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Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data

文献类型：期刊论文

作者Wu, Hua1,2,3; Ying, Wangmin1,2
刊名REMOTE SENSING
出版日期2019-11-01
卷号11期号:21页码:20
关键词net surface shortwave radiation MODIS FLUXNET Random Forest Artificial Neural Network Support Vector Regression
DOI10.3390/rs11212520
通讯作者Wu, Hua(wuhua@igsnrr.ac.cn)
英文摘要Net surface shortwave radiation (NSSR) is one of the most important fundamental parameters in various land processes. Benefiting from its efficient nonlinear fitting ability, machine learning algorithms have a great potential in the retrieval of NSSR. However, few studies have explored the level of accuracy that machine learning algorithms can reach for different land covers on the worldwide scale and what the optimal independent variables are in the machine learning-based NSSR model. To guide the use of machine learning algorithms correctly in the retrieval of NSSR, it is necessary to give a comprehensive analysis from algorithm complexity, accuracy, and other aspects. In this study, three classic machine learning algorithms, including Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR), were built well to estimate instantaneous NSSR with optimal hyperparameters by elaborately selecting different independent variables, including top of atmosphere (TOA) channel spectral reflectance, geographic parameters, surface information, and atmosphere conditions. Global FLUXNET in situ measurements throughout 2014 were used to validate the accuracies of retrieved NSSR over various land cover types. The root mean square error (RMSE) is below 55 W/m(2), and the distributions of error histogram are also similar. Approximately 50% of absolute error were within 25 W/m(2). There was a performance difference of NSSR estimations in various surface types, and the performance of three machine learning methods in a specific surface type was also different. However, the RF method may be considered as the optimal methodology to retrieve NSSR from MODIS data, owing to its relatively better precision and concise hyperparameter-tuned process. The importance analysis of the proposed independent variables of NSSR retrieval shows that the introduction of geographic information can effectively reduce the error of NSSR retrieval, and surface information and atmosphere information are not necessary. It was also found that a combination of geographic information and blue band TOA reflectance already have a pretty good accuracy in NSSR retrieval, which implies there is a possibility to transfer our NSSR model to other satellite sensors, especially with insufficient channels. In a word, the NSSR model with machine learning algorithms would be an efficient, concise, and general method in the future.
WOS关键词ARTIFICIAL NEURAL-NETWORKS ; SOLAR-RADIATION ; TEMPERATURE PRODUCT ; MODIS ; MODEL ; INDEX
资助项目Strategic Priority Research Program of Chinese Academy of Sciences[XDA20030302] ; National Natural Science Foundation of China[41871267]
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000504716700061
出版者MDPI
资助机构Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/131228]  
专题中国科学院地理科学与资源研究所
通讯作者Wu, Hua
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Hua,Ying, Wangmin. Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data[J]. REMOTE SENSING,2019,11(21):20.
APA Wu, Hua,&Ying, Wangmin.(2019).Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data.REMOTE SENSING,11(21),20.
MLA Wu, Hua,et al."Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data".REMOTE SENSING 11.21(2019):20.

入库方式： OAI收割

来源：地理科学与资源研究所

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