Spatial Resolution Matching of Microwave Radiometer Measurements Using Iterative Deconvolution With Close Loop Priors (ICLP)

Passive multifrequency microwave sensors frequently struggle with difficulties of nonuniform spatial resolution among multiple channels. The raw measurements in the land–sea transition zone are seriously contaminated. Conventional analytical deconvolution techniques suffer from the tradeoff between spatial resolution enhancement and noise amplification, leading to low data integrity in the practical spatial resolution matching application. To provide multichannel microwave radiometer (MWR) data with matching levels of spatial resolution, a method based on iterative deconvolution with close loop priors (ICLP) is proposed. Specifically, a destriping module is first utilized as a preprocessing step to maintain high data integrity. Then, the close loop mechanism using sparse adaptive priors is proposed to balance the spatial resolution and data integrity enhancement. Also, progressively iterative deconvolution is introduced to realize controllable levels of spatial resolution enhancement (spatial resolution matching) for multichannel data to reach a consistent level. Experiments performed using both simulated and actual microwave radiation imager (MWRI) data demonstrate the validity and effectiveness of the method.

I. Introduction

Microwave radiometer (MWR) is designed to obtain radiometric measurements with a combination of frequency and polarization [1]. Due to the continuous, all-weather, all-day observing capabilities, the MWR has been extensively employed in worldwide weather parameters monitoring [2]. The retrieval of meteorological parameters, such as cloud liquid water, total precipitation water, sea wind speed, snow depth, and snow water equivalent, relies tremendously on the MWR instrument [3], [4], [5].