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Title: Surface daytime net radiation estimation using artificial neural networks

Journal Article · · Remote Sensing
DOI:https://doi.org/10.3390/rs61111031· OSTI ID:1222969
 [1];  [1];  [2];  [1];  [1]
  1. Beijing Normal Univ., Beijing (China)
  2. Beijing Normal Univ., Beijing (China); Univ. of Maryland, College Park, MD (United States)
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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.

Research Organization:
Oregon State Univ., Corvallis, OR (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
FG02-04ER63917
OSTI ID:
1222969
Journal Information:
Remote Sensing, Vol. 6, Issue 11; ISSN 2072-4292
Publisher:
MDPICopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 27 works
Citation information provided by
Web of Science

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Cited By (4)

Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis journal February 2018
Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data journal October 2019
Surface Shortwave Net Radiation Estimation from FengYun-3 MERSI Data journal May 2015
GLASS Daytime All-Wave Net Radiation Product: Algorithm Development and Preliminary Validation journal March 2016

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14 SOLAR ENERGY
net radiation
artificial neural network
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remotely sensed products