Accurate delineation of spring phenology (e.g., start of growing season, SOS) of deciduous forests is essential for understanding its responses to environmental changes. To date, SOS dates from analyses of satellite images and vegetation index (VI) −based phenological models have notable discrepancies but they have not been fully evaluated, primarily due to the lack of ground reference data for evaluation. This study evaluated the SOS dates of a deciduous broadleaf forest estimated by VI-based phenological models from three satellite sensors (PlanetScope, Sentinel-2A/B, and Landsat-7/8/9) by using ground reference data collected by a small unmanned aerial vehicle (sUAV). Daily sUAV imagery (0.035-meter resolution) was used to identify and generate green leaf maps. These green leaf maps were further aggregated to generate Green Leaf Fraction (GLF) maps at the spatial resolutions of PlanetScope (3-meter), Sentinel-2A/B (10-meter), and Landsat-7/8/9 (30-meter). The temporal changes of GLF differ from those of vegetation indices in spring, with the peak dates of GLF being much earlier than those of VIs. At the SOS dates estimated by VI-based phenological models in 2022 (Julian days from 105 to 111), GLF already ranges from 62% to 96%. The moderate accuracy and large uncertainties of SOS dates from VI-based phenological models arise from the limitations of vegetation indices in accurately tracking the number of green leaves and the inherent uncertainties of the mathematical models used. The results of this study clearly highlight the need for new research on spring phenology of deciduous forests.

中文翻译：

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时间序列 sUAV 数据揭示了基于植被指数的物候模型估计的落叶阔叶林春季物候指标的中等精度和较大的不确定性


准确描绘落叶林的春季物候（例如，生长季节的开始，SOS）对于了解其对环境变化的响应至关重要。迄今为止，来自卫星图像分析和基于植被指数 （VI） 的物候模型的 SOS 日期存在显着差异，但尚未得到充分评估，主要是由于缺乏用于评估的地面参考数据。本研究使用小型无人机 （sUAV） 收集的地面参考数据，评估了由三个卫星传感器（PlanetScope、Sentinel-2A/B 和 Landsat-7/8/9）的基于 VI 的物候模型估计的落叶阔叶林的 SOS 日期。每日 sUAV 图像（0.035 米分辨率）用于识别和生成绿叶地图。这些绿叶地图被进一步聚合，以生成 PlanetScope（3 米）、Sentinel-2A/B（10 米）和 Landsat-7/8/9（30 米）空间分辨率的绿叶分数 （GLF） 地图。GLF 的时间变化与春季植被指数的时间变化不同，GLF 的峰值日期远早于 VIs。在 2022 年基于 VI 的物候模型估计的 SOS 日期（儒略日从 105 到 111）时，GLF 已经在 62% 到 96% 之间。基于 VI 的物候模型的 SOS 测年具有中等准确性和较大的不确定性，这是由于植被指数在准确跟踪绿叶数量方面的局限性以及所用数学模型的固有不确定性。这项研究的结果清楚地强调了对落叶林春季物候进行新研究的必要性。