Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning
Accurate high spatial resolution snow depth mapping in arid and semi-arid regions is of great importance for snow disaster assessment and hydrological modeling. However, due to the complex topography and low spatial-resolution microwave remote-sensing data, the existing snow depth datasets have larg...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/4/584 |
| _version_ | 1797413316182671360 |
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| author | Linglong Zhu Yonghong Zhang Jiangeng Wang Wei Tian Qi Liu Guangyi Ma Xi Kan Ya Chu |
| author_facet | Linglong Zhu Yonghong Zhang Jiangeng Wang Wei Tian Qi Liu Guangyi Ma Xi Kan Ya Chu |
| author_sort | Linglong Zhu |
| collection | DOAJ |
| description | Accurate high spatial resolution snow depth mapping in arid and semi-arid regions is of great importance for snow disaster assessment and hydrological modeling. However, due to the complex topography and low spatial-resolution microwave remote-sensing data, the existing snow depth datasets have large errors and uncertainty, and actual spatiotemporal heterogeneity of snow depth cannot be effectively detected. This paper proposed a deep learning approach based on downscaling snow depth retrieval by fusion of satellite remote-sensing data with multiple spatial scales and diverse characteristics. The (Fengyun-3 Microwave Radiation Imager) FY-3 MWRI data were downscaled to 500 m resolution to match Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover, meteorological and geographic data. A deep neural network was constructed to capture detailed spectral and radiation signals and trained to retrieve the higher spatial resolution snow depth from the aforementioned input data and ground observation. Verified by in situ measurements, downscaled snow depth has the lowest root mean square error (RMSE) and mean absolute error (MAE) (8.16 cm, 4.73 cm respectively) among Environmental and Ecological Science Data Center for West China Snow Depth (WESTDC_SD, 9.38 cm and 5.36 cm), the Microwave Radiation Imager (MWRI) Ascend Snow Depth (MWRI_A_SD, 9.45 cm and 5.49 cm) and MWRI Descend Snow Depth (MWRI_D_SD, 10.55 cm and 6.13 cm) in the study area. Meanwhile, downscaled snow depth could provide more detailed information in spatial distribution, which has been used to analyze the decrease of retrieval accuracy by various topography factors. |
| first_indexed | 2024-03-09T05:17:07Z |
| format | Article |
| id | doaj.art-553e8d4385cd41fe890bb0d57fd0e614 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:17:07Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-553e8d4385cd41fe890bb0d57fd0e6142023-12-03T12:44:53ZengMDPI AGRemote Sensing2072-42922021-02-0113458410.3390/rs13040584Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep LearningLinglong Zhu0Yonghong Zhang1Jiangeng Wang2Wei Tian3Qi Liu4Guangyi Ma5Xi Kan6Ya Chu7Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, ChinaJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaBinjiang College, Nanjing University of Information Science and Technology, Wuxi 214105, ChinaHuayun Information Technology Engineering Co., Ltd, China Meteorological Administration, Beijing 100081, ChinaAccurate high spatial resolution snow depth mapping in arid and semi-arid regions is of great importance for snow disaster assessment and hydrological modeling. However, due to the complex topography and low spatial-resolution microwave remote-sensing data, the existing snow depth datasets have large errors and uncertainty, and actual spatiotemporal heterogeneity of snow depth cannot be effectively detected. This paper proposed a deep learning approach based on downscaling snow depth retrieval by fusion of satellite remote-sensing data with multiple spatial scales and diverse characteristics. The (Fengyun-3 Microwave Radiation Imager) FY-3 MWRI data were downscaled to 500 m resolution to match Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover, meteorological and geographic data. A deep neural network was constructed to capture detailed spectral and radiation signals and trained to retrieve the higher spatial resolution snow depth from the aforementioned input data and ground observation. Verified by in situ measurements, downscaled snow depth has the lowest root mean square error (RMSE) and mean absolute error (MAE) (8.16 cm, 4.73 cm respectively) among Environmental and Ecological Science Data Center for West China Snow Depth (WESTDC_SD, 9.38 cm and 5.36 cm), the Microwave Radiation Imager (MWRI) Ascend Snow Depth (MWRI_A_SD, 9.45 cm and 5.49 cm) and MWRI Descend Snow Depth (MWRI_D_SD, 10.55 cm and 6.13 cm) in the study area. Meanwhile, downscaled snow depth could provide more detailed information in spatial distribution, which has been used to analyze the decrease of retrieval accuracy by various topography factors.https://www.mdpi.com/2072-4292/13/4/584downscalingdeep learningsnow depthMWRIdata fusion |
| spellingShingle | Linglong Zhu Yonghong Zhang Jiangeng Wang Wei Tian Qi Liu Guangyi Ma Xi Kan Ya Chu Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning Remote Sensing downscaling deep learning snow depth MWRI data fusion |
| title | Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning |
| title_full | Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning |
| title_fullStr | Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning |
| title_full_unstemmed | Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning |
| title_short | Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning |
| title_sort | downscaling snow depth mapping by fusion of microwave and optical remote sensing data based on deep learning |
| topic | downscaling deep learning snow depth MWRI data fusion |
| url | https://www.mdpi.com/2072-4292/13/4/584 |
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