The Utilization of Remote Sensing and GIS Integration for Rainfall Distribution Modeling and Drought Prediction in Agricultural Regions

Abstract

Rainfall variability and drought events pose significant challenges to agricultural sustainability and food security in many regions. Accurate monitoring and prediction of rainfall distribution are therefore essential for effective agricultural planning and water resource management. This study aims to examine the utilization of remote sensing and Geographic Information Systems (GIS) integration for rainfall distribution modeling and drought prediction in agricultural regions. The research employs a qualitative literature review approach to analyze and synthesize findings from previous studies related to the application of geospatial technologies in climate monitoring and drought assessment. The analysis focuses on the role of satellite-based precipitation data, GIS spatial modeling techniques, and environmental indicators used in drought prediction frameworks. The results indicate that remote sensing provides extensive spatial coverage and continuous observation capabilities that enable the monitoring of rainfall variability across large geographic areas. When integrated with GIS-based spatial analysis, these datasets can be transformed into detailed rainfall distribution models that support the identification of drought-prone agricultural zones. The study also finds that effective drought prediction requires the integration of multiple environmental variables, including soil moisture, vegetation conditions, and land surface temperature. Overall, the integration of remote sensing and GIS technologies offers a comprehensive geospatial framework that improves rainfall analysis, supports drought early warning systems, and enhances decision-making for sustainable agricultural management.