Assessing topographic effects on forest responses to drought with multiple seasonal metrics from Sentinel-2

Topography determines run-off direction, redistributes groundwater, and affects land surface solar radiation loads and the associated evaporative forcing, consequently, topography can modulate the impact of drought and heat waves on ecosystems. This topographic modulation effect, which typically occurs at the local scale, is often overlooked when assessing ecosystem drought responses using moderate-to-coarse spatial resolution satellite observations, such as the Moderate-resolution Imaging Spectroradiometer (MODIS) imagery. Sentinel-2 and Landsat imagery with finer resolution are suitable for monitoring changes at the local scale, however, studies relying on single vegetation metrics may fail to get a holistic picture of vegetation drought responses, particularly for forests that have complex physiological mechanisms. Here, we performed a comprehensive assessment of the topographic effects on coniferous forest responses to the severe 2018 drought in Scandinavia, using 6 vegetation seasonal metrics during 2017–2021 from the Sentienl-2 High-Resolution Vegetation Phenology and Productivity (HR-VPP) products. We found significant differences (p < 0.05) between sunny and shady aspects, between higher and lower elevations, and between steep and gentle slopes, regarding the maximum impact time, forest drought resistance, and resilience. Specifically, the sunny aspects and steep slopes were related to higher risks of delayed impacts and low resistance and resilience, and elevation and slope were more powerful in regulating the phenology shift and greening rate loss. We also identified different sensitivity in greenness and productivity to topographic effect and greater sensitivity of spring phenology to topographic differences as compared to autumn phenology. The study demonstrates vegetation drought responses represented by multiple seasonal metrics, reveals the prevalent topographic effects at the local scale, and quantifies the magnitudes of the effects with regional statistics.