## 6 Conclusions  
The estimation of accurate soil moisture using satellite imagery is essential for evaluating irrigation efficiency. It can be applied to several areas such as in the urban lawn, golf courses, agriculture, or at a higher scale considering a whole river basin. Nonetheless, the existing proposed models have relatively low accuracies.  
This paper shows the combined use of remote sensing and a soil moisture sensor network to generate soil sensor indexes based on satellite imagery. Our results indicate that it is feasible to evaluate the soil moisture at different points of the golf course to achieve better water efficiency by using Sentinel-2 images. Nonetheless, indexes offered accurate results only in the natural soil, precluding remote soil moisture estimation in the fairways and greens. The accuracy of obtained models upgrades the existing solutions based on the R\({}^{2}\) and the MAEs of the calibration dataset. We added a validation dataset to verify our findings. The validation indicated that the model obtained with bands at 20 m has greater MAE than the training dataset. This aspect was not considered in previous contributions, and it is essential to ensure its application to new areas. To verify our results, a comparison with other indexes is performed. The indexes were applied to the nearby area of the golf course, and the obtained results correspond to the observed changes in the different land uses.  
As future work, we will include data of other seasons to decrease the MAEs of the validation dataset. Our main objective with future work is to evaluate the option of creating seasonal soil moisture indexes. Moreover, other remote sensing sources will be combined with Sentinel-2 data to attain the generation of soil moisture models suitable for fairways and green areas.  
Conceptualisation, L.P. and D.M.-C.; methodology, L.P.; software, L.P. and D.M.-C.; validation, P.V.M., J.F.M. and J.L.; formal analysis, L.P.; data curation, L.P.; writing--original draft preparation, L.P., L.G. and D.M.-C.; writing--review and editing, L.G. and L.P.; supervision, P.V.M., J.F.M. and J.L.; project administration, P.V.M.; funding acquisition, P.V.M., J.F.M. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by project PDR18-XEROCESPED, under the PDR-CM 2014-2020, by the EU (European Agricultural Fund for Rural Development, EAFRD), Ministerio de Agricultura, Pesca y Alimentation (MAPA) and Comunidad de Madrid regional government through MIDRA, by the \"Program Estatica de l+D+i Orientado a los Retos de la Sociedad, en el marco del Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2017-2020\" (Project code: PID2020-114467 RR-C33), and by \"Proyectos de innovacion de interes general por grupos operativos de la Asociacion Europea para la Innovacion en materia de productividad y sostenibilidad agricolas (AEI-Agri)\" in the framework \"Programa Nacional de Desarrollo Rural 2014-2020\", GO TECNOGAR,and by Conselleria de Educacion, Cultura y Deporte, through \"Subvenciones para la contratacion de personal investigador en fase postdoctoral\" APOSTD/2019/04.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy constraints.  
**Conflicts of Interest:** The authors declare no conflict of interest.