Introduction

River basin management continues to challenge humanity worldwide in finding a balance between anthropogenic hydrological water needs and those of the environment (Hoekstra et al. , 2012; Wheeler et al. , 2017; Bouckaert et al. , 2018). Both surface water and groundwater resources continue to diminish via over abstraction and declining rainfall across many countries, resulting in poor riverine ecological function and environmental condition (Vörösmarty et al. , 2010; Rolls and Bond, 2017; Bouckaert et al. , 2018). To arrest environmental decline in Australia’s food-bowl, the Murray-Darling Basin (MDB; Figure 1), the Murray-Darling Basin Plan was developed to ensure sustainable water use while supporting reliant industries and the environment (MDBA, 2009).
Recognising the MDB will remain a highly managed system, central to the Murray-Darling Basin Plan is water for the environment to support and where possible, restore ecological condition to achieve long-term environmental outcomes in the absence of natural flows (MDBA, 2009). However, prioritising when and where environmental water is required across the MDB is extremely complex and to a large extent, relies onin-situ monitoring of ecological assets such as fish, birds and both woody and non-woody vegetation. As the MDB covers ~14% of Australia’s land area (or 1,000,000 km2), reliance on in-situ monitoring is not only costly, it impedes the ability to understand large-scale ecological water requirements and ecological responses. Remote sensing methods, however, can provide robust broad scale monitoring options, underpinned by in-situ observations.
Over the last decade, it has become evident that field measurement of water loss from floodplain woody vegetation via transpiration and evapotranspiration (ET), provides a way to observe forest or woodland ecological condition and water needs (Doody et al. , 2015; Jarchowet al. , 2017; Wallace et al ., 2019). When tree water requirements are not met, both transpiration and ET decline in response to tree water saving adaptations such as stomatal closure to prevent xylem cavitation and tree death (Baird et al. , 2005; Doodyet al. , 2009, 2015). Severity of decline is related to extent of continued water deficit/drought. Broadscale monitoring of woody vegetation transpiration and ET during drought can highlight tree condition decline trajectories and inform prioritisation of environmental water over both short and longer timeframes. Similarly, monitoring tree response to increasing water availability is equally important in a water management context related to decisions around where and when environmental water should be delivered.
Considerable investigation has demonstrated that remote sensing provides a broadscale means to monitor woody vegetation water loss via ET (Guerschman et al. , 2009; Glenn et al. , 2011; Mu et al. , 2011). In particular, MODIS imagery, providing 8-day composite greenness products (normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI), provides excellent temporal resolution from the year 2000 from which to develop vegetation monitoring solutions (Glenn et al. , 2011; Nagler et al. , 2016). Evapotranspiration algorithms, such as those developed by Mu (2013), Guerschman (2009) and Nagler (2016), lend themselves to broadscale ecological monitoring. However, the MODIS spatial scale of 250 m can be limiting when it comes to calibrating remote sensing outputs within-situ data collected over smaller scales, as often happens. For example, in the MDB, collection of woody vegetation ET typically occurs using 50 x 50 m plots, with highly heterogenous canopy cover (Doodyet al. , 2015).
To augment development of temporal fine scale remote sensing monitoring methods which are applicable to ecological vegetation systems worldwide, a method to downscale low spatial resolution ET estimates based on high spatial resolution FTCC maps requires development. Improved downscaling will reduce the risk of over or underestimating vegetation ET during the field calibration process and improve large-scale remote sensing ET estimates. While several fractional vegetation cover products exist in Australia (Guerschman et al. , 2015; Guerschman and Hill, 2018; DEE, 2019), the information they impart is not suitable to downscale ET, due to lack of fine scale classification of vegetation cover or discrimination between groundcover vegetation and trees.
The objective of this study, therefore, was to develop a method to determine fractional tree canopy cover (FTCC) at a pixel resolution of 20 m. The intent was to provide a fine scale evaluation of the proportion of tree canopy in each MODIS pixel and field measurement area. Understanding the amount of remote sensing ET related directly to tree ET (from field measurement) allows improved calibration accuracy between the two. This in turn, improves broadscale ET estimates used for monitoring, by accounting for heterogenous canopy cover in each pixel. The innovative method reported, combines radar (Sentinel-1) and multi-spectral (Sentinel-2) imagery for the first time, to estimate FTCC. Imagery based on LiDAR (Light Detection and Ranging) data was employed as a surrogate for field canopy cover and used to train and build a model that can estimate FTCC across large areas of the MDB. Given the paucity of methods to identify and map canopy cover at fine scales, the research presented within is likely to be important to many aspects of environmental management and hydrology, specifically catchment water management and improving our understanding of the underlying hydrological processes related to vegetation presence and absence.