Journal
REMOTE SENSING
Volume 9, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/rs9060608
Keywords
Gross Primary Production (GPP); Remote Sensing (RS); footprint; mismatch; light use efficiency; f(PAR); PRI; semi-empirical GPP model; MODIS GPP
Categories
Funding
- COST Action OPTIMISE a Pacheco-Labrador's Short Scientific Mission at DISAT (University of Milano-Bicocca)
- Spanish Ministry of Economy and Competitiveness [CGL2012-34383]
- EUFAR
- Ministry of Science and Innovation [CGL2008-02301/CLI]
- Ministry of Economy and Competitiveness [AYA2011-29334-C02-01]
- German Aerospace Center (DLR) [50EE1621]
- German Federal Ministry of Economic Affairs and Energy
- MPI-BGC
- Alexander Von Humsboldt Foundation through the Markus Reichstein Prize
Ask authors/readers for more resources
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 (GPP(hh)) 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available