Deep Learning-Based Spectral Reconstruction Technology for Water Color Remote Sensing and Error Analysis
文献类型：期刊论文
| 作者 | Tang, Rugang2; He, Li3; Guo, Biyun1,2,4; Ye, Cuishuo2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025-08-17 |
| 卷号 | 17期号:16页码:2860 |
| 关键词 | spectral reconstruction deep learning coastal zone water color remote sensing |
| DOI | 10.3390/rs17162860 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Land observation multispectral satellites (e.g., Landsat-8/9 and Sentinel-2) offer high spatial resolution but have limited spectral bands for water color observation and insufficient spectral resolution. This study proposes a spectral reconstruction model based on a residual neural network (Deep Spectral Reconstruction Learning Network, DSR-Net) to provide additional spectral bands support for nearshore water observations. The model is trained on 60 million pairs of quasi-synchronous reflectance data, and achieves stable reconstruction of 15 water color channels of the surface level reflectance for water pixels (rho w) from visible to near-infrared bands, considering sensor noise and atmospheric correction errors. Validation results based on AERONET-OC data show that the root mean square error of reconstructed rho w by DSR-Net ranges from 4.09 to 5.18 x 10-3, representing a reduction of 25% to 43% compared to original atmospheric correction results. The reconstruction accuracy reaches the observation level of the Sentinel-3/OLCI water color sensor and is universally applicable to different water categories, effectively supporting nearshore water color observation tasks such as colored dissolved organic matter inversion and cyanobacteria monitoring. The errors in the multispectral reflectance-based rho w primarily arise from sensor noise and atmospheric correction errors. After DSR-Net reconstruction, approximately 59% of the uncertainty caused by sensor noise and 38% of that caused by atmospheric correction errors are reduced. In summary, the spectral reconstruction products generated by DSR-Net not only significantly enhance the water color observation capabilities of current satellite sensors but also provide critical technical support for marine environmental monitoring and the design of next-generation sensors. |
| URL标识 | 查看原文 |
| WOS关键词 | OCEAN COLOR ; PHYTOPLANKTON PIGMENTS ; ATMOSPHERIC CORRECTION ; YANGTZE ESTUARY ; REFLECTANCE ; ALGORITHM ; COASTAL ; INLAND ; MODEL ; SPECTROMETER |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001558506000001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216141]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | He, Li |
| 作者单位 | 1.Minist Water Resources, Key Lab Water Ecol Remediat & Protect Headwater Re, Xining 810016, Peoples R China 2.Zhejiang Ocean Univ, Marine Sci & Technol Coll, Zhoushan 316022, Peoples R China; 3.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 4.Qinghai Univ, Sch Civil Engn & Water Resources, Xining 810016, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Tang, Rugang,He, Li,Guo, Biyun,et al. Deep Learning-Based Spectral Reconstruction Technology for Water Color Remote Sensing and Error Analysis[J]. REMOTE SENSING,2025,17(16):2860. |
| APA | Tang, Rugang,He, Li,Guo, Biyun,&Ye, Cuishuo.(2025).Deep Learning-Based Spectral Reconstruction Technology for Water Color Remote Sensing and Error Analysis.REMOTE SENSING,17(16),2860. |
| MLA | Tang, Rugang,et al."Deep Learning-Based Spectral Reconstruction Technology for Water Color Remote Sensing and Error Analysis".REMOTE SENSING 17.16(2025):2860. |
入库方式： OAI收割
来源：地理科学与资源研究所
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