Surface daytime net radiation estimation using artificial neural networks

Jiang, Bo; Zhang, Yi; Liang, Shunlin; Zhang, Xiaotong; Xiao, Zhiqiang

doi:10.3390/rs61111031

Surface daytime net radiation estimation using artificial neural networks

Journal Article · Mon Nov 10 23:00:00 EST 2014 · Remote Sensing

DOI:https://doi.org/10.3390/rs61111031· OSTI ID:1222969

Jiang, Bo ^[1]; Zhang, Yi ^[2]; Liang, Shunlin ^[3]; Zhang, Xiaotong ^[2]; Xiao, Zhiqiang ^[2]

Beijing Normal Univ., Beijing (China); Oregon State University
Beijing Normal Univ., Beijing (China)
Beijing Normal Univ., Beijing (China); Univ. of Maryland, College Park, MD (United States)

Net all-wave surface radiation (R_n) is one of the most important fundamental parameters in various applications. However, conventional R_n measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical R_n 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 R_n globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. R_n 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 R_n estimation.

View Accepted Manuscript (DOE)

Research Organization:: Oregon State Univ., Corvallis, OR (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)

Grant/Contract Number:: FG02-04ER63917

OSTI ID:: 1222969

Journal Information:: Remote Sensing, Journal Name: Remote Sensing Journal Issue: 11 Vol. 6; ISSN 2072-4292

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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