Estimation of latent heat flux over savannah vegetation across the North Australian Tropical Transect from multiple sensors and global meteorological data

Publisher:
Elsevier Masson
Publication Type:
Journal Article
Citation:
Agricultural and Forest Meteorology, 2017, 232 pp. 689 - 703
Issue Date:
2017-01-15
Full metadata record
Latent heat flux (LE) and corresponding water loss in non-moisture-limited ecosystems are well corre-lated to radiation and temperature. By contrast, in savannahs and arid and semi-arid lands LE is mostlydriven by available water and the vegetation exerts a strong control over the rate of transpiration.Therefore, LE models that use optical vegetation indices (VIs) to represent the vegetation component(transpiration as a function of surface conductance, Gs) generally overestimate water fluxes in water-limited ecosystems. In this study, we evaluated and compared optical and passive microwave indexbased retrievals of Gsand LE derived using the Penman-Monteith (PM) formulation over the North Aus-tralian Tropical Transect (NATT). The methodology was evaluated at six eddy covariance (EC) sites fromthe OzFlux network. To parameterize the PM equation for retrievals of LE (PM-Gs), a subset of Gsvalueswas derived from meteorological and EC flux observations and regressed against individual and com-bined satellite indices, from (1) MODIS AQUA: the Normalized Difference Water Index (NDWI) and theEnhanced Vegetation Index (EVI); and from (2) AMSR-E passive microwave: frequency index (FI), polar-ization index (PI), vegetation optical depth (VOD) and soil moisture (SM) products. Similarly, we combinedoptical and passive microwave indices (multi-sensor model) to estimate weekly Gsvalues, and evaluatedtheir spatial and temporal synergies. The multi-sensor approach explained 40–80% of LE variance at somesites, with root mean square errors (RMSE) lower than 20 W/m2and demonstrated better performanceto other satellite-based estimates of LE. The optical indices represented potential Gsassociated with thephenological status of the vegetation (e.g. leaf area index, chlorophyll content) at finer spatial resolution.The microwave indices provided information about water availability and moisture stress (e.g. watercontent in leaves and shallow soil depths, atmospheric demand) at a high temporal resolution, therebyproviding a scaling factor for potential Gs. We applied the newly proposed Gsmodel to estimate LE atregional scale using global meteorological data. Our derivation could be extended to continental scalesproviding equally robust estimates of LE in arid and semi-arid biomes. A more accurate estimation of Gsand LE across different savannah classes will improve the analysis of water use efficiency under droughtconditions, which is of importance to climate change studies of water, carbon and energy cycling.
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