A land surface temperature model-data differencing approach to quantifying subsurface water use by vegetation: Application in the Condamine region, south-eastern Queensland

Abstract

Water plays a crucial role in plant community and landscape function; plants use stomata to regulate water loss via transpiration as the 'cost' of carbon assimilation, which in turn regulates surface temperature of leaves. Presently there are a number of aspects of vegetation water-use dynamics that are poorly understood, including the degree of dependence on water sources at different depths in the soil profile; timing, frequency, duration and magnitude of water use; and ecosystem strategies to acquiring water from different sources as water availability varies.The research presented in this thesis aimed to: (1) quantify timing and frequency of the use of subsurface water by woody vegetation, (2) quantify the confidence of these predictions, and (3) identify subsurface water-use strategies and physiological responses employed by woody vegetation with variation in climatic and water-deficit conditions. These aims were achieved through development, application and assessment of a novel land surface temperature (LST) model-data differencing approach applied to a subtropical woodland environment in eastern Australia. The approach detects subsurface water use by vegetation in space and time within 95% confidence intervals, through differences in modelled LST (Ts.mod) and satellite observations of LST (Ts.obs) after accounting for random and systematic error in the model and data.Modelled LST was derived using a two-layer surface energy balance (SEB) model and captures water use from surface water and soil water in the top 30 cm of the soil profile (i.e. shallow soil water). Satellite observations of LST were obtained from Terra-MODIS thermal infrared imagery and captures water use from all available sources. When compared, temperature differences between independently-derived Ts.mod and Ts.obs reveal subsurface water use (i.e. below 30 cm depth) plus systematic and random error. Systematic error or bias, was estimated from temperature differences of grassland vegetation based on an assumption that these vegetation communities do not access subsurface water under certain climatic conditions. Therefore Ts.mod should equal Ts.obs.Random error in Ts.obs was estimated from the literature while sensitivity and uncertainty analyses were used to quantify Ts.mod random error arising from five SEB model resistance terms and their associated input parameters. A Student's t-test was then used to account for systematic and random error in the detection of subsurface water use by vegetation within 95% confidence intervals.The LST model-data differencing approach was applied in a 3200 km 2 region of the subtropical Condamine River Catchment, south-eastern Queensland Australia. The study area contains a mixture of native remnant vegetation, agriculturally-intensive areas, and coal seam gas development. Extensive eucalypt woodlands dominate low-lying hill slopes, while rainfed pastoral grasses and irrigated crops are common throughout the alluvial floodplain and topographic lows adjacent to hill slopes. Timing and frequency ...

Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review

“…There are now many examples of these types of models. For instance, Watts et al (2014) [23] extended a terrestrial flux model that allows for satellite data as primary inputs to estimate CO 2 and CH 4 fluxes, and Gow (2016) combined satellite observations of land surface temperature with a surface energy balance model to estimate groundwater use by vegetation [24]. These physics-based approaches are typically based on sophisticated algorithms that consider sensor performance and multiple environmental impacts from the atmosphere and sea surface, as well as the optical properties of the water body and seafloor.…”