Journal Article10.1109/jstars.2024.3468627
Mapping of utility-scale solar panel areas from 2000 to 2022 in China using Google Earth Engine
Xin Lyu,Xiaobing Li,Haishuo Wei,Junxu Wu,Dongliang Dang,Chenhao Zhang,Kai Wang,Anru Lou +7 more
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TL;DR: This study maps China's utility-scale solar panel areas from 2000 to 2022 using Google Earth Engine, providing a comprehensive dataset and revealing spatiotemporal variation and development trends, with implications for renewable energy distribution and carbon reduction targets.
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Abstract: Solar power generation is an effective way to reduce carbon emissions and has a wide range of applications worldwide. China's newly installed photovoltaic capacity has ranked first in the world in recent years. Timely and accurate monitoring of the spatiotemporal distribution characteristics of solar power plants is essential to optimize China's renewable energy power distribution and achieve carbon reduction targets. However, long-term solar panel (SP) datasets are still lacking. Based on the Google Earth Engine platform, this study proposed a fine extraction method framework of SPs in large and complex geographical environments by integrating stratified sampling and zonal modeling and obtained the first comprehensive dataset of SP distribution in China covering 2000–2022 to fill the gap in relevant research and practice. It has been verified that the F1-score of the SP datasets was higher than 0.87, which could meet the research needs. On this basis, this study revealed the spatiotemporal variation and development trends of SPs in China over the past 20 years. The proposed method framework can serve as a scientific reference for relevant research, while the interpreted SP datasets and analysis results can provide a basis for regional decision making and related research in the context of future climate change.
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Citations
Artificial intelligence-based optimization of a giga-scale bifacial photovoltaic power plant using partitioned random vector reinforcement learning and Eurasian-Lynx optimization
Mohamed E. Zayed,Shafiqur Rehman,Hassan Z. Al Garni,Kashif Irshad,Atif Saeed Alzahrani,Kotb M. Kotb +5 more
Spatiotemporal dynamics and ecological impact of photovoltaic power plants in China (2000 <i>–</i> 2023): a remote sensing approach
Li-Wen Yang,Luguang Jiang,Ye Liu +2 more
Abstract: The global proliferation of photovoltaic power plants (PV) has greatly contributed to alleviating the energy crisis and addressing global warming caused by carbon emissions. Accurately assessing the ecological impacts of PV power plants is crucial for balancing ecological protection and the sustainable development of the PV industry. This study used the Google Earth Engine (GEE) platform to construct an object-oriented random forest model, obtaining annual spatial distribution data for PV in China from 2000 to 2023, and quantifying the ecological effects of PV installations at the regional level. The results revealed a phased expansion of PV power plants since 2000, including a germination phase, start-up phase, expansion phase, and boom phase. As of 2023, the total PV area in China was 7716.13 km2, with the northwest region having the largest installed PV capacity, accounting for 35.76% of the total PV area in China. Most PV installations were located in grassland, cropland, and barren land, which together accounted for more than 85% of the total. Compared to cropland, where about half of the samples improved, PV installations on barren land and grassland showed around 90% and 85% improvement respectively, with smaller NDVI declines and stronger recovery. Furthermore, large-scale PV construction enhanced ecological functions such as shading, cooling, wind-proofing, and sand fixation, thus contributing more effectively to promoting local vegetation growth. This study provides data support and a scientific basis for optimizing the distribution of PV power plants and promoting the development of “PV+” compound projects.
The spatiotemporal patterns of national-subsidized PV power projects in China: evolution and influencing factors
Chenxuan Xiao,Lisha Hao +1 more
- 01 Nov 2025
Scenario-based simulation of future photovoltaic land expansion in China: Balancing energy demands and ecological conservation
Luguang Jiang,Ye Liu,Liwen Yang +2 more
More than doubled in 2023: Mapping the photovoltaic power plants in China based on satellite data and machine learning
Liwen Yang,Luguang Jiang,Ye Liu +2 more
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Use of normalized difference built-up index in automatically mapping urban areas from TM imagery
Y. Zha,Jay Gao,Shaoxiang Ni +2 more
TL;DR: The devised NDBI method was applied to map urban land in the city of Nanjing, eastern China and results at an accuracy of 92.6% indicate that it can be used to fulfil the mapping objective reliably.
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Solar energy development impacts on land cover change and protected areas
Rebecca R. Hernandez,Rebecca R. Hernandez,Madison K. Hoffacker,Michelle L. Murphy-Mariscal,Grace C. Wu,Michael F. Allen +5 more
TL;DR: In this paper, the Carnegie Energy and Environmental Compatibility model was used to quantify each installation according to environmental and technical compatibility and evaluated installations according to their proximity to protected areas, including inventoried roadless areas, endangered and threatened species habitat, and federally protected areas.
Tracking annual changes of coastal tidal flats in China during 1986–2016 through analyses of Landsat images with Google Earth Engine
Xinxin Wang,Xiangming Xiao,Zhenhua Zou,Bangqian Chen,Jun Ma,Jinwei Dong,Russell Doughty,Qiaoyan Zhong,Yuanwei Qin,Shengqi Dai,Xiangping Li,Bin Zhao,Bo Li +12 more
TL;DR: The interannual dynamics of coastal tidal flats area in China over the last three decades can be divided into three periods: a stable period during 1986-1992, an increasing period during 1993-2001 and a decreasing period during 2002-2016.
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The Global Availability of Landsat 5 TM and Landsat 7 ETM+ Land Surface Observations and Implications for Global 30m Landsat Data Product Generation
V. Kovalskyy,David P. Roy +1 more
TL;DR: In this paper, a statistical Landsat metadata analysis is reported considering more than 800,000 Landsat 5 TM and Landsat 7 ETM+ acquisitions obtained from the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center archive.
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