Machine Learning Methods for Predicting Argania spinosa Crop Yield and Leaf Area Index: A Combined Drought Index Approach from Multisource Remote Sensing Data
Abstract
:1. Introduction
- (I)
- Downscaling monthly CHIRPS data from a 5 km to 1 km scale using topographic and vegetation variables as predictors with the random forest model.
- (II)
- Investigating and comparing the performance of various models to identify the optimal machine learning approach for predicting crop yield and LAI in Argane forest stands.
- (III)
- Identifying correlations between crop yield and LAI with predictor variables, particularly drought indices.
- (IV)
- Establishing a combined drought index (CDI) from multisource remote sensing data to monitor and evaluate long-term agricultural drought in Argane forest areas from 2001 to 2021.
2. Materials and Methods
2.1. Study Areas
2.2. Field Measurements and Yield Data
3. Datasets
3.1. CHIRPS Precipitation Data
3.2. NDVI Data
3.3. LST Data
3.4. Evapotranspiration Data
3.5. Soil Moisture Data
3.6. DEM Data
3.7. Rain Gauge Data
4. Methods
4.1. Drought Stress Indices
4.2. Agricultural Drought Condition Index
4.3. Machine Learning Algorithms
4.4. Downscaling of Original CHIRPS Precipitation Data
- Re-sampling original predictors such as elevation, aspect, slope, longitude, latitude and NDVI from 1 km resolution to 0.05° resolution using pixel averaging. These were then reprojected to the same projection as CHIRPS data.
- Establishing relationships within the resampled environmental factors and CHIRPS precipitation data via a random forest regression model. This provided an estimated monthly precipitation at the 0.05° scale.
- Computing residual precipitation estimates with a spatial resolution of 25 km by subtracting predicted CHIRPS monthly precipitation from original CHIRPS monthly data.
- Generating CHIRPS monthly precipitation at 1 km from environmental variables at 1 km data using the nonparametric regression equation obtained in step 2.
- Correcting CHIRPS downscaled precipitation results by incorporating the 1 km resolution residual to 1 km downscaled precipitation.
5. Accuracy Evaluation
6. Results and Discussion
6.1. Spatial Downscaling of CHIRPS Precipitation Data
6.2. Construction of Crop Yield and LAI Estimation Model Based on Drought Indices
6.3. Correlation Analysis
6.4. Drought Monitoring
6.4.1. Agricultural Drought Monitoring Using CDI
6.4.2. Validation of Results
6.4.3. Limitation of the CDI
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Municipalities | Provinces | Latitude | Longitude | Altitude |
---|---|---|---|---|
Tafedna | Essaouira | 31.11 | –9.80 | 100 m–250 m |
Sidi Hmad ou Hamed | Essaouira | 31.35 | –9.67 | 100 m–250 m |
Imi Mqourn | Ait Baha | 30.21 | –9.21 | 200 m–300 m |
Lqliaa | Inzegane Ait Melloul | 30.31 | –9.54 | 40 m–70 m |
Drargua | Agadir | 30.45 | –9.48 | 100 m–900 m |
Sidi Ahmed Ou Abdallah | Taroudannt | 30.34 | –8.61 | 900 m–1100 m |
Bigoudine | Taroudannt | 30.67 | –9.22 | 700 m–1100 m |
Bounrar | Taroudannt | 30.31 | –8.77 | 900 m–1100 m |
Tioughza | Sidi ifni | 29.43 | –9.98 | 150 m–700 m |
