Coupling Remote Sensing Data and AquaCrop Model for Simulation of Winter Wheat Growth under Rainfed and Irrigated Conditions in a Mediterranean Environment
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Field Trials
2.2. Field Observations and Measurements
2.3. Satellite Data and Vegetation Indices
2.4. AquaCrop Model
2.4.1. Model Description, Calibration, and Validation
2.4.2. Coupling of AquaCrop and Sentinel Data
2.4.3. Statistical Analysis
3. Results and Discussion
3.1. Relationship between the Sentinel 2—Vegetation Indices and Field Data
3.2. Performance of AquaCrop Model to Simulate Winter Wheat Growth and Yield under Different Water Inputs
3.2.1. AquaCrop Calibration
3.2.2. AquaCrop Validation
3.3. Results of S2-Derived CC Insertion in AquaCrop
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Month | Rain [mm Month−1] | Tmax [°C] | Tmin [°C] | RHmean [%] | WS [m s−1] | Rs [W m−21] | ETo [mm Month−1] |
---|---|---|---|---|---|---|---|
November-16 | 29.00 | 20.35 | 2.81 | 45.65 | 1.41 | 89.12 | 59.40 |
December-16 | 105.20 | 10.29 | 0.43 | 83.68 | 1.91 | 61.77 | 26.50 |
January-17 | 119.40 | 10.87 | −0.73 | 78.87 | 1.69 | 68.20 | 30.70 |
February-17 | 14.20 | 13.70 | −2.12 | 60.54 | 1.38 | 82.75 | 43.00 |
March-17 | 49.40 | 16.15 | 4.01 | 72.03 | 1.98 | 118.38 | 62.00 |
April-17 | 12.60 | 22.33 | 5.94 | 53.87 | 2.11 | 261.81 | 126.40 |
May-17 | 3.80 | 27.30 | 9.02 | 47.35 | 2.04 | 320.03 | 175.30 |
June-17 | 2.40 | 32.25 | 12.04 | 41.97 | 1.80 | 346.29 | 197.40 |
July-17 | 0.00 | 34.92 | 17.46 | 36.64 | 1.18 | 351.36 | 198.10 |
August-17 | 0.00 | 35.25 | 13.20 | 43.79 | 1.34 | 305.01 | 182.80 |
September-17 | 0.20 | 33.32 | 12.73 | 45.17 | 1.29 | 250.94 | 142.60 |
October-17 | 13.80 | 25.43 | 8.74 | 54.80 | 1.18 | 174.53 | 85.90 |
November-17 | 22.80 | 19.42 | 5.02 | 67.04 | 1.15 | 119.52 | 48.3 |
December-17 | 0.20 | 16.78 | 2.28 | 68.03 | 1.28 | 96.30 | 39.7 |
January-18 | 168.20 | 11.88 | 1.13 | 82.68 | 1.50 | 84.84 | 29.40 |
February-18 | 131.80 | 16.32 | 3.26 | 70.45 | 1.35 | 123.62 | 45.80 |
March-18 | 18.20 | 20.96 | 3.95 | 57.61 | 1.66 | 206.32 | 93.20 |
April-18 | 17.60 | 24.35 | 6.74 | 52.57 | 1.37 | 248.99 | 117.10 |
May-18 | 19.80 | 27.05 | 12.48 | 50.98 | 1.30 | 279.58 | 147.00 |
June-18 | 12.20 | 30.73 | 14.67 | 46.51 | 1.78 | 322.78 | 183.30 |
July-18 | 0.00 | 33.40 | 14.02 | 45.74 | 1.93 | 324.94 | 203.90 |
August-18 | 0.00 | 33.80 | 14.25 | 50.05 | 1.64 | 291.17 | 179.40 |
September-18 | 0.00 | 32.50 | 13.61 | 46.94 | 1.36 | 232.73 | 136.60 |
October-18 | 70.80 | 26.43 | 10.77 | 60.09 | 1.26 | 165.64 | 86.80 |
November-18 | 46.20 | 17.88 | 6.28 | 78.19 | 1.17 | 106.52 | 40.50 |
December-18 | 122.20 | 12.05 | 4.06 | 88.77 | 1.50 | 76.20 | 24.30 |
January-19 | 288.00 | 10.70 | −0.15 | 83.45 | 1.73 | 102.05 | 29.80 |
February-19 | 214.40 | 12.69 | 1.34 | 81.97 | 1.45 | 126.49 | 37.10 |
March-19 | 130.00 | 14.78 | 3.09 | 75.97 | 1.70 | 172.19 | 64.40 |
April-19 | 75.60 | 18.38 | 4.71 | 72.63 | 1.31 | 222.68 | 86.90 |
May-19 | 13.80 | 29.62 | 9.35 | 45.12 | 1.19 | 320.70 | 163.60 |
June-19 | 4.00 | 32.63 | 13.81 | 49.31 | 1.46 | 332.87 | 183.00 |
