Prediction of diffuse solar radiation by integrating radiative transfer model and machine-learning techniques

Yunbo Lu; Renlan Zhang; Lunche Wang; Xin Su; Ming Zhang; Huaping Li; Shiyu Li; Jiaojiao Zhou

doi:10.1016/j.scitotenv.2022.160269

Prediction of diffuse solar radiation by integrating radiative transfer model and machine-learning techniques

Sci Total Environ. 2023 Feb 10;859(Pt 1):160269. doi: 10.1016/j.scitotenv.2022.160269. Epub 2022 Nov 17.

Authors

Yunbo Lu¹, Renlan Zhang¹, Lunche Wang², Xin Su¹, Ming Zhang¹, Huaping Li¹, Shiyu Li¹, Jiaojiao Zhou¹

Affiliations

¹ Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; Hunan Key Laboratory of Remote Sensing of Ecological Environment in Dongting Lake Area, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China.
² Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; Hunan Key Laboratory of Remote Sensing of Ecological Environment in Dongting Lake Area, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China. Electronic address: wang@cug.edu.cn.

PMID: 36402326
DOI: 10.1016/j.scitotenv.2022.160269

Abstract

Diffuse radiation is a major component of solar radiation that is important in carbon exchanges and material, energy, and information flows in agricultural ecosystems; however, measuring diffuse radiation is difficult and expensive, leaving only few stations in China that can record diffuse radiation. Therefore, five high-speed and highly accurate hybrid models were developed and compared to simulate diffuse radiation based on the aerosol optical properties and radiation parameters provided by the Aerosol Robotic Network (AERONET), Baseline Surface Radiation Network (BSRN), Wuhan University, Chinese Ecosystem Research Network (CERN), GLASS surface albedo data, and combined radiative transfer model (RTM) with machine learning (ML) models that include random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron (MLP), deep neural networks (DNN), and convolutional neural network (CNN). Furthermore, the uncertainty in the simulated diffuse radiation due to the measurement uncertainties of aerosol optical properties and land surface albedo was quantified, and the relative contributions of multiple variables to diffuse radiation were analyzed. The results showed that RTM-RF was the most successful, with determination coefficients (R²) of 0.95, 0.94, and 0.98, and minimum root mean square errors (RMSE) of 9.56, 10.05, and 13.27 W m^-2 at the Lulin, Wuhan, and Xianghe sites, respectively. The largest measurement uncertainty in the aerosol optical depth (AOD) was found at the Lulin site, while that of the single-scattering albedo led to the largest errors in Wuhan and Xianghe. AOD, solar zenith angle (SZA), and single-scattering albedo contributed significantly more than the asymmetry factor, land surface albedo, precipitable water vapor, and ozone. This was especially true for AOD, which was higher than 28 % at all sites. Overall, the proposed RTM-RF method exhibited superior performance, therefore we recommend it for estimating diffuse radiation in China.

Keywords: Diffuse solar radiation; Hybrid model; Machine-learning technique; Radiative transfer model; Uncertainty estimates.

MeSH terms

Aerosols
Ecosystem
Humans
Machine Learning
Ozone*
Solar Energy*

Substances

Aerosols
Ozone