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Title: Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data

Abstract

High-quality retrieval of land surface phenology (LSP) is increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. We analyzed four state-of-the-art phenology methods: threshold, logistic-function, moving-average and first derivative based approaches, and retrieved LSP in the North Hemisphere for the period 1999–2017 from Copernicus Global Land Service (CGLS) SPOT-VEGETATION and PROBA-V leaf area index (LAI) 1 km V2.0 time series. We validated the LSP estimates with near-surface PhenoCam and eddy covariance FLUXNET data over 80 sites of deciduous forests. Results showed a strong correlation (R2 > 0.7) between the satellite LSP and ground-based observations from both PhenoCam and FLUXNET for the timing of the start (SoS) and R2 > 0.5 for the end of season (EoS). The threshold-based method performed the best with a root mean square error of ~9 d with PhenoCam and ~7 d with FLUXNET for the timing of SoS (30th percentile of the annual amplitude), and ~12 d and ~10 d, respectively, for the timing of EoS (40th percentile).

Authors:
 [1];  [2]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. CREAF, Bellaterra, Catalonia (Spain); Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Catalonia (Spain). CSIC
  2. Northern Arizona Univ., Flagstaff, AZ (United States). Center for Ecosystem Science and Society; Northern Arizona Univ., Flagstaff, AZ (United States). School of Informatics, Computing and Cyber Systems
Publication Date:
Research Org.:
Princeton Univ., NJ (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1802813
Grant/Contract Number:  
SC0016011
Resource Type:
Accepted Manuscript
Journal Name:
Remote Sensing
Additional Journal Information:
Journal Volume: 12; Journal Issue: 18; Journal ID: ISSN 2072-4292
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; 47 OTHER INSTRUMENTATION; Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology; Land-surface phenology; SPOT-VEGETATION; PROBA-V; leaf area index; PhenoCam; FLUXNET

Citation Formats

Bórnez, Kevin, Richardson, Andrew D., Verger, Aleixandre, Descals, Adrià, and Peñuelas, Josep. Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data. United States: N. p., 2020. Web. doi:10.3390/rs12183077.
Bórnez, Kevin, Richardson, Andrew D., Verger, Aleixandre, Descals, Adrià, & Peñuelas, Josep. Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data. United States. https://doi.org/10.3390/rs12183077
Bórnez, Kevin, Richardson, Andrew D., Verger, Aleixandre, Descals, Adrià, and Peñuelas, Josep. Sat . "Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data". United States. https://doi.org/10.3390/rs12183077. https://www.osti.gov/servlets/purl/1802813.
@article{osti_1802813,
title = {Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data},
author = {Bórnez, Kevin and Richardson, Andrew D. and Verger, Aleixandre and Descals, Adrià and Peñuelas, Josep},
abstractNote = {High-quality retrieval of land surface phenology (LSP) is increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. We analyzed four state-of-the-art phenology methods: threshold, logistic-function, moving-average and first derivative based approaches, and retrieved LSP in the North Hemisphere for the period 1999–2017 from Copernicus Global Land Service (CGLS) SPOT-VEGETATION and PROBA-V leaf area index (LAI) 1 km V2.0 time series. We validated the LSP estimates with near-surface PhenoCam and eddy covariance FLUXNET data over 80 sites of deciduous forests. Results showed a strong correlation (R2 > 0.7) between the satellite LSP and ground-based observations from both PhenoCam and FLUXNET for the timing of the start (SoS) and R2 > 0.5 for the end of season (EoS). The threshold-based method performed the best with a root mean square error of ~9 d with PhenoCam and ~7 d with FLUXNET for the timing of SoS (30th percentile of the annual amplitude), and ~12 d and ~10 d, respectively, for the timing of EoS (40th percentile).},
doi = {10.3390/rs12183077},
journal = {Remote Sensing},
number = 18,
volume = 12,
place = {United States},
year = {Sat Sep 19 00:00:00 EDT 2020},
month = {Sat Sep 19 00:00:00 EDT 2020}
}

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