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Assessing the Impact of Climate Change On Seasonal Variation In Agricultural Land Use Using Sentinel-2 and Machine Learning
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1  Euromed University of Fez
Academic Editor: Riccardo Buccolieri


Food security and agriculture are essential for sustainable development, urbanization, and the well-being of population growth, while climate change in the Middle East and North Africa (MENA) region is causing erratic rainfall and rising temperatures, resulting in changes in agricultural land use. The purpose of this study is to examine land changes in the Fez region of Morocco over six years (2017-2022). The study utilized Sentinel-2 satellite multispectral images, spectral indices (NDVI, NBI, and NHFD), climate data and drought index to achieve the research objective. Using ground truth data, feature selection bands and spectral indices two machine learning algorithms: Random Forest (RF) and Gradient Tree Boost algorithms(GTB) were trained and tested via the Google Earth Engine (GEE) platform for the classification of LULC into four classes (Built-up Area, Water, Agricultural land and barren land), and ArcGIS Pro software was used for analysis. The overall performance of the algorithms was assessed, and the result shows that RF outperformed the GTB algorithm with 90% and 98 in the Kaffa coefficient and accuracy respectively. The final LULC analysis result shows that agricultural land use has fallen by 20% over the last five years, while builtup area has expanded by 10%, water decreased by 4% and barren land has increased by 6%. Correlation research between the change and the drought index revealed that climate change has an impact on agricultural land use in the region.

Keywords: Sentinel-2A; supervised learning; GEE; Spectral Indices; Morocco
Comments on this paper
Michael Webb
How can remote sensing and GIS technologies be further leveraged to monitor and mitigate the impacts of climate change on land use in the MENA region ?