Merging Satellite Measurements of Rainfall Using Multi-scale Imagery Technique

TL;DR

This paper introduces an iterative image fusion algorithm that combines satellite rainfall measurements to improve detection and intensity estimates, addressing data gaps and low revisit issues in satellite observations.

Contribution

The paper presents a novel multi-scale image fusion technique specifically designed for merging satellite rainfall data, enhancing accuracy over existing methods.

Findings

Significant improvement in rain detection accuracy.

Enhanced rain intensity estimation in merged data.

Effective filling of data gaps over multiple years.

Abstract

Several passive microwave satellites orbit the Earth and measure rainfall. These measurements have the advantage of almost full global coverage when compared to surface rain gauges. However, these satellites have low temporal revisit and missing data over some regions. Image fusion is a useful technique to fill in the gaps of one image (one satellite measurement) using another one. The proposed algorithm uses an iterative fusion scheme to integrate information from two satellite measurements. The algorithm is implemented on two datasets for 7 years of half-hourly data. The results show significant improvements in rain detection and rain intensity in the merged measurements.