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Remote Sens. 2017, 9(6), 608; doi:10.3390/rs9060608

Spatio-Temporal Relationships between Optical Information and Carbon Fluxes in a Mediterranean Tree-Grass Ecosystem

1
Max Planck Institute for Biogeochemistry, Hans Knöll Straße 10, Jena D-07745, Germany
2
Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab), Institute of Economic, Geography and Demography (IEGD-CCHS), Spanish National Research Council (CSIC), C/Albasanz 26-28, 28037 Madrid, Spain
3
Remote Sensing of Environmental Dynamics Laboratory, Department of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
4
Fundación Centro de Estudios Ambientales del Mediterráneo (CEAM), Charles Darwin 14, Parc Tecnològic, 46980 Paterna, Spain
5
European Commission, Joint Research Centre (JRC), Directorate D—Sustainable Resources, Via E. Fermi 2749, I-21027 Ispra, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Alfredo R. Huete and Prasad S. Thenkabail
Received: 16 March 2017 / Revised: 24 May 2017 / Accepted: 8 June 2017 / Published: 14 June 2017
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Abstract

Spatio-temporal mismatches between Remote Sensing (RS) and Eddy Covariance (EC) data as well as spatial heterogeneity jeopardize terrestrial Gross Primary Production (GPP) modeling. This article combines: (a) high spatial resolution hyperspectral imagery; (b) EC footprint climatology estimates; and (c) semi-empirical models of increasing complexity to analyze the impact of these factors on GPP estimation. Analyses are carried out in a Mediterranean Tree-Grass Ecosystem (TGE) that combines vegetation with very different physiologies and structure. Half-hourly GPP (GPPhh) were predicted with relative errors ~36%. Results suggest that, at EC footprint scale, the ecosystem signals are quite homogeneous, despite tree and grass mixture. Models fit using EC and RS data with high degree of spatial and temporal match did not significantly improved models performance; in fact, errors were explained by meteorological variables instead. In addition, the performance of the different models was quite similar. This suggests that none of the models accurately represented light use efficiency or the fraction of absorbed photosynthetically active radiation. This is partly due to model formulation; however, results also suggest that the mixture of the different vegetation types might contribute to hamper such modeling, and should be accounted for GPP models in TGE and other heterogeneous ecosystems. View Full-Text
Keywords: Gross Primary Production (GPP); Remote Sensing (RS); footprint; mismatch; light use efficiency; fPAR; PRI; semi-empirical GPP model; MODIS GPP Gross Primary Production (GPP); Remote Sensing (RS); footprint; mismatch; light use efficiency; fPAR; PRI; semi-empirical GPP model; MODIS GPP
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pacheco-Labrador, J.; El-Madany, T.S.; Martín, M.P.; Migliavacca, M.; Rossini, M.; Carrara, A.; Zarco-Tejada, P.J. Spatio-Temporal Relationships between Optical Information and Carbon Fluxes in a Mediterranean Tree-Grass Ecosystem. Remote Sens. 2017, 9, 608.

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