Detecting the Lake Area Seasonal Variations in the Tibetan Plateau from Multi-Sensor Satellite Data Using Deep Learning
Abstract
:1. Introduction
2. Study Area and Datasets
2.1. Study Area
2.2. Landsat-8 and Sentinel-1 Datasets
2.3. Remote Sensing Database Generation from Various Sensor Observations
2.4. Data for Comparison
3. Method
3.1. Improving the CloudNet+
3.2. Preprocessing Remote Sensing Data
3.3. Extracting Lake Area Using Deep Learning
3.3.1. Extracting Lake Area Using Optical Datasets
3.3.2. Extracting Lake Area Using Multi-Sensor Datasets
3.4. Postprocessing
3.5. Error Metrics
4. Results
4.1. Performance of the AttCloudNet+
4.2. Evaluating Detected Lake Boundaries
4.3. Seasonal Variations of Lake Area from 2015 to 2020
5. Discussion
5.1. Effect of Snow Cover
5.2. Effect of Radar Shadow
5.3. Lake Area Extracted Using Conventional Methods
5.4. Future Improvement
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Landsat-8 OLI | Sentinel-1 SAR | |
---|---|---|
Time range | 2013–present | 2014–present |
Spatial resolution | 30 m | 10 m |
Temporal resolution | 16 days | 6 days |
Channels | 11 bands | HH/VV/HV |
Spectral range | 0.43–12.50 μm | 3.75–7.50 cm |
Data Source | Data Type | Coverage Region | Coverage Period | Number of Matches |
---|---|---|---|---|
LEGOS | Lake level | 230 lakes of the world | 2015–2017 | 12 |
CAS | Lake level | 70 lakes in the Tibetan Plateau | 2003–2020 | 15 |
Lake area | Lakes (>1 km2) in the Tibetan Plateau | 1970s–2018 | 15 | |
WHU | Lake area | Lakes (>50 km2) in the Tibetan Plateau | 2015–2017 | 15 |
Model Structure | Accuracy | Precision | Recall | F1_Score | mIoU |
---|---|---|---|---|---|
UNet | 0.619 | 0.619 | 0.988 | 0.716 | 0.309 |
DeepUNet | 0.986 | 0.969 | 0.964 | 0.964 | 0.910 |
DeepLabv3+ | 0.971 | 0.974 | 0.975 | 0.975 | 0.939 |
AttResUNet | 0.986 | 0.968 | 0.965 | 0.964 | 0.910 |
SegNet | 0.986 | 0.970 | 0.965 | 0.965 | 0.913 |
Original CloudNet+ | 0.983 | 0.981 | 0.990 | 0.985 | 0.938 |
CloudNet+ -Sequential Channel and Spatial Attention | 0.979 | 0.974 | 0.989 | 0.981 | 0.930 |
AttCloudNet+ | 0.985 | 0.982 | 0.992 | 0.986 | 0.945 |
Model | Comparison Sites | Combined RMSE (m) | Combined MAE (m) | Optical RMSE (m) | Optical MAE (m) |
---|---|---|---|---|---|
DeepLabv3+ | Selin Co | 229.1 | 173.5 | 58.2 | 42.5 |
Nam Co | 10.6 | 11.0 | 30.1 | 24.6 | |
Yamzho Yumco | 43.9 | 17.5 | 32.8 | 22.2 | |
Mean | 99.5 | 76.0 | 40.3 | 29.8 | |
UNet | Selin Co | 232.9 | 162.2 | 29.1 | 21.9 |
Nam Co | 24.6 | 19.4 | 24.1 | 20.2 | |
Yamzho Yumco | 15.8 | 13.0 | 28.6 | 22.9 | |
Mean | 91.1 | 64.9 | 27.3 | 21.7 | |
AttCloudNet+ | Selin Co | 30.0 | 21.8 | ||
Nam Co | 16.7 | 12.7 | |||
Yamzho Yumco | 18.2 | 15.2 | |||
Mean | 21.6 | 16.6 | |||
LaeNet | Selin Co | 30.8 | 22.5 | ||
Nam Co | 20.1 | 16.0 | |||
Yamzho Yumco | 23.7 | 18.8 | |||
Mean | 24.9 | 19.1 |
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Chen, X.; Zhang, X.; Zhuang, C.; Hu, X. Detecting the Lake Area Seasonal Variations in the Tibetan Plateau from Multi-Sensor Satellite Data Using Deep Learning. Water 2025, 17, 68. https://doi.org/10.3390/w17010068
Chen X, Zhang X, Zhuang C, Hu X. Detecting the Lake Area Seasonal Variations in the Tibetan Plateau from Multi-Sensor Satellite Data Using Deep Learning. Water. 2025; 17(1):68. https://doi.org/10.3390/w17010068
Chicago/Turabian StyleChen, Xingyu, Xiuyu Zhang, Changwei Zhuang, and Xibang Hu. 2025. "Detecting the Lake Area Seasonal Variations in the Tibetan Plateau from Multi-Sensor Satellite Data Using Deep Learning" Water 17, no. 1: 68. https://doi.org/10.3390/w17010068
APA StyleChen, X., Zhang, X., Zhuang, C., & Hu, X. (2025). Detecting the Lake Area Seasonal Variations in the Tibetan Plateau from Multi-Sensor Satellite Data Using Deep Learning. Water, 17(1), 68. https://doi.org/10.3390/w17010068