Spatial Downscaling of Land Surface Temperature Based on a Multi-Factor Geographically Weighted Machine Learning Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Description and Image Preprocessing
2.3. LST Retrieval from Landsat 8 Data
2.4. Feature Selection
2.4.1. Candidate Explanatory Variables
2.4.2. Determination of the Optimal Feature Combination
2.5. MFGWML Downscaling Method
2.5.1. Principles of Base Learners
- 1.
- XGBoost
- 2.
- MARS
- 3.
- BRR
2.5.2. Principle of GWR
2.5.3. Geographically Weighted Ensemble Learning
2.6. Classic LST Downscaling Algorithms
2.6.1. TsHARP Algorithm
2.6.2. HUTS Algorithm
2.7. Downscaling Accuracy Validation Strategies
2.8. Overall Methodological Workflow
3. Results
3.1. Feature Selection Procedure
3.1.1. Correlations between Features and LST
3.1.2. Correlations among Features
3.1.3. Variable Importance Assessment
3.2. MFGWML Model Analysis
3.2.1. Base Model Correlations
3.2.2. MFGWML Model Parameters
3.3. Accuracy Validation and Comparison
3.3.1. Comparing MFGWML with TsHARP
3.3.2. Comparing MFGWML with HUTS
4. Discussion
4.1. Sources of LST Errors
4.2. Limitations
4.3. Future Research
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
Appendix A
Features | Albedo | BI | Blue | DEM | FVC | Green | IBI | IVI | MNDWI | MSAVI | NDBI | NDDI | NDVI | NDWI | NIR | OSAVI | Red | SAVI | Slope | SWIR1 | SWIR2 | TC1 | TC2 | TC3 | UI |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Albedo | 1.000 | 0.196 | 0.456 | −0.029 | −0.040 | 0.549 | 0.149 | 0.092 | −0.239 | 0.187 | 0.242 | 0.043 | −0.003 | −0.088 | 0.671 | 0.082 | 0.465 | 0.161 | −0.081 | 0.870 | 0.627 | 0.958 | 0.137 | −0.416 | 0.220 |
BI | 0.196 | 1.000 | 0.797 | −0.499 | −0.913 | 0.813 | 0.968 | −0.843 | 0.532 | −0.854 | 0.978 | −0.815 | −0.862 | 0.771 | −0.529 | −0.868 | 0.875 | −0.851 | −0.463 | 0.543 | 0.824 | 0.460 | −0.852 | −0.796 | 0.972 |
Blue | 0.456 | 0.797 | 1.000 | −0.505 | −0.828 | 0.960 | 0.776 | −0.788 | 0.575 | −0.750 | 0.805 | −0.787 | −0.814 | 0.749 | −0.335 | −0.800 | 0.950 | −0.767 | −0.491 | 0.575 | 0.858 | 0.642 | −0.795 | −0.623 | 0.864 |
DEM | −0.029 | −0.499 | −0.505 | 1.000 | 0.611 | −0.486 | −0.435 | 0.534 | −0.513 | 0.555 | −0.454 | 0.529 | 0.534 | −0.520 | 0.381 | 0.540 | −0.482 | 0.533 | 0.679 | −0.139 | −0.375 | −0.161 | 0.536 | 0.276 | −0.507 |
FVC | −0.040 | −0.913 | −0.828 | 0.611 | 1.000 | −0.808 | −0.845 | 0.963 | −0.807 | 0.951 | −0.858 | 0.953 | 0.974 | −0.932 | 0.674 | 0.973 | −0.858 | 0.955 | 0.595 | −0.302 | −0.693 | −0.294 | 0.937 | 0.580 | −0.922 |
