Abstract
To achieve carbon neutrality, solar photovoltaic (PV) in China has undergone enormous development over the past few years. PV datasets with high accuracy and fine temporal span are crucial to assess the corresponding carbon reductions. In this study, we employed the random forest classifier to extract PV installations throughout China in 2015 and 2020 using Landsat-8 imagery in Google Earth Engine. The results were further visually inspected and refined by morphological filtering, cavity filling and manual adjustment. Validation analysis revealed that the initial classification achieved an overall accuracy over 96% for both 2015 and 2020. Further validation using independent test samples demonstrated that the final dataset outperformed the accuracies of existing PV datasets. In 2015, the total area of installed PV in China was 663.09 km(2), which were mainly distributed in the northwest, Beijing-Tianjin-Hebei, and the Yangtze River Delta region. By 2020, the total area of PV reached to 2847.36 km(2), with net increase of almost 3.3 times. Installed PV was intensified in the northwest and extended to eastern China.