A Spatiotemporal Enhanced SMAP Freeze/Thaw Product (1980–2020) over China and Its Preliminary Analyses
Abstract
:1. Introduction
2. Materials
2.1. Study Area and In Situ Observations
2.2. Data
2.2.1. Target Datasets for ConvLSTM
2.2.2. Predictor Datasets for ConvLSTM
2.2.3. Auxiliary Datasets
3. Methods
3.1. Convolutional Long Short-Term Memory Model Based on the Decision Level
3.2. Validation Method
3.2.1. Direct Validation
3.2.2. Indirect Validation
4. Results
4.1. The Temporal and Spatial Distribution of CovLSTM_FT
4.2. Direct Validation
4.3. Indirect Validation
5. Discussion
5.1. Analysis of ConvLSTM_FT’s Performances Based on CTC-Derived Result
5.2. Soil Freeze/Thaw Trend in the ConvLSTM_FT over 1980–2020
Region | SFG | Permafrost | Slope (d/a) | |
---|---|---|---|---|
Area Percent | Thermal Stability | Area Percent | ||
Northwest | 43.04% | / | 56.96% | −0.32 |
North | 97.00% | / | 2.00% | −0.29 |
Qinghai–Tibet | 41.60% | / | 47.92% | −0.06 |
Within Qinghai–Tibet | / | Very stable | 0.36% | −0.31 |
Stable | 4.01% | −0.22 | ||
Semi-stable | 16.02% | −0.11 | ||
Transitional | 17.62% | −0.09 | ||
Unstable | 9.92% | −0.05 | ||
41.60% | / | / | −0.02 |
6. Conclusions
- (1)
- The ConvLSTM model can capture spatiotemporal information effectively, and the introduction of decision-level fusion can further improve the prediction accuracy of ConvLSTM. Therefore, the decision-level spatiotemporal fusion architecture based on the ConvLSTM model is an effective method worth trying in the research of data fusion, time series extension, and classification accuracy improvement.
- (2)
- As a temporal expanding product of SMAP_E_FT, ConvLSTM_FT overall outperforms SMAP_E_FT. Direct verification results show that the overall classification accuracy of ConvLSTM_FT has an improvement of 2.44% relative to SMAP_E_FT, especially in frozen seasons (improved by an average of 7.03%). Indirect verification results show that ConvLSTM_FT is more stable than ERA5_FT and Meterology_FT, ranking second in the accuracy of FT soil identification regardless of land cover types, climate types, and terrain complexity.
- (3)
- The analysis result of the classification accuracy of ConvLSTM_FT from 1980 to 2020 shows that the annual frozen days and their changes are reasonable in the northwest, north, and Qinghai–Tibet regions of China. Especially in the Qinghai–Tibet region, with the decrease in permafrost thermal stability, the rate of frozen soil degradation slows down. These results are reasonable and can effectively reflect the impact of climate change on frozen soils in the past 41 years.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Networks | Genhe | Maqu | Naqu | Pali | Saihanba | SDR |
---|---|---|---|---|---|---|
Selected/Total nodes | 18/23 | 19/26 | 33/71 | 13/25 | 11/29 | 27/34 |
Depth | 5 cm | 5 cm | 0–5 cm | 5 cm | 5 cm | 3 cm |
Interval | 30 min | 15 min | 30 min | 30 min | 30 min | 10–15 min |
Begin coverage | April 2018 | April 2018 | September 2018 | |||
End coverage | December 2020 | December 2019 | December 2020 |
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Cui, H.; Chai, L.; Li, H.; Zhao, S.; Li, X.; Liu, S. A Spatiotemporal Enhanced SMAP Freeze/Thaw Product (1980–2020) over China and Its Preliminary Analyses. Remote Sens. 2024, 16, 950. https://doi.org/10.3390/rs16060950
Cui H, Chai L, Li H, Zhao S, Li X, Liu S. A Spatiotemporal Enhanced SMAP Freeze/Thaw Product (1980–2020) over China and Its Preliminary Analyses. Remote Sensing. 2024; 16(6):950. https://doi.org/10.3390/rs16060950
Chicago/Turabian StyleCui, Hong**g, Linna Chai, Heng Li, Shaojie Zhao, **aoyan Li, and Shaomin Liu. 2024. "A Spatiotemporal Enhanced SMAP Freeze/Thaw Product (1980–2020) over China and Its Preliminary Analyses" Remote Sensing 16, no. 6: 950. https://doi.org/10.3390/rs16060950