Differential semblance optimisation based on the adaptive quadratic Wasserstein distance

As the robustness for the wave equation-based inversion methods, wave equation migration velocity analysis (WEMVA) is stable for overcoming the multipathing problem and has become popular in recent years. As a rapidly developed method, differential semblance optimisation (DSO) is convenient to implement and can automatically detect the moveout existing in common image gathers (CIGs). However, by implementing in the image domain with the target of minimising moveouts and improving coherence of the CIGs, the DSO method often suffers from imaging artefacts caused by uneven illumination and irregular observation geometry, which may produce poor velocity updates with artefact contamination. To deal with this issue, in this paper, by introducing Wiener-like filters, we modify the conventional image matching-based objective function to a new one by introducing the quadratic Wasserstein metric technique. The new misfit function measures the distance of two distributions obtained by the convolutional filters and target functions. With the new misfit function, the adjoint sources and the corresponding gradients are improved. We apply the new method to two numerical examples and one field dataset. The corresponding results indicate that the new method is robust to compensate low frequency components of velocity models.

Influence of Indirect Solar Irradiance on Satellite Observation

With the development of Space Domain Awareness(SDA), satellites’ optical characteristics are becoming attention-grabbing. Sunlight was usually considered the only light source for the satellites. However, in the actual observation, researchers have found that earthshine and moonlight would increase errors of the observation results, which have greatly influence the estimation of the satellite’s state. In order to avoid this influence, we propose an observation strategy. Firstly, we propose an accurate earthshine model, which considers the earth’s volume and favors long-time continuous satellite observation. Then, we explore the earthshine and moonlight’s impact on satellite observation results and find that this impact varies with the satellite attributes. Furthermore, we Figure out the law of this impact and establish a connection between this law and observation geometry. Finally, a Period Contribution model is proposed to provide a corresponding observation strategy to avoid the influence of earthshine and moonlight.

ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE

This paper examines algorithms for estimating terrestrial albedo from the products of the Global Change Observation Mission – Climate (GCOM-C) / Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency. We selected two algorithms: one based on a bidirectional reflectance distribution function (BRDF) model and one based on multi-regression models. The former determines kernel-driven BRDF model parameters from multiple sets of reflectance and estimates the land surface albedo from those parameters. The latter estimates the land surface albedo from a single set of reflectance with multi-regression models. The multi-regression models are derived for an arbitrary geometry from datasets of simulated albedo and multi-angular reflectance. In experiments using in situ multi-temporal data for barren land, deciduous broadleaf forests, and paddy fields, the albedos estimated by the BRDF-based and multi-regression-based algorithms achieve reasonable root-mean-square errors. However, the latter algorithm requires information about the land cover of the pixel of interest, and the variance of its estimated albedo is sensitive to the observation geometry. We therefore conclude that the BRDF-based algorithm is more robust and can be applied to SGLI operational albedo products for various applications, including climate-change research.

Estimating the Earth’s Outgoing Longwave Radiation Measured from a Moon-Based Platform

Moon-based Earth observations have attracted significant attention across many large-scale phenomena. As the only natural satellite of the Earth, and having a stable lunar surface as well as a particular orbit, Moon-based Earth observations allow the Earth to be viewed as a single point. Furthermore, in contrast with artificial satellites, the varied inclination of Moon-based observations can improve angular samplings of specific locations on Earth. However, the potential for estimating the global outgoing longwave radiation (OLR) from the Earth with such a platform has not yet been fully explored. To evaluate the possibility of calculating OLR using specific Earth observation geometry, we constructed a model to estimate Moon-based OLR measurements and investigated the potential of a Moon-based platform to acquire the necessary data to estimate global mean OLR. The primary method of our study is the discretization of the observational scope into various elements and the consequent integration of the OLR of all elements. Our results indicate that a Moon-based platform is suitable for global sampling related to the calculation of global mean OLR. By separating the geometric and anisotropic factors from the measurement calculations, we ensured that measured values include the effects of the Moon-based Earth observation geometry and the anisotropy of the scenes in the observational scope. Although our results indicate that higher measured values can be achieved if the platform is located near the center of the lunar disk, a maximum difference between locations of approximately 9 × 10−4 W m−2 indicates that the effect of location is too small to remarkably improve observation performance of the platform. In conclusion, our analysis demonstrates that a Moon-based platform has the potential to provide continuous, adequate, and long-term data for estimating global mean OLR.

