Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula
Abstract
:1. Introduction
2. Data and Methods
2.1. Remote Sensing Data
2.1.1. GEO-KOMPSAT 2A (GK2A)
2.1.2. Precipitation Data
2.2. Numerical Model and Elevation Data
2.3. In-Situ Measurements
2.4. Processing
2.4.1. Extraterrestrial Solar Radiation (ESR)
2.4.2. Penman–Monteith Evapotranspiration (PM-ET)
2.4.3. Standardization of Input Variables
2.5. ANN Model
2.5.1. Model Structure
2.5.2. Mean Decrease Accuracy (MDA)
2.6. Statistical Analysis
3. Results
3.1. Input Data Correlation
3.2. MLP Model
3.3. Evaluation against KMA Stations
4. Discussions
4.1. NIFoS Flux Towers
4.2. Comparison with MODIS
4.3. Previous Studies on the Korean Peninsula
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ADAM | A Method for Stochastic Optimization |
AET | Actual Evapotranspiration |
AMI | Advanced Meteorological Imager |
ANN | Artificial Neural Networks |
ASR | Absorbed Shortwave Radiation |
BN | Batch Normalization |
COMS | Communication, Ocean and Meteorological Satellite |
DEM | Digital Elevation Model |
DLR | Downward Longwave Radiation |
DSR | Downward Shortwave Radiation |
EC | Eddy Covariance |
ELU | Exponential Linear Unit |
ESR | Extraterrestrial Solar Radiation |
ET | Evapotranspiration |
FAO | Food and Agriculture Organization of the United Nations |
GEO | Geostationary Orbit |
GK2A | GEOstationary Korea Multi-Purpose SATellite 2A |
IMERG | Integrated Multi-satellitE Retrievals for Global Precipitation Measurement |
KMA | Korea Meteorological Administration |
LDAPS | Local Data Assimilation and Prediction System |
LEO | Low Earth Orbit |
MDA | Mean Decrease Accuracy |
ML | Machine Learning |
MLP | Multilayer Perceptron |
MODIS | Moderate Resolution Imaging Spectroradiometer |
NDVI | Normalized Difference Vegetation Index |
NIFoS | National Institute of Forest Science |
NMSC | National Meteorological Satellite Center |
NWP | Numerical Weather Prediction |
OLR | Outgoing Longwave Radiation |
PET | Potential Evapotranspiration |
PM | Penman-Monteith |
RSR | Reflected Shortwave Radiation |
SPI6 | Standardized Precipitation Index for Six Months |
SRTM | Shuttle Radar Topography Mission |
ULR | Upward Longwave Radiation |
UM | Unified Model |
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Channel No. | Channel Name | Wavelength Range (μm) | Resolution (km) |
---|---|---|---|
1 | VIS004 | 0.431–0.479 | 1.0 × 1.0 |
2 | VIS005 | 0.5025–0.5175 | 1.0 × 1.0 |
3 | VIS006 | 0.625–0.66 | 0.5 × 0.5 |
4 | VIS008 | 0.8495–0.8705 | 1.0 × 1.0 |
5 | NR013 | 1.373–1.383 | 2.0 × 2.0 |
6 | NR016 | 1.601–1.619 | 2.0 × 2.0 |
7 | SW038 | 3.74–3.96 | 2.0 × 2.0 |
8 | WV063 | 6.061–6.425 | 2.0 × 2.0 |
9 | WV069 | 6.89–7.01 | 2.0 × 2.0 |
10 | WV073 | 7.258–7.433 | 2.0 × 2.0 |
11 | IR087 | 8.44–8.76 | 2.0 × 2.0 |
12 | IR096 | 9.543–9.717 | 2.0 × 2.0 |
13 | IR105 | 10.25–10.61 | 2.0 × 2.0 |
14 | IR112 | 11.08–11.32 | 2.0 × 2.0 |
15 | IR123 | 12.15–12.45 | 2.0 × 2.0 |
16 | IR133 | 13.21–13.39 | 2.0 × 2.0 |
Data | Variables | Spatial Resolution (Temporal Resolution) | Processing | Source |
---|---|---|---|---|
GK2A/AMI | RSR | 2 km × 2 km (10 min) | KMA NMSC | |
DSR | ||||
ASR | ||||
OLR | ||||
