Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Theoretical Frameworks
2.1.1. Convolutional Neural Network
2.1.2. Gated Recurrent Unit Network
2.1.3. Hybrid CNN–GRU. Neural Network
2.1.4. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)
- Step 1: The decomposition of p-realizations of using EMD to develop their first intrinsic approach, as explained according to the equation:
- Step 2: Putting k = 1, the 1st residue is computed following Equation (7).
- Step 3: Putting k = 2, the 2nd residual is obtained as:
- Step 4: Setting k = 2… K calculates the kth residue as:
- Step 5: Now we decompose the realisations until their first model of EMD is reached; here, the (k + 1) is:
- Step 6: Now the k value is incremented, and steps 4–6 are repeated. Consequently, the final residue is achieved:
2.1.5. Feature Selection: Neighbourhood Component Analysis
3. Study Area and Data
3.1. Study Area and Description of Predictive Model Development Dataset
3.1.1. MODIS Satellite Dataset
3.1.2. Scientific Information for Landowners (SILO) Dataset
3.1.3. Climate Indices
3.2. Predictive Model Development
3.2.1. Feature Selection
3.2.2. Hybrid Deep Learning Algorithm Implementation
3.2.3. Predictive Model Evaluation
4. Results
5. Discussions
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Station Name | BOM Station ID | SILO Position (MODIS Grid Area) | Major Climate Class [68] | Soil Type [69] | Elevation [70] |
---|---|---|---|---|---|
Menindee | 047019 | 32.39°S, 142.42°E (142.5°E, 32.5°S, 142.25°E, 32.25°S) | Desert | Calcarosol | 61 |
Deniliquin | 074128 | 35.53°S, 144.97° (145°E, 35.25°S, 144.75°E, 35°S) | Savannah | Calcarosol | 94 |
Fairfield | 066137 | 33.92°S, 150.98°E (149.75°E, 37.75°S, 150.0°E, 37.5°S) | Savannah | Vertosol | 15 |
Gabo Island | 084016 | 37.57°S, 149.92°E (150°E, 37.75°S, 149.75°E, 37.5°S) | Sub-Tropical | Sodosol | 15 |
GLDAS 2.0: Modis Satellite Data from Giovanni Repository | |||
---|---|---|---|
Predictor Variable | Notation | Description | Units |
SurT | St | Average Surface Skin temperature | K |
CSW | CW | Plant canopy surface water | Kg m−2 |
CWE | CE | Canopy water evaporation | kg m−2 s−1 |
Esoil | Es | Direct Evaporation from Bare Soil | kg m−2 s−1 |
ET | ET | Evapotranspiration | kg m−2 s−1 |
Esnow | Es | Snow Evaporation | kg m−2 s−1 |
GWS | GW | Groundwater storage | mm |
LWR. | LW | Net longwave radiation flux | W m−2 |
Qg | Qg | Ground heat flux | W m−2 |
Qh | Qh | Sensible heat net flux | W m−2 |
Qle | Qle | Latent heat net flux | W m−2 |
Qs | Qs | Storm surface runoff | Kg m−2 s−1 |
Qsb | Qb | Baseflow-groundwater runoff | Kg m−2 s−1 |
Qsm | Qm | Snow-melt | Kg m−2 s−1 |
Snd | Sn | Snow depth | m |
Snt | Snt | Snow Surface temperature | m |
SMp | Sp | Profile Soil moisture | Kg m−2 |
SMrz | Sz | Root Zone Soil moisture | Kg m−2 |
