Tracking seasonal drought effects on ecosystem light use efficiency in a mediterranean forest using climatic and remote sensing data

2012 ◽  
Author(s):  
M.A. Gilabert ◽  
A. Moreno ◽  
F. Maselli ◽  
M. Chiesi ◽  
B. Martinez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Mediterranean Forest ◽  
Light Use Efficiency ◽  
Seasonal Drought ◽  
Sensing Data ◽  
Use Efficiency ◽  
Drought Effects

scholarly journals LINKING PHOTOSYNTHETIC LIGHT USE EFFICIENCY AND OPTICAL VEGETATION ACTIVE INDICATORS: IMPLICATIONS FOR GROSS PRIMARY PRODUCTION ESTIMATION BY REMOTE SENSING

2020 ◽  
Vol V-3-2020 ◽  
pp. 571-578
Author(s):  
S. Wang ◽  
Z. Li ◽  
Y. Zhang ◽  
D. Yang ◽  
C. Ni
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Seasonal Changes ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Canopy Structure ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Light Use Efficiency ◽  
Use Efficiency

Abstract. Over the last 40 years, the light use efficiency (LUE) model has become a popular approach for gross primary productivity (GPP) estimation in the carbon and remote sensing communities. Despite the fact that the LUE model provides a simple but effective way to approximate GPP at ecosystem to global scales from remote sensing data, when implemented in real GPP modelling, however, the practical form of the model can vary. By reviewing different forms of LUE model and their performances at ecosystem to global scales, we conclude that the relationships between LUE and optical vegetation active indicators (OVAIs, including vegetation indices and sun-induced chlorophyll fluorescence-based products) across time and space are key for understanding and applying the LUE model. In this work, the relationships between LUE and OVAIs are investigated at flux-tower scale, using both remotely sensed and simulated datasets. We find that i) LUE-OVAI relationships during the season are highly site-dependent, which is complexed by seasonal changes of leaf pigment concentration, canopy structure, radiation and Vcmax; ii) LUE tends to converge during peak growing season, which enables applying pure OVAI-based LUE models without specifically parameterizing LUE and iii) Chlorophyll-sensitive OVAIs, especially machine-learning-based SIF-like signal, exhibits a potential to represent spatial variability of LUE during the peak growing season.We also show the power of time-series model simulations to improve the understanding of LUE-OVAI relationships at seasonal scale.

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