Green | 0.549 | 0.813 | 0.960 | −0.486 | −0.808 | 1.000 | 0.755 | −0.746 | 0.540 | −0.695 | 0.798 | −0.754 | −0.783 | 0.713 | −0.225 | −0.755 | 0.974 | −0.712 | −0.477 | 0.660 | 0.871 | 0.733 | −0.730 | −0.623 | 0.840 |
IBI | 0.149 | 0.968 | 0.776 | −0.435 | −0.845 | 0.755 | 1.000 | −0.796 | 0.415 | −0.836 | 0.984 | −0.744 | −0.798 | 0.694 | −0.561 | −0.828 | 0.835 | −0.830 | −0.383 | 0.514 | 0.809 | 0.411 | −0.850 | −0.812 | 0.961 |
IVI | 0.092 | −0.843 | −0.788 | 0.534 | 0.963 | −0.746 | −0.796 | 1.000 | −0.872 | 0.966 | −0.799 | 0.990 | 0.989 | −0.979 | 0.766 | 0.994 | −0.798 | 0.983 | 0.515 | −0.162 | −0.598 | −0.160 | 0.961 | 0.489 | −0.874 |
MNDWI | −0.239 | 0.532 | 0.575 | −0.513 | −0.807 | 0.540 | 0.415 | −0.872 | 1.000 | −0.798 | 0.443 | −0.904 | −0.860 | 0.932 | −0.698 | −0.839 | 0.543 | −0.819 | −0.510 | −0.129 | 0.276 | −0.063 | −0.770 | −0.124 | 0.568 |
MSAVI | 0.187 | −0.854 | −0.750 | 0.555 | 0.951 | −0.695 | −0.836 | 0.966 | −0.798 | 1.000 | −0.804 | 0.929 | 0.943 | −0.912 | 0.845 | 0.982 | −0.772 | 0.995 | 0.515 | −0.108 | −0.570 | −0.080 | 0.993 | 0.501 | −0.867 |
NDBI | 0.242 | 0.978 | 0.805 | −0.454 | −0.858 | 0.798 | 0.984 | −0.799 | 0.443 | −0.804 | 1.000 | −0.768 | −0.813 | 0.721 | −0.485 | −0.820 | 0.853 | −0.805 | −0.406 | 0.597 | 0.857 | 0.497 | −0.818 | −0.851 | 0.976 |
NDDI | 0.043 | −0.815 | −0.787 | 0.529 | 0.953 | −0.754 | −0.744 | 0.990 | −0.904 | 0.929 | −0.768 | 1.000 | 0.991 | −0.996 | 0.707 | 0.976 | −0.789 | 0.953 | 0.524 | −0.184 | −0.595 | −0.195 | 0.921 | 0.469 | −0.850 |
NDVI | −0.003 | −0.862 | −0.814 | 0.534 | 0.974 | −0.783 | −0.798 | 0.989 | −0.860 | 0.943 | −0.813 | 0.991 | 1.000 | −0.977 | 0.692 | 0.987 | −0.836 | 0.964 | 0.542 | −0.242 | −0.650 | −0.249 | 0.935 | 0.522 | −0.891 |
NDWI | −0.088 | 0.771 | 0.749 | −0.520 | −0.932 | 0.713 | 0.694 | −0.979 | 0.932 | −0.912 | 0.721 | −0.996 | −0.977 | 1.000 | −0.718 | −0.959 | 0.740 | −0.936 | −0.513 | 0.131 | 0.543 | 0.142 | −0.901 | −0.425 | 0.808 |
NIR | 0.671 | −0.529 | −0.335 | 0.381 | 0.674 | −0.225 | −0.561 | 0.766 | −0.698 | 0.845 | −0.485 | 0.707 | 0.692 | −0.718 | 1.000 | 0.771 | −0.326 | 0.828 | 0.316 | 0.370 | −0.111 | 0.449 | 0.824 | 0.184 | −0.540 |
OSAVI | 0.082 | −0.868 | −0.800 | 0.540 | 0.973 | −0.755 | −0.828 | 0.994 | −0.839 | 0.982 | −0.820 | 0.976 | 0.987 | −0.959 | 0.771 | 1.000 | −0.821 | 0.993 | 0.527 | −0.183 | −0.623 | −0.177 | 0.976 | 0.516 | −0.893 |
Red | 0.465 | 0.875 | 0.950 | −0.482 | −0.858 | 0.974 | 0.835 | −0.798 | 0.543 | −0.772 | 0.853 | −0.789 | −0.836 | 0.740 | −0.326 | −0.821 | 1.000 | −0.786 | −0.491 | 0.630 | 0.886 | 0.677 | −0.800 | −0.671 | 0.895 |
