Seasonal Monitoring and Analysis of Soil Moisture and Vegetation Health in Oil Palm Plantations Using Remote Sensing

Malaysia, a prominent producer of palm oil in Asia, faces the challenge of regulating plantations to ensure optimal productivity. Manual monitoring proves impractical due to the associated energy, expense, and time constraints. Consequently, cost-effective, and time-saving alternatives such as remote sensing (RS) and Geographic Information System (GIS) utilizing satellite imagery become crucial for palm oil monitoring. These technologies leverage spectral and texture studies for vegetation classification. This study integrates remote sensing techniques to assess soil moisture dynamics and vegetation health in oil palm plantations, aiming to enhance sustainable agricultural management practices. The research utilizes the Soil Moisture Index (SMI) and Normalised Difference Vegetation Index (NDVI), derived from Landsat 9 satellite imagery accessed and processed using Climate Engine and ArcGIS Pro software, to analyze seasonal variations and their implications. Key findings reveal distinct seasonal patterns: high soil moisture levels during wet periods (SMI Level 5) support optimal vegetation health (NDVI Level 3) and face challenges of waterlogging, contrasted with low soil moisture during dry seasons (SMI Level 1), requiring adaptive irrigation strategies to maintain vegetation health. Correlation analysis underscores the significant relationship between soil moisture, vegetation health, and climatic variables (temperature, precipitation), guiding the development of targeted management strategies. This research contributes novel insights into the integrated use of SMI and NDVI for agricultural sustainability, offering practical implications for adaptive management and resource optimization in oil palm plantations, such as developing irrigation schedules and drainage systems tailored to seasonal conditions.