Intercomparison and evaluation of spring phenology products using National Phenology Network and AmeriFlux observations in the contiguous United States

Dailiang Peng, Xiaoyang Zhang, Chaoyang Wu, Wenjiang Huang, Alemu Gonsamo, Alfredo R. Huete, Kamel Didan, Bin Tan, Xinjie Liu, Bing Zhang

Research output: Contribution to journalArticle

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Abstract

Many remote sensing based spring phenology products have been developed to monitor and study vegetation phenology at regional and global scales. It is important to understand how these products perform relative to each other and to ground observations. In this study, we extracted spring green-up onset dates (GUD) over the contiguous United States (CONUS) from six major land surface phenology (LSP) products: (1) Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics Phenology (MCD12Q2); (2) Vegetation Index and Phenology Multi-sensor Phenology (VIPPHENEVI2); (3) Global Long-Term Climate Modeling Grid Land Surface Phenology (CMGLSP); (4 and 5) North American Carbon Program (NACP) Phenology (MOD09Q1PEVI and MOD15PHN); and (6) USGS/EROS advanced very high resolution radiometer (AVHRR) phenology (AVHRRP). We characterized and compared the GUD data in these LSP products, and evaluated their accuracy using ground-based phenology observations [i.e., human observations of first leaf and sensor readings of gross primary productivity (GPP)] from the USA National Phenology Network (USA-NPN) and AmeriFlux. The results revealed the consistencies and discrepancies of GUD estimates among LSP products. Intercomparison of the six products indicated that the root mean square error (RMSE) of these products range from 17.8 days to 31.5 days, whereas AVHRRP GUD has the lowest correlation and largest RMSE (∼30 days) relative to other products. When compared to ground observations, GUD estimates in six LSP products generally have RMSE values of ∼20 days and significant correlations (p < 0.001). For the products (MCD12Q2, AVHRRP, MOD09Q1PEVI, and MOD15PHN) available for comparisons in the short-term period (from 2001–2007), AVHRRP GUD presented relatively weaker correlations and a lower index of agreement (IOA), however, MCD12Q2 GUD showed overall slightly better consistencies with ground observations. In the two long-term products (CMGLSP and VIPPHENEVI2 from 1982–2013), CMGLSP exhibited stronger correlations, lower RMSE, and higher IOA with ground observations of the first leaf dates than VIPPHENEVI2 did. To our knowledge, our study provides the first comprehensive evaluation of phenology products using two independent ground-based datasets.

Original languageEnglish (US)
Pages (from-to)33-46
Number of pages14
JournalAgricultural and Forest Meteorology
Volume242
DOIs
StatePublished - Aug 15 2017

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phenology
AVHRR
land surface
radiometers
product
evaluation
climate modeling
climate
sensors (equipment)
sensor
moderate resolution imaging spectroradiometer
vegetation index
land cover

Keywords

  • Evaluation
  • First leaf dates
  • Green-up onset date
  • Land surface phenology
  • Remote sensing

ASJC Scopus subject areas

  • Forestry
  • Global and Planetary Change
  • Agronomy and Crop Science
  • Atmospheric Science

Cite this

Intercomparison and evaluation of spring phenology products using National Phenology Network and AmeriFlux observations in the contiguous United States. / Peng, Dailiang; Zhang, Xiaoyang; Wu, Chaoyang; Huang, Wenjiang; Gonsamo, Alemu; Huete, Alfredo R.; Didan, Kamel; Tan, Bin; Liu, Xinjie; Zhang, Bing.

In: Agricultural and Forest Meteorology, Vol. 242, 15.08.2017, p. 33-46.

Research output: Contribution to journalArticle

Peng, Dailiang ; Zhang, Xiaoyang ; Wu, Chaoyang ; Huang, Wenjiang ; Gonsamo, Alemu ; Huete, Alfredo R. ; Didan, Kamel ; Tan, Bin ; Liu, Xinjie ; Zhang, Bing. / Intercomparison and evaluation of spring phenology products using National Phenology Network and AmeriFlux observations in the contiguous United States. In: Agricultural and Forest Meteorology. 2017 ; Vol. 242. pp. 33-46.
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AU - Zhang, Xiaoyang

AU - Wu, Chaoyang

AU - Huang, Wenjiang

AU - Gonsamo, Alemu

AU - Huete, Alfredo R.

AU - Didan, Kamel

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