Exploring Soil Moisture Index to Analyze Cavendish Banana (Musa Acuminata) Yield and Stress Pattern in Cabadbaran City, Agusan del Norte, Philippines

Abstract. Cavendish bananas are essential to the agricultural economy of Barangay Soriano, Cabadbaran City, but their growth and yield are highly sensitive to soil moisture conditions. This study explored the use of remote sensing and GIS technologies to assess soil moisture levels, stress patterns, and their effects on banana yield through the Soil Moisture Index (SMI). The main objective was to create SMI maps, analyze stress patterns, and determine the correlation between SMI and banana yield. This was achieved using satellite imagery from Landsat 8 OLI and TIRS, combined with field validation using gravimetric soil moisture measurements. The study provided a detailed understanding of moisture conditions across the plantation and their impact on crop performance. Results revealed that an SMI range of 0.46 to 0.82 or 46% to 82% supports optimal growth, while extreme dryness or wetness significantly reduces yield. Areas with balanced soil moisture showed the highest productivity, contributing to the overall health and sustainability of the plantation. Also, SMI and Cavendish banana yield has a significant positive correlation with an R² that is equal to 0.7976 or 79.76%. Furthermore, the study demonstrated that remote sensing and GIS are effective tools for monitoring soil moisture and managing Cavendish banana plantations. By applying these methods, farmers can optimize resource management, reduce stress-related crop losses, and enhance agricultural productivity.