| Performance Assessment of Four Data-Driven Machine Learning Models: A Case to Generate Sentinel-2 Albedo at 10 Meters | |
Chen, Hao1; Lin, Xingwen1; Sun, Yibo2; Wen, Jianguang3; Wu, XD(吴小丹)4 ; You, Dongqin3; Cheng, Juan5; Zhang, Zhenzhen1; Zhang, Zhaoyang1; Wu, Chaofan1; Zhang, Fei1; Yin, Kechen1; Jian, Huaxue1; Guan, Xinyu1 | |
| 2023-05 | |
| Source Publication | Remote Sensing Impact Factor & Quartile Of Published Year The Latest Impact Factor & Quartile |
| EISSN | 2072-4292 |
| Volume | 15Issue:10 |
| page numbers | 23 |
| Abstract | 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 brands, 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 R2 larger than 0.9472. © 2023 by the authors. |
| Keyword | 3D modeling Digital storage Forestry Land use Landforms Learning algorithms Learning systems Machine learning Nearest neighbor search Neural networks Solar radiation Albedo Data driven Data-driven machine learning algorithm High resolution Machine learning algorithms Machine learning models Performance Remote-sensing Sentinel-2 Surface albedo albedo data-driven machine learning algorithms remote sensing |
| Publisher | MDPI |
| DOI | 10.3390/rs15102684 |
| Indexed By | EI ; SCIE |
| Language | 英语 |
| WOS Research Area | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS Subject | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000998132300001 |
| EI Accession Number | 20232414212271 |
| EI Keywords | Remote sensing |
| EI Classification Number | 403 Urban and Regional Planning and Development ; 481.1 Geology ; 657.1 Solar Energy and Phenomena ; 722.1 Data Storage, Equipment and Techniques ; 723.2 Data Processing and Image Processing ; 723.4 Artificial Intelligence ; 723.4.2 Machine Learning ; 821 Agricultural Equipment and Methods ; Vegetation and Pest Control ; 921.5 Optimization Techniques |
| Original Document Type | Journal article (JA) ; Article |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://ir.lzu.edu.cn/handle/262010/592600 |
| Collection | 兰州大学 资源环境学院 |
| Affiliation | 1.College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua; 321004, China; 2.State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing; 100012, China; 3.The State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academic of Sciences and University of Chinese Academic of Sciences, Beijing; 100083, China; 4.The College of Earth and Environmental Sciences, Lanzhou University, Lanzhou; 730000, China; 5.The Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an; 710119, China |
| Recommended Citation 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). |
| Files in This Item: | |||||
| File Name/Size | DocType | Version | Access | License | |
| Chen-2023-Performanc（13289KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | Application Full Text |
