A brief survey on the availability of satellite air pollution data

Satellite air pollution data sources are presented in this article. Satellite missions are listed and described, and corresponding instruments measuring the concentration of greenhouse gases or other parameters that affect air pollution. Main data hubs are enumerated, and other platforms providing access to the data in different formats. Some platforms contain tools and services that have the capabilities to process and store the satellite data further. Most of them also provide the visualisation of the data, and other simplifications for data selection.

Low-orbit satellite grouping of the system for monitoring geodesic parameters of the Earth and the direction of its development

Спутниковые миссии наряду с наземными геодезическими сетями различных космических технологий являются второй важной составляющей российского сегмента системы контроля геодезических параметров Земли (ГПЗ). Спутники имеют неоспоримое преимущество в том, что они непрерывно собирают однородные данные над большими частями земной поверхности. Они позволяют сбор данных, которые не могут быть зарегистрированы на поверхности Земли. Такие спутники в наше время оборудованы множеством датчиков, контролирующих поверхности суши, океана и льда, а также гравитационное поле Земли и его временные изменения. Потенциал и влияние спутниковых миссий для наблюдений Земли значительно возрастут в связи с тем, что: (1) будут запускаться все больше и больше спутниковых созвездий, вместо отдельных спутников, увеличивая тем самым временное и пространственное разрешение получаемых данных; (2) спутники будут летать в «формированиях», образуя большие наблюдательные приборы, состоящие из датчиков, расположенных на нескольких спутниках. В работе автор, с учетом рекомендаций международного геодезического сообщества, предлагает набор низкоорбитальных спутниковых миссий различных направлений, необходимых для полноценного функционирования системы контроля ГПЗ, и рассматривает направления развития данных миссий. Satellite missions, along with ground-based geodesic networks of various space technologies, will be the second important component of the Russian segment of the system for monitoring geodesic parameters of the Earth (GPE). Satellites have the undeniable advantage of continuously collecting uniform data over large parts of the Earth’s surface. They allow the collection of data that cannot be registered on the Earth’s surface. Such satellites are now equipped with a variety of sensors that monitor the surface of land, ocean and ice, as well as the Earth’s gravitational field and its temporal changes. The potential and impact of satellite missions for Earth observations will increase significantly as: (1) more and more satellite constellations will be launched, instead of individual satellites, thereby increasing the temporal and spatial resolution of the data received; (2) satellites will fly in “formations”, forming large observation instruments consisting of sensors located on several satellites. In this article the author taking into account the international geodetic community recommendations proposes a set of low-orbit satellite missions to various areas, necessary for proper operation of the system for monitoring GPE, and the directions of development of these missions are considered.

Geohazards Monitoring and Assessment Using Multi-Source Earth Observation Techniques

Geological disasters are responsible for the loss of human lives and for significant economic and financial damage every year. Considering that these disasters may occur anywhere—both in remote and/or in highly populated areas—and anytime, continuously monitoring areas known to be more prone to geohazards can help to determine preventive or alert actions to safeguard human life, property and businesses. Remote sensing technology—especially satellite-based—can be of help due to its high spatial and temporal coverage. Indeed, data acquired from the most recent satellite missions is considered suitable for a detailed reconstruction of past events but also to continuously monitor sensitive areas on the lookout for potential geohazards. This work aims to apply different techniques and methods for extensive exploitation and analysis of remote sensing data, with special emphasis given to landslide hazard, risk management and disaster prevention. Multi-temporal SAR (Synthetic Aperture Radar) interferometry, SAR tomography, high-resolution image matching and data modelling are used to map out landslides and other geohazards and to also monitor possible hazardous geological activity, addressing different study areas: (i) surface deformation of mountain slopes and glaciers; (ii) land surface displacement; and (iii) subsidence, landslides and ground fissure. Results from both the processing and analysis of a dataset of earth observation (EO) multi-source data support the conclusion that geohazards can be identified, studied and monitored in an effective way using new techniques applied to multi-source EO data. As future work, the aim is threefold: extend this study to sensitive areas located in different countries; monitor structures that have strategic, cultural and/or economical relevance; and resort to artificial intelligence (AI) techniques to be able to analyse the huge amount of data generated by satellite missions and extract useful information in due course.

Analysis of Methods for CubeSat Mission Design Based on in-orbit Results of KRAKsat Mission

The success rate of currently performed CubeSat missions shows that despite their popularity, small satellites are still not as reliable as larger platforms. This research was conducted to analyse in-orbit experience from the KRAKsat mission and discuss methods for mission design and engineering that can increase CubeSats reliability and prevent their failures. The main purpose was to define best practices and rules that should be followed during mission development and operations to ensure its success based on the overview of the lessons learned from KRAKsat and problems encountered during its mission. This paper summarizes the experiences obtained and provides methods that can be used while carrying out future robust CubeSat projects. It was written to prove that there are some parts of the small satellite missions that are often neglected in the university-led projects and, by ensuring proper testing and planning before the actual mission, its reliability can increase. The following analysis could be used as a guide during the development of the next CubeSat projects.

The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings

The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed using data acquired by different satellite missions: Montescaglioso landslide (2013, Italy), Scillato landslide (2015, Italy), Bingham Canyon Mine landslide (2013, UT, USA), Big Sur landslide (2017, CA, USA) and Xinmo landslide (2017, China). This paper aimed at providing a contribution to improve the knowledge within the subject area of landslide forecasting using monitoring data, in particular exploring the suitability of satellite InSAR for spatial and temporal prediction of large landslides. The study confirmed that satellite InSAR can be successful in the early detection of slopes prone to collapse; its limitations due to phase aliasing and low sampling frequency are also underlined. According to the results, we propose a novel landslide predictability classification discerning five different levels of predictability by satellite InSAR. Finally, the big step forward made for landslide forecasting applications since the beginning of the first SAR systems (ERS and Envisat) is shown, highlighting that future perspectives are encouraging thanks to the expected improvement of upcoming satellