Spectral-Feature-Driven photovoltaic Detection: A universal Physics-Based index for rapid Localization
文献类型：期刊论文
| 作者 | He, Shuang1,2,3; Tian, Qingjiu1,2; Tian, Jia1,4; Hao, Lina5 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2026-03-01 |
| 卷号 | 147页码:105164 |
| 关键词 | Renewable Energy Photovoltaic Panel Detection Photovoltaic Spectral Signature SolarPhotovoltaic Panels Index (SPPI) Spectral Reflectance Analysis |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2026.105164 |
| 产权排序 | 5 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| URL标识 | 查看原文 |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001707688200001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221217]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Tian, Jia |
| 作者单位 | 1.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210023, Peoples R China; 2.Nanjing Univ, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Peoples R China; 3.Nanjing Univ, Collaborat Innovat Ctr South Sea Studies, Nanjing 210093, Peoples R China; 4.Beihang Univ, Sch Instrumentat & Optoelect Engn, Beijing 100191, Peoples R China; 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | He, Shuang,Tian, Qingjiu,Tian, Jia,et al. Spectral-Feature-Driven photovoltaic Detection: A universal Physics-Based index for rapid Localization[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2026,147:105164. |
| APA | He, Shuang,Tian, Qingjiu,Tian, Jia,&Hao, Lina.(2026).Spectral-Feature-Driven photovoltaic Detection: A universal Physics-Based index for rapid Localization.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,147,105164. |
| MLA | He, Shuang,et al."Spectral-Feature-Driven photovoltaic Detection: A universal Physics-Based index for rapid Localization".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 147(2026):105164. |
入库方式： OAI收割
来源：地理科学与资源研究所
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