Time series sUAV data reveal moderate accuracy and large uncertainties in spring phenology metric of deciduous broadleaf forest as estimated by vegetation index-based phenological models

Abstract

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.