Improved estimates of arctic land surface phenology using Sentinel-2 time series
Descals, Adrià (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Yin, Gaofei (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Peñuelas, Josep (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Verger, Aleixandre (Centre de Recerca Ecològica i d'Aplicacions Forestals)

Date:	2020
Abstract:	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.
Grants:	European Commission 610028 European Commission 835541 Ministerio de Ciencia e Innovación PID2019-110521GB-I00 Ministerio de Ciencia e Innovación BES-2017-080197 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1005
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Land surface phenology ; Vegetation monitoring ; Sentinel-2 ; Arctic ; Cloud computing ; Google Earth Engine
Published in:	Remote sensing (Basel), Vol. 12, Issue 22 (November 2020) , art. 3738, ISSN 2072-4292

DOI: 10.3390/rs12223738

13 p, 8.2 MB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Experimental sciences > CREAF (Centre de Recerca Ecològica i d'Aplicacions Forestals) > Imbalance-P
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