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Letter

Improved Estimates of Arctic Land Surface Phenology Using Sentinel-2 Time Series

1
CREAF, Cerdanyola del Vallès, 08193 Barcelona, Catalonia, Spain
2
CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain
3
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(22), 3738; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223738
Received: 30 September 2020 / Revised: 1 November 2020 / Accepted: 11 November 2020 / Published: 13 November 2020
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. View Full-Text
Keywords: land surface phenology; vegetation monitoring; Sentinel-2; arctic; cloud computing; Google Earth Engine land surface phenology; vegetation monitoring; Sentinel-2; arctic; cloud computing; Google Earth Engine
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MDPI and ACS Style

Descals, A.; Verger, A.; Yin, G.; Peñuelas, J. Improved Estimates of Arctic Land Surface Phenology Using Sentinel-2 Time Series. Remote Sens. 2020, 12, 3738. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223738

AMA Style

Descals A, Verger A, Yin G, Peñuelas J. Improved Estimates of Arctic Land Surface Phenology Using Sentinel-2 Time Series. Remote Sensing. 2020; 12(22):3738. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223738

Chicago/Turabian Style

Descals, Adrià, Aleixandre Verger, Gaofei Yin, and Josep Peñuelas. 2020. "Improved Estimates of Arctic Land Surface Phenology Using Sentinel-2 Time Series" Remote Sensing 12, no. 22: 3738. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223738

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