Spectral-Feature-Driven photovoltaic Detection: A universal Physics-Based index for rapid Localization

• In-depth exploration of optical reflection in photovoltaic (PV) panels. • Overcoming the background complexity, heterogeneity, and diversity of PV installations. • Proposing a universal spectral index (SPPI) for mapping PV panels. • Enhancing PV detection through distinct and physics-based spectral features. • Proven efficiency, generalizability, and robustness across multiple case studies. Photovoltaic (PV) energy is critical to the transition towards a net-zero economy and plays a vital role in meeting the Sustainable Development Goals (SDGs), particularly regarding affordable clean energy (SDG 7) and climate action (SDG 13). Timely and accurate acquisition of the spatial distribution of PV installations is critical for regional energy planning, capacity estimation, and policy adjustment. However, accurately detecting PV installations remains challenging due to their environmental complexity and structural diversity. Through multi-platform spectral analysis (including Sentinel-2, Landsat-8, and GF-2 imagery), this study identifies distinctive spectral reflectance properties of PV materials, characterized by a prominent peak in the 400–500 nm range and significantly lower reflectance in the visible to near-infrared spectrum compared to natural landscapes, while exhibiting higher reflectance than water bodies. Leveraging physics-based spectral signatures that remain consistent across diverse geographical settings, we introduce the Spectral Ratio-Normalized Difference Solar Photovoltaic Panel Index (SPPI), a universal approach for efficient PV detection using optical satellite imagery. Quantitative validation across multiple regions (urban, rural, and mountainous environments) demonstrates that SPPI achieves exceptional performance with 94.34% overall accuracy and a robust Kappa coefficient of 0.778, outperforming existing index-based methodologies while producing results comparable to more computationally intensive deep learning approaches. The SPPI methodology’s distinctive advantage lies in its ability to generate precise PV polygon boundaries while maintaining computational efficiency, enabling rapid large-scale mapping without specialized hardware requirements. While installation variations and extreme viewing angles may affect performance, the physics-based nature of the index ensures consistent results under normal imaging conditions. This universal, computationally efficient approach facilitates effective PV installation monitoring and energy capacity estimation, enhancing renewable energy analytics for carbon neutrality initiatives.