Improved estimates of arctic land surface phenology using Sentinel-2 time series
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 characterist...
Published in: | Remote Sensing |
---|---|
Main Authors: | , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://ddd.uab.cat/record/233934 |
id |
ftuabarcelonapb:oai:ddd.uab.cat:233934 |
---|---|
record_format |
openpolar |
spelling |
ftuabarcelonapb:oai:ddd.uab.cat:233934 2024-09-09T19:21:45+00:00 Improved estimates of arctic land surface phenology using Sentinel-2 time series Descals, Adrià Yin, Gaofei Peñuelas, Josep Verger, Aleixandre 2020 application/pdf https://ddd.uab.cat/record/233934 eng eng 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 Remote sensing (Basel) Vol. 12, Issue 22 (November 2020), art. 3738 https://ddd.uab.cat/record/233934 urn:10.3390/rs12223738 urn:oai:ddd.uab.cat:233934 urn:articleid:20724292v12n22p3738 open access 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. https://creativecommons.org/licenses/by/4.0/ Land surface phenology Vegetation monitoring Sentinel-2 Arctic Cloud computing Google Earth Engine Article 2020 ftuabarcelonapb 2024-08-06T14:30:50Z 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. Article in Journal/Newspaper Arctic Universitat Autònoma de Barcelona: Dipòsit Digital de Documents de la UAB Arctic The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983) Remote Sensing 12 22 3738 |
institution |
Open Polar |
collection |
Universitat Autònoma de Barcelona: Dipòsit Digital de Documents de la UAB |
op_collection_id |
ftuabarcelonapb |
language |
English |
topic |
Land surface phenology Vegetation monitoring Sentinel-2 Arctic Cloud computing Google Earth Engine |
spellingShingle |
Land surface phenology Vegetation monitoring Sentinel-2 Arctic Cloud computing Google Earth Engine Descals, Adrià Yin, Gaofei Peñuelas, Josep Verger, Aleixandre Improved estimates of arctic land surface phenology using Sentinel-2 time series |
topic_facet |
Land surface phenology Vegetation monitoring Sentinel-2 Arctic Cloud computing Google Earth Engine |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Descals, Adrià Yin, Gaofei Peñuelas, Josep Verger, Aleixandre |
author_facet |
Descals, Adrià Yin, Gaofei Peñuelas, Josep Verger, Aleixandre |
author_sort |
Descals, Adrià |
title |
Improved estimates of arctic land surface phenology using Sentinel-2 time series |
title_short |
Improved estimates of arctic land surface phenology using Sentinel-2 time series |
title_full |
Improved estimates of arctic land surface phenology using Sentinel-2 time series |
title_fullStr |
Improved estimates of arctic land surface phenology using Sentinel-2 time series |
title_full_unstemmed |
Improved estimates of arctic land surface phenology using Sentinel-2 time series |
title_sort |
improved estimates of arctic land surface phenology using sentinel-2 time series |
publishDate |
2020 |
url |
https://ddd.uab.cat/record/233934 |
long_lat |
ENVELOPE(73.317,73.317,-52.983,-52.983) |
geographic |
Arctic The Sentinel |
geographic_facet |
Arctic The Sentinel |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
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 Remote sensing (Basel) Vol. 12, Issue 22 (November 2020), art. 3738 https://ddd.uab.cat/record/233934 urn:10.3390/rs12223738 urn:oai:ddd.uab.cat:233934 urn:articleid:20724292v12n22p3738 |
op_rights |
open access 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. https://creativecommons.org/licenses/by/4.0/ |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
22 |
container_start_page |
3738 |
_version_ |
1809762009725534208 |