SSM | SSM | Surface Soil moisture | Kg m−2 |
SWE | SW | Snow depth water equivalent | Kg m−2 |
SWR | SR | Net short-wave radiation flux | W m−2 |
Tra | Tr | Transpiration | Kg m−2 s−1 |
TWS | TW | Terrestrial water storage | mm |
SILO (Ground-Based Observations) | |||
T.Max | Tx | Maximum Temperature | °C |
T.Min | Tn | Minimum Temperature | °C |
Rain | r | Rainfall | mm |
Evap | Ep | Evaporation | mm |
Radn | Rd | Radiation | MJ m−2 |
VP | VP | Vapour Pressure | hPa |
RHmaxT | Rx | Relative Humidity at Temperature T.Max | % |
RHminT | Rn | Relative Humidity at Temperature T.Min | % |
Mpot | Mp | Morton potential evapotranspiration overland | mm |
SYNOPTIC-SCALE (Climate Mode Indices) | |||
Nino3.0 | N3 | Average SSTA over 150°–90°W and 5°N–5°S | NONE |
Nino3.4 | N34 | Average SSTA over 170°E–120°W and 5°N–5°S | |
Nino4.0 | N4 | Average SSTA over 160°E–150°W and 5°N–5°S | |
Nino1+2 | N12 | Average SSTA over 90°W–80°W and 0°–10°S | |
AO | A | Arctic Oscillation | |
AAO | AO | Antarctic Oscillation | |
MJO1 | MJ1 | Madden Julian Oscillation-1 | |
MJO2 | MJ2 | Madden Julian Oscillation-2 | |
MJO4 | MJ4 | Madden Julian Oscillation-4 | |
MJO5 | MJ5 | Madden Julian Oscillation-5 | |
MJO6 | MJ6 | Madden Julian Oscillation-6 | |
MJO7 | MJ7 | Madden Julian Oscillation-7 | |
MJO8 | MJ8 | Madden Julian Oscillation-8 | |
MJO10 | MJ10 | Madden Julian Oscillation-10 | |
EPO | EP | East Pacific Oscillation | |
GBI | G | Greenland Blocking Index (GBI) | |
WPO | WP | Western Pacific Oscillation (WPO.) | |
PNA | PN | Pacific North American Index | |
NAO | N | North Atlantic Oscillation | |
SAM | SM | Southern Annular Mode index | |
SOI | SOI | Southern Oscillation Index, as per Troup [84] |
(a) Tested Range of Model Hyper-Parameters | ||
Model | Model Hyper-parameter Names | Search Space for Optimal Hyper-Parameters |
CNN-GRU | Filter 1 | (70, 80, 100, 150) |
Filter 2 | (70, 80, 100,150) | |
Filter 3 | (70, 80, 100, 150) | |
GRU Cell Units | (40, 50, 70, 80, 100, 150) | |
Epochs | (500, 800, 1000) | |
Activation function | (ReLU) | |
Optimiser | (Adam, SGD) | |
Batch Size | (5, 10, 20, 50, 100) | |
GRU | GRU Cell 1 | (70, 80, 100, 110) |
GRU Cell 2 | (70, 80, 100,150, 200, 210) | |
Epochs | (500, 800, 1000) | |
Activation function | (ReLU) | |
Optimiser | (Adam, SGD) | |
Batch Size | (5, 10, 20, 50, 100) | |
(b) Optimally Selected Hyper-Parameters | ||
CNN-GRU | Convolution Layer 1 (C1) | 80 |
C1-Activation function | ReLU | |
C1-Pooling Size | 1 | |
Convolution Layer 2 (C2) | 70 | |
C2-Activation function | ReLU | |
C2-Pooling Size | 1 | |
Convolution Layer 3 (C3) | 80 | |
C3-Activation function | ReLU | |
C3-Pooling Size | 1 | |
GRU Layer 1 (L1) | 200 | |
L1-Activation function | ReLU | |
GRU Layer 2 (L2) | 60 | |
L2-Activation function | ReLU | |
Drop-out rate | 0.2 | |
Optimiser | Adam | |
