DOI: 10.1016/j.atmosres.2017.09.006
Scopus记录号: 2-s2.0-85032025974
论文题名: Estimating solar radiation using NOAA/AVHRR and ground measurement data
作者: Fallahi S. ; Amanollahi J. ; Tzanis C.G. ; Ramli M.F.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2018
卷: 199 起始页码: 93
结束页码: 102
语种: 英语
英文关键词: AVHRR sensor
; Emissivity
; Land surface temperature
; NDVI
Scopus关键词: Electromagnetic wave emission
; Linear regression
; Neural networks
; Remote sensing
; Solar radiation
; Surface measurement
; Surface properties
; Emissivity
; Ground measurements
; Land surface temperature
; Mean temperature
; Multiple linear regression models
; NDVI
; Remote sensing data
; Renewable energies
; Atmospheric temperature
; AVHRR
; estimation method
; land surface
; measurement method
; NDVI
; NOAA satellite
; remote sensing
; sensor
; solar radiation
; surface temperature
; Iran
; Kordestan [Iran]
英文摘要: Solar radiation (SR) data are commonly used in different areas of renewable energy research. Researchers are often compelled to predict SR at ground stations for areas with no proper equipment. The objective of this study was to test the accuracy of the artificial neural network (ANN) and multiple linear regression (MLR) models for estimating monthly average SR over Kurdistan Province, Iran. Input data of the models were two data series with similar longitude, latitude, altitude, and month (number of months) data, but there were differences between the monthly mean temperatures in the first data series obtained from AVHRR sensor of NOAA satellite (DS1) and in the second data series measured at ground stations (DS2). In order to retrieve land surface temperature (LST) from AVHRR sensor, emissivity of the area was considered and for that purpose normalized vegetation difference index (NDVI) calculated from channels 1 and 2 of AVHRR sensor was utilized. The acquired results showed that the ANN model with DS1 data input with R2 = 0.96, RMSE = 1.04, MAE = 1.1 in the training phase and R2 = 0.96, RMSE = 1.06, MAE = 1.15 in the testing phase achieved more satisfactory performance compared with MLR model. It can be concluded that ANN model with remote sensing data has the potential to predict SR in locations with no ground measurement stations. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/109024
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
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作者单位: Department of Environmental Sciences, Faculty of Natural Resources, University of Kurdistan, Iran; National and Kapodistrian University of Athens, Department of Physics, Section of Environmental Physics and Meteorology, University Campus, Bldg Phys-5, Athens, 15784, Greece; Faculty of Environmental Studies, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia
Recommended Citation:
Fallahi S.,Amanollahi J.,Tzanis C.G.,et al. Estimating solar radiation using NOAA/AVHRR and ground measurement data[J]. Atmospheric Research,2018-01-01,199