SAVI | 0.161 | −0.851 | −0.767 | 0.533 | 0.955 | −0.712 | −0.830 | 0.983 | −0.819 | 0.995 | −0.805 | 0.953 | 0.964 | −0.936 | 0.828 | 0.993 | −0.786 | 1.000 | 0.506 | −0.120 | −0.580 | −0.102 | 0.991 | 0.495 | −0.873 |
Slope | −0.081 | −0.463 | −0.491 | 0.679 | 0.595 | −0.477 | −0.383 | 0.515 | −0.510 | 0.515 | −0.406 | 0.524 | 0.542 | −0.513 | 0.316 | 0.527 | −0.491 | 0.506 | 1.000 | −0.144 | −0.382 | −0.197 | 0.496 | 0.241 | −0.490 |
SWIR1 | 0.870 | 0.543 | 0.575 | −0.139 | −0.302 | 0.660 | 0.514 | −0.162 | −0.129 | −0.108 | 0.597 | −0.184 | −0.242 | 0.131 | 0.370 | −0.183 | 0.630 | −0.120 | −0.144 | 1.000 | 0.852 | 0.949 | −0.148 | −0.795 | 0.531 |
SWIR2 | 0.627 | 0.824 | 0.858 | −0.375 | −0.693 | 0.871 | 0.809 | −0.598 | 0.276 | −0.570 | 0.857 | −0.595 | −0.650 | 0.543 | −0.111 | −0.623 | 0.886 | −0.580 | −0.382 | 0.852 | 1.000 | 0.812 | −0.610 | −0.896 | 0.860 |
TC1 | 0.958 | 0.460 | 0.642 | −0.161 | −0.294 | 0.733 | 0.411 | −0.160 | −0.063 | −0.080 | 0.497 | −0.195 | −0.249 | 0.142 | 0.449 | −0.177 | 0.677 | −0.102 | −0.197 | 0.949 | 0.812 | 1.000 | −0.127 | −0.619 | 0.474 |
TC2 | 0.137 | −0.852 | −0.795 | 0.536 | 0.937 | −0.730 | −0.850 | 0.961 | −0.770 | 0.993 | −0.818 | 0.921 | 0.935 | −0.901 | 0.824 | 0.976 | −0.800 | 0.991 | 0.496 | −0.148 | −0.610 | −0.127 | 1.000 | 0.526 | −0.880 |
TC3 | −0.416 | −0.796 | −0.623 | 0.276 | 0.580 | −0.623 | −0.812 | 0.489 | −0.124 | 0.501 | −0.851 | 0.469 | 0.522 | −0.425 | 0.184 | 0.516 | −0.671 | 0.495 | 0.241 | −0.795 | −0.896 | −0.619 | 0.526 | 1.000 | −0.793 |
UI | 0.220 | 0.972 | 0.864 | −0.507 | −0.922 | 0.840 | 0.961 | −0.874 | 0.568 | −0.867 | 0.976 | −0.850 | −0.891 | 0.808 | −0.540 | −0.893 | 0.895 | −0.873 | −0.490 | 0.531 | 0.860 | 0.474 | −0.880 | −0.793 | 1.000 |
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Satellite | Band No. | Band Name | Wavelength () | Central Wavelength () | Spatial Resolution () |
---|---|---|---|---|---|
Landsat 8 | Band 2 | Blue | 452–512 | 483 | 30 |
Band 3 | Green | 533–590 | 561 | ||
Band 4 | Red | 636–673 | 655 | ||
Band 5 | NIR | 851–879 | 865 | ||
Band 6 | SWIR1 | 1566–1651 | 1609 | ||
Band 7 | SWIR2 | 2107–2294 | 2201 | ||
Band 10 | TIR1 | 1060–1119 | 10,900 | 100 | |
Sentinel-2A | Band 2 | Blue | 458–523 | 490 | 10 |
Band 3 | Green | 543–578 | 560 | ||
Band 4 | Red | 650–680 | 665 | ||
Band 8 | NIR | 785–899 | 842 | ||
Band 11 | SWIR | 1565–1655 | 1610 | 20 | |
Band 12 | SWIR | 2100–2280 | 2190 |