Padding | Same | |
Batch Size | 5 | |
Epochs | 400 | |
GRU | GRU Cell 1 (G1) | 110 |
G1-Activation function | ReLU | |
GRU Cell 2 (G2) | 250 | |
G2-Activation function | ReLU | |
Epochs | 300 | |
Optimiser | SGD | |
Drop-out rate | 0.2 | |
Batch Size | 15 | |
Epochs | 1000 |
Soil Moisture Forecasting Horizon, nth Day Lead Time | ||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1st Day | 5th Day | 7th Day | 14th Day | 21st Day | 30th Day | |||||||||||||||||||||||||
r | NSE | RMSE | MAE | APB | r | NSE | RMSE | MAE | APB | r | NSE | RMSE | MAE | APB | r | NSE | RMSE | MAE | APB | r | NSE | RMSE | MAE | APB | r | NSE | RMSE | MAE | APB | |
Study Station 1: Menindee | ||||||||||||||||||||||||||||||
CEEMDAN-CNN-GRU | 0.996 | 0.995 | 0.021 | 0.013 | 0.359 | 0.993 | 0.991 | 0.040 | 0.030 | 0.823 | 0.985 | 0.967 | 0.075 | 0.057 | 1.559 | 0.906 | 0.896 | 0.226 | 0.185 | 5.079 | 0.895 | 0.787 | 0.230 | 0.186 | 5.098 | 0.869 | 0.714 | 0.255 | 0.201 | 5.493 |
CNN-GRU | 0.967 | 0.892 | 0.135 | 0.112 | 3.061 | 0.966 | 0.918 | 0.117 | 0.094 | 2.569 | 0.945 | 0.861 | 0.152 | 0.121 | 3.330 | 0.892 | 0.770 | 0.235 | 0.193 | 5.285 | 0.899 | 0.788 | 0.210 | 0.168 | 4.594 | 0.851 | 0.765 | 0.238 | 0.181 | 4.945 |
CEEMDAN-GRU | 0.976 | 0.937 | 0.116 | 0.094 | 2.234 | 0.970 | 0.933 | 0.120 | 0.095 | 2.265 | 0.957 | 0.909 | 0.140 | 0.110 | 2.613 | 0.882 | 0.738 | 0.237 | 0.186 | 4.424 | 0.864 | 0.781 | 0.262 | 0.206 | 4.918 | 0.866 | 0.742 | 0.275 | 0.217 | 5.163 |
GRU | 0.962 | 0.893 | 0.134 | 0.110 | 3.020 | 0.962 | 0.933 | 0.121 | 0.094 | 2.589 | 0.940 | 0.851 | 0.158 | 0.126 | 3.452 | 0.882 | 0.745 | 0.244 | 0.197 | 5.390 | 0.887 | 0.748 | 0.243 | 0.196 | 5.360 | 0.863 | 0.726 | 0.251 | 0.197 | 5.386 |
Study Station 2: Deniliquin | ||||||||||||||||||||||||||||||
CEEMDAN-CNN-GRU | 0.990 | 0.899 | 0.048 | 0.034 | 0.778 | 0.989 | 0.975 | 0.091 | 0.065 | 1.489 | 0.959 | 0.917 | 0.165 | 0.113 | 2.611 | 0.801 | 0.607 | 0.355 | 0.247 | 5.716 | 0.768 | 0.573 | 0.374 | 0.266 | 6.130 | 0.703 | 0.465 | 0.415 | 0.295 | 6.807 |
CNN-GRU | 0.979 | 0.955 | 0.098 | 0.075 | 1.799 | 0.945 | 0.866 | 0.169 | 0.137 | 3.270 | 0.929 | 0.846 | 0.181 | 0.143 | 3.405 | 0.866 | 0.624 | 0.283 | 0.224 | 5.333 | 0.873 | 0.749 | 0.231 | 0.181 | 4.298 | 0.848 | 0.687 | 0.258 | 0.202 | 4.806 |
CEEMDAN-GRU | 0.987 | 0.958 | 0.106 | 0.081 | 1.930 | 0.968 | 0.929 | 0.123 | 0.096 | 2.279 | 0.969 | 0.920 | 0.131 | 0.106 | 2.524 | 0.872 | 0.730 | 0.240 | 0.189 | 4.505 | 0.859 | 0.712 | 0.249 | 0.197 | 4.701 | 0.869 | 0.671 | 0.264 | 0.207 | 4.926 |
GRU | 0.967 | 0.927 | 0.125 | 0.099 | 2.350 | 0.947 | 0.889 | 0.154 | 0.121 | 2.874 | 0.918 | 0.822 | 0.195 | 0.153 | 3.655 | 0.867 | 0.722 | 0.244 | 0.191 | 4.560 | 0.868 | 0.695 | 0.256 | 0.201 | 4.787 | 0.850 | 0.659 | 0.269 | 0.217 | 5.152 |
