## 4 Conclusions  
The maintenance of photovoltaic solar plants plays a key role in terms of their performance and useful life. The INSPECTORMAP tool presented in this article allows for the maintenance of both small and large photovoltaic solar plants without the need for periodic on-site inspections.  
Figure 14: Bodies of water detected in the vicinity of plant 4 for a specific instant of time based on: (**a**) the NDWI with a 10-m spatial resolution, (**b**) the NDWI with a 20-m spatial resolution, and (**c**) the SWI with a 20-m spatial resolution.  
Thanks to the revisit period of the satellites, different spectral indices can be calculated periodically and free of charge to detect those areas of the installation that require maintenance due to the presence of water or vegetation. The process of downloading and calculating these indexes is performed automatically thanks to a Python script implemented in the proposed SDI. Likewise, the integration of Landsat-8 and Sentinel-2 data is based on the Open Data Cube standard and the Local Nested Grid tool developed. This integration allows for maximum use of these products in geometric, radiometric, and temporal terms. In this way, the INSPECTORMAP user does not have to perform any calculations; they only have to analyze the results offered by the tool and be aware of the warnings the tool automatically generates when certain thresholds are exceeded.  
Although the estimation of spectral indices for the detection of anomalous vegetation and water bodies is very useful, it must be considered that, in some cases, the quantified surfaces are not sufficiently precise. For this reason, the SDI INSPECTORMAP serves as a support tool to aid the maintenance operators to know the vegetation activity and water bodies growth in the surroundings of photovoltaic plants, which can be a potential risk for the operation of these facilities. As has been demonstrated with the estimation of the areas affected by vegetation, in most cases, the tool is very useful to know exactly the location where the operator will proceed to withdrawal.  
Although there are several indices for estimating water bodies by satellite images, this contribution confirms that the NDWI index allows for a precise estimation of the affected areas compared to other indices such as the SWI, which tends to overestimate the areas with presence of water. As has been shown, the improved resolution of satellite imagery allows a more precise quantification. This improvement in precision is key for sensitive cases of large floods, in which a highly precise quantification of the affected area is required without involving a large computational expense.  
Conceptualization, E.R.d.O. and D.G.-A.; methodology, E.R.d.O., M.S.-A., S.D.P. and D.G.-A.; software, E.R.d.O.; validation, M.S.-A. and S.D.P.; formal analysis, D.G.-A., M.S.-A. and S.D.P.; writing--original draft preparation, M.S.-A.; writing--review and editing, S.D.P. and D.G.-A. All authors have read and agreed to the published version of the manuscript. This research was funded by the CDTI, grant number IDI-20170947. Institutional Review Board Statement: Not applicable.  
Not applicable. This work was financed by the CDTI, belonging to the Ministry of Science and Innovation of Spain, through the call for CIEN strategic projects within the framework of the Project INSPECTOR: industrial inspection and maintenance of complex or unattended facilities. Authors would like to thank Junta de Castilla y Leon with the Fondo Social Europeo for the financial support given through programs for human resources (EDU/1100/2017). The authors declare no conflict of interest.