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

Javier Pacheco-Labrador,Arnaud Carrara,Tarek El-Madany,Mirco Migliavacca,M Martín,Micol Rossini,Pablo J Zarco-Tejada

doi:10.3390/rs9060608

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.

Highlights

Monitoring of terrestrial carbon fluxes is critical to assess impact of human activities and ClimateChange in ecosystem functions and distribution [1]
Carbon exchanges are measured using the eddy covariance technique [6]. This approach is spatially limited because the spatial representativeness of the EC flux data is limited (from few meters to ~1 km2 around the Eddy Covariance (EC) system depending on the measurement and canopy height)
The impact of pixel-footprint spatial mismatch on Gross Primary Production (GPP) estimation becomes more acute in heterogeneous environments that combine species with different ecological and phenological strategies or different land uses [12,13,14]

Summary

Introduction

Monitoring of terrestrial carbon fluxes is critical to assess impact of human activities and ClimateChange in ecosystem functions and distribution [1]. The footprint climatology has been demonstrated as an essential tool to get information about the vegetation sampled by the EC flux measurements [7] In this context, Remote Sensing (RS) becomes an opportunity to provide exhaustive spatial information of plant function and structure and estimate carbon uptake at global scale [8]. EC footprint shows a large variability driven by wind direction and atmospheric stability that requires high or medium RS spatial resolution images to be characterized [7,11]. Such spatial resolutions can only be reached at global scale at the expenses of frequency of acquisition. The impact of pixel-footprint spatial mismatch on GPP estimation becomes more acute in heterogeneous environments that combine species with different ecological and phenological strategies or different land uses [12,13,14]

Methods

Results

Discussion

Conclusion