Satellite | Scene Number | Acquisition Date | Acquisition Time (UTC) | ) | ) | Cloud Cover (%) |
---|---|---|---|---|---|---|
Landsat 8 | 123/32 | 10 July 2017 | 02:53:19 | 128.809 | 64.468 | 0.010 1 |
123/33 | 10 July 2017 | 02:53:43 | 125.826 | 65.120 | 0.040 1 | |
124/32 2 | 17 July 2017 | 02:59:32 | 130.009 | 63.551 | 13.180 1 | |
Sentinel-2A | T50SLJ | 27 June 2017 | 03:13:58 | 134.493 | 68.972 | 0.244 1 |
T50SMJ | 27 June 2017 | 03:13:58 | 136.915 | 69.583 | 0.000 | |
T50TLK | 27 June 2017 | 03:13:58 | 135.946 | 68.288 | 0.486 1 | |
T50TMK | 27 June 2017 | 03:13:58 | 138.372 | 68.882 | 0.000 | |
T50TML | 27 June 2017 | 03:13:58 | 139.730 | 68.168 | 0.000 | |
T50TNK | 27 June 2017 | 03:13:58 | 140.896 | 69.458 | 0.000 | |
T50TNL | 27 June 2017 | 03:13:58 | 142.248 | 68.728 | 0.000 |
Full Name | Formula | Reference |
---|---|---|
Normalized difference vegetation index (NDVI) | [42] | |
Soil adjusted vegetation index (SAVI) | [43] | |
Modified soil adjusted vegetation index (MSAVI) | [44] | |
Optimal soil adjusted vegetation index (OSAVI) | [45] | |
Fractional vegetation cover (FVC) 1 | [46] | |
Index-based vegetation index (IVI) | [33] | |
Normalized difference water index (NDWI) | [47] | |
Modified normalized difference water index (MNDWI) | [48] | |
Index-based built-up index (IBI) | [49] | |
Normalized difference built-up index (NDBI) | [50] | |
Urban index (UI) | [51] | |
Bare soil index (BI) | [52] | |
Normalized difference drought index (NDDI) | [53] |
Resolution (m) | LST Retrieved from Landsat 8 Data | Variables Extracted from Landsat 8 Data | Terrain Factors Extracted from SRTM Data | Variables Extracted from Sentinel-2A Data |
---|---|---|---|---|
Original | 30 | 30 | 30 | 10 |
Aggregated | 60, 90, 120, 150, 180, 270, 360, 450, and 540 | 90, 180, 270, 360, 450, and 540 | 60, 90, 120, 150, 180, 270, 360, 450, and 540 | 30, 60, 90, 120, 150, and 180 |
Feature | Albedo | Aspect | BI | Blue | DEM | FVC | Green | Hillshade | IBI |
---|---|---|---|---|---|---|---|---|---|
P | 0.236 | −0.057 | 0.733 | 0.750 | −0.726 | −0.759 | 0.726 | 0.062 | 0.697 |
Feature | IVI | MNDWI | MSAVI | NDBI | NDDI | NDVI | NDWI | NIR | OSAVI |
P | −0.683 | 0.503 | −0.679 | 0.716 | −0.670 | −0.700 | 0.634 | −0.379 | −0.698 |
Feature | Red | SAVI | Slope | SWIR1 | SWIR2 | TC1 | TC2 | TC3 | UI |
P | 0.735 | −0.677 | −0.575 | 0.383 | 0.649 | 0.409 | −0.687 | −0.502 | 0.766 |
Downscaling Schemes | Fitting Equation | RMSE (K) | MAE (K) | |||||
---|---|---|---|---|---|---|---|---|
M 1 | T 1 | M 1 | T 1 | M 1 | T 1 | M 1 | T 1 | |
