Performance Assessment of Four Data-Driven Machine Learning Models: A Case to Generate Sentinel-2 Albedo at 10 Meters
文献类型:期刊论文
作者 | Chen, Hao6; Lin, Xingwen6; Sun, Yibo5; Wen, Jianguang3,4; Wu, Xiaodan2; You, Dongqin3,4; Cheng, Juan1; Zhang, Zhenzhen6; Zhang, Zhaoyang6; Wu, Chaofan6 |
刊名 | REMOTE SENSING |
出版日期 | 2023-05-22 |
卷号 | 15期号:10 |
ISSN号 | 2072-4292 |
关键词 | Sentinel-2 albedo data-driven machine learning algorithms remote sensing |
DOI | 10.3390/rs15102684 |
产权排序 | 6 |
英文摘要 | High-resolution albedo has the advantage of a higher spatial scale from tens to hundreds of meters, which can fill the gaps of albedo applications from the global scale to the regional scale and can solve problems related to land use change and ecosystems. The Sentinel-2 satellite provides high-resolution observations in the visible-to-NIR bands, giving possibilities to generate a high-resolution surface albedo at 10 m. This study attempted to evaluate the performance of the four data-driven machine learning algorithms (i.e., random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and XGBoost (XGBT)) for the generation of a Sentinel-2 albedo over flat and rugged terrain. First, we used the RossThick-LiSparseR model and the 3D discrete anisotropic radiative transfer (DART) model to build the narrowband surface reflectance and broadband surface albedo, which acted as the training and testing datasets over flat and rugged terrain. Second, we used the training and testing datasets to drive the four machine learning models, and evaluated the performance of these machine learning models for the generation of Sentinel-2 albedo. Finally, we used the four machine learning models to generate a Sentinel-2 albedo and compared them with in situ albedos to show the models' application potentials. The results show that these machine learning models have great performance in estimating Sentinel-2 albedos at a 10 m spatial scale. The comparison with in situ albedos shows that the random forest model outperformed the others in estimating a high-resolution surface albedo based on Sentinel-2 datasets over the flat and rugged terrain, with an RMSE smaller than 0.0308 and R-2 larger than 0.9472. |
语种 | 英语 |
出版者 | MDPI |
WOS记录号 | WOS:000998132300001 |
源URL | [http://ir.opt.ac.cn/handle/181661/96525] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Lin, Xingwen |
作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China 2.Lanzhou Univ, Coll Earth & Environm Sci, Lanzhou 730000, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100083, Peoples R China 4.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100083, Peoples R China 5.Chinese Res Inst Environm Sci, State Key Lab Environm Criteria & Risk Assessment, Beijing 100012, Peoples R China 6.Zhejiang Normal Univ, Coll Geog & Environm Sci, Jinhua 321004, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Hao,Lin, Xingwen,Sun, Yibo,et al. Performance Assessment of Four Data-Driven Machine Learning Models: A Case to Generate Sentinel-2 Albedo at 10 Meters[J]. REMOTE SENSING,2023,15(10). |
APA | Chen, Hao.,Lin, Xingwen.,Sun, Yibo.,Wen, Jianguang.,Wu, Xiaodan.,...&Guan, Xinyu.(2023).Performance Assessment of Four Data-Driven Machine Learning Models: A Case to Generate Sentinel-2 Albedo at 10 Meters.REMOTE SENSING,15(10). |
MLA | Chen, Hao,et al."Performance Assessment of Four Data-Driven Machine Learning Models: A Case to Generate Sentinel-2 Albedo at 10 Meters".REMOTE SENSING 15.10(2023). |
入库方式: OAI收割
来源:西安光学精密机械研究所
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