**4. Resolution**

The resolution of all systems refers to the minimum spatial area to which their conclusions can be applied. It is usually expressed in square meters or in hectares. There is no specific way for every system to certify their resolution. The results of a specific interpolation method can produce a different accuracy for different datasets coming from the same W/S network. In W/S-based systems, the resolution is dependent on the interpolation resolution. In remote sensing systems, the resolution is limited by the resolution of satellite images and the resolution of weather forecast models.

The spatial resolution of all systems can vary. For W/S-based systems, IRMA\_SYS reports 200 m while CIMIS reports 2 km. For remote sensing systems, the Manna Irrigation resolution is 5 km, Irrisat reports ET0 with a spatial resolution of 375 m, and that of OPENET is 33 m. Comparing the resolution of all systems, there is no sure conclusion about which type of system is more accurate or not.

### **5. Results and Conclusions**

The study of operational Decision Support Systems (DSSs) for irrigation water managemen<sup>t</sup> based on weather stations has yielded some useful results. DSSs can be classified into those that use in situ data and those that use remote sensing information. Despite this, weather stations are still necessary for remote sensing systems, as remote sensing data rely on in situ measurements for evaluation and there is often an option/suggestion for the installation of a weather station in the field of interest.

All systems use the evapotranspiration method (ET0) to calculate crop water needs. To calculate the water balance, all systems require or have the option of irrigation water volumes to be added from farmers. Remote sensing systems face the challenge to accurately estimate precipitation. Manna Irrigation is the only remote sensing system that describes the actual method used to produce irrigation suggestions. It also has a unique approach to estimate water balance using Kc values and the correlation between Kc and the NDVI index.

Remote sensing systems introduced the concept of virtual weather stations by using meteorological data to measure ET0, incorporating forecasting methods and AI algorithms to produce results. IRMA\_SYS has also adopted the method of a virtual weather station based on the interpolation of measurements that can be placed virtually on a field.

The spatial resolution of the systems varies. Theoretically, W/S-based systems are expected to be more accurate due to the use of actual measurements compared to the combination of satellite and meteorological data. A simultaneous comparison between systems on the same field, using several benchmarks against actual data, could provide a means for evaluating their performance.

**Author Contributions:** C.K., I.T. and N.M. conceptualized the research, C.K. wrote the manuscript, I.T. and N.M. reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