Scheme 1 | y = 1.030∗x − 9.098 | y = 1.029∗x − 6.381 | 1.304 | 1.833 | 0.942 | 1.420 | 0.928 | 0.868 |
Scheme 2 | y = 1.040∗x − 12.140 | y = 1.032∗x − 7.072 | 1.404 | 1.839 | 1.028 | 1.422 | 0.919 | 0.867 |
Scheme 3 | y = 1.027∗x − 8.373 | y = 1.033∗x − 7.250 | 1.447 | 1.739 | 1.059 | 1.345 | 0.912 | 0.879 |
Scheme 4 | y = 1.041∗x − 12.686 | y = 1.035∗x − 7.944 | 1.411 | 1.697 | 1.046 | 1.307 | 0.918 | 0.884 |
Scheme 5 | y = 1.025∗x − 7.721 | y = 1.033∗x − 7.344 | 1.483 | 1.716 | 1.088 | 1.322 | 0.906 | 0.880 |
Scheme 6 | y = 1.070∗x − 21.590 | y = 1.039∗x − 9.028 | 1.444 | 1.657 | 1.087 | 1.274 | 0.917 | 0.888 |
Downscaling Schemes | Mean (K) | Median (K) | The 25th Percentile (K) | The 75th Percentile (K) | IQR | |||||
---|---|---|---|---|---|---|---|---|---|---|
M 1 | T 1 | M 1 | T 1 | M 1 | T 1 | M 1 | T 1 | M 1 | T 1 | |
Scheme 1 | 0.008 | −2.612 | 0.233 | −2.426 | −0.585 | −3.686 | 0.770 | −1.376 | 1.355 | 2.310 |
Scheme 2 | 0.021 | −2.747 | 0.211 | −2.597 | −0.670 | −3.842 | 0.882 | −1.511 | 1.552 | 2.331 |
Scheme 3 | 0.008 | −2.808 | 0.259 | −2.675 | −0.697 | −3.844 | 0.884 | −1.646 | 1.581 | 2.198 |
Scheme 4 | −0.007 | −2.839 | 0.276 | −2.729 | −0.737 | −3.845 | 0.889 | −1.713 | 1.626 | 2.131 |
Scheme 5 | 0.001 | −2.860 | 0.256 | −2.758 | −0.767 | −3.880 | 0.894 | −1.764 | 1.662 | 2.116 |
Scheme 6 | −0.017 | −2.883 | 0.270 | −2.792 | −0.899 | −3.895 | 0.990 | −1.797 | 1.889 | 2.098 |
Downscaling Schemes | <−3 K (%) | −3 K~−1.5 K (%) | −1.5 K~1.5 K (%) | 1.5 K~3 K (%) | >3 K (%) | Outliers (%) | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
M 1 | T 1 | M 1 | T 1 | M 1 | T 1 | M 1 | T 1 | M 1 | T 1 | M 1 | T 1 | |
Scheme 1 | 2.929 | 37.389 | 9.344 | 34.695 | 80.735 | 26.939 | 6.118 | 0.703 | 0.874 | 0.274 | 5.433 | 1.991 |
Scheme 2 | 3.467 | 40.900 | 9.875 | 34.337 | 76.548 | 23.896 | 8.953 | 0.656 | 1.157 | 0.211 | 4.392 | 1.983 |
Scheme 3 | 3.841 | 42.199 | 10.434 | 35.790 | 76.443 | 21.421 | 8.063 | 0.417 | 1.219 | 0.173 | 4.517 | 1.768 |
Scheme 4 | 3.671 | 43.212 | 11.153 | 36.377 | 76.294 | 19.821 | 8.009 | 0.395 | 0.873 | 0.195 | 3.710 | 1.835 |
Scheme 5 | 3.931 | 43.861 | 11.145 | 36.735 | 75.574 | 18.880 | 7.858 | 0.321 | 1.492 | 0.203 | 3.995 | 1.570 |
Scheme 6 | 3.712 | 44.663 | 12.232 | 36.677 | 73.452 | 18.108 | 9.722 | 0.357 | 0.882 | 0.195 | 1.932 | 1.672 |
Downscaling Schemes | RMSE (K) | MAE (K) | MBE (K) | NSE | Pearson a | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
M b | T b | M b | T b | M b | T b | M b | T b | M b | T b | M b | T b | |
Scheme 1 | 1.312 | 3.196 | 0.961 | 2.707 | 0.006 | −2.614 | 0.917 | 0.510 | 0.964 | 0.932 | 0.917 | 0.631 |
