-meter, every 2 to 3 days).


We developed two indicators. The White Flower Index (WFI) detects white flowers using blue, green, and red bands. The Chlorophyll and Green Leaf Indicator (CGLI) identifies green leaf growth when green reflectance is higher than both blue and red.


Only PlanetScope, with its high spatial and temporal resolution, clearly captured both flower and leaf timing. Sentinel-2 had too few usable images, and HLS pixels were too large, often mixing trees with other land types.


This study shows that fine-scale, frequent satellite images combined with WFI and CGLI can accurately track flower and leaf changes in spring.




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