Extracting Solar Photovoltaic Installations from Satellite Images in China
DOI:
https://doi.org/10.36777/jag2023.2.1.2Keywords:
Google Earth Engine, Photovoltaic, Random Forest, Remote Sensing, SentinelAbstract
Developing renewable energy and accelerating the energy transition are crucial for achieving the Sustainable Development Goals and carbon neutrality. Solar photovoltaic (PV) systems are rapidly expanding across China as a widely adopted renewable energy technology. Remote sensing (RS) technology provides an effective means of detecting and monitoring existing PV installations. Previous studies have primarily focused on PV detection at relatively small spatial scales. This study aims to detect large-scale PV installations across China using openly available, multi-source remote sensing data. Multi-source satellite imagery, including Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral data, was used to construct classification features. A random forest classifier was developed on the Google Earth Engine platform to detect PV installations across China. Manually collected samples and an existing PV database were used to evaluate the accuracy of the detection results. The results show that PV installations can be detected nationwide with high overall accuracy (OA = 98.90%) and a kappa coefficient of 0.86. The detection rate reached 80.11% when compared with the existing PV database. These findings provide valuable reference information for the future development and monitoring of solar energy infrastructure and can support progress towards the Sustainable Development Goals.
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