Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine

• Published in: Earth system science data Vol. 14; no. 8; pp. 3743 - 3755
• Main Authors: Zhang, Xunhe, Xu, Ming, Wang, Shujian, Huang, Yongkai, Xie, Zunyi
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 17.08.2022; Copernicus Publications
• Subjects: Accuracy; Agricultural land; Air pollution; Algorithms; Alternative energy sources; Barren lands; Classification; Climate change; Cloud computing; Datasets; Deep learning; Earth resources technology satellites; Electric power distribution; Emissions; Engines; Environmental assessment; Environmental impact; Environmental Impact Assessment; Environmental management; Fossil fuels; Global temperature changes; Grasslands; Greenhouse gases; Image classification; Industrial plant emissions; Landsat; Landsat satellites; Machine learning; Mapping; Neural networks; Photovoltaic cells; Photovoltaics; Plant cover; Power plants; Remote sensing; Satellite data; Solar power plants; Spatial discrimination; Spatial resolution; Vegetation; Vegetation cover; Visual discrimination learning; Workflow; China
• ISSN: 1866-3516; 1866-3508; 1866-3516
• DOI: 10.5194/essd-14-3743-2022

Photovoltaic (PV) technology, an efficient solution for mitigating the impacts of climate change, has been increasingly used across the world to replace fossil fuel power to minimize greenhouse gas emissions. With the world's highest cumulative and fastest built PV capacity, China needs to assess the environmental and social impacts of these established PV power plants. However, a comprehensive map regarding the PV power plants' locations and extent remains scarce on the country scale. This study developed a workflow, combining machine learning and visual interpretation methods with big satellite data, to map PV power plants across China. We applied a pixel-based random forest (RF) model to classify the PV power plants from composite images in 2020 with a 30 m spatial resolution on the Google Earth Engine (GEE). The resulting classification map was further improved by a visual interpretation approach. Eventually, we established a map of PV power plants in China by 2020, covering a total area of 2917 km2. We found that most PV power plants were situated on cropland, followed by barren land and grassland, based on the derived national PV map. In addition, the installation of PV power plants has generally decreased the vegetation cover. This new dataset is expected to be conducive to policy management, environmental assessment, and further classification of PV power plants. The dataset of photovoltaic power plant distribution in China by 2020 is available to the public at https://doi.org/10.5281/zenodo.6849477 (Zhang et al., 2022).