## 5 Future Applications with Emphasis in Agriculture
### EO & Field Irrigation  
To date, EO approaches to optimise in field irrigation decision support have focused on optical satellite technology ([PERSON], 2010; [PERSON], 2010) via the estimation of key biophysical parameters such as leaf area index (LAI), surface albedo, crop height and in turn key crop coefficient (Kc) required to estimate water requirements. The system reported by [PERSON] et al. (2010) has been deployed in Italy (IRRISAT), Austria (EO4 Water) and Australia (IRRIEYE) with a resolution of 20 m. However, all optical EO applications for agricultural irrigation decision support require high temporal frequencies (days not weeks) of image data. There are now increasing numbers of EO platforms that can provide the necessary suite of data and the issue of cloud cover can be overcome using microwave data. This is a critical issue for irrigation support within agriculture. The working capital deployed by farmers to grow crops is extremely high, so in global regions that have significant cloud cover precision irrigation techniques reliant on optical EO techniques to estimate ET and SSM are unlikely to be commercially robust. Precision irrigation techniques also require high spatial resolutions as SSM is known to vary considerably within fields ([PERSON], 2010). These complex spatial variances in soil moisture are driven by multiple factors such as changes in site elevations, soil type, soil density, underlying changes in geology, variances in local crop growth, nutrition etc. For field and precision irrigation, it is critical that on site spatial variance is well understood. However, even ground-based sensors (such as TDR and neutron probes) are of little value in understanding field scale variances in soil moisture. These devices have very small fields of view (cm) and interpolation over field scales would require prohibitive numbers of instruments. However, radar-based EO sensors offer an emerging approach to estimation of soil moisture spatial variability on a field scale (km\({}^{2}\)). However, robust higher spatial resolutions with EO can only be achieved with calibration against accurate ground truth instruments that measure at within field scale resolutions. Recent advances in novel ground sensors; in particular cosmic neutron albedo detectors (cosmos sensors) will facilitate necessary ground truth data provision. Cosmos sensors provide direct field scale measurement of soil moisture to within a c. 150 m radius of the sensor ([PERSON], 2010). Recently, [PERSON] et al. (2014), who described a UK network of ground-based cosmos sensors, showed a reasonable correlation on two different sites (\(R^{2}=0.46\), \(n=837\) days and \(R^{2}=0.59\), \(n=0.59\)) between soil moisture retrieved from ASCAT (grid 12.5 km\({}^{2}\)) and the novel ground sensors. These results were achieved despite a mismatch in the spatial resolutions of the ASCAT and cosmos stations. A further manifestation of cosmos sensor technology has been described by [PERSON] et al. (2015); this group developed a mobile version of the cosmos sensor (\"cosmos rover\"), which can be deployed on a utility vehicle. The rover was designed to provide rapid estimations of soil moisture across large geographical regions (where there is a lack of ground truth data), or high spatial resolution measurements within a single field. We do foresee that the combination of novel ground truth data (e.g., cosmos sensors) with new high resolution EO active MW platforms (e.g., Sentinel-1) offers significant potential to develop high resolution operational products within field maps of soil moisture. This combination could enable the large-scale and cloud independent, use of EO to underpin in field and precision irrigation decision support. In addition, irrigation decision support also requires estimates of biophysical parameters in particular LAI and the crop parameter Kc (for a full description of the approach see ([PERSON], 2010; [PERSON], 2010)). If cloud denies optical recognition of these parameters then SAR must also be used.