Articles | Volume 14, issue 8
https://doi.org/10.5194/essd-14-3743-2022
https://doi.org/10.5194/essd-14-3743-2022
Data description paper
 | 
17 Aug 2022
Data description paper |  | 17 Aug 2022

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

Xunhe Zhang, Ming Xu, Shujian Wang, Yongkai Huang, and Zunyi Xie

Related subject area

Domain: ESSD – Land | Subject: Energy and Emissions
Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution
Mark Omara, Ritesh Gautam, Madeleine A. O'Brien, Anthony Himmelberger, Alex Franco, Kelsey Meisenhelder, Grace Hauser, David R. Lyon, Apisada Chulakadabba, Christopher Chan Miller, Jonathan Franklin, Steven C. Wofsy, and Steven P. Hamburg
Earth Syst. Sci. Data, 15, 3761–3790, https://doi.org/10.5194/essd-15-3761-2023,https://doi.org/10.5194/essd-15-3761-2023, 2023
Short summary
An adapted hourly Himawari-8 fire product for China: principle, methodology and verification
Jie Chen, Qiancheng Lv, Shuang Wu, Yelu Zeng, Manchun Li, Ziyue Chen, Enze Zhou, Wei Zheng, Cheng Liu, Xiao Chen, Jing Yang, and Bingbo Gao
Earth Syst. Sci. Data, 15, 1911–1931, https://doi.org/10.5194/essd-15-1911-2023,https://doi.org/10.5194/essd-15-1911-2023, 2023
Short summary
A GeoNEX-based high-spatiotemporal-resolution product of land surface downward shortwave radiation and photosynthetically active radiation
Ruohan Li, Dongdong Wang, Weile Wang, and Ramakrishna Nemani
Earth Syst. Sci. Data, 15, 1419–1436, https://doi.org/10.5194/essd-15-1419-2023,https://doi.org/10.5194/essd-15-1419-2023, 2023
Short summary

Cited articles

Al Garni, H. Z. and Awasthi, A.: Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia, Appl. Energ., 206, 1225–1240, https://doi.org/10.1016/j.apenergy.2017.10.024, 2017. 
Aydin, N. Y., Kentel, E., and Duzgun, H. S.: GIS-based site selection methodology for hybrid renewable energy systems: A case study from western Turkey, Energ. Convers. Manage., 70, 90–106, 2013. 
Belgiu, M. and Drăguţ, L.: Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016. 
Bradbury, K., Saboo, R., L. Johnson, T., Malof, J. M., Devarajan, A., Zhang, W., M. Collins, L., and G. Newell, R.: Distributed solar photovoltaic array location and extent dataset for remote sensing object identification, Sci. Data, 3, 160106, https://doi.org/10.1038/sdata.2016.106, 2016. 
Capellán-Pérez, I., de Castro, C., and Arto, I.: Assessing vulnerabilities and limits in the transition to renewable energies: Land requirements under 100 % solar energy scenarios, Renew. Sust. Energ. Rev., 77, 760–782, https://doi.org/10.1016/j.rser.2017.03.137, 2017. 
Download
Short summary
Photovoltaic (PV) power plants have 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 km2. Based on the derived map, we found that most PV power plants were situated on cropland. In addition, the installation of PV power plants has generally decreased the vegetation cover.
Altmetrics
Final-revised paper
Preprint