Surface Shortwave Net Radiation Estimation from Landsat TM/ETM+ Data Using Four Machine Learning Algorithms
Abstract
:1. Introduction
2. Data and Methods
2.1. Data
2.1.1. In-Situ Measurements
2.1.2. Remotely Sensed Data
2.1.3. MERRA-2 Reanalysis Data
2.1.4. Other Parameters
2.2. Methods
3. Results and Analysis
3.1. Model Development
3.1.1. Ins-SSNR
3.1.2. D-SSNR
3.2. Validation and Comparison
3.2.1. Direct Validation
3.2.2. Comparison with GLASS Product
3.3. Model Performance Analysis
3.4. Extended Use to Landsat 7/ETM+ Data
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
References
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Abbr. | Name | Unit | Temporal Resolution | Source | |
---|---|---|---|---|---|
Response variable | SSNR | Surface shortwave net radiation | instantaneous/daily | In-situ | |
Independent variables | SZA | Solar zenith angle | degree | instantaneous | Landsat data |
SAA | Solar azimuth angle | degree | instantaneous | Landsat data | |
WV | Water vapor | hourly/daily | MERRA-2 | ||
LAT | Latitude | degree | - | In-situ | |
ri1 | Top-of-atmosphere (TOA) reflectance | \ | instantaneous | Landsat data | |
BT | Brightness temperature | K | instantaneous | Landsat data | |
CI2 | Clearness index | \ | daily | Calculated |
Abbreviations | Time Period | Instrument | Reference | Temporal Resolution |
---|---|---|---|---|
ARM1 | 1994–2011 | Kipp&Zonen Pyrgeometer | [34] | 1 min |
AsiaFlux | 2000–2009 | Kipp&Zonen, CM-6F | [35] | 30 min |
BSRN2 | 1995–2011 | Eppley, PIR/Kipp&Zonen CG4 | [36] | 1 or 3 min |
CEOP3 | 2008–2009 | - | - | 30 min |
EOL4 | 2006–2007 | Kipp&Zonen CM21, Kipp&Zonen CG4s | [37] | 1 h |
GC_NET5 | 1997–1998 | Li Cor Photodiode & REBS Q* 7 | [38] | 1 h |
La Thuile6 | 1997–2011 | Kipp&Zonen Pyrgeometer, etc | [39] | 30 min |
SAFARI.20007 | 2000 | Kipp&Zonen Pyrgeometer | [40,41] | 30 min |
SURFRAD8 | 1995–2011 | Eppley, PIR | [42] | 3 min |
Main Types | Total Number of Samples | |
---|---|---|
Instantaneous SSNR | Daily SSNR | |
ENF1 | 2246 | 2165 |
EBF2 | 127 | 123 |
DNF3 | 90 | 97 |
DBF4 | 1072 | 1004 |
MF5 | 488 | 552 |
PW6 | 55 | 61 |
CRO7 | 3311 | 2619 |
ICE8 | 9 | - |
BR9 | 512 | 228 |
SHB10 | 411 | 405 |
SAV11 | 269 | 271 |
GRA12 | 7794 | 4629 |
Total | 16,384 | 12,154 |
Landsat 5/TM | Landsat 7/ETM+ | ||||
---|---|---|---|---|---|
Band | Wavelength | Resolution | Band | Wavelength | Resolution |
1 | 0.45–0.52 | 30 m | 1 | 0.45–0.52 | 30 m |
2 | 0.52–0.60 | 30 m | 2 | 0.52–0.60 | 30 m |
3 | 0.63–0.69 | 30 m | 3 | 0.63–0.69 | 30 m |
4 | 0.76–0.90 | 30 m | 4 | 0.77–0.90 | 30 m |
5 | 1.55–1.75 | 30 m | 5 | 1.55–1.75 | 30 m |
