@article{02ffed0eda0242a4bfafaae2d29abaf9,
title = "Assessing the effects of photovoltaic powerplants on surface temperature using remote sensing techniques",
abstract = "The rapid development of photovoltaic (PV) powerplants in the world has drawn attention on their climate and environmental impacts. In this study, we assessed the effects of PV powerplants on surface temperature using 23 largest PV powerplants in the world with thermal infrared remote sensing technique. Our result showed that the installation of the PV powerplants had significantly reduced the daily mean surface temperature by 0.53 °C in the PV powerplant areas. The cooling effect with the installation of the PV powerplants was much stronger during the daytime than the nighttime with the surface temperature dropped by 0.81 °C and 0.24 °C respectively. This cooling effect was also depended on the capacity of the powerplants with a cooling rate of-0.32,-0.48, and-0.14 °C/TWh, respectively, for daily mean, daytime, and nighttime temperature. We also found that the construction of the powerplants significantly decreased the surface albedo from 0.22 to 0.184, but significantly increased the effective albedo (surface albedo plus electricity conversion) from 0.22 to 0.244, suggesting conversion of solar energy to electrical energy is a major contributor to the observed surface cooling. Our further analyses showed that the nighttime cooling in the powerplants was significantly correlated with the latitude and elevation of the powerplants as well as the annual mean temperature, precipitation, solar radiation, and normalized difference vegetation index (NDVI). This means the temperature effect of the PV powerplants depended on regional geography, climate and vegetation conditions. This finding can be used to guide the selection of the sites of PV powerplants in the future.",
keywords = "Convective cooling, Effective albedo, MODIS, NDVI, Photovoltaic powerplants",
author = "Xunhe Zhang and Ming Xu",
note = "Funding Information: mean view angle from MODIS Terra during daytime in 2018. Figure S2: The annual mean view time from MODIS Terra during daytime in 2018. Figure S3: Diagrammatic sketch of PV panels. The α is tilt angle of PV panel, which classification-based emissivity model. Table S2: Comparison of the emissivity of the sands, dry vegetation and is equal to the latitude of the PV powerplant [77], and the β is solar altitude angle (SAA). Table S1: The emissivity values used for the MODIS LST products (band 31 and 32) based on the classification-based emissivity model. Table dSa2y: tCimome (p1a0r:i3s0onlocoaf ltthime eem) oinssJiuvnitey Sooflsthtiec es,aSnedpst,e mdrbye rveEgqeutiantoioxna annddD PeVce msoblaerr cSeolllsst.i cTea.bTleh eS3so: lTahrealctaitvuidtye eafnfgeclet aotf the PV panels in different PV power stations. Table S4: The solar altitude angle (SAA) and proportion of shade surface of each powerplant at the MODIS passing time during daytime (10:30 local time) on June Solstice, ASeupthteomr bCeorn Etrqiubiuntoioxn asn: dC oDnecceepmtubaelriz Saotilosnti,cXe.. ZT.haen dsoMla.rXa.;ltmiteutdheo daonlgolgey a, Xt .1Z3.:3a0n d(loMc.aXl .;tismofet)w iasr eeq, Xu.aZl .;tovatlhide astoiolanr, X.Z.; writing—original draft preparation, X.Z.; writing—review and editing, M.X.; visualization, X.Z.; supervision, M.X.; funding acquisition, M.X. All authors have read and agreed to the published version of the manuscript. Author Contributions: Conceptualization, X.Z. and M.X.; methodology, X.Z and M.X.; software, X.Z.; Funding: This study was supported by the National Key Research and Development Program of China validation, X.Z.; writing—original draft preparation, X.Z.; writing—review and editing, M.X.; visualization, X.Z.; supervision, M.X.; funding acquisition, M.X. All authors have read and agreed to the published version of the manuscript. Funding Information: Funding: This study was supported by the National Key Research and Development Program of China (2018YFA0606500, 2017YFA0604302). Publisher Copyright: {\textcopyright} 2020 by the authors.",
year = "2020",
month = jun,
day = "1",
doi = "10.3390/rs12111825",
language = "English (US)",
volume = "12",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "11",
}