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Studies pertaining to small scale structures producing scintillations using synthetic aperture radars (SARs) have predominantly been conducted at low-latitude regions. The high-latitude region (auroral belt and polar caps) is highly dynamic and varies in response to stimuli from solar winds and the magnetosphere in complex ways. In this paper, the authors have shown the capability of SAR for scintillation observation in the auroral region. An attempt has been made to fit an irregularity anisotropy model to SAR measurements for characterizing the ionospheric irregularities in the auroral regions. The dependency of anisotropy irregularity model on parameters, such as irregularity structure (axial ratio), their orientation with respect to magnetic field lines, and the ionospheric plasma drift, is closely studied using Advanced Land Observing Satellite (ALOS)-2 datasets acquired over Alaska. Geomagnetic indices and total electron content data are consistent with the occurrence of the scintillation event under study. Drift velocity measurements from high-frequency radars in the super dual array radar network (SuperDARN) showed that the anisotropy is independent of the magnitude and the azimuth angle of the plasma drift. The typical range of orientation angle suitable for the high latitude regions probed by ALOS-2 is demonstrated to be between 120°–135°. This paper explores the idea of inferring irregularity anisotropy by comparing the amplitude scintillation (S4) index measured in SAR data pairs using two well-established techniques. The image contrast technique heavily relies on the accurate modeling of anisotropy, whereas the radar cross-sectional enhancement method is independent of it. This feature has been exploited in the $S_{4}$ comparison to finally fit the choice of irregularity axial ratio and conclude that the sheet-like structures best describe the ionospheric irregularity structure in the region under observation. [ABSTRACT FROM AUTHOR]

Continuous, consistent, and long time-series from remote sensing are essential to monitoring changes on Earth’s surface. However, analyzing such data sets is often challenging due to missing values introduced by cloud cover, missing orbits, sensor geometry artifacts, and so on. We propose a new and accurate spatio-temporal prediction method to replace missing values in remote sensing data sets. The method exploits the spatial coherence and temporal seasonal regularity that are inherent in many data sets. The key parts of the method are: 1) the adaptively chosen spatio-temporal subsets around missing values; 2) the ranking of images within the subsets based on a scoring algorithm; 3) the estimation of empirical quantiles characterizing the missing values; and 4) the prediction of missing values through quantile regression. One advantage of quantile regression is the robustness to outliers, which enables more accurate parameter retrieval in the analysis of remote sensing data sets. In addition, we provide bootstrap-based quantification of prediction uncertainties. The proposed prediction method was applied to a Normalized Difference Vegetation Index data set from the Moderate Resolution Imaging Spectroradiometer and assessed with realistic test data sets featuring between 20% and 50% missing values. Validation against established methods showed that the proposed method has a good performance in terms of the root-mean-squared prediction error and significantly outperforms its competitors. This paper is accompanied by the open-source R package gapfill, which provides a flexible, fast, and ready-to-use implementation of the method. [ABSTRACT FROM AUTHOR]

The dielectric measurements were made for five organic soils taken from the tundra territories of Alaska, Yamal, and Taimyr, with the content of organic matter varying from 35% to 80%. The measurements were carried out in the temperature range from −30 °C to 25 °C, frequencies from 0.45 to 16 GHz and soil moisture from close to zero to the field moisture capacity. The refractive mixing model was applied to fit the measurements of the soil’s complex refractive index (CRI) as a function of soil moisture, with the values of temperature being fixed. As a result, a respective dielectric model was developed. The amounts of bound and transient water in the thawed and frozen soils were introduced as parameters of the developed model and derived as a function of temperature and content of soil organic matter. The other parameters which concern the CRIs of soil solids as well as bound, transient, and liquid soil water or ice components were derived as a function of temperature. The errors of the proposed model estimated in terms of the values of normalized root-mean-sqaure error for the real and imaginary parts of the soil complex relative permittivity appeared to be 6%–7% and 23%, respectively. The proposed dielectric model can be applied in active and passive remote sensing, in particular, for the SMOS, SMAP, and Aquarius missions after testing in ground-based experiments. [ABSTRACT FROM AUTHOR]