Surface daytime net radiation estimation using artificial neural networks
Abstract
Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010 both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R2) of 0.92, a root mean square error (RMSE) of 34.27 W·m–2 , and a bias of –0.61 W·m–2 in global mode based onmore »
- Authors:
-
- Beijing Normal Univ., Beijing (China)
- Beijing Normal Univ., Beijing (China); Univ. of Maryland, College Park, MD (United States)
- Publication Date:
- Research Org.:
- Oregon State Univ., Corvallis, OR (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1222969
- Grant/Contract Number:
- FG02-04ER63917
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Remote Sensing
- Additional Journal Information:
- Journal Volume: 6; Journal Issue: 11; Journal ID: ISSN 2072-4292
- Publisher:
- MDPI
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 14 SOLAR ENERGY; net radiation; artificial neural network; modeling; remotely sensed products
Citation Formats
Jiang, Bo, Zhang, Yi, Liang, Shunlin, Zhang, Xiaotong, and Xiao, Zhiqiang. Surface daytime net radiation estimation using artificial neural networks. United States: N. p., 2014.
Web. doi:10.3390/rs61111031.
Jiang, Bo, Zhang, Yi, Liang, Shunlin, Zhang, Xiaotong, & Xiao, Zhiqiang. Surface daytime net radiation estimation using artificial neural networks. United States. https://doi.org/10.3390/rs61111031
Jiang, Bo, Zhang, Yi, Liang, Shunlin, Zhang, Xiaotong, and Xiao, Zhiqiang. Tue .
"Surface daytime net radiation estimation using artificial neural networks". United States. https://doi.org/10.3390/rs61111031. https://www.osti.gov/servlets/purl/1222969.
@article{osti_1222969,
title = {Surface daytime net radiation estimation using artificial neural networks},
author = {Jiang, Bo and Zhang, Yi and Liang, Shunlin and Zhang, Xiaotong and Xiao, Zhiqiang},
abstractNote = {Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010 both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R2) of 0.92, a root mean square error (RMSE) of 34.27 W·m–2 , and a bias of –0.61 W·m–2 in global mode based on the validation dataset. In conclusion, ANN methods are a potentially powerful tool for global Rn estimation.},
doi = {10.3390/rs61111031},
journal = {Remote Sensing},
number = 11,
volume = 6,
place = {United States},
year = {Tue Nov 11 00:00:00 EST 2014},
month = {Tue Nov 11 00:00:00 EST 2014}
}
Web of Science
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