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Article

Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco

1
ProcEDE, Department of Applied Physique, Faculty of Sciences and Technologies, Cadi Ayyad University, Marrakech 40000, Morocco
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CESBIO, University of Toulouse, IRD/CNRS/UPS/CNES, 31400 Toulouse, France
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Center for Remote Sensing Applications (CRSA), University Mohammed VI Polytechnic (UM6P), Benguerir 43150, Morocco
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National Institute for Agronomic Research (INRA), Rabat 10000, Morocco
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G-EAU, University Montpellier, AgroParisTech, CIRAD, IRD, INRAE, Institut Agro, 34000 Montpellier, France
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LMFE, Department of Physics, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech 40000, Morocco
*
Author to whom correspondence should be addressed.
Academic Editors: Bin Chen, Yufang Jin and Le Yu
Remote Sens. 2021, 13(16), 3101; https://doi.org/10.3390/rs13163101
Received: 4 June 2021 / Revised: 28 July 2021 / Accepted: 30 July 2021 / Published: 6 August 2021
(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Yield Estimation)
Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting. View Full-Text
Keywords: crop yield forecasting; machine learning; remote sensing drought indices; climate indices; weather data; semiarid region crop yield forecasting; machine learning; remote sensing drought indices; climate indices; weather data; semiarid region
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MDPI and ACS Style

Bouras, E.h.; Jarlan, L.; Er-Raki, S.; Balaghi, R.; Amazirh, A.; Richard, B.; Khabba, S. Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco. Remote Sens. 2021, 13, 3101. https://doi.org/10.3390/rs13163101

AMA Style

Bouras Eh, Jarlan L, Er-Raki S, Balaghi R, Amazirh A, Richard B, Khabba S. Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco. Remote Sensing. 2021; 13(16):3101. https://doi.org/10.3390/rs13163101

Chicago/Turabian Style

Bouras, El h., Lionel Jarlan, Salah Er-Raki, Riad Balaghi, Abdelhakim Amazirh, Bastien Richard, and Saïd Khabba. 2021. "Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco" Remote Sensing 13, no. 16: 3101. https://doi.org/10.3390/rs13163101

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