## Challenges in Operational Estimation of ET/SSM  
### Challenges in Operational Estimation of ET  
One major challenge in satellite ET estimation is the availability of high resolution imagery at both spatial and temporal scales. Generally, low or moderate resolution imagery is typically available at higher temporal resolution for e.g., MODIS but facing challenges with respect to spatial resolution \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Product Name** & **Sensor Type** & **Distribution** & **Spatial Resolution** & **Temporal Resolution** \\  & & & **Resolution** & **(Days)/Coverage** \\ \hline MetOp/ASCAT soil moisture & Microwave scatterometer (C-band) & [[http://www.eumestat.int](http://www.eumestat.int)]([http://www.eumestat.int](http://www.eumestat.int)) & 12.5-25 km & 2/global (2007+) \\ \hline SMOS 12/L3 soil moisture & Microwave radiometer (L-band) & [[https://smos-ds-02.eo.esa.int/](https://smos-ds-02.eo.esa.int/)]([https://smos-ds-02.eo.esa.int/](https://smos-ds-02.eo.esa.int/)) & 35–60 km & 2-3/global (2010+) \\ \hline GCM-WI/AMSR2 soil moisture & Microwave radiometer (C and X bands) & [[https://earthdata.nasa.gov/](https://earthdata.nasa.gov/)]([https://earthdata.nasa.gov/](https://earthdata.nasa.gov/)) & 25 km & 1/global (2012+) \\ \hline SMAP L2/L3 soil moisture & Microwave radiometer (L-band) & [[https://nsidc.org/data/smap](https://nsidc.org/data/smap)]([https://nsidc.org/data/smap](https://nsidc.org/data/smap)) & 40 km & 2-3/global (2015+) \\ \hline SMOS BEC L4 soil moisture & Fusion of microwave (L-band) and optical (VIS/IR) & bec.icm.csic.es & 1 km & 2-3/regional (2010+) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Present (June 2017) remote sensing soil moisture data products with near real-time capabilities. (\(\sim\)1 km) and available only at 1 week or greater temporal resolution. On the other hand, SEVIRI has high temporal resolution but available at 1-3 km spatial resolution. Data from new remote sensing platforms could prove very useful for downscaling ET estimates to scales that more closely approximate actual agricultural fields. Some researchers attempted multi-sensor fusion to obtain high spatial temporal ET information; yet, presently not available for operational applications. Satellites such as Sentinels 2 and 3 by the European Space Agency (ESA), could provide ET data at high spatial and temporal frequency. Sentinel-2 can provide data in different regions of the electromagnetic spectrum with a revisit time of 5 days at the equator and 2-3 days at mid-latitudes. Therefore, such data can be very useful for vegetation-based ET models, including NDVI and the leaf area index (LAI). In addition, Sentinel-3 is able to provide thermal imagery at 1 km resolution with revisit times of approximately 1 day at the equator. Such data can be utilised for developing methodologies to downscale coarser resolution SSM operational products (e.g., from SMOS) to 1 km. However, such approaches are at present at a relatively early stage of development and more research is needed in this direction. Another major problem is developing algorithms for estimation of ET in heterogeneous landscapes such as in case of crops where agricultural plots are small and need different irrigation practices with respect to crop types. In developing countries like India, Pakistan, Bangladesh etc. are having marginal lands and following either a canal-irrigated system or from groundwater wells using the pumps. This cause a problem in retrieval of ET because of high variation in water level in the agricultural plots, as multiple cropping practices are dominating in these countries to optimize the yield. Most globally-available datasets are at a resolution of 1 km (MOD16) or coarser (PT-JPL, MOD16, GLEAM, SEBS-LF), which is significantly larger than irrigated patches in many areas and hence is a major challenge in obtaining a good quality ET. Even in the United States, where agricultural fields are large, 1 km resolution can be too coarse to resolve individual fields and to map ET differences by crop [87].