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Radio occultation (RO) provides a cost-effective component of the overall sensor mix required to characterise the ionosphere over wide areas and in areas where it is not possible to deploy ground sensors. The paper describes the RO constellation that has been developed and deployed by Spire Global. This constellation and its associated ground infrastructure are now producing data that can be used to characterise the bulk ionosphere, lower ionosphere perturbations, and ionospheric scintillation.

Glacier velocity is an important parameter to characterize glacier dynamics and to derive ice thickness and mass balance. The 2-pass/3-pass Differential Synthetic Aperture Radar Interferometry (DInSAR) techniques are advantageous in estimating glacier movements. However, the 2-pass DInSAR requires an external Digital Elevation Model (DEM), whereas the 3-pass DInSAR does not. The 3-pass DInSAR technique is adopted with Sentinel-1A/1B to map the Svalbard glacier flow velocities. Furthermore, the effect of glacier surface elevation change on the 2-pass DInSAR results were revealed by comparing glacier velocity with the two time period topographic information. The coherence indicates that Sentinel-1A/1B has a high potential to infer operational glacier velocity using the 3-pass DInSAR method with an average atmospheric uncertainty of 0.24cm/day over the studied region. The precision of the 3-pass DInSAR is analyzed by comparing 2-pass derived line-of-sight (LOS) velocity on the same dates. The 3- pass DInSAR achieves high-resolution and detailed information. [ABSTRACT FROM AUTHOR]

In this article, an algorithm for snow depth (SD) and snow water equivalent (SWE) retrieval is proposed based on a polarimetric synthetic aperture radar (SAR) decomposition model and field measured snow data. The field campaigns were conducted at the Dhundi observatory (in the Indian Himalaya) in January 2016 and 2018. The field-measured data are used here to build a linear regression between wetness (w) and the imaginary part of the snow permittivity (ε''), and the validation of retrieved SD and SWE. The snow density (ρs) and w are calculated with a generalized volume parameter derived using a theoretical model and SAR data (coherency matrix). These snow parameters and the field-based regression relating w and ε'' are eventually used for the SD and SWE retrieval. Three TerraSAR-X scenes of the quad-polarization X-band data acquired in January 2016 are used to study the effect of the snow conditions on the accuracy of the proposed algorithm. The mean absolute error (MAE), root-mean-square error (RMSE), and index of agreement (IOA) for SD are 4.84 cm, 5.12 cm, and 0.71, respectively. On the other hand, for SWE, it is 1.42 cm, 1.53 cm, and 0.71, respectively. [ABSTRACT FROM AUTHOR]

The highly dynamic nature of snow requires frequent observations to study its various properties. Keeping this in mind, the present investigation presents results from the analysis of fully polarimetric synthetic aperture radar (POLSAR) parameters for the development of a snow depth (SD) inversion model for SD retrieval. Snow depth retrieved using ground penetrating radar (GPR) at 500 MHz over Austre Grønfjordbreen in the Svalbard region was used to understand the behaviour of certain polarimetric parameters. A significant correlation was found between field-measured SD and POLSAR parameters, namely coherence and normalized volume scattering power (R2 = 0.84 and R2 = 0.73, respectively.) Using the POLSAR scattering powers obtained from the six-component model-based decomposition (6SD), the heterogeneity and anisotropic behaviour in the firn areas are also explained. Further, based on the analyses shown in this work, a polarimetric parameter-based SD inversion algorithm have been proposed and validated. The univariate model with co-polarization coherence has the highest correlation (R2 = 0.84, Root Mean Square Error (RMSE) = 0.18). We have even tested several multivariate models for the same, to conclude that a combination of coherence, normalized volume and double-bounce scattering have a high correlation with SD (R2 = 0.84, RMSE = 0.18). Additionally, temporal and spatial variability in SD was also observed from three polarimetric SAR images acquired between 4 April 2015 and 15 May 2015 over the Western Nordenskiöld Land region. Increase in snow depth corresponding to snow precipitation events were also detected using the POLSAR data. [ABSTRACT FROM AUTHOR]