Merging Satellite Measurements of Rainfall Using Multi-scale Imagery Technique
This work addresses data gaps in satellite rainfall measurements for meteorology and climate research, but it is incremental as it builds on existing image fusion techniques.
The paper tackles the problem of incomplete rainfall data from passive microwave satellites by proposing an iterative fusion algorithm to merge measurements from two satellites, resulting in significant improvements in rain detection and intensity over 7 years of half-hourly data.
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.