Remote estimation of grassland gross primary production during extreme 
meteorological seasons

Rossini, Micol; Migliavacca, Mirco; Galvagno, Marta; Meroni, Michele; Cogliati, Sergio; Cremonese, Edoardo; Fava, Francesco; Gitelson, Anatoly; Julitta, Tommaso; di Cella, Umberto Morra; Siniscalco, Consolata; Colombo, Roberto

doi:10.1016/j.jag.2013.12.008

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Remote estimation of grassland gross primary production during extreme meteorological seasons

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Migliavacca, Mirco
Biosphere-Atmosphere Interactions and Experimentation, Dr. M. Migliavacca, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Citation

Rossini, M., Migliavacca, M., Galvagno, M., Meroni, M., Cogliati, S., Cremonese, E., et al. (2014). Remote estimation of grassland gross primary production during extreme meteorological seasons. International Journal of Applied Earth Observation and Geoinformation, 29, 1-10. doi:10.1016/j.jag.2013.12.008.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0015-154E-9

Abstract

Different models driven by remotely sensed vegetation indexes (VIs) and incident photosyntheticallyactive radiation (PAR) were developed to estimate gross primary production (GPP) in a subalpine grass-land equipped with an eddy covariance flux tower. Hyperspectral reflectance was collected using anautomatic system designed for high temporal frequency acquisitions for three consecutive years, includ-ing one (2011) characterized by a strong reduction of the carbon sequestration rate during the vegetativeseason. Models based on remotely sensed and meteorological data were used to estimate GPP, and across-validation approach was used to compare the predictive capabilities of different model formula-tions. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of thevariability in GPP in the ecosystem investigated, characterized by a strong seasonal dynamic. Modelperformances improved when including also PARpotentialdefined as the maximal value of incident PARunder clear sky conditions in model formulations. Best performing models are based entirely on remotelysensed data. This finding could contribute to the development of methods for quantifying the temporalvariation of GPP also on a broader scale using current and future satellite sensors