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The greatest advantage of a satellite-borne microwave radiometer (MWR) is that it can obtain information on the Earth’s surface with less influence by rain and clouds compared to an infrared radiometer; however, it has the disadvantage that the observing area is large so the spatial resolution is low. One example is the Advanced Microwave Scanning Radiometer-2 (AMSR2) loaded on the GCOM-W satellite. Hardware technologies to overcome this disadvantage are in progress, but software processings to improve spatial resolution are also needed, especially to process previously obtained data. The author was inspired by the experience of developing the L1R product for the AMSR2 and has investigated optimizing the Backus–Gilbert (BG) method to enhance spatial resolution. First, the BG method was used to calculate weighting coefficients to synthesize the brightness temperature equivalently obtained in a small observation area from those obtained in multiple larger areas. Next, a viable algorithm was developed and implemented in order to solve the problem appearing in this simple application of the BG method. The algorithm was verified and shown to reduce the observation area by about 17%, enhancing the spatial resolution. [ABSTRACT FROM AUTHOR]

The Global Change Observation Mission 1st-Water (GCOM-W1) satellite, which was launched by the Japan Aerospace Exploration Agency in May 2012, contains an Advanced Microwave Scanning Radiometer-2 (AMSR2). The AMSR2 provides multifrequency measurements of microwave energy (brightness temperature) emitted by the Earth's surface and atmosphere. However, each frequency field of view (FOV) differs in size because of its hardware design. For the retrieval of more accurate geophysical parameters from multifrequency brightness temperatures, the brightness temperatures should be modified to be the same as measured in the same FOV. The Backus–Gilbert (BG) method is one of the antenna pattern matching techniques used for this modification. We applied the BG method to the AMSR2 data to define a new data set of modified brightness temperatures, a level 1R (L1R) product that is freely and widely available. We optimized the implementation of the BG method to obtain the L1R product, with smoothing factors dynamically determined for all modified brightness temperatures. This paper describes the implementation method, including the criterion used to determine the smoothing factors and presents a quality assessment of the L1R product. [ABSTRACT FROM PUBLISHER]