Satellite-Based Rainfall Datasets: A Global Systematic Review of Applications, Accuracy, and Research Gaps

Abstract

In the context of increasing climate variability and the gradual decline of ground-based observation networks, satellite-based rainfall estimates (SREs) have become indispensable tools for hydrological monitoring disaster preparedness, and climate modeling. Satellite technology has evolved rapidly in recent years, with new missions, sensors, and techniques implemented by agencies and researchers to improve SRE products. This study presents a global systematic review explicitly applying the PRISMA methodology, offering a structured and reproducible framework for evidence synthesis. It evaluates the performance of the most widely used SREs across all continents from January 2018 to November 2025, with particular emphasis on their application in data-scarce and hydrologically complex regions. Drawing from 636 peer-reviewed studies, the review identifies key factors affecting the accuracy of SREs, including topography, rainfall type, and seasonality. Notably, products that integrate satellite data with ground-based observations consistently demonstrate superior performance compared to satellite-only estimates. Among them, IMERG-Final and CHIRPS stand out as the most widely used datasets worldwide, with IMERG-Final showing particularly promising performance across most continents. The findings highlight the need for future research to prioritize the development of advanced bias correction algorithms, region-specific calibration methods, and hybrid models that incorporate additional meteorological variables. Although previous reviews have addressed this approach, the present synthesis offers an updated and concise reference for selecting suitable SREs across diverse environmental and operational contexts.