Detection of Solar Photovoltaic Power Plants Using Satellite and Airborne Hyperspectral Imaging

Abstract

Solar photovoltaic panels (PV) provide great potential to reduce greenhouse gas emissions as a renewable energy technology. The number of solar PV has increased significantly in recent years and is expected to increase even further. Therefore, accurate and global mapping and monitoring of PV modules with remote sensing methods is important for predicting energy production potentials, revealing socio-economic drivers, supporting urban planning, and estimating ecological impacts. Hyperspectral imagery provides crucial information to identify PV modules based on their physical absorption and reflection properties. This study investigated spectral signatures of spaceborne PRISMA data of 30 m low resolution for the first time, as well as airborne AVIRIS-NG data of 5.3 m medium resolution for the detection of solar PV. The study region is located around Irlbach in southern Germany. A physics-based approach using the spectral indices nHI, NSPI, aVNIR, PEP, and VPEP was used for the classification of the hyperspectral images. By validation with a solar PV ground truth dataset of the study area, a user’s accuracy of 70.53% and a producer’s accuracy of 88.06% for the PRISMA hyperspectral data, and a user’s accuracy of 65.94% and a producer’s accuracy of 82.77% for AVIRIS-NG were achieved.