Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco
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 a...
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ftmdpi:oai:mdpi.com:/2072-4292/13/16/3101/ 2023-08-20T04:08:34+02:00 Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco El houssaine Bouras Lionel Jarlan Salah Er-Raki Riad Balaghi Abdelhakim Amazirh Bastien Richard Saïd Khabba agris 2021-08-06 application/pdf https://doi.org/10.3390/rs13163101 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Agriculture and Vegetation https://dx.doi.org/10.3390/rs13163101 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 16; Pages: 3101 crop yield forecasting machine learning remote sensing drought indices climate indices weather data semiarid region Text 2021 ftmdpi https://doi.org/10.3390/rs13163101 2023-08-01T02:22:26Z 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, ... Text North Atlantic North Atlantic oscillation MDPI Open Access Publishing Remote Sensing 13 16 3101 |
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MDPI Open Access Publishing |
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language |
English |
topic |
crop yield forecasting machine learning remote sensing drought indices climate indices weather data semiarid region |
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crop yield forecasting machine learning remote sensing drought indices climate indices weather data semiarid region El houssaine Bouras Lionel Jarlan Salah Er-Raki Riad Balaghi Abdelhakim Amazirh Bastien Richard Saïd Khabba Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco |
topic_facet |
crop yield forecasting machine learning remote sensing drought indices climate indices weather data semiarid region |
description |
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, ... |
format |
Text |
author |
El houssaine Bouras Lionel Jarlan Salah Er-Raki Riad Balaghi Abdelhakim Amazirh Bastien Richard Saïd Khabba |
author_facet |
El houssaine Bouras Lionel Jarlan Salah Er-Raki Riad Balaghi Abdelhakim Amazirh Bastien Richard Saïd Khabba |
author_sort |
El houssaine Bouras |
title |
Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco |
title_short |
Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco |
title_full |
Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco |
title_fullStr |
Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco |
title_full_unstemmed |
Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco |
title_sort |
cereal yield forecasting with satellite drought-based indices, weather data and regional climate indices using machine learning in morocco |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13163101 |
op_coverage |
agris |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Remote Sensing; Volume 13; Issue 16; Pages: 3101 |
op_relation |
Remote Sensing in Agriculture and Vegetation https://dx.doi.org/10.3390/rs13163101 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13163101 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
16 |
container_start_page |
3101 |
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1774720894138056704 |