Vegetation Index | Regression Models | R2 | RMSE (t ha−1) |
---|---|---|---|
NDVI | y = 10.728x1.4315 | 0.68 | 2.26 |
EVI | y = 3.8732x0.072 | 0.12 | 4.89 |
fAPAR | y = 15.333x2.5657 | 0.71 | 1.09 |
LAI | y = 2.7865x0.9011 | 0.78 | 1.90 |
FVC | y = 0.0005x2.2673 | 0.85 | 1.34 |
Parameter Description | Wheat Calibration | Method of Determination |
---|---|---|
Conservative parameters | ||
Base temperature (°C) | 0 | e ** |
Cut-off temperature (°C) | 26 | e |
Canopy cover per seeding at 90% emergence (CC0) (cm2 plant−1) | 1.5 | e |
Canopy growth coefficient (CGC) (%/degree-day) | 0.0061 | c * |
Crop coefficient for transpiration at CC = 100% | 1.1 | c |
Canopy decline coefficient (CDC) at senescence (%/degree-day) | 0.004 | c |
Biomass water productivity (WP), normalized for ETo before yield formation (g m−2) | 16 | e |
Biomass water productivity, normalized for ETo during yield formation (% of WP) | 100 | e |
Leaf growth threshold p-upper | 0.2 | c |
Leaf growth threshold p-lower | 0.55 | c |
Leaf growth stress coefficient curve shape | 5 | c |
Stomatal conductance threshold p-upper | 0.55 | e |
Stomata stress coefficient curve shape | 0.5 | c |
Senescence stress coefficient p-upper | 0.55 | c |
Senescence stress coefficient curve shape | 2.5 | c |
Non-conservative parameters | ||
Time from sowing to emergence (GDD) | 150 | e |
Maximum canopy cover (CCx) (%) | 82 | m *** |
Time from sowing to start senescence (GDD) | 1422 | e |
Time from sowing to maturity (GDD) | 2451 | e |
Time from sowing to flowering (GDD) | 1100 | e |
Maximum effective rooting depth, Zx (m) | 1 | m |
Minimum effective rooting depth, Zn (m) | 0.3 | d **** |
Reference harvest index, HIo | 45 | m |
GD range where crop transpiration is affected by cold stress (°C–day) | 0–17.9 | c |
Calibration Dataset | ||||
---|---|---|---|---|
Variables | Measured | Simulated | % of Deviation | |
Biomass (t ha−1) | I-100 | 10.7 | 10.7 | 0.04 |
I-rainfed | 8.4 | 7.2 | −14.7 | |
Yield (t ha−1) | I-100 | 3.6 | 3.7 | 2.1 |
I-rainfed | 2.5 | 2.0 | −21.8 | |
Canopy cover—CC (%) | I-100 | 82.0 | 81.8 | −0.3 |
I-rainfed | 80.2 | 78.1 | −2.6 |
Calibration Dataset | ||||||||
---|---|---|---|---|---|---|---|---|
Biomass (t ha−1) | Yield (t ha−1) | CC (%) | SWD (mm) | |||||
Statistical Indicators | I-100 | I-Rainfed | I-100 | I-Rainfed | I-100 | I-Rainfed | I-100 | I-Rainfed |
RMSE | 0.62 | 1.34 | 0.08 | 0.54 | 5.74 | 7.47 | 12.47 | 28.76 |
CV (RMSE) | 0.12 | 0.36 | 0.02 | 0.22 | 0.13 | 0.11 | 0.12 | 0.58 |
dIA | 0.99 | 0.96 | _ | _ | 0.99 | 0.98 | 0.82 | 0.73 |
NSE | 0.99 | 0.95 | 0.99 | 0.95 | 0.99 | 0.98 | 0.99 | 0.78 |
Validation Datasets | |||||||
---|---|---|---|---|---|---|---|
Season 2017–2018 | Season 2018–2019 | ||||||
Variables | Measured | Simulated | % of Deviation | Measured | Simulated | % of Deviation | |
Biomass (t ha−1) | I-100 | 10.8 | 10.4 | −3.9 | 15.0 | 15.6 | 3.9 |
I-50 | _ | _ | _ | 14.2 | 15.4 | 8.5 | |
I-rainfed | 6.8 | 7.7 | 13.8 | 12.2 | 15.0 | 23.3 | |
Yield (t ha−1) | I-100 | 5.6 | 5.1 | −9.3 | 5.3 | 5.4 | 1.7 |
I-50 | _ | _ | _ | 4.8 | 5.2 | 9.7 | |
I-rainfed | 3.1 | 2.6 | −18.9 | 4.3 | 5.0 | 16.1 | |
Canopy cover (%) | I-100 | 75 | 81.7 | 8.9 | 77.3 | 81.8 | 5.8 |
