Journal
REMOTE SENSING
Volume 12, Issue 22, Pages -Publisher
MDPI
DOI: 10.3390/rs12223738
Keywords
land surface phenology; vegetation monitoring; Sentinel-2; arctic; cloud computing; Google Earth Engine
Categories
Funding
- European Research Council ERC [SyG-610028]
- Marie Sklodowska-Curie grant of European Union's Horizon 2020 research and innovation programme [835541]
- Spanish project [PID2019-110521GB-I00]
- Ministry of Science and Innovation of Spain FPI grant [BES-2017-080197]
- Catalan grant [SGR 2017-1005]
- Marie Curie Actions (MSCA) [835541] Funding Source: Marie Curie Actions (MSCA)
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The high spatial resolution and revisit time of Sentinel-2A/B tandem satellites allow a potentially improved retrieval of land surface phenology (LSP). The biome and regional characteristics, however, greatly constrain the design of the LSP algorithms. In the Arctic, such biome-specific characteristics include prolonged periods of snow cover, persistent cloud cover, and shortness of the growing season. Here, we evaluate the feasibility of Sentinel-2 for deriving high-resolution LSP maps of the Arctic. We extracted the timing of the start and end of season (SoS and EoS, respectively) for the years 2019 and 2020 with a simple implementation of the threshold method in Google Earth Engine (GEE). We found a high level of similarity between Sentinel-2 and PhenoCam metrics; the best results were observed with Sentinel-2 enhanced vegetation index (EVI) (root mean squared error (RMSE) and mean error (ME) of 3.0 d and -0.3 d for the SoS, and 6.5 d and -3.8 d for the EoS, respectively), although other vegetation indices presented similar performances. The phenological maps of Sentinel-2 EVI compared well with the same maps extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) in homogeneous landscapes (RMSE and ME of 9.2 d and 2.9 d for the SoS, and 6.4 and -0.9 d for the EoS, respectively). Unreliable LSP estimates were filtered and a quality flag indicator was activated when the Sentinel-2 time series presented a long period (>40 d) of missing data; discontinuities were lower in spring and early summer (9.2%) than in late summer and autumn (39.4%). The Sentinel-2 high-resolution LSP maps and the GEE phenological extraction method will support vegetation monitoring and contribute to improving the representation of Artic vegetation phenology in land surface models.
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