Scheme 2 | 1.416 | 3.309 | 1.057 | 2.830 | 0.020 | −2.747 | 0.903 | 0.471 | 0.959 | 0.931 | 0.903 | 0.613 |
Scheme 3 | 1.453 | 3.303 | 1.081 | 2.870 | 0.010 | −2.804 | 0.898 | 0.471 | 0.955 | 0.938 | 0.898 | 0.617 |
Scheme 4 | 1.423 | 3.313 | 1.078 | 2.902 | −0.008 | −2.841 | 0.901 | 0.463 | 0.958 | 0.940 | 0.901 | 0.615 |
Scheme 5 | 1.487 | 3.338 | 1.109 | 2.927 | 0.001 | −2.859 | 0.891 | 0.451 | 0.952 | 0.938 | 0.891 | 0.609 |
Scheme 6 | 1.478 | 3.328 | 1.155 | 2.940 | −0.017 | −2.881 | 0.891 | 0.449 | 0.958 | 0.942 | 0.891 | 0.610 |
Downscaling Schemes | Fitting Equation | RMSE (K) | MAE (K) | |||||
---|---|---|---|---|---|---|---|---|
M 1 | H 1 | M 1 | H 1 | M 1 | H 1 | M 1 | H 1 | |
Scheme 1 | y = 0.836∗x + 51.721 | y = 0.754∗x + 79.651 | 1.636 | 1.784 | 1.283 | 1.359 | 0.818 | 0.754 |
Scheme 2 | y = 0.800∗x + 62.735 | y = 0.752∗x + 80.494 | 1.766 | 1.808 | 1.372 | 1.370 | 0.778 | 0.747 |
Scheme 3 | y = 0.750∗x + 78.661 | y = 0.750∗x + 81.057 | 1.727 | 1.776 | 1.335 | 1.345 | 0.760 | 0.750 |
Scheme 4 | y = 0.786∗x + 67.558 | y = 0.755∗x + 79.735 | 1.627 | 1.754 | 1.277 | 1.324 | 0.795 | 0.755 |
Scheme 5 | y = 0.739∗x + 82.234 | y = 0.786∗x + 70.213 | 1.749 | 1.638 | 1.344 | 1.255 | 0.744 | 0.789 |
Scheme 6 | y = 0.808∗x + 60.561 | y = 0.770∗x + 75.074 | 1.687 | 1.689 | 1.336 | 1.288 | 0.784 | 0.768 |
Downscaling Schemes | Mean (K) | Median (K) | The 25th Percentile (K) | The 75th Percentile (K) | IQR | |||||
---|---|---|---|---|---|---|---|---|---|---|
M 1 | H 1 | M 1 | H 1 | M 1 | H 1 | M 1 | H1 | M 1 | H 1 | |
Scheme 1 | −0.471 | −2.667 | −0.282 | −2.483 | −1.581 | −3.885 | 0.710 | −1.272 | 2.291 | 2.613 |
Scheme 2 | −0.345 | −2.903 | −0.239 | −2.734 | −1.547 | −4.147 | 0.860 | −1.522 | 2.407 | 2.626 |
Scheme 3 | −0.482 | −3.044 | −0.380 | −2.923 | −1.767 | −4.231 | 0.762 | −1.786 | 2.528 | 2.445 |
Scheme 4 | −0.577 | −3.155 | −0.477 | −3.055 | −1.801 | −4.344 | 0.652 | −1.921 | 2.453 | 2.423 |
Scheme 5 | −0.565 | −3.302 | −0.585 | −3.219 | −1.892 | −4.434 | 0.643 | −2.106 | 2.535 | 2.329 |
Scheme 6 | −0.475 | −3.342 | −0.451 | −3.281 | −1.692 | −4.478 | 0.743 | −2.153 | 2.436 | 2.325 |
Downscaling Schemes | <−3 K (%) | −3 K~−1.5 K (%) | −1.5 K~1.5 K (%) | 1.5 K~3 K (%) | >3 K (%) | Outliers (%) | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
M 1 | H 1 | M 1 | H 1 | M 1 | H 1 | M 1 | H 1 | M 1 | H 1 | M 1 | H 1 | |
Scheme 1 | 8.790 | 39.714 | 17.512 | 30.830 | 62.838 | 28.643 | 9.259 | 0.673 | 1.601 | 0.140 | 1.359 | 1.332 |
