Merging Infrared and Microwave SST Data at South China Sea

Abstract

The sea surface temperature (SST) data availability by infrared measurement was low compared to microwave measurement. The infrared radiation cannot penetrate the cloud, so their availabilities are severely limited by clouds. The microwave can penetrate the clouds and give accurate SST measurements under clouds. The TRMM Microwave Imager (TMI) derived SST data has an RMS error of 0.6-0.7 K. TMI is a microwave radiometer onboard the Tropical Rainfall Measurement Mission (TRMM) Satellite. The infrared derived SST data has low data availability but has higher spatial resolution compared to microwave derived data. The spatial resolution of infrared radiometer, Moderate-resolution Imaging Spectroradiometer (MODIS) is about 1km, but the spatial resolution of TMI is 25km. The objective of our study is to increase the spatial resolution of microwave derived SST data and increase the availability of infrared derived SST data. In this study, we used MODIS SST data with grid size of about 0.01? and TMI SST data with grid size of 0.25?. We re-sampled these data into the new map with grid size of 0.1?. We find the new merged SST data from the MODIS SST and TMI SST. If any of the MODIS or TMI SST data is available, then the merged SST was assigned the value of the existing data. If both of the SST data are available, then the new merged SST was given the value of the average of these data. Otherwise, the new SST data was determined by interpolation method. We used the neighbourhood pixels for interpolation. All of the processing steps were programmed in MATLAB code. . However we checked our results by comparing the SST data availability of TMI and MODIS images before processing and data availability of new image after processing.