globalchange  > 气候变化事实与影响
DOI: 10.1016/j.jag.2013.12.008
Scopus记录号: 2-s2.0-84897505864
论文题名:
Remote estimation of grassland gross primary production during extreme meteorological seasons
作者: Rossini M; , Migliavacca M; , Galvagno M; , Meroni M; , Cogliati S; , Cremonese E; , Fava F; , Gitelson A; , Julitta T; , di Cella U; M; , Siniscalco C; , Colombo R
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 29, 期:1
起始页码: 1
结束页码: 10
语种: 英语
英文关键词: Extreme events ; Grassland ; Gross primary production ; Potential photosynthetically active radiation ; PRI ; Vegetation index
Scopus关键词: data acquisition ; eddy covariance ; estimation method ; grassland ; performance assessment ; photosynthetically active radiation ; primary production ; remote sensing ; spectral reflectance ; subalpine environment ; vegetation index
英文摘要: Different models driven by remotely sensed vegetation indexes (VIs) and incident photosynthetically active radiation (PAR) were developed to estimate gross primary production (GPP) in a subalpine grassland equipped with an eddy covariance flux tower. Hyperspectral reflectance was collected using an automatic system designed for high temporal frequency acquisitions for three consecutive years, including one (2011) characterized by a strong reduction of the carbon sequestration rate during the vegetative season. Models based on remotely sensed and meteorological data were used to estimate GPP, and a cross-validation approach was used to compare the predictive capabilities of different model formulations. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterized by a strong seasonal dynamic. Model performances improved when including also PARpotential defined as the maximal value of incident PAR under clear sky conditions in model formulations. Best performing models are based entirely on remotely sensed data. This finding could contribute to the development of methods for quantifying the temporal variation of GPP also on a broader scale using current and future satellite sensors. © 2013 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79714
Appears in Collections:气候变化事实与影响

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作者单位: Remote Sensing of Environmental Dynamics Laboratory, DISAT, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy; Max Planck Institute for Biogeochemistry, Biogeochemical Data Integration Department, Hans Knoll Str 10, Jena, Germany; Agenzia Regionale per la Protezione dell'Ambiente della Valle d'Aosta, Sez. Agenti Fisici, Aosta, Italy; European Commission, DG-JRC, Institute for Environment and Sustainability, Monitoring Agricultural Resources Unit, Ispra, VA, Italy; Center for Advanced Land Management Information Technologies, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68588-0973, United States; Department of Life Science and Systems Biology, University of Torino, Torino, Italy

Recommended Citation:
Rossini M,, Migliavacca M,, Galvagno M,et al. Remote estimation of grassland gross primary production during extreme meteorological seasons[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,29(1)
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