## Challenges in Operational Estimation of ET/SSM
### Challenges in Operational Estimation of SSM  
There are number of challenges in the satellite soil moisture estimation for agricultural applications. A key challenge is that the spatial and/or temporal resolutions provided at present by the available operational products do not sufficiently represent the spatio-temporal dynamics/variationsand uncertainties of SSM at small scales [21; 46]. Many hydrological applications require information on soil moisture at sub-daily temporal resolutions, where this time-dependent data is needed to initialise and update forecast, storm water management, flood or hydrological models. Therefore, there is a requirement for the development of operational SSM information at the requisite spatio-temporal scale for the monitoring of hydrological processes and applications.  
Furthermore, another challenge is related to the penetration depth of the EO signal and the depth on which the soil moisture can be retrieved. In visible/IR or even for some wavelengths in the microwave region of the spectrum the penetration depth is very poor. The latter leads to an incomplete picture of the soil moisture profile and is found to be not very suitable for agricultural applications which involves depth wise soil moisture measurements, such as in is the case for of irrigation water demand estimation. The other important problem relevant to this in satellite soil moisture retrieval is the presence of dense vegetation covers, which causes uncertainty in soil moisture retrievals [89]. Therefore, retrieval models able to quantify the vegetation contribution to emissivity are needed for robust soil moisture retrievals, (see recent retrieval approaches for soil moisture and vegetation optical depth at L-band: [90; 91]).  
A further challenge is related to the acquisition conditions under which SSM can be retrieved. During cloudy conditions soil moisture retrieval from visible/IR satellite produce is not possible. Microwave can provide retrievals under all weather conditions however, they are strongly affected by Radio Frequency Interference (RFI) [92]. In addition, SSM retrieval in areas with high pixel heterogeneity, specially in agro-ecosystems with varying crop type, can cause very significant differences in the behaviour of retrieved SSM [93; 94]. This includes regions with the presence of free water within the pixel. Other important factors are the spatial and temporal resolutions of the retrieved soil moisture [9]. As soil moisture is highly variable, sub daily or hourly records are needed for an accurate application of soil moisture especially for irrigation scheduling, this is not possible with present microwave sensors which are on polar orbits and provide measurements during dawn and dusk [95]. Possible solutions to this would be a microwave sensor on a geostationary platform or a retrieval approach combining a polar-orbiting microwave sensor with a VIS/IR geostationary [55].  
Lesser accuracy in the soil moisture retrieval due to the variety of reasons mentioned above can cause uncertainty in irrigation scheduling practices. Thus, providing a full characterization of the errors associated with an operational product is another challenge. Remote sensing scientists need to improve the characterization of the errors associated to the soil moisture products and their consistency. To ensure that these operational products can be exploited at full potential, efforts should be directed towards their validation and error characterisation under different climatic and vegetation biomes [18; 35]. Such studies should also include the implementation of sensitivity analysis as integrated part of the verification [96].