I-50 | _ | _ | _ | 77.0 | 81.8 | 6.2 | |
I-rainfed | 69 | 80 | 15.9 | 76.9 | 81.8 | 6.4 |
Season 2017–2018 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Biomass (t ha−1) | Yield (t ha−1) | CC (%) | SWD (mm) | |||||||||
Statistical Indicators | I-100 | I-50 | I-Rainfed | I-100 | I-50 | I-Rainfed | I-100 | I-50 | I-Rainfed | I-100 | I-50 | I-Rainfed |
RMSE | 0.67 | _ | 0.92 | 1.48 | _ | 0.7 | 11.11 | _ | 21.48 | 21.93 | _ | 29.34 |
CV (RMSE) | 0.13 | _ | 0.26 | 0.26 | _ | 0.23 | 0.21 | _ | 0.51 | 0.16 | _ | 0.32 |
dIA | 0.98 | _ | 0.98 | _ | _ | _ | 0.95 | _ | 0.77 | 0.75 | _ | 0.72 |
NSE | 0.99 | _ | 0.96 | 0.93 | _ | 0.95 | 0.98 | _ | 0.86 | 0.98 | _ | 0.9 |
Season 2018–2019 | ||||||||||||
RMSE | 1.32 | 1.36 | 1.6 | 0.09 | 0.46 | 0.69 | 6.99 | 7.16 | 9.49 | 10.97 | 14.33 | 17.25 |
CV (RMSE) | 0.21 | 0.22 | 0.27 | 0.02 | 0.1 | 0.16 | 0.14 | 0.14 | 0.19 | 0.33 | 0.4 | 0.43 |
dIA | 0.99 | 0.99 | 0.98 | _ | _ | _ | 0.99 | 0.99 | 0.98 | 0.87 | 0.78 | 0.83 |
NSE | 0.98 | 0.98 | 0.97 | 0.99 | 0.99 | 0.97 | 0.99 | 0.99 | 0.97 | 0.96 | 0.94 | 0.93 |
2016–2017 Dataset | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Biomass (t ha−1) | Yield (t ha−1) | CC (%) | SWD (mm) | |||||||||
Statistical Indicators | I-100 | I-50 | I-Rainfed | I-100 | I-50 | I-Rainfed | I-100 | I-50 | I-Rainfed | I-100 | I-50 | I-Rainfed |
RMSE | 1.06 | _ | 0.48 | 0.05 | _ | 0.34 | 4.45 | _ | 5.65 | 24.45 | _ | 21.96 |
CV (RMSE) | 0.2 | _ | 0.14 | 0.3 | _ | 0.24 | 0.09 | _ | 0.13 | 0.27 | _ | 0.16 |
dIA | 0.98 | _ | 0.99 | _ | _ | _ | 0.99 | _ | 0.98 | 0.82 | _ | 0.84 |
NSE | 0.98 | _ | 0.99 | 0.91 | _ | 0.96 | 1 | _ | 0.99 | 0.93 | _ | 0.98 |
2018–2019 Dataset | ||||||||||||
RMSE | 1.15 | 1 | 1.26 | 0.04 | 0.23 | 0.42 | 5.28 | 4.42 | 7.17 | 10.86 | 14.43 | 17.54 |
CV (RMSE) | 0.18 | 0.16 | 0.21 | 0.01 | 0.05 | 0.12 | 0.11 | 0.09 | 0.15 | 0.33 | 0.4 | 0.44 |
dIA | 0.99 | 0.99 | 0.99 | _ | _ | _ | 0.99 | 0.99 | 0.98 | 0.87 | 0.77 | 0.74 |
NSE | 0.99 | 0.99 | 0.98 | 1 | 1 | 0.99 | 0.99 | 0.99 | 0.99 | 0.96 | 0.94 | 0.93 |
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Abi Saab, M.T.; El Alam, R.; Jomaa, I.; Skaf, S.; Fahed, S.; Albrizio, R.; Todorovic, M. Coupling Remote Sensing Data and AquaCrop Model for Simulation of Winter Wheat Growth under Rainfed and Irrigated Conditions in a Mediterranean Environment. Agronomy 2021, 11, 2265. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11112265
Abi Saab MT, El Alam R, Jomaa I, Skaf S, Fahed S, Albrizio R, Todorovic M. Coupling Remote Sensing Data and AquaCrop Model for Simulation of Winter Wheat Growth under Rainfed and Irrigated Conditions in a Mediterranean Environment. Agronomy. 2021; 11(11):2265. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11112265
Chicago/Turabian StyleAbi Saab, Marie Therese, Razane El Alam, Ihab Jomaa, Sleiman Skaf, Salim Fahed, Rossella Albrizio, and Mladen Todorovic. 2021. "Coupling Remote Sensing Data and AquaCrop Model for Simulation of Winter Wheat Growth under Rainfed and Irrigated Conditions in a Mediterranean Environment" Agronomy 11, no. 11: 2265. https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11112265