Data Fusion Between Microwave and Thermal Infrared Radiometer Data and Its Application to Skin Sea Surface Temperature, Wind Speed and Salinity Retrievals

Kohei Arai

doi:10.14569/ijacsa.2013.040236

Abstract

Method for data fusion between Microwave Scanning Radiometer: MSR and Thermal Infrared Radiometer: TIR derived skin sea surface temperature: SSST, wind speed: WS and salinity is proposed. SSST can be estimated with MSR and TIR radiometer data. Although the contribution ocean depth to MSR and TIR radiometer data are different each other, SSST estimation can be refined through comparisons between MSR and TIR derived SSST. Also WS and salinity can be estimated with MSR data under the condition of the refined SSST. Simulation study results support the idea of the proposed data fusion method.

Highlights

Microwave Scanning Radiometer: MSR onboard remote sensing satellites allow estimations of salinity, soil moisture, ocean wind speed, precipitable water, rainfall rate, air temperature, atmospheric pressure, and Skin Sea Surface Temperature: SSST
SSST can be estimated with MSR and TIR radiometer data
The contribution ocean depth to MSR and TIR radiometer data are different each other, SSST estimation can be refined through comparisons between MSR and TIR derived SSST

Summary

INTRODUCTION

Microwave Scanning Radiometer: MSR onboard remote sensing satellites allow estimations of salinity, soil moisture, ocean wind speed, precipitable water, rainfall rate, air temperature (profile), atmospheric pressure, and Skin Sea Surface Temperature: SSST. The conventional geophysical parameter estimation method is based on regressive analysis with a plenty of truth data and the corresponding microwave radiometer data [11]. Both radiometers observe same sea surface through same atmosphere. Atmospheric model and sea surface model for both thermal infrared wavelength region and microwave wavelength region are known Both radiometer data can be used for improvement of the estimation accuracy. The proposed method of data fusion between thermal infrared and microwave radiometers is eliminate influences due to emissivity changes by ocean winds based on the aforementioned models.

Process Flow of the Proposed Data Fusion Method

CONCLUSION