Spatio-temporal relationships between optical information and carbon fluxes in a Mediterranean tree-grass ecosystem

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) footprint climatology and c) models of increasing complexity to analyze the impact of these factors on GPP prediction. Analyses are carried out in a Mediterranean Tree-Grass Ecosystem (TGE) that combines vegetation with very different physiologies and structure. Half-hourly GPP (GPPhh) was predicted with relative errors ~36%. Results suggest that at EC footprint scale the ecosystem signals are quite homogeneous despite of trees and grass mixture. Models fit using EC and RS data with different degree of spatial and temporal match did not significantly improved models performance; on the contrary errors resulted explained by meteorological variables. Also, the performance of the different models was quite similar. This suggests that none of the models accurately represented light use efficiency (ε) or the fraction of photosynthetically absorbed radiation (fPAR). 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 included by GPP models in TGE and other heterogeneous ecosystems.

PACHECO-LABRADOR Javier;  EL-MADANY Tarek S.;  MARTIN M.Pilar;  MIGLIAVACCA Mirco;  ROSSINI Micol;  CARRARA Arnaud;  ZARCO TEJADA Pablo Jesus; 

2017-06-26

MDPI AG

JRC106768

2072-4292,   

http://www.mdpi.com/2072-4292/9/6/608,    https://publications.jrc.ec.europa.eu/repository/handle/JRC106768,   

10.3390/rs9060608,