Preprints
https://doi.org/10.5194/essd-2022-16
https://doi.org/10.5194/essd-2022-16
 
25 Jan 2022
25 Jan 2022
Status: a revised version of this preprint is currently under review for the journal ESSD.

Mapping photovoltaic power plants in China using Landsat, Random Forest, and Google Earth Engine

Xunhe Zhang1,2,3, Shujian Wang1, Yongkai Huang1, Zunyi Xie1,2, and Ming Xu1,2,3 Xunhe Zhang et al.
  • 1College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
  • 2Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China
  • 3Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng 475004, China

Abstract. Photovoltaic (PV) technology, as an efficient solution for mitigating 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 photovoltaic (PV) power plants. However, a comprehensive map regarding the locations and extent of the PV power plants remains to be scarce at the country scale. This study developed a workflow combining machine learning and visual interpretation methods with big satellite data to map the PV power plants in China. We applied a pixel-based Random Forest (RF) model to classify the PV power plants from composite images in 2020 with 30-meter spatial resolution on Google Earth Engine (GEE). The result 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. Based on the derived national PV map, we found that most PV power plants were sited on cropland, followed by barren land and grassland. 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.

Xunhe Zhang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-16', Anonymous Referee #1, 21 Feb 2022
    • AC1: 'Reply on RC1', Xunhe Zhang, 20 Apr 2022
  • RC2: 'Comment on essd-2022-16', Anonymous Referee #2, 20 Mar 2022
    • AC2: 'Reply on RC2', Xunhe Zhang, 20 Apr 2022

Xunhe Zhang et al.

Data sets

The dataset of photovoltaic power plant distribution in China by 2020 Xunhe Zhang https://doi.org/10.5281/zenodo.4552918

Xunhe Zhang et al.

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Short summary
Photovoltaic (PV) power plants has been increasingly built across the world to mitigate climate change. A map of the PV power plants is important for policy management and environmental assessment. We established a map of PV power plants in China by 2020, covering a total area of 2917 square kilometers. Based on the derived map, we found that most PV power plants were sited on cropland. In addition, the installation of PV power plants has generally decreased the vegetation cover.