6 | 10.40–12.50 | 120 m | 6 | 10.40–12.50 | 60 m |
7 | 2.08–2.35 | 30 m | 7 | 2.08–2.35 | 30 m |
8 | 0.520–0.900 | 15 m |
BT WV | WV | BT | None | TrainingTime | Fitting Time | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R2 | RMSE (W·m-2) | MAE (W·m-2) | bias (W·m-2) | R2 | RMSE (W·m-2) | MAE (W·m-2) | bias (W·m-2) | R2 | RMSE (W·m-2) | MAE (W·m-2) | bias (W·m-2) | R2 | RMSE (W·m-2) | MAE (W·m-2) | bias (W·m-2) | |||
MARS | 0.84 | 83.39 | 58.06 | 0.05 | 0.84 | 83.44 | 58.17 | 0.18 | 0.83 | 85.46 | 60.04 | 0.17 | 0.83 | 85.46 | 60.04 | 0.17 | 1 min | 0.01 s |
BPNN | 0.84 | 82.85 | 57.45 | 0.20 | 0.84 | 82.19 | 57.19 | 0.04 | 0.84 | 82.97 | 57.61 | 0.22 | 0.84 | 83.80 | 57.53 | –0.04 | 35 min | 0.06 s |
SVR | 0.85 | 80.08 | 52.04 | 7.65 | 0.85 | 80.56 | 52.34 | 8.11 | 0.85 | 81.45 | 53.67 | 7.43 | 0.85 | 82.16 | 54.22 | 7.67 | 30 min | 1 s |
GBRT | 0.86 | 75.72 | 51.51 | –0.38 | 0.86 | 76.88 | 52.25 | –0.11 | 0.86 | 77.47 | 52.92 | –1.05 | 0.86 | 78.15 | 53.49 | –0.91 | 2 h | 0.03 s |
GGM | GCM | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BT WV | BT WV | WV | BT | None | |||||||||||||||||
Samples | R2 | RMSE (W·m-2) | MAE (W·m-2) | bias (W·m-2) | R2 | RMSE (W·m-2) | MAE (W·m-2) | bias (W·m-2) | R2 | RMSE (W·m-2) | MAE (W·m-2) | bias (W·m-2) | R2 | RMSE (W·m-2) | MAE (W·m-2) | bias (W·m-2) | R2 | RMSE (W·m-2) | MAE (W·m-2) | bias (W·m-2) | |
Clear | 12,403 | 0.86 | 69.92 | 46.22 | –1.01 | 0.86 | 71.64 | 47.49 | 0.10 | 0.86 | 71.76 | 47.59 | –0.49 | 0.85 | 72.67 | 48.75 | –0.97 | 0.85 | 73.38 | 48.87 | –0.51 |
Cloud | 3094 | 0.78 | 91.32 | 67.71 | –1.93 | 0.78 | 92.31 | 66.96 | –2.87 | 0.78 | 92.76 | 67.93 | –4.09 | 0.77 | 93.59 | 67.96 | –3.87 | 0.76 | 95.06 | 69.44 | –6.61 |
Cloud shadow | 887 | 0.78 | 90.89 | 67.89 | 13.91 | 0.71 | 102.35 | 77.06 | 2.13 | 0.71 | 103.02 | 78.06 | –1.41 | 0.70 | 105.33 | 79.43 | –1.74 | 0.71 | 104.05 | 79.15 | –7.85 |
Overall | 13,116 | 0.86 | 75.72 | 51.51 | –0.38 | 0.86 | 77.92 | 52.88 | –0.36 | 0.86 | 78.25 | 53.16 | –1.23 | 0.86 | 78.35 | 53.24 | –1.57 | 0.85 | 79.43 | 53.99 | –2.16 |
CI | Without CI | Training Time | Fitting Time | |||||||
---|---|---|---|---|---|---|---|---|---|---|
R2 | RMSE (W·m-2) | MAE (W·m-2) | bias (W·m-2) | R2 | RMSE (W·m-2) | MAE (W·m-2) | Bias (W·m-2) | |||
MARS | 0.91 | 24.10 | 15.30 | 0.04 | 0.86 | 29.13 | 20.50 | 0.19 | 1 min | 0.02 s |
BPNN | 0.92 | 22.39 | 14.78 | –0.026 | 0.86 | 28.94 | 20.30 | 0.13 | 1.5 h | 0.03 s |
SVR | 0.92 | 21.96 | 13.90 | 1.21 | 0.88 | 27.98 | 18.70 | 2.99 | 1.2 h | 0.80 s |
GBRT | 0.93 | 21.01 | 14.07 | 0.12 | 0.88 | 27.21 | 18.94 | –0.35 | 2.5 h | 0.02 s |
