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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]

It was experimentally confirmed that microwave energy is emitted during the compression of rocks. Land-surface deformations are likely to be accompanied by rock failures. From the experimental results, we could conclude that the microwave signals generated by rock failures near the land surface are detectable by a satellite-borne radiometer. Therefore, we developed an algorithm to evaluate the microwave energy generated by rock failures on the land surface and applied the algorithm to the data of the Advanced Microwave Scanning Radiometer for the Earth Observing System for the earthquake that struck Al Hoceima, Morocco, on February 24, 2004. As a result, we detected definitive microwave signals around the epicenter two days before the earthquake only in the entire observation period of six years. This paper presents the details of the algorithm and the analysis results. [ABSTRACT FROM AUTHOR]

In order to extract a local and faint variation generated by a specific source from a physical value obtained by a remote-sensing satellite, we should focus on the difference between physical values at two points. We have developed a new data- restructuring algorithm based on this concept. This algorithm enables us to obtain a distribution of physical values at any interval and to calculate the difference between any two points regardless of the sensor-sampling interval. This paper presents this algorithm and how to implement it on a computer. Based on this data-restructuring algorithm, we have established a new data-processing method and analyzed the brightness-temperature data obtained by a microwave radiometer. As a result, we have successfully extracted the feature associated with a volcanic eruption. This feature cannot be extracted from brightness temperatures directly. This paper demonstrates this data-processing method by analyzing a volcanic eruption. [ABSTRACT FROM AUTHOR]