Study Station 3: Fairfield | ||||||||||||||||||||||||||||||
CEEMDAN-CNN-GRU | 0.975 | 0.976 | 0.035 | 0.024 | 0.554 | 0.972 | 0.975 | 0.069 | 0.052 | 1.189 | 0.959 | 0.920 | 0.162 | 0.110 | 2.524 | 0.842 | 0.628 | 0.349 | 0.238 | 5.493 | 0.762 | 0.573 | 0.374 | 0.264 | 6.088 | 0.746 | 0.523 | 0.374 | 0.261 | 6.078 |
CNN-GRU | 0.945 | 0.935 | 0.061 | 0.048 | 1.099 | 0.962 | 0.943 | 0.135 | 0.091 | 2.107 | 0.907 | 0.821 | 0.240 | 0.156 | 3.612 | 0.764 | 0.560 | 0.376 | 0.264 | 6.109 | 0.759 | 0.554 | 0.379 | 0.259 | 5.988 | 0.708 | 0.477 | 0.410 | 0.289 | 6.671 |
CEEMDAN-GRU | 0.947 | 0.943 | 0.048 | 0.034 | 0.778 | 0.939 | 0.935 | 0.091 | 0.065 | 1.489 | 0.929 | 0.917 | 0.165 | 0.113 | 2.611 | 0.801 | 0.607 | 0.355 | 0.247 | 5.716 | 0.768 | 0.573 | 0.374 | 0.266 | 6.130 | 0.703 | 0.465 | 0.415 | 0.295 | 6.807 |
GRU | 0.925 | 0.919 | 0.153 | 0.096 | 2.205 | 0.913 | 0.905 | 0.177 | 0.115 | 2.659 | 0.904 | 0.809 | 0.250 | 0.168 | 3.864 | 0.778 | 0.585 | 0.369 | 0.254 | 5.850 | 0.775 | 0.568 | 0.376 | 0.267 | 6.165 | 0.666 | 0.411 | 0.435 | 0.314 | 7.267 |
Study Station 4: Gabo Island | ||||||||||||||||||||||||||||||
CEEMDAN-CNN- GRU | 0.988 | 0.966 | 0.085 | 0.067 | 1.455 | 0.987 | 0.971 | 0.079 | 0.062 | 1.346 | 0.978 | 0.944 | 0.109 | 0.086 | 1.887 | 0.931 | 0.899 | 0.188 | 0.147 | 3.206 | 0.909 | 0.764 | 0.224 | 0.175 | 3.829 | 0.913 | 0.807 | 0.202 | 0.158 | 3.456 |
CNN-GRU | 0.979 | 0.951 | 0.101 | 0.078 | 1.707 | 0.973 | 0.944 | 0.109 | 0.084 | 1.826 | 0.948 | 0.897 | 0.147 | 0.113 | 2.457 | 0.921 | 0.843 | 0.182 | 0.141 | 3.087 | 0.911 | 0.803 | 0.204 | 0.160 | 3.493 | 0.879 | 0.862 | 0.193 | 0.151 | 3.284 |
CEEMDAN-GRU | 0.986 | 0.966 | 0.085 | 0.067 | 1.472 | 0.983 | 0.964 | 0.087 | 0.069 | 1.508 | 0.974 | 0.945 | 0.107 | 0.085 | 1.844 | 0.924 | 0.821 | 0.194 | 0.153 | 3.340 | 0.913 | 0.814 | 0.198 | 0.156 | 3.394 | 0.912 | 0.798 | 0.206 | 0.161 | 3.520 |
GRU | 0.977 | 0.950 | 0.102 | 0.081 | 1.773 | 0.970 | 0.940 | 0.113 | 0.086 | 1.868 | 0.951 | 0.902 | 0.144 | 0.111 | 2.423 | 0.919 | 0.825 | 0.192 | 0.150 | 3.283 | 0.912 | 0.813 | 0.199 | 0.156 | 3.411 | 0.815 | 0.743 | 0.203 | 0.160 | 3.499 |
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Ahmed, A.A.M.; Deo, R.C.; Raj, N.; Ghahramani, A.; Feng, Q.; Yin, Z.; Yang, L. Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data. Remote Sens. 2021, 13, 554. https://doi.org/10.3390/rs13040554
Ahmed AAM, Deo RC, Raj N, Ghahramani A, Feng Q, Yin Z, Yang L. Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data. Remote Sensing. 2021; 13(4):554. https://doi.org/10.3390/rs13040554
Chicago/Turabian StyleAhmed, A. A. Masrur, Ravinesh C Deo, Nawin Raj, Afshin Ghahramani, Qi Feng, Zhenliang Yin, and Linshan Yang. 2021. "Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data" Remote Sensing 13, no. 4: 554. https://doi.org/10.3390/rs13040554