Congalton, R. G.:
A review of assessing the accuracy of classifications of remotely sensed data, Remote Sens. Environ., 37, 35–46, 1991.
Creutzig, F., Agoston, P., Goldschmidt, J. C., Luderer, G., Nemet, G., and Pietzcker, R. C.:
The underestimated potential of solar energy to mitigate climate change, Nat. Energy, 2, 1–9, https://doi.org/10.1038/nenergy.2017.140, 2017.
Deines, J. M., Kendall, A. D., Crowley, M. A., Rapp, J., Cardille, J. A., and Hyndman, D. W.:
Mapping three decades of annual irrigation across the US High Plains Aquifer using Landsat and Google Earth Engine, Remote Sens. Environ., 233, 111400, https://doi.org/10.1016/j.rse.2019.111400, 2019.
Dunnett, S., Sorichetta, A., Taylor, G., and Eigenbrod, F.:
Harmonised global datasets of wind and solar farm locations and power, Sci. Data, 7, 130, https://doi.org/10.1038/s41597-020-0469-8, 2020.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L.:
The shuttle radar topography mission, Rev. Geophys., 45, 1–33,
https://doi.org/10.1029/2005RG000183, 2007.
Flood, N.:
Seasonal Composite Landsat TM/ETM
+ Images Using the Medoid (a Multi-Dimensional Median), Remote Sens.-Basel, 5, 6481–6500, https://doi.org/10.3390/rs5126481, 2013.
Gong, P., Li, X., and Zhang, W.:
40-Year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing, Sci. Bull., 64, 756–763, https://doi.org/10.1016/j.scib.2019.04.024, 2019.
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B., Yang, J., Zhang, W., and Zhou, Y.:
Annual maps of global artificial impervious area (GAIA) between 1985 and 2018, Remote Sens. Environ., 236, 111510, https://doi.org/10.1016/j.rse.2019.111510, 2020.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.:
Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, 2017.
Grodsky, S. M. and Hernandez, R. R.:
Reduced ecosystem services of desert plants from ground-mounted solar energy development, Nat. Sustain., 3, 1036–1043, https://doi.org/10.1038/s41893-020-0574-x, 2020.
Hammoud, M., Shokr, B., Assi, A., Hallal, J., and Khoury, P.:
Effect of dust cleaning on the enhancement of the power generation of a coastal PV-power plant at Zahrani Lebanon, Sol. Energy, 184, 195–201, 2019.
He, K., Zhang, X., Ren, S., and Sun, J.:
Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 770–778, https://doi.org/10.1109/CVPR.2016.90, 27–30 June 2016.
Hernandez, R. R., Easter, S., Murphy-Mariscal, M. L., Maestre, F. T., Tavassoli, M., Allen, E. B., Barrows, C. W., Belnap, J., Ochoa-Hueso, R., and Ravi, S.:
Environmental impacts of utility-scale solar energy, Renew. Sust. Energ. Rev., 29, 766–779, 2014.
Hernandez, R. R., Hoffacker, M. K., and Field, C. B.:
Efficient use of land to meet sustainable energy needs, Nat. Clim. Change., 5, 353–358, https://doi.org/10.1038/nclimate2556, 2015.
Hou, X., Wang, B., Hu, W., Yin, L., and Wu, H.:
SolarNet: A Deep Learning Framework to Map Solar Power Plants In China From Satellite Imagery, arXiv [preprint], arXiv:1912.03685, 2019.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L. G.:
Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83, 195–213, 2002.
Ji, C., Bachmann, M., Esch, T., Feilhauer, H., Heiden, U., Heldens, W., Hueni, A., Lakes, T., Metz-Marconcini, A., and Schroedter-Homscheidt, M.:
Solar photovoltaic module detection using laboratory and airborne imaging spectroscopy data, Remote Sens. Environ., 266, 112692, https://doi.org/10.1016/j.rse.2021.112692, 2021.
Kruitwagen, L., Story, K., Friedrich, J., Byers, L., Skillman, S., and Hepburn, C.:
A global inventory of photovoltaic solar energy generating units, Nature, 598, 604–610, 2021.
Li, X., Zhou, Y., Meng, L., Asrar, G. R., Lu, C., and Wu, Q.:
A dataset of 30
m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States, Earth Syst. Sci. Data, 11, 881–894, https://doi.org/10.5194/essd-11-881-2019, 2019.
Li, Y., Kalnay, E., Motesharrei, S., Rivas, J., Kucharski, F., Kirk-Davidoff, D., Bach, E., and Zeng, N.:
Climate model shows large-scale wind and solar farms in the Sahara increase rain and vegetation, Science, 361, 1019–1022, 2018.
Liu, Y., Zhang, R. Q., Huang, Z., Cheng, Z., López-Vicente, M., Ma, X. R., and Wu, G. L.:
Solar photovoltaic panels significantly promote vegetation recovery by modifying the soil surface microhabitats in an arid sandy ecosystem, Land Degrad. Dev., 30, 2177–2186, https://doi.org/10.1002/ldr.3408, 2019.
Malof, J. M., Bradbury, K., Collins, L. M., and Newell, R. G.:
Automatic detection of solar photovoltaic arrays in high resolution aerial imagery, Appl. Energ., 183, 229–240, https://doi.org/10.1016/j.apenergy.2016.08.191, 2016a.
Malof, J. M., Bradbury, K., Collins, L. M., Newell, R. G., Serrano, A., Wu, H., and Keene, S.:
Image features for pixel-wise detection of solar photovoltaic arrays in aerial imagery using a random forest classifier, 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, UK,
799–803, 20–23 November 2016b.
