dc.contributor.author |
Descals, Adrià |
dc.contributor.author |
Yin, Gaofei |
dc.contributor.author |
Peñuelas, Josep |
dc.contributor.author |
Verger, Aleixandre |
dc.date |
2020 |
dc.identifier |
https://ddd.uab.cat/record/233934 |
dc.identifier |
10.3390/rs12223738 |
dc.identifier |
oai:ddd.uab.cat:233934 |
dc.identifier |
20724292v12n22p3738 |
dc.format |
application/pdf |
dc.language |
eng |
dc.publisher |
|
dc.relation |
European Commission 610028 |
dc.relation |
European Commission 835541 |
dc.relation |
Ministerio de Ciencia e Innovación PID2019-110521GB-I00 |
dc.relation |
Ministerio de Ciencia e Innovación BES-2017-080197 |
dc.relation |
Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1005 |
dc.relation |
Remote sensing (Basel) ; Vol. 12, Issue 22 (November 2020), art. 3738 |
dc.rights |
open access |
dc.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. |
dc.rights |
https://creativecommons.org/licenses/by/4.0/ |
dc.subject |
Land surface phenology |
dc.subject |
Vegetation monitoring |
dc.subject |
Sentinel-2 |
dc.subject |
Arctic |
dc.subject |
Cloud computing |
dc.subject |
Google Earth Engine |
dc.title |
Improved estimates of arctic land surface phenology using Sentinel-2 time series |
dc.type |
Article |
dc.description.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. |