Title: Remote estimation of grassland gross primary production during extreme meteorological seasons
Authors: ROSSINI MicolMIGLIAVACCA M.GALVAGNO MartaMERONI MICHELECOGLIATI SergioCREMONESE EdoardoFAVA FrancescoGITELSON AnatolyJULITTA TommasoMORRA DI CELLA UmbertoSINISCALCO ConsolataCOLOMBO Roberto
Citation: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION vol. 29 p. 1-10
Publisher: ELSEVIER SCIENCE BV
Publication Year: 2014
JRC N°: JRC92454
ISSN: 0303-2434
URI: http://www.sciencedirect.com/science/article/pii/S0303243413001761
http://publications.jrc.ec.europa.eu/repository/handle/JRC92454
DOI: 10.1016/j.jag.2013.12.008
Type: Articles in periodicals and books
Abstract: 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.
JRC Directorate:Sustainable Resources

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