Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data
Jiang, Hou2,3; Lu, Ning1,3,6; Huang, Guanghui4; Yao, Ling1,3,6; Qin, Jun5; Liu, Hengzi2,3
刊名APPLIED ENERGY
2020-07-15
卷号270页码:14
关键词Scale effect Surface solar radiation Convolutional neural network Artificial neural network Multivariate linear regression
ISSN号0306-2619
DOI10.1016/j.apenergy.2020.115178
通讯作者Lu, Ning(lvn@lreis.ac.cn)
英文摘要The presence of nonhomogeneous clouds and their induced radiation interactions result in significant horizontal photon transport and spatially adjacent effects on surface solar radiation (SSR), making spatial estimation scale-dependent. Overlooking scale effects during SSR retrieval from satellite data is responsible for variations in retrieval accuracy and deviations in associated applications. In this paper, the spatial scale effects on SSR retrieval accuracy are investigated using multivariate linear regression and artificial neural network and convolutional neural network models. Scale effects are quantified through changes in retrieval accuracy under varying satellite data input size and compared among different models to reveal the merits and defects of classic linear, ordinary nonlinear, and spatially nonlinear models. The results show that scale effects have considerable impacts on retrieval accuracy in each of the three models for both site-specific and general conditions. The maximum improvement in terms of the root mean square error can reach up to 9% after involving scale information. The performance of site-specific models is continually enhanced with the expansion of spatial scale, while that of general models will drop to some extent beyond a particular threshold. Approximate distances of 20 km and 40 km from the central location are identified as the optimal scale for artificial neural and convolutional neural networks, respectively. This study also concludes that the robustness of general models is relevant to various atmospheric factors, providing perspectives for further improvements including the fusion of time series images, integration with physical modules, and the combination of multi-resolution data.
资助项目National Natural Science Foundation of China[41971312] ; National Natural Science Foundation of China[41890854] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)[GML2019ZD0301]
WOS关键词SUNSHINE DURATION ; ENERGY-PRODUCTION ; QUALITY-CONTROL ; AIR-POLLUTION ; IRRADIANCE ; NETWORK ; MODELS ; PARAMETERIZATION ; IMPACT
WOS研究方向Energy & Fuels ; Engineering
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:000540433000031
资助机构National Natural Science Foundation of China ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)
内容类型期刊论文
源URL[http://ir.igsnrr.ac.cn/handle/311030/162346]  
专题中国科学院地理科学与资源研究所
通讯作者Lu, Ning
作者单位1.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China
5.Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing 100085, Peoples R China
6.Southern Marine Sci & Engn Guangdong Lab, Guangzhou 511458, Peoples R China
推荐引用方式
GB/T 7714
Jiang, Hou,Lu, Ning,Huang, Guanghui,et al. Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data[J]. APPLIED ENERGY,2020,270:14.
APA Jiang, Hou,Lu, Ning,Huang, Guanghui,Yao, Ling,Qin, Jun,&Liu, Hengzi.(2020).Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data.APPLIED ENERGY,270,14.
MLA Jiang, Hou,et al."Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data".APPLIED ENERGY 270(2020):14.
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