气候变化领域集成服务门户(CCPortal): Estimating Downward Shortwave Solar Radiation on Clear-Sky Days in Heterogeneous Surface Using LM-BP Neural Network

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DOI	10.3390/en14020273
	Estimating Downward Shortwave Solar Radiation on Clear-Sky Days in Heterogeneous Surface Using LM-BP Neural Network
	Wang, Weizhen; Feng, Jiaojiao; Xu, Feinan
通讯作者	Wang, WZ (通讯作者)，Chinese Acad Sci, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China. ; Wang, WZ (通讯作者)，Chinese Acad Sci, Key Lab Land Surface Proc & Climate Change Cold &, Lanzhou 730000, Peoples R China.
发表日期	2021
EISSN	1996-1073
卷号	14 期号:2
英文摘要	Downward surface shortwave radiation (DSSR) plays an important role in the energy balance of the earth's surface. Accurate estimate of DSSR is of great significance for the rational and effective use of solar energy. Some parameterization schemes were proposed to estimate DSSR using meteorological measurements given ground-based radiation observation sites are scare and uneven. With the development of remote sensing technique, remotely sensed data can be applied to obtain continuous DSSR in space. Commonly, the spatial resolution of most radiation products is relatively low and cannot meet the needs of certain fields. Moreover, some retrieval algorithms based on the radiation transfer models are complicated for non-professionals. In this study, a back-propagation (BP) neural network method with Levenberg-Marquardt (LM) algorithm (hereafter referred to as LM-BP) was applied to predict DSSR by building the relationship between measured DSSR and high-resolution remote sensing data from the Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER). The DSSR observations from the four-component radiation sensor installed at the land covered by vegetable, village, maize, orchard, Gobi, sandy desert, desert steppe, and wetland were used to validate the model estimates. The results showed that the estimates of DSSR from LM-BP agreed well with the site measurements, with the root mean square error (RMSE) and the mean bias error (MBE) values of 27.34 W/m(2) and -1.59 W/m(2), respectively. This indicates that by combining the LM-BP network model and ASTER images can obtain precise DSSR in heterogenous surface. The DSSR results of this study can provide accurate high-spatial resolution input data for hydrological, evapotranspiration, and crop models.
关键词	MODIS MODELS ANN LST
英文关键词	DSSR; finer spatial resolution; LM-BP neural network; ASTER; heterogeneous surface
语种	英语
WOS研究方向	Energy & Fuels
WOS类目	Energy & Fuels
WOS记录号	WOS:000611199800001
来源期刊	ENERGIES
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254658
作者单位	[Wang, Weizhen; Feng, Jiaojiao; Xu, Feinan] Chinese Acad Sci, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China; [Wang, Weizhen] Chinese Acad Sci, Key Lab Land Surface Proc & Climate Change Cold &, Lanzhou 730000, Peoples R China; [Feng, Jiaojiao] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Wang, Weizhen,Feng, Jiaojiao,Xu, Feinan. Estimating Downward Shortwave Solar Radiation on Clear-Sky Days in Heterogeneous Surface Using LM-BP Neural Network[J]. 中国科学院西北生态环境资源研究院,2021,14(2).
APA	Wang, Weizhen,Feng, Jiaojiao,&Xu, Feinan.(2021).Estimating Downward Shortwave Solar Radiation on Clear-Sky Days in Heterogeneous Surface Using LM-BP Neural Network.ENERGIES,14(2).
MLA	Wang, Weizhen,et al."Estimating Downward Shortwave Solar Radiation on Clear-Sky Days in Heterogeneous Surface Using LM-BP Neural Network".ENERGIES 14.2(2021).