Samples | GGM | GCM | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CI | CI | Without CI | |||||||||||
R2 | RMSE (W·m-2) | MAE (W·m-2) | bias (W·m-2) | R2 | RMSE (W·m-2) | MAE (W·m-2) | bias (W·m-2) | R2 | RMSE (W·m-2) | MAE (W·m-2) | bias (W·m-2) | ||
CI < 0.2 | 414 | 0.63 | 21.52 | 11.70 | –0.22 | 0.57 | 23.35 | 12.38 | –3.16 | 0.56 | 23.77 | 12.52 | –2.94 |
0.2 ≤ CI < 0.7 | 9136 | 0.92 | 20.87 | 14.19 | 0.05 | 0.92 | 21.06 | 14.55 | 0.24 | 0.87 | 27.08 | 19.42 | –2.53 |
CI ≥ 0.7 | 172 | 0.94 | 27.56 | 14.96 | –1.74 | 0.93 | 29.52 | 18.11 | 0.93 | 0.93 | 30.09 | 17.82 | –0.66 |
Overall | 9722 | 0.93 | 21.01 | 14.07 | 0.12 | 0.92 | 21.34 | 14.52 | 0.04 | 0.88 | 26.97 | 19.08 | –2.51 |
Elevation Interval | Total Number of Samples | |
---|---|---|
Instantaneous SSNR | Daily SSNR | |
<200 m | 582 | 370 |
200~400 m | 1157 | 978 |
400~600 m | 530 | 435 |
600~1000 m | 390 | 287 |
1000~1500 m | 242 | 156 |
1500~2000 m | 276 | 114 |
2000~3000 m | 40 | 33 |
≥3000 m | 51 | 59 |
Total | 3268 | 2432 |
Site | Lat, Lon | Land Cover | Height (m) |
---|---|---|---|
Larned, Kansas: E01 | 38.20N, 99.32W | Cropland | 632 |
Hillsboro, Kansas: E02 | 38.31N, 97.30W | Grassland | 450 |
LeRoy, Kansas: E03 | 38.20N, 95.60W | Cropland | 338 |
Plevna, Kansas: E04 | 37.95N, 98.33W | Rangeland | 513 |
Halstead, Kansas: E05 | 38.11N, 97.51W | Wheat | 440 |
Towanda, Kansas: E06 | 37.84N, 97.02W | Alfalfa | 409 |
Elk Falls, Kansas: E07 | 37.38N, 96.18W | Pasture | 283 |
Coldwater, Kansas: E08 | 37.33N, 99.31W | Rangeland | 664 |
Ashton, Kansas: E09 | 37.13N, 97.27W | Grassland | 386 |
Tyro, Kansas: E10 | 37.07N, 95.79W | Alfalfa | 248 |
Byron, Oklahoma: E11 | 36.88N, 98.29W | Alfalfa | 360 |
Pawhuska, Oklahoma: E12 | 36.84N, 96.43W | Prairie | 331 |
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Wang, Y.; Jiang, B.; Liang, S.; Wang, D.; He, T.; Wang, Q.; Zhao, X.; Xu, J. Surface Shortwave Net Radiation Estimation from Landsat TM/ETM+ Data Using Four Machine Learning Algorithms. Remote Sens. 2019, 11, 2847. https://doi.org/10.3390/rs11232847
Wang Y, Jiang B, Liang S, Wang D, He T, Wang Q, Zhao X, Xu J. Surface Shortwave Net Radiation Estimation from Landsat TM/ETM+ Data Using Four Machine Learning Algorithms. Remote Sensing. 2019; 11(23):2847. https://doi.org/10.3390/rs11232847
Chicago/Turabian StyleWang, Yezhe, Bo Jiang, Shunlin Liang, Dongdong Wang, Tao He, Qian Wang, **ang Zhao, and Jianglei Xu. 2019. "Surface Shortwave Net Radiation Estimation from Landsat TM/ETM+ Data Using Four Machine Learning Algorithms" Remote Sensing 11, no. 23: 2847. https://doi.org/10.3390/rs11232847