Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning
Abstract
:1. Introduction
2. Study Area and Data Preprocessing
2.1. Study Area
2.2. Satellite Remote Sensing Data
2.3. Geographic Data
2.4. Ground Observation
3. Methodology
3.1. Construction of Space–Time Extra Trees Model
3.2. Design of Temporal and Spatial Dimensional Information
3.3. Applicability Evaluation and Factor Optimization of the Model
3.4. Snow Recognition of Landsat
3.5. Metrics for Evaluating the Accuracy of Snow after Cloud Removal
4. Results
4.1. Mapping of Snow Cover Reconstruction
4.2. Accuracy of Snow Reconstruction and Cloud Coverage Variations
4.3. Validation of Individual Snow Cover Mapping
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Properties | Variable Description |
---|---|
Numerical Range | 0–1000 μm (0: Snow-free, Others: Snow) |
Data Format | GeoTiff |
Space Scope | 82.33° E~88.12° E; 42.03° N~44.37° N |
Spatial Resolution | 0.005° (500 m) |
Time Range | 27 February 2000~7 May 2020 |
Time Resolution | Daily |
Channel 1 | Channel 2 | Image Classification |
---|---|---|
0 | 0 | Cloud |
0 | 1 | Snow-free |
1~1000 μm | 0 | Snow-cover |
1 | 1 | None |
Ground True | Snow | Snow-Free | |
---|---|---|---|
Snow Reconstruction | |||
Snow | TP | FP | |
Snow-free | FN | TN |
The Value Interval for Kappa | The Consistency Level of the Image |
---|---|
0 < Kappa ≤ 0.20 | Very low consistency |
0.20 < Kappa ≤ 0.40 | General consistency |
0.40 < Kappa ≤ 0.60 | Medium consistency |
0.60 < Kappa ≤ 0.80 | High consistency |
0.80 < Kappa ≤ l | Almost complete consistency |
Methods | RMSE (μm) | MAE (μm) |
---|---|---|
Classification and Regression Tree, CART | 68.048 | 49.934 |
K-Nearest Neighbor, KNN | 57.108 | 42.516 |
Random Forest, RF | 55.822 | 41.692 |
Ridge Regression, RR | 57.054 | 45.048 |
Support Vector Regression, SVR | 56.710 | 42.880 |
Denoising Autoencoder Artificial Neural Network, DAANN | 54.186 | 40.852 |
Space–Time Extra Randomized Trees | 52.751 | 40.109 |
Accuracy (%) | Case 1: 6 March 2014 | Case 2: 21 April 2016 | Case 3: 2 November 2017 | |||
---|---|---|---|---|---|---|
Before Reconstruction | After Reconstruction | Before Reconstruction | After Reconstruction | Before Reconstruction | After Reconstruction | |
OA | 64.15 | 81.03 | 38.26 | 88.45 | 77.46 | 85.23 |
Precision | 65.28 | 82.86 | 4.38 | 35.74 | 82.48 | 88.53 |
Recall | 85.79 | 95.28 | 34.24 | 65.16 | 81.24 | 86.68 |
F1-Score | 74.14 | 88.64 | 7.77 | 46.16 | 81.86 | 87.60 |
Kappa | 17.79 | 32.84 | 6.83 | 86.74 | 81.46 | 86.93 |
Hydrological Year | Trainable Days | Un-Trainable Days | SCD of MODIS | SCD of Reconstructed Snow | SCD of the Station |
---|---|---|---|---|---|
2000–2001 | 216 | 149 | 46 | 104 | 159 |
2001–2002 | 244 | 121 | 22 | 83 | 111 |
2002–2003 | 234 | 131 | 3 | 3 | 16 |
2003–2004 | 252 | 114 | 44 | 72 | 78 |
2004–2005 | 229 | 136 | 59 | 103 | 144 |
2005–2006 | 246 | 119 | 58 | 108 | 148 |
2006–2007 | 243 | 122 | 29 | 40 | 49 |
2007–2008 | 248 | 118 | 31 | 39 | 29 |
2008–2009 | 227 | 138 | 58 | 75 | 110 |
2009–2010 | 225 | 140 | 67 | 90 | 127 |
2010–2011 | 256 | 109 | 78 | 142 | 173 |
2011–2012 | 238 | 127 | 69 | 77 | 89 |
2012–2013 | 236 | 129 | 39 | 74 | 124 |
2013–2014 | 250 | 115 | 69 | 99 | 137 |
2014–2015 | 225 | 140 | 63 | 87 | 116 |
2015–2016 | 215 | 150 | 35 | 94 | 134 |
2016–2017 | 231 | 134 | 64 | 96 | 141 |
2017–2018 | 229 | 136 | 54 | 92 | 146 |
2018–2019 | 228 | 137 | 53 | 94 | 136 |
2019–2020 | 221 | 144 | 85 | 122 | 146 |
Mean days | 234.65 | 130.45 | 51.30 | 84.7 | 115.65 |
Snow Cover Data | Annual Average Accuracy (%) | Average Annual Coverage (%) | |||||
---|---|---|---|---|---|---|---|
OA | Precision | Recall | F1 | Snow | Cloud | Snow-Free | |
MODIS Snow | 93.69 | 82.54 | 86.67 | 84.55 | 21.52 | 52.46 | 26.02 |
Reconstructed snow | 92.96 | 83.91 | 89.02 | 86.39 | 33.84 | 34.41 | 31.75 |
Accuracy (%) | Validation Day 1 | Validation Day 2 | Validation Day 3 | Validation Day 4 | ||||
---|---|---|---|---|---|---|---|---|
Our’s | Hao’s | Our’s | Hao’s | Our’s | Hao’s | Our’s | Hao’s | |
OA | 92.89 | 94.43 | 86.99 | 92.58 | 91.44 | 89.45 | 95.77 | 94.96 |
Precision | 70.12 | 75.23 | 87.55 | 78.54 | 80.34 | 69.02 | 73.13 | 63.72 |
Recall | 90.40 | 87.65 | 54.69 | 91.52 | 90.84 | 96.62 | 76.21 | 65.80 |
F1-Score | 78.98 | 80.97 | 67.32 | 84.53 | 85.27 | 80.52 | 74.64 | 64.75 |
Kappa | 74.78 | 77.72 | 59.74 | 79.69 | 79.26 | 73.56 | 72.33 | 62.03 |
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Zhu, L.; Ma, G.; Zhang, Y.; Wang, J.; Kan, X. Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning. Water 2023, 15, 3726. https://doi.org/10.3390/w15213726
Zhu L, Ma G, Zhang Y, Wang J, Kan X. Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning. Water. 2023; 15(21):3726. https://doi.org/10.3390/w15213726
Chicago/Turabian StyleZhu, Linglong, Guangyi Ma, Yonghong Zhang, Jiangeng Wang, and Xi Kan. 2023. "Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning" Water 15, no. 21: 3726. https://doi.org/10.3390/w15213726
APA StyleZhu, L., Ma, G., Zhang, Y., Wang, J., & Kan, X. (2023). Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning. Water, 15(21), 3726. https://doi.org/10.3390/w15213726