DLR | ||||
ULR | ||||
NDVI | 2 km × 2 km (1 day) | |||
GPM IMERG | SPI6 | 10 km × 10 km (1 day) | NASA | |
UM LDAPS | Ta | 1.5 km × 1.5 km (3 h) | , , | Met Office |
Ts | ||||
RH | ||||
Static data | ESR | – | – | – |
DEM | 30 m × 30 m | – | NASA |
Parameter | Hyperparameter | ||
---|---|---|---|
Activation | ELU | Alpha | 1 |
Optimizer | ADAM | Learning rate | |
Beta1 | 0.9 | ||
Beta2 | 0.999 | ||
Epsilon | |||
Loss function | RMSE | ||
Epochs | 100 | ||
Batch size | 500 |
Month | Observed PET (mm day−1) | Estimated PET (mm day−1) | ||||
---|---|---|---|---|---|---|
Minimum | Maximum | Mean | Minimum | Maximum | Mean | |
August 2020 | 0.52 | 9.74 | 4.29 | 0.18 | 8.32 | 4.09 |
September 2020 | 0.69 | 8.66 | 3.78 | 0.00 | 8.25 | 3.27 |
October 2020 | 0.79 | 9.44 | 3.90 | 0.00 | 7.88 | 3.37 |
November 2020 | 0.39 | 6.96 | 2.76 | 0.00 | 5.98 | 2.52 |
December 2020 | 0.34 | 5.06 | 2.06 | 0.06 | 4.47 | 2.09 |
January 2021 | 0.30 | 6.52 | 1.87 | 0.00 | 4.76 | 1.84 |
February 2021 | 0.50 | 10.16 | 2.97 | 0.00 | 7.61 | 2.83 |
March 2021 | 0.28 | 9.04 | 3.67 | 0.08 | 8.72 | 3.59 |
April 2021 | 0.66 | 12.28 | 5.44 | 0.42 | 10.59 | 5.48 |
May 2021 | 0.54 | 14.41 | 5.14 | 0.65 | 11.10 | 5.27 |
June 2021 | 0.40 | 11.30 | 5.25 | 0.94 | 10.32 | 5.32 |
July 2021 | 0.52 | 10.22 | 5.50 | 0.92 | 9.47 | 5.31 |
Month | No. | Bias (mm day−1) | RMSE (mm day−1) | MAE (mm day−1) | STD (mm day−1) | nRMSE | R | IOA |
---|---|---|---|---|---|---|---|---|
August 2020 | 1260 | −0.208 | 0.671 | 0.510 | 0.638 | 0.156 | 0.949 | 0.968 |
September 2020 | 1234 | −0.506 | 0.782 | 0.645 | 0.597 | 0.207 | 0.931 | 0.940 |
October 2020 | 1286 | −0.529 | 0.804 | 0.651 | 0.605 | 0.206 | 0.901 | 0.913 |
November 2020 | 1241 | −0.237 | 0.575 | 0.446 | 0.524 | 0.208 | 0.908 | 0.941 |
December 2020 | 1289 | 0.027 | 0.399 | 0.304 | 0.398 | 0.193 | 0.881 | 0.937 |
January 2021 | 1291 | −0.028 | 0.456 | 0.353 | 0.455 | 0.244 | 0.883 | 0.932 |
February 2021 | 1170 | −0.142 | 0.671 | 0.466 | 0.625 | 0.216 | 0.885 | 0.936 |
March 2021 | 1294 | −0.073 | 0.585 | 0.448 | 0.581 | 0.160 | 0.954 | 0.974 |
April 2021 | 1250 | 0.035 | 0.763 | 0.582 | 0.762 | 0.140 | 0.928 | 0.963 |
May 2021 | 1293 | 0.131 | 0.704 | 0.512 | 0.692 | 0.137 | 0.960 | 0.979 |
June 2021 | 1249 | 0.067 | 0.609 | 0.457 | 0.605 | 0.116 | 0.955 | 0.977 |
July 2021 | 1275 | −0.186 | 0.710 | 0.530 | 0.685 | 0.129 | 0.940 | 0.965 |
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Jang, J.-C.; Sohn, E.-H.; Park, K.-H.; Lee, S. Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula. Hydrology 2021, 8, 129. https://doi.org/10.3390/hydrology8030129
Jang J-C, Sohn E-H, Park K-H, Lee S. Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula. Hydrology. 2021; 8(3):129. https://doi.org/10.3390/hydrology8030129
Chicago/Turabian StyleJang, Jae-Cheol, Eun-Ha Sohn, Ki-Hong Park, and Soobong Lee. 2021. "Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula" Hydrology 8, no. 3: 129. https://doi.org/10.3390/hydrology8030129
APA StyleJang, J. -C., Sohn, E. -H., Park, K. -H., & Lee, S. (2021). Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula. Hydrology, 8(3), 129. https://doi.org/10.3390/hydrology8030129