Special Sensor Microwave Imager Radiometer Research Articles

Two-Dimensional TSVD to Enhance the Spatial Resolution of Radiometer Data

A reconstruction technique, based on the 2-D truncated singular value decomposition, is first proposed to enhance the spatial resolution of radiometer earth observation measurements. The technique is very computer time effective when the kernel is a 2-D tensor product. The key issue regarding the selection of the truncation parameter is addressed by the statistically based generalized cross-validation approach. Experiments undertaken on a data set consisting of both simulated and actual 2-D special sensor microwave imager radiometer measurements show the robustness of the technique against the additive noise and its effectiveness in terms of processing time. A typical 2-D radiometer scene is processed in seconds by a standard PC processor.

On the Spatial Resolution Enhancement of Microwave Radiometer Data in Banach Spaces

A reconstruction technique, mathematically based on a generalization of the gradient method in Banach spaces, is first proposed to enhance the spatial resolution of radiometer earth observation measurements. This approach allows reducing the over-smoothing effects and the oscillations that are often present in standard Hilbert-spaces procedures without any drawback on the numerical complexity. Experiments undertaken on a data set consisting of both simulated and actual 2-D special sensor microwave imager radiometer measurements show the accuracy and the effectiveness of the proposed technique. A typical radiometer scene is processed in few minutes by a standard PC processor. Furthermore, since the proposed approach is iterative, the processing time increases slowly with the problem's size.

2006 Greenland Ice Sheet snowmelt from spaceborne microwave brightness temperatures

Brightness temperatures over the Greenland ice sheet, as measured by the Special Sensor Microwave Imager radiometer (SSM/I) aboard the U.S. Air Force Defense Meteorological Satellite (DMSP) Program's F13 satellite, have now been released (available at ftp://sidads.colorado.edu/pub/DATASETS/brightness‐temperatures/easegrid/ssmi/). These data support the quantification of the number of snow melting days in 2006 (Figure l a ) and the 2006 melting days anomaly (e.g., the difference between 2006 melting days and the average number of melting days between 1988 and 2005; Figure l b ). Updated results are fundamental to longterm trend analyses of snowmelt extent and duration.The 2006 melting index (e.g., the number of melting days × melt extent) follows the trend derived from the analysis of 1988–2005 data, and ranks seventh when using the 19.35‐gigahertz data (with 2005, 2002, 1998, 2004, 1999, and 2003 in this order having a greater melting index). The 2006 melting index ranks fifth when using the 37‐gigahertz data, which are more sensitive to surface processes (with 1998, 2002, 2005, and 2004 in this order having a greater melting index).

Northern Hemisphere Snow-Covered Area Mapping: Optical Versus Active and Passive Microwave Data

Spaceborne passive microwave data have been available for the past 27 years, and have supported the development of several algorithms for the retrieval of snow water equivalent and snow depth that, in turn, can be used for mapping snow-covered areas. In contrast, only recently has the application of spaceborne active microwave instruments been investigated for remote sensing of snow on a global scale. This raises the question of whether a technique combining active and passive microwave data can improve the mapping of snow parameters with respect to techniques based solely on passive data. In this letter, we report results concerning the mapping of snow-covered area (SCA) in the Northern Hemisphere between the years 2000 and 2004 derived from the combination of the brightness temperatures at 19.35 and 37 GHz measured by the Special Sensor Microwave Imager Radiometer with backscatter coefficients at 13.4 GHz measured by the NASA's QuickSCAT. SCA derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) is used as a reference to evaluate the performance of the microwave-based techniques and their combination. Results show that, generally, the technique using passive data provides better agreement with MODIS SCA than the technique using only scatterometer data. However, the results when both datasets are used show considerable improvement, demonstrating the potential benefits of a multisensor approach

Multifrequency polarimetric synthetic aperture radar observations of sea ice

The first known fully polarimetric airborne synthetic aperture radar (SAR) data set of sea ice is introduced. Images were acquired in the Beaufort, Bering and Chukchi seas in March 1988, during a campaign for validation of Defense Meteorological Satellite Program Special Sensor Microwave Imager radiometer ice products. Statistics of the magnitude, phase and polarization of complex backscattered signals recorded by the Jet Propulsion Laboratory three‐frequency SAR are examined in detail for various scenes with different ice characteristics. The full Stokes matrix information generated from C, L, and P band data characterize the scattering behavior of different ice types. Polarization ratios and phase differences between linear copolarized returns are used for discrimination between particular image features and mechanisms are suggested for the observed polarimetric characteristics. Results indicate that combinations of frequency and polarization enhance current capability to distinguish ice of different properties using single frequency, fixed polarization micro‐wave radar. A specific example is the polarimetric identification of new ice formation which may not be easily distinguished in ERS‐1 5.3GHz, VV polarization SAR data. Such findings are consistent with theoretical model simulations of scattering characteristics of sea ice. Overall, these preliminary results demonstrate that radar polarimetry will likely add a new dimension to our current capability for extracting geophysically important ice variables using radar remote sensing methods.