Assessing the Impact of Climate Change on Seasonal Variation in Agricultural Land Use Using Sentinel-2 and Machine Learning

Abstract

The Fez region in Morocco has experienced changes in agricultural land use as a result of climate change. These changes include erratic rainfall, rising temperatures, and evapotranspiration. The objective of this research is to investigate the impact of these changes on agricultural land use between 2018 and 2022 using remote sensing data (sentinel-2 and MODIS), climate data, drought index (Vegetation Condition Index (VCI)) and two machine learning algorithms (Random Forest (RF) and Gradient Tree Boost (GTB). The RF and GTB algorithms were trained and tested, and their performance was analyzed, revealing that the GTB algorithm is more efficient than the RF, with a Kaffa coefficient of 91% and overall accuracy of 93%. The analysis of climate change on land use and land cover (LULC) variations revealed a significant (54%) reduction in rainfall. Furthermore, agricultural land use and water were reduced by 41% and 17%, respectively. Conversely, barren land and built-up areas increased by 58% and 4%, respectively, and the annual mean VCI decreased from 39.72 in 2018 to 19.9 in 2022. The study concluded that climate change had a significant impact on the region’s agricultural land cover, and decreases in rainfall directly affect agricultural land use.

Keywords

• climate change
• Google Earth Engine
• LULC
• Sentinel-2
• supervised learning
• Morocco