The dataset of photovoltaic power plant distribution in China by 2020

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 photovoltaic (PV) power plants. However, a comprehensive map regarding the PV power plants' locations and extent remain 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 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. We found that most PV power plants were sited 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.光伏（Photovoltaic，PV）技术作为缓解气候变化影响的高效解决方案，已在全球范围内愈发广泛地用于替代化石燃料发电，以最大限度减少温室气体排放。作为全球累计光伏装机容量最大、新增装机增速最快的国家，中国亟需对已投运的光伏电站的环境与社会影响开展评估。然而，在国家尺度上，能够全面覆盖光伏电站位置与分布范围的专题地图仍较为匮乏。本研究构建了一套融合机器学习、目视解译方法与海量卫星数据的工作流程，用于绘制全国范围内的光伏电站分布地图。本研究基于谷歌地球引擎（Google Earth Engine，GEE）平台，利用2020年空间分辨率为30米的合成影像，采用基于像素的随机森林（Random Forest，RF）模型对光伏电站进行分类识别。后续通过目视解译方法对初步分类结果图进行了优化完善。最终，本研究构建了截至2020年的中国光伏电站分布地图，总覆盖面积达2917平方千米。基于所生成的全国光伏电站分布图，研究发现绝大多数光伏电站选址于耕地，其次为裸地与草地。此外，光伏电站的建设普遍导致了植被覆盖度的下降。本数据集有望为政策制定管理、环境影响评估以及后续光伏电站分类研究提供有力支撑。