The study applied remote sensing techniques to detect dust deposits on photovoltaic solar panels in Kita, Mali, using the Google Earth Engine (GEE) platform. Sentinel-2 images (2021-2023) recorded on the GEE platform were used to derive sand indices, including the Ratio Normalized Difference Soil Index (RNDSI) and the Dry Bare Soil Index (DBSI), enabling monthly detection of dust accumulation patterns. Results indicate DBSI values ranging from 0.05 to 0.35 and RNDSI from 0.02 to 0.34. Seasonal trends were evident: June to September corresponded to the least dusty months, favoring optimal production, while December to May marked peak dust accumulation, highlighting critical periods for intensified cleaning and maintenance. The remote sensing analysis further provided the spatial distribution of deposited dust, allowing operators to target specific areas for prioritized cleaning interventions. By identifying both seasonal and spatial variations, this approach supports more efficient resource allocation and performance optimization of large-scale PV systems. Our findings demonstrate that freely available satellite data, combined with semi-automated GEE processing, is a cost-effective and near-real-time alternative to labor-intensive manual monitoring. This method can guide companies in planning a regular maintenance schedule improving long-term system efficiency, and sustaining reliable solar energy production in dust prone environments.
Cite this paper
Dembélé, Y. , Bonkaney, A. L. , Sanogo, S. , Kumar, N. , Tischbein, B. , Saï and Madougou, D. (2025). Remote Sensing of Dust Deposition on Solar Panels: Assessing the Impact of Climate Variables in Sub-Saharan Africa—A Case Study in Mali. Open Access Library Journal, 12, e14260. doi: http://dx.doi.org/10.4236/oalib.1114260.
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