Malof, J. M., Collins, L. M., and Bradbury, K.:
A deep convolutional neural network, with pre-training, for solar photovoltaic array detection in aerial imagery, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, Texas, USA, 874–877, 23–28 July 2017.
Mao, Y., Harris, D. L., Xie, Z., and Phinn, S.:
Efficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine, ISPRS J. Photogramm., 181, 385–399, 2021.
Maxwell, A. E., Warner, T. A., and Fang, F.:
Implementation of machine-learning classification in remote sensing: an applied review, Int. J. Remote Sens., 39, 2784–2817, https://doi.org/10.1080/01431161.2018.1433343, 2018.
Nemet, G. F.:
Net radiative forcing from widespread deployment of photovoltaics, Environ. Sci. Technol., 43, 2173–2178, 2009.
Nghiem, J., Potter, C., and Baiman, R.:
Detection of Vegetation Cover Change in Renewable Energy Development Zones of Southern California Using MODIS NDVI Time Series Analysis, 2000 to 2018, Environments, 6, 40, https://doi.org/10.3390/environments6040040, 2019.
Phalke, A. R., Özdoğan, M., Thenkabail, P. S., Erickson, T., Gorelick, N., Yadav, K., and Congalton, R. G.:
Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine, ISPRS J. Photogramm., 167, 104–122, https://doi.org/10.1016/j.isprsjprs.2020.06.022, 2020.
Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., and Kennedy, R.:
Landsat-8: Science and product vision for terrestrial global change research, Remote Sens. Environ., 145, 154–172, 2014.
Sahu, A., Yadav, N., and Sudhakar, K.:
Floating photovoltaic power plant: A review, Renew. Sust. Energ. Rev., 66, 815–824, https://doi.org/10.1016/j.rser.2016.08.051, 2016.
Schmidhuber, J.:
Deep learning in neural networks: An overview, Neural Networks, 61, 85–117, 2015.
Copernicus Climate Change Service:
ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service Climate Data Store (CDS),
https://cds.climate.copernicus.eu/cdsapp#!/home (last access: 11 August 2022), 2017.
Taha, H.:
The potential for air-temperature impact from large-scale deployment of solar photovoltaic arrays in urban areas, Sol. Energy, 91, 358–367, 2013.
Tatem, A. J.:
WorldPop, open data for spatial demography, Sci. Data, 4, 1–4, 2017.
Tucker, C. J.:
Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8, 127–150, 1979.
Wen, J., Liu, Q., Xiao, Q., Liu, Q., You, D., Hao, D., Wu, S., and Lin, X.:
Characterizing Land Surface Anisotropic Reflectance over Rugged Terrain: A Review of Concepts and Recent Developments, Remote Sens.-Basel, 10, 370, https://doi.org/10.3390/rs10030370, 2018.
Xie, Z., Phinn, S. R., Game, E. T., Pannell, D. J., Hobbs, R. J., Briggs, P. R., and McDonald-Madden, E.:
Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands – A first step towards identifying degraded lands for conservation, Remote Sens. Environ., 232, 111317, https://doi.org/10.1016/j.rse.2019.111317, 2019.
Xu, H.:
Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, Int. J. Remote Sens., 27, 3025–3033, 2006.
Yadav, A. K. and Chandel, S. S.:
Tilt angle optimization to maximize incident solar radiation: A review, Renew. Sust. Energ. Rev., 23, 503–513, https://doi.org/10.1016/j.rser.2013.02.027, 2013.
Yu, J., Wang, Z., Majumdar, A., and Rajagopal, R.:
DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States, Joule, 2, 2605–2617, https://doi.org/10.1016/j.joule.2018.11.021, 2018.
Zha, Y., Gao, J., and Ni, S.:
Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, Int. J. Remote Sens., 24, 583–594, 2003.
Zhang, X. and Xu, M.:
Assessing the Effects of Photovoltaic Powerplants on Surface Temperature Using Remote Sensing Techniques, Remote Sens.-Basel, 12, 1825, https://doi.org/10.3390/rs12111825, 2020.
Zhang, X., Zeraatpisheh, M., Rahman, M. M., Wang, S., and Xu, M.:
Texture Is Important in Improving the Accuracy of Mapping Photovoltaic Power Plants: A Case Study of Ningxia Autonomous Region, China, Remote Sens., 13, 3909, https://doi.org/10.3390/rs13193909,
2021.
Zhang, X., Wang, S., Huang, Y., Zunyi Xie, Z., and Xu, M.:
The dataset of photovoltaic power plant distribution in China by 2020 (002), Zenodo [data set], https://doi.org/10.5281/zenodo.6849477, 2022.
Zhou, B., Okin, G. S., and Zhang, J.:
Leveraging Google Earth Engine (GEE) and machine learning algorithms to incorporate in situ measurement from different times for rangelands monitoring, Remote Sens. Environ., 236, 111521, https://doi.org/10.1016/j.rse.2019.111521, 2020.
Zhu, Z., Woodcock, C. E., Rogan, J., and Kellndorfer, J.:
Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data, Remote Sens. Environ., 117, 72–82, 2012.
Zou, H., Du, H., Brown, M. A., and Mao, G.:
Large-scale PV power generation in China: A grid parity and techno-economic analysis, Energy, 134, 256–268, https://doi.org/10.1016/j.energy.2017.05.192, 2017.