The soiling of solar panels from dry deposition affects the overall efficiency of power output from solar power plants. This study focuses on the detection and monitoring of sand deposition (wind-blown dust) on photovoltaic (PV) solar panels in arid regions using multitemporal remote sensing data. The study area is located in Bhadla solar park of Rajasthan, India which receives numerous sandstorms every year, carried by westerly and north-westerly winds. This study aims to use Google Earth Engine (GEE) in monitoring the soiling phenomenon on PV panels. Optical imageries archived in the GEE platform were processed for the generation of various sand indices such as the normalized differential sand index (NDSI), the ratio normalized differential soil index (RNDSI), and the dry bare soil index (DBSI). Land surface temperature (LST) derived from Landsat 8 thermal bands were also used to correlate with sand indices and to observe the pattern of sand accumulation in the target region. Additionally, high-resolution PlanetScope images were used to quantitatively validate the sand indices. Our study suggests that the use of freely available satellite data with semiautomated processing on GEE can be a useful alternative to manual methods. The developed method can provide near real-time monitoring of soiling on PV panels cost-effectively. This study concludes that the DBSI method has a comparatively higher potential (89.6% Accuracy, 0.77 Kappa) in the detection of sand deposition on PV panels as compared to other indices. The findings of this study can be useful to solar energy companies in the development of an operational plan for the cleaning of PV panels regularly.

The rapid expansion of solar energy in India poses challenges in the context of cropland encroachment and water-resource scarcity. To help address these challenges, we used open access satellite observations and GIS technologies over Earth Engine platform to devise the Solar Panel Index (SPI) for efficient detection of solar farms, achieving a Land use/Land cover classification accuracy of 89 %. We have used cropland, water availability, power grid and land surface temperature data-sets for generation of land suitability map. Sentinel-2 data-sets along with SPI were used to develop solar farm locations and their estimated installed capacity within the study area. The outcomes from the study depicted that over 40 % of all solar farms in the country are located on agricultural land, with the highest seen in Karnataka (73.55 %) followed by Tamil Nadu (68.81 %). Furthermore, high installed capacity coincides with low groundwater depths, exacerbating local water strain. Madhya Pradesh was found to have almost no power plant located within 5 km of surface water bodies. Our findings emphasize the significance of the solar-agriculture-water (SAW) nexus for sustainable development. Using high-resolution Land Suitability Map (LSM) as a decision-making criteria for allocating land for power generation reveals that Rajasthan holds the highest potential for solar energy installation. Our integrated approach considering the interplay between solar-energy production, agriculture, and water-resource management contributes to the sustainable growth of the solar-energy sector and regional development.