TY - JOUR
T1 - Remote estimation of grassland gross primary production during extreme meteorological seasons
AU - Rossini, Micol
AU - Migliavacca, Mirco
AU - Galvagno, Marta
AU - Meroni, Michele
AU - Cogliati, Sergio
AU - Cremonese, Edoardo
AU - Fava, Francesco
AU - Gitelson, Anatoly
AU - Julitta, Tommaso
AU - di Cella, Umberto Morra
AU - Siniscalco, Consolata
AU - Colombo, Roberto
N1 - Funding Information:
This study was supported by the PhenoALP project, an Interreg project co-funded by the European Regional Development Fund , under the operational program for territorial cooperation Italy-France (ALCOTRA) 2007–2013. The authors acknowledge the staff of the Remote Sensing of Environmental Dynamics Laboratory (DISAT, UNIMIB) for the support during field campaigns.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Extreme events
KW - Grassland
KW - Gross primary production
KW - PRI
KW - Potential photosynthetically active radiation
KW - Vegetation index
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U2 - 10.1016/j.jag.2013.12.008
DO - 10.1016/j.jag.2013.12.008
M3 - Article
AN - SCOPUS:84897505864
VL - 29
SP - 1
EP - 10
JO - International Journal of Applied Earth Observation and Geoinformation
JF - International Journal of Applied Earth Observation and Geoinformation
SN - 1569-8432
IS - 1
ER -