EGU24-731, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Exploring Drought Monitoring in Morocco: A Review of Remote Sensing and Machine Learning Techniques

Said El Goumi1, Mustapha Namous1, Abdenbi Elaloui1, Samira Krimissa1, and Nafia El-alaouy2
Said El Goumi et al.
  • 1Data Science for Sustainable Earth Laboratory (Data4Earth) Sultan Moulay Slimane University, Beni Mellal, 23000, Morocco
  • 2Geosciences Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakesh, 40000, Morocco

The challenges of climate change and water scarcity in Morocco highlight the need for Remote Sensing (RS) and Machine Learning (ML) for drought monitoring. Droughts pose socio-economic and environmental challenges and have significant impacts on the country's agriculture-based economy and water management strategies. This study provides a comprehensive review of advanced RS technologies and ML algorithms, with a focus on their effectiveness in monitoring and forecasting drought conditions. RS provides extensive spatial coverage and captures important data on factors such as vegetation health, soil moisture, and precipitation trends, which are crucial for early detection and response to droughts. Incorporating ML algorithms significantly improves the precision and efficiency of drought prediction models, aiding in the development of comprehensive drought indices and forecasting models for agricultural planning and effective water resource management.

The study evaluates various RS methods utilized in Morocco, including the analysis of satellite imagery and vegetation indices such as NDVI, and assesses ML techniques like support vector machines (SVM) and artificial neural networks (ANN) for predicting drought-induced agricultural impacts. The combined use of these technologies provides a holistic approach to drought monitoring, enabling timely interventions to assist communities affected by drought. However, the study also highlights challenges in areas such as data availability, model validation, and associated costs. To effectively manage drought risks, the paper recommends that Moroccan policymakers and stakeholders leverage these technological advancements while emphasizing the importance of continuing research, interdisciplinary collaboration, and capacity building in these areas.

Key words: Drought,remote sensing, machine learning, climate change, Morocco

How to cite: El Goumi, S., Namous, M., Elaloui, A., Krimissa, S., and El-alaouy, N.: Exploring Drought Monitoring in Morocco: A Review of Remote Sensing and Machine Learning Techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-731,, 2024.