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RSS FeedsRemote Sensing, Vol. 11, Pages 682: Wide Field of View Microwave Interferometric Radiometer Imaging (Remote Sensing)


21 march 2019 17:01:13

Remote Sensing, Vol. 11, Pages 682: Wide Field of View Microwave Interferometric Radiometer Imaging (Remote Sensing)

In microwave interferometric radiometers with a large field of view, as for example the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) onboard the Soil Moisture and Ocean Salinity (SMOS) satellite, one of the major causes of reconstruction error is the contribution to the visibility of the brightness temperature outside the fundamental period, defined on the basis of reciprocal grids. A mitigation method consisting of estimating this contribution through the application of a brightness temperature model outside the fundamental period is proposed. The main advantage is that it does not require any a posteriori addition of artificial scenes to the reconstructed image. Additionally, a method to avoid the sophisticated matrix regularization and inversion techniques usually applied in microwave interferometry is presented. Image reconstruction algorithms are implemented on a minimum grid size in order to maximize their numerical efficiency. An improved method to apply an apodization window to the reconstructed image for reducing Gibbs oscillations is also proposed. All procedures are generally described considering the single polarization case and successively implemented applying the MIRAS layout in both its single polarization and full polarimetric modes. Results show similar performance of the proposed algorithm with respect to the nominal one applied by SMOS. All algorithms are implemented in the MIRAS Testing Software and have been successfully used for scientific studies by other teams. Digg Facebook Google StumbleUpon Twitter
23 viewsCategory: Geology, Physics
Remote Sensing, Vol. 11, Pages 683: Rebuilding a Microwave Soil Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness Temperature and SMAP over the Qinghai-Tibet Plateau, China (Remote Sensing)
Remote Sensing, Vol. 11, Pages 681: A Comparative Review of Manifold Learning Techniques for Hyperspectral and Polarimetric SAR Image Fusion (Remote Sensing)
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