From LiDAR intensity to reflectance: Fast and spatially resolved cleanliness assessment in solar fields

Mirror soiling reduces reflectance and decreases plant yield in concentrating solar power systems. Conventional pointwise reflectometers for cleanliness assessment are accurate yet slow and sparse. In this study, we refine a LiDAR-based method that converts raw laser scanner intensity into a calibrated, viewpoint-independent backscatter signal and maps cleanliness across heliostat fields. A calibration pipeline corrects detector response, distance, and incidence angle to produce standardized backscatter, followed by a monotonic empirical mapping to reflectometer cleanliness. Field tests at an operational tower plant show a strong inverse correlation between LiDAR and reflectometer measurements (Spearman’s rank correlation of −0.94 for calibration). Across four independent perimeter validations, the LiDAR mean cleanliness matched portable handheld reflectometer (pFlex) within 0.10–0.72% per mirror (absolute mean difference 0.34% for validation), while paired-sample MAE and RMSD were 2.31–2.91% and 2.88–3.72%, respectively. Compared to pointwise reflectometers, the method enables fast, illumination-independent, centimetre-resolution mapping, supporting improved yield estimates, optimized receiver operation, and water- and cost-efficient cleaning strategies.