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

Abstract

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