Intersensor Calibration of Microwave Brightness Temperature between AMSR-E and AMSR2

Brightness temperature (TB) observations from Advanced Microwave Scanning Radiometer (AMSR-E) onboard NASA’s Aqua satellite and its successor, Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite, have been playing important roles in advancing our understanding of Earth's climate system through their contributions in generating satellite products related to hydrological cycle, sea ice dynamics, and land surface processes. However, satellite products are usually based on one of these two sensors, or with the assumptions that there is no significant difference between these two sensors. Given the fact that AMSR-E and AMSR2 are different instruments with variations in their design, technology, and measurement characteristics, brightness temperature observations from these two may have systematic bias, which would lead to an inconsistency in products derived from them. To ensure the continuity and reliability of satellite products retrieved from AMSR-E and AMSR2 for climate research, a rigorous inter-sensor brightness temperature calibration process has to be performed.

In this study, TB from AMSR-E are calibrated to TB from AMSR2 for all AMSR-E channels using an approach similar to the Simultaneous Conical Overpass (SCO). Overlapping data from 2012 to 2015 from AMSR-E slow rotation mode and AMSR2 are used to generate the regression relationship for the calibration at pixel level with spatial resolution of 0.25-degree. Orbital data are first gridded to 0.25-degree, and footprint pairs within each grid that has minimal observation time difference are identified and used to build a linear regression relationship for calibrations. Results show that, at least 50 paired TB observations are identified for each grid to perform the regression relationship. Over 95 percent of pixels have regression correlation coefficient value of above 0.95. Validation of the calibrated AMSR-E TB product are performed in different land cover type regions using the mean TB time series, and the results show significant improvements in TB consistency between AMSR-E and AMSR2. At global scale, the mean TB difference between AMSR-E and AMSR2 are reduced from over 3K to almost zero before and after calibration, depending on the channels and spatial locations. Figure 1, for example, shows that the mean TB difference for channel 6.9GHz (V-pol) are all improved to very close too zero.

A complete calibrated AMSR-E TB product is generated from 2002 to 2015. This calibrated AMSR-E brightness temperature product, when integrated with AMSR2 data, offer a wealth of applications across climate science, environmental monitoring, and numerous other scientific fields. The long-term, consistent, and accurate dataset they provide is a valuable resource for understanding and addressing complex environmental and climate-related challenges.