## 1 Introduction  
Water secures livelihoods and economic growth. Agriculture alone consumes 70% of global fresh water [1, 2]. As global population and the human desire for a higher protein diet grows, pressure on the world's scarce water supply will increase. This pressure and demand for water shows a high geographic variance and there is no doubt that global droughts but also flood risk, are threatening regional food security. These risks are reflected in the Davos Risk Register [3] that rates \"water crises\" as the 3 rd highest global risk for impact. Furthermore, agriculture is competing with industrial, household and environmental users for scarce water supply [4]. These demands combined with the potential forward impact of climate change demonstrate that there is an urgent need to garner a better understanding of the natural processes driving the global hydrological cycle [5, 6, 7]. In particular, more exact information on the spatio-temporal variation of parameters such as surface soil moisture (SSM) and evapotranspiration (ET) rates are of key significance. Specifically, these parameters exert a strong control on the Earth's water, carbon cycles and ecosystem functioning [8; 9]. Tools to quantify both ET and SSM have a crucial role in understanding the Earth's climatic system on the longer term but by enabling increased water use efficiency they can also have significant economic, environmental and social impact on the shorter term. For the latter, data is required at high spatial and temporal resolutions to inform decision makers on sustainable utilisation and management of water [10]. There are numerous techniques available to derive SSM and ET using ground instrumentation. Ground measurements have certain advantages, such as providing a relatively direct measurement, instrument portability, simple installation, operation and maintenance, the ability to provide measurement at the desired depth and also the relative maturity and stability of the methods. Nevertheless, such techniques have proven difficult to implement, especially over large areas. This reflects the complex, expensive and labour-intensive challenges in deploying a network of in-situ sensors at a study site. In addition, current field equipment typically determines only localized estimates of SSM which must be aggregated for measuring the parameter over large spatial scales. The placement of the in-situ sensors is then key to obtain representative estimates [11]. Various ground-based observational networks have been established in basins of different characteristics around the globe, aiming to systematically collect, archive and distribute a wide variety of relevant data for use in research activities [12; 13]. A review of the available ground-based methods including their relative strengths and limitations and available operational networks can be found in [14; 15]. Conversely, the advent of Earth Observation (EO) technology has provided novel and economically feasible means to derive temporally consistent coverage of ET and SSM at different spatial scales [16; 17; 18]. EO provides regional to global scale parameter estimation and enables non-invasive synoptic views in a spatially contiguous fashion, providing estimates from otherwise inaccessible regions. Another advantage is that observations can be obtained over remote areas which are otherwise inaccessible for in-situ measurements. In addition, the development of EO has enabled the scientific community to investigate previously unsolvable problems, such as spatial variability and scale of observation [19]. These are the main characteristics that make EO one of the most efficient and cost-effective techniques for obtaining spatio-temporal estimates of ET as well as of SSM [20; 21]. Remote sensing instruments do not directly measure either soil water content and/or the turbulent heat fluxes. The spectral measures they provide have to be combined with a physical or semi-empirical model and an inversion algorithm in order to estimate the parameters.