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| DOI | 10.3390/rs16081394 |
| Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism | |
| 发表日期 | 2024 |
| EISSN | 2072-4292 |
| 起始页码 | 16 |
| 结束页码 | 8 |
| 卷号 | 16期号:8 |
| 英文摘要 | Soil moisture (SM) is a critical variable affecting ecosystem carbon and water cycles and their feedback to climate change. In this study, we proposed a convolutional neural network (CNN) model embedded with a residual block and attention module, named SMNet, to spatially downscale the European Space Agency (ESA) Climate Change Initiative (CCI) SM product. In the SMNet model, a lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism was integrated to comprehensively extract the spatial and channel information from the high-resolution input remote sensing products, the reanalysis meteorological dataset, and the topographic data. The model was employed to downscale the ESA CCI SM from its original spatial resolution of 25 km to 1 km in California, USA, in the annual growing season (1 May to 30 September) from 2003 to 2021. The original ESA CCI SM data and in situ SM measurements (0-5 cm depth) from the International Soil Moisture Network were used to validate the model's performance. The results show that compared with the original ESA CCI SM data, the downscaled SM data have comparable accuracy with a mean correlation (R) and root mean square error (RMSE) of 0.82 and 0.052 m3/m3, respectively. Moreover, the model generates reasonable spatiotemporal SM patterns with higher accuracy in the western region and relatively lower accuracy in the eastern Nevada mountainous area. In situ site validation results in the SCAN, the SNOTEL network, and the USCRN reveal that the R and RMSE are 0.62, 0.63, and 0.77, and 0.077 m3/m3, 0.093 m3/m3, and 0.078 m3/m3, respectively. The results are slightly lower than the validation results from the original ESA CCI SM data. Overall, the validation results suggest that the SMNet downscaling model proposed in this study has satisfactory performance in handling the task of soil moisture downscaling. The downscaled SM model not only preserves a high level of spatial consistency with the original ESA CCI SM model but also offers more intricate spatial variations in SM depending on the spatial resolution of model input data. |
| 英文关键词 | deep learning; convolutional neural network (CNN); convolutional block attention module (CBAM); attention mechanisms; soil moisture; ESA CCI SM |
| 语种 | 英语 |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS类目 | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001210347800001 |
| 来源期刊 | REMOTE SENSING
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/291480 |
| 作者单位 | Qingdao University; Chinese Academy of Sciences; Aerospace Information Research Institute, CAS |
| 推荐引用方式 GB/T 7714 | . Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism[J],2024,16(8). |
| APA | (2024).Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism.REMOTE SENSING,16(8). |
| MLA | "Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism".REMOTE SENSING 16.8(2024). |
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