Sidi Bouabdelli | Tiznit | 29.47 | –9.83 | 300 m–800 m |
Drought Index | Name | Formulation | Data Source |
---|---|---|---|
PCI | Precipitation Condition Index | CHIRPS | |
VCI | Vegetation Condition Index | MODIS | |
TCI | Temperature Condition Index | MODIS | |
ETCI | Evapotranspiration Condition Index | MODIS | |
SMCI | Soil Moisture Condition Index | SMAP |
Drought Severity | TCI, VCI, PCI, ETCI & VHI Values | CDI Values |
---|---|---|
Exceptional drought | VCI ≤ 10 | CDI ≤ 10 |
Critical drought | 10 < VCI ≤ 20 | 10 < CDI ≤ 20 |
Moderate drought | 20 < VCI ≤ 30 | 20 < CDI ≤ 30 |
Slight drought | 30 < VCI ≤ 40 | 30 < CDI ≤ 40 |
No drought | VCI ≥ 40 | CDI ≥ 40 |
Trait | Model | Training Set | Testing Set | All Data Set | ||||||
---|---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | MAE | R2 | RMSE | MAE | R2 | RMSE | MAE | ||
Crop Yield | XGBoost | 0.93 | 6.86 | 1.36 | 0.60 | 16.33 | 7.30 | 0.94 | 6.25 | 1.44 |
GBDT | 0.93 | 6.88 | 1.49 | 0.44 | 19.38 | 8.14 | 0.94 | 6.22 | 1.41 | |
RF | 0.87 | 9.17 | 3.57 | 0.56 | 17.17 | 7.87 | 0.88 | 8.72 | 3.31 | |
DT | 0.66 | 15.03 | 6.75 | 0.54 | 17.50 | 7.79 | 0.67 | 14.62 | 6.47 | |
SVR | 0.21 | 22.78 | 8.20 | 0.25 | 22.29 | 7.80 | 0.33 | 20.94 | 7.95 | |
ANN | 0.71 | 13.79 | 6.39 | 0.67 | 14.87 | 7.93 | 0.73 | 13.38 | 6.09 | |
LR | 0.63 | 15.52 | 8.70 | 0.54 | 17.52 | 10.81 | 0.69 | 14.30 | 7.61 | |
LAI | XGBoost | 0.64 | 0.65 | 0.51 | 0.38 | 0.93 | 0.69 | 0.62 | 0.67 | 0.52 |
GBDT | 0.64 | 0.68 | 0.54 | 0.38 | 0.94 | 0.70 | 0.62 | 0.67 | 0.52 | |
RF | 0.63 | 0.66 | 0.52 | 0.41 | 0.90 | 0.66 | 0.62 | 0.68 | 0.53 | |
DT | 0.58 | 0.70 | 0.56 | 0.34 | 0.96 | 0.72 | 0.57 | 0.72 | 0.56 | |
SVR | 0.59 | 0.69 | 0.55 | 0.34 | 0.96 | 0.67 | 0.59 | 0.70 | 0.55 | |
ANN | 0.63 | 0.66 | 0.51 | 0.38 | 0.93 | 0.69 | 0.62 | 0.67 | 0.53 | |
LR | 0.57 | 0.71 | 0.56 | 0.37 | 0.93 | 0.72 | 0.56 | 0.72 | 0.58 |
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Mouafik, M.; Fouad, M.; El Aboudi, A. Machine Learning Methods for Predicting Argania spinosa Crop Yield and Leaf Area Index: A Combined Drought Index Approach from Multisource Remote Sensing Data. AgriEngineering 2024, 6, 2283-2305. https://doi.org/10.3390/agriengineering6030134
Mouafik M, Fouad M, El Aboudi A. Machine Learning Methods for Predicting Argania spinosa Crop Yield and Leaf Area Index: A Combined Drought Index Approach from Multisource Remote Sensing Data. AgriEngineering. 2024; 6(3):2283-2305. https://doi.org/10.3390/agriengineering6030134
Chicago/Turabian StyleMouafik, Mohamed, Mounir Fouad, and Ahmed El Aboudi. 2024. "Machine Learning Methods for Predicting Argania spinosa Crop Yield and Leaf Area Index: A Combined Drought Index Approach from Multisource Remote Sensing Data" AgriEngineering 6, no. 3: 2283-2305. https://doi.org/10.3390/agriengineering6030134
APA StyleMouafik, M., Fouad, M., & El Aboudi, A. (2024). Machine Learning Methods for Predicting Argania spinosa Crop Yield and Leaf Area Index: A Combined Drought Index Approach from Multisource Remote Sensing Data. AgriEngineering, 6(3), 2283-2305. https://doi.org/10.3390/agriengineering6030134