Visibility study of Galileo satellites from a VLBI network

<p>Over the last years, ideas have been proposed to install a Very Long Baseline Interferometry (VLBI) transmitter on one or more satellites of the Galileo constellation. Satellites transmitting signals that can be observed by VLBI telescopes provide the opportunity of extending the current VLBI research with observations to geodetic satellites. These observations offer a variety of new possibilities such as high precision tying of space geodetic techniques but also the direct determination of the absolute orientation of the satellite constellation with respect to the International Celestial Reference Frame (ICRF) and have implications on the determination of long-term reference frames. </p><p>This contribution provides a visibility study of the Galileo satellites from a VLBI network. The newly developed satellite scheduling module in VieSched++ is used to determine the time periods during which a satellite is observable from a VLBI network. The possible satellite observations are evaluated through the number of stations from which a satellite is observable. Moreover, the impact on determining the orientation of the satellite constellation, caused by the observation geometry, is investigated with using the UT1-UTC Dilution of Precision (UDOP) factor.</p>

Comparison of polar stratospheric cloud detection and composition as observed by ground-based lidar and CALIOP at Dome C.

<p>Polar stratospheric clouds have been observed at Dome C by a ground-based lidar from 2014 up to the present, possibly in coincidence with nearby overpasses of the CALIPSO satellite, with the CALIOP lidar on board.</p><p>A thorough study has been made in terms of detection efficiency and composition classification of near coincident lidar observations, with the goal to identify the main biases between the two lidars.</p><p>When comparing ground-based lidar observations with nearby CALIOP overpasses, several biases might occur, due to the distance between ground-based lidar and nearest overpass, observation geometry and integration times and different algorithms used for data analysis.</p><p>The bias resulting from different data analysis has been reduced by applying an algorithm for PSC detection and composition classification to the ground-based data which is very similar to the V2 algorithm used for CALIOP.</p><p>By comparing 5 years of PSC observations at Dome C, considering both detection efficiency and composition of the observed PSCs, the impact of all biases will be discussed and possibly quantified. </p>

Impact of the Galileo constellation on GNSS Tropospheric Tomography

<p>GNSS Tomography is a promising tool to reconstruct the wet refractivity field (Nw) related to water vapor due to the continuous pass of GNSS rays through the atmosphere. To improve observation geometry compared to a sole GPS/ Glonass system scenario, applying further multi-GNSS observations in GNSS Tomography has become an essential research point in the recent decade. Therefore, the aim of this presentation is to investigate the impact of different constellations to solve the ill-posed inverse problem to retrieve a wet refractivity field by focusing on Galileo's effect on the accuracy of the estimated refractivity. Regarding this, the designed models loosely constrained due to provide an optimum situation for assessing the influence of Galileo constellation in the tomography solution. Test computations are based on data from a regional RTK-GNSS network close to Vienna operated by the Austrian power-supply company EVN (EnergieVersorgung Niederösterreich) and mostly located in the west part of Austria. The span DoYs 233-246 in August 2019 was chosen as a period of interest due to the high precipitation during that time. Consequently, we have considered the following processing schemes: 1- GPS+ Glonass (GR), 2- GPS+ Galileo (GE), and 3- GPS + Glonass + Galileo (GRE) to generate the reconstructed Nw field by means of the in-house Tomography software TOMTRP. Furthermore, as the Slant Tropospheric Delays (SWDs) and corresponding residuals are used as input data for GNSS tomography, so the impact of the mentioned schemes to estimate SWDs has been investigated here. Finally, in order to analyze the efficiency of the three schemes, the reconstructed refractivity profiles are compared to radiosonde profiles available in that area.</p>

Tomographic Performance of Multi-Static Radar Formations: Theory and Simulations

3D imaging of Earth’s surface layers (such as canopy, sub-surface, or ice) requires not just the penetration of radar signal into the medium, but also the ability to discriminate multiple scatterers within a slant-range and azimuth resolution cell. The latter requires having multiple radar channels distributed in across-track direction. Here, we describe the theory of multi-static radar tomography with emphasis on resolution, SNR, sidelobes, and nearest ambiguity location vs. platform distribution, observation geometry, and different multi-static modes. Signal-based 1D and 2D simulations are developed and results for various observation geometries, target distributions, acquisition modes, and radar parameters are shown and compared with the theory. Pros and cons of multi-static modes are compared and discussed. Results for various platform formations are shown, revealing that unequal spacing is useful to suppress ambiguities at the cost of increased multiplicative noise. In particular, we demonstrate that the multiple-input multiple-output (MIMO) mode, in combination with nonlinear spacing, outperforms the other modes in terms of ambiguity, sidelobe levels, and noise suppression. These findings are key to guiding the design of tomographic SAR formations for accurate surface topography and vegetation mapping.