Snow persistence influences vegetation metrics central to Arctic greening analyses

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Satellite imagery is critical for understanding land-surface change in the rapidly warming Arctic. Since the 1980s, studies have found positive trends in the normalised difference vegetation index (NDVI) derived from satellite imagery over the Arctic - commonly referred to as ‘Arctic greening’ and assumed to represent increased vegetation productivity. However, greening analyses use satellite imagery with pixel sizes ranging from tens to hundreds of metres and do not account for the integration of abiotic phenomena such as snow within vegetation indices. Here, we use high resolution drone data from one Arctic and one sub-Arctic site to show that fine-scale snow persistence within satellite pixels is associated with both reduced magnitude and delayed timing of annual peak NDVI, the base metric of Arctic greening analyses. We found higher snow persistence within Sentinel-2 pixels is associated with a lower magnitude and later peak NDVI, with a mean difference in NDVI of 0.088 and seven days between high and low snow persistence pixels. These effects were stronger in NASA HLSS30 data, representative of Landsat data commonly used in greening analyses. Our findings indicate that unaccounted changes in fine-scale snow persistence may contribute to Arctic spectral greening and browning trends through either ecological responses of vegetation to snow cover or abiotic interactions between snow and the estimated peak NDVI. In order to improve our understanding of Arctic land-surface change, studies should integrate very-high-resolution data to estimate the dynamics of late season snow within coarser satellite pixels. https://doi.org/10.32942/X29G88 Environmental Monitoring Arctic greening, snow, Spatial resolution, NDVI, drones, plants, phenology Published: 2024-07-20 03:54 CC BY Attribution 4.0 International Data and Code Availability Statement: The data and code that support the findings of this study are openly available at the following URL/DOI: https://github.com/calumhoad/snowpersistence/tree/main Language: English