Spatial and temporal patterns of land surface fluxes from remotely sensed surface temperatures within an uncertainty modelling framework

Abstract

Abstract. Characterising the development of evapotranspiration through time is a difficult task, particularly when utilising remote sensing data, because retrieved information is often spatially dense, but temporally sparse. Techniques to expand these essentially instantaneous measures are not only limited, they are restricted by the general paucity of information describing the spatial distribution and temporal evolution of evaporative patterns. In a novel approach, temporal changes in land surface temperatures, derived from NOAA-AVHRR imagery and a generalised split-window algorithm, are used as a calibration variable in a simple land surface scheme (TOPUP) and combined within the Generalised Likelihood Uncertainty Estimation (GLUE) methodology to provide estimates of areal evapotranspiration at the pixel scale. Such an approach offers an innovative means of transcending the patch or landscape scale of SVAT type models, to spatially distributed estimates of model output. The resulting spatial and temporal patterns of land surface fluxes and surface resistance are used to more fully understand the hydro-ecological trends observed across a study catchment in eastern Australia. The modelling approach is assessed by comparing predicted cumulative evapotranspiration values with surface fluxes determined from Bowen ratio systems and using auxiliary information such as in-situ soil moisture measurements and depth to groundwater to corroborate observed responses.

High-resolution sensing for precision agriculture: from Earth-observing satellites to unmanned aerial vehicles

“…Over the last few decades, there has been considerable effort directed towards the estimation of a range of terrestrial variables, such as land cover changes 13 , land surface fluxes 6,14 and vegetation retrievals 15 via an array of government agency led space satellite programs. These include sensors and satellites that have been launched by the National Aeronautics and Space Administration (NASA), the European Space Agency (ESA), Japan's Aerospace Exploration Agency (JAXA) and more recently the Chinese National Space Agency (CNSA).…”

“…Through detailed observation and monitoring of agricultural systems, precision farming essentially aims to optimize the use of inputs (water, nutrients, chemicals) in order to maximize outputs (the crop yield). To do this demands that a wide range of information be collected in a dynamic manner, mapping both the spatial and temporal change in variables such as soil moisture 5 , evaporation 6 , vegetation indices 7 , trace elements and other metrics of crop development and health 8 . Given the scale of agricultural operations, the only feasible option to monitor such systems on a regional scale is via remote sensing 9 .…”

Actual daily evapotranspiration estimated from MERIS and AATSR data over the Chinese Loess Plateau

“…In the past decades, many algorithms using remote sensing information to estimate ET have been developed, and from simple and empirical approaches to complex and data consuming ones. These can be put into two groups broadly: (1) to estimate ET by set empirical relation between ET and parameters (vegetation index, temperature, albedo) that could be measured from meteorological satellites (Index, 1994;Boegh, 2002;McCabe et al, 2005); (2) to calculate the sensible heat flux first and then obtain the latent heat flux as the residual of the energy balance equation, SEBAL (Bastiaanssen et al, 1998), SEBS (Su et al, 1999;Su, 2002), NTDI (McVicar and Jupp, 1999). Most models require a complex and high-quality input data to obtain accurate results and each method has limitations (Bashir et al, 2008).…”

Examining the relationship between intermediate-scale soil moisture and terrestrial evaporation within a semi-arid grassland

“…Although coupling between evaporation and soil moisture is expected to be high in arid and semi-arid regions, the dynamics of surfaceatmosphere feedbacks are not well understood in such environments (Wang et al, 2012). Several studies have attempted to describe these links, with the aim of predicting one variable through knowledge of the other (Mintz and Walker, 1993;Wetzel and Chang, 1987) or to use developed relationships to inform upon linked hydrological responses such as evaporation (McCabe et al, 2005;Stisen et al, 2011), soil moisture (Liu et al, 2012), drought (Entekhabi et al, 1992;Fischer et al, 2007;Oglesby and Erickson, 1989), precipitation (Findell et al, 2011;Held et al, 2005), and even vegetation response to soil moisture stress (Liu et al, 2011). A common feature of such studies is the use of model estimates of terrestrial evaporation across a wide range of study areas and land cover and biome types.…”