Profile

Azza Gorrab ep El Khedhri
Postdoctoral fellow
Department of Environmental Sciences
UQTR
azzagorrab@gmail.com

Supervised by:

Alexandre Roy (Regular member)

Co-supervised by:

Oliver Sonnentag (Regular member)

Research project description

Monitoring tree hydraulic traits from vegetation optical depth (VOD) products derived from passive microwave observations: the case of the boreal forest

Introduction: The characterization of the hydraulic functioning of trees in the Canadian boreal forest is essential to understand the vulnerability of the forest to climate change, particularly periods of drought, extreme temperatures, and freeze-thaw cycles. The water content of the vegetation is a key parameter for monitoring the evolution of the water status of trees, which could condition the productivity of our forests. During the last decades, different studies have been carried out with passive microwave radiometers for the characterization of the vegetation at a global scale and a high revisit frequency. These works have proven the relevance of radiometers for the study of the aerial biomass and the water status of the vegetation, especially in L-band. Derived from passive microwaves, the vegetation optical depth (VOD) has shown its strong sensitivity to the water content of the vegetation: the more the vegetation cover is waterlogged, the more the VOD increases. Objectives: In this context, we propose to analyze the potential of VOD products to restore the specific hydraulic characteristics of the boreal forest. For this analysis, we will consider interannual and seasonal variations of VOD products derived from different L-band radiometers (SMOS and SMAP). Study sites: We operate a large in situ database from 21 boreal research sites covering the width of Canada. At several of these sites, we have continuous measurements of sap flow density (heat dissipation probe), stem water content (frequency domain reflectometry) and diameter (automatic girth dendrometer). Another set of meteorological and environmental data is available and acquired at half-hourly time steps, including wind speed and direction, precipitation, soil moisture and temperature at different depths, snow depth, net radiation, air temperature and relative humidity, etc., as well as repeated records of permafrost conditions (such as frost depth) and vegetation structure (such as leaf area index). Material and methods: In the first part, we work on the optimization of the SMOS-IC model inversion in boreal forest (determine a new parameterization of soil roughness, vegetation parameters, albedo, integration of the water fraction in the calculation of soil brightness temperature...) in order to obtain better inversions of VOD and soil moisture. In the second part, we will establish empirical relationships between SMOS and SMAP L-VOD products and tree hydraulic traits (in particular trunk diameter growth, gross sap flow, soil water potential/vegetation...). In addition, soil moisture products derived from SMOS and SMAP will be analyzed in comparison to our in-situ measurements. Expected results: It is recognized that microwave satellite products of moisture and optical depth (VOD) provide rather poor results in the boreal forest. New calibrated values of soil roughness and simple scattering albedo parameters will be defined based on comparisons of model-simulated and observed moisture at numerous boreal forest sites. These values will allow for better correlation and proportionality of VOD estimates with tree hydraulic traits, including diameter and gross trunk sap measurements that are directly related to tree moisture content. Improved correlation of SMOS-IC products (moisture and VOD) should be observed by incorporating the water fraction into the calculation of soil brightness temperature within the model. The improved estimates of vegetation water status, through VOD, will be used to improve the description of tree hydraulics in a flux estimation model (CLASSIC, Canadian Land Surface Scheme), for better adaptation to the effects of extreme climate change.

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