Monitoring Reservoir Surface Area Using Google Earth Engine (GEE): A Case Study in Southwest Rote District, NTT

Abstract

This study aims to monitor the surface area of 13 small reservoirs in the Southwest Rote District, East Nusa Tenggara (NTT), using the Google Earth Engine (GEE) platform over a five-year period (2019–2024). The methodology involved the collection and processing of landsat satellite imagery, the integration of rainfall and rainy-day data from CHIRPS, and an analysis of changes in reservoir surface area over time. The study leveraged GEE’s cloud computing capabilities to efficiently process large-scale data. The results demonstrate that GEE is effective for mapping and monitoring changes in reservoir surface area, evidenced by a model accuracy (R²) of 0.8178 and a positive correlation (r) of 0.668 when compared against manual delineation measurements. Changes in reservoir surface area were analyzed to identify trends and patterns associated with factors such as rainfall, irrigation demands, and human activity. These findings provide crucial information for sustainable water resource management, particularly in the context of arid regions like NTT. The analysis also contributes to the understanding of hydrological dynamics in the area and establishes a foundation for improved decision-making regarding reservoir management. This study highlights the potential of GEE as a powerful tool for monitoring water resources in hard-to-reach areas and contributes to the development of remote sensing-based monitoring methodologies.