Scheme 2 | 9.030 | 44.557 | 16.672 | 30.794 | 59.442 | 23.735 | 11.224 | 0.687 | 3.632 | 0.227 | 1.975 | 1.526 |
Scheme 3 | 10.630 | 48.267 | 18.621 | 31.982 | 57.892 | 18.739 | 9.214 | 0.691 | 3.643 | 0.321 | 1.739 | 1.817 |
Scheme 4 | 9.933 | 51.177 | 20.155 | 31.106 | 58.955 | 16.745 | 8.639 | 0.677 | 2.318 | 0.295 | 1.058 | 1.835 |
Scheme 5 | 10.585 | 55.280 | 21.100 | 29.794 | 55.761 | 14.357 | 8.458 | 0.414 | 4.096 | 0.155 | 2.057 | 1.755 |
Scheme 6 | 8.531 | 56.624 | 19.706 | 28.793 | 58.766 | 13.940 | 10.198 | 0.500 | 2.799 | 0.143 | 1.137 | 1.854 |
Downscaling Schemes | RMSE (K) | MAE (K) | MBE (K) | NSE | Pearson a | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
M b | H b | M b | H b | M b | H b | M b | H b | M b | H b | M b | H b | |
Scheme 1 | 1.834 | 3.366 | 1.407 | 2.779 | −0.471 | −2.665 | 0.805 | 0.342 | 0.904 | 0.869 | 0.807 | 0.534 |
Scheme 2 | 1.980 | 3.571 | 1.514 | 3.007 | −0.345 | −2.903 | 0.771 | 0.253 | 0.882 | 0.864 | 0.772 | 0.500 |
Scheme 3 | 2.066 | 3.675 | 1.584 | 3.163 | −0.483 | −3.049 | 0.746 | 0.198 | 0.872 | 0.866 | 0.75 | 0.483 |
Scheme 4 | 1.936 | 3.745 | 1.508 | 3.261 | −0.579 | −3.155 | 0.774 | 0.156 | 0.891 | 0.869 | 0.779 | 0.472 |
Scheme 5 | 2.119 | 3.786 | 1.635 | 3.358 | −0.565 | −3.302 | 0.724 | 0.118 | 0.862 | 0.888 | 0.729 | 0.472 |
Scheme 6 | 1.913 | 3.856 | 1.501 | 3.413 | −0.477 | −3.344 | 0.769 | 0.063 | 0.886 | 0.876 | 0.773 | 0.450 |
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Xu, S.; Zhao, Q.; Yin, K.; He, G.; Zhang, Z.; Wang, G.; Wen, M.; Zhang, N. Spatial Downscaling of Land Surface Temperature Based on a Multi-Factor Geographically Weighted Machine Learning Model. Remote Sens. 2021, 13, 1186. https://doi.org/10.3390/rs13061186
Xu S, Zhao Q, Yin K, He G, Zhang Z, Wang G, Wen M, Zhang N. Spatial Downscaling of Land Surface Temperature Based on a Multi-Factor Geographically Weighted Machine Learning Model. Remote Sensing. 2021; 13(6):1186. https://doi.org/10.3390/rs13061186
Chicago/Turabian StyleXu, Saiping, Qianjun Zhao, Kai Yin, Guojin He, Zhaoming Zhang, Guizhou Wang, Meiping Wen, and Ning Zhang. 2021. "Spatial Downscaling of Land Surface Temperature Based on a Multi-Factor Geographically Weighted Machine Learning Model" Remote Sensing 13, no. 6: 1186. https://doi.org/10.3390/rs13061186
APA StyleXu, S., Zhao, Q., Yin, K., He, G., Zhang, Z., Wang, G., Wen, M., & Zhang, N. (2021). Spatial Downscaling of Land Surface Temperature Based on a Multi-Factor Geographically Weighted Machine Learning Model. Remote Sensing, 13(6), 1186. https://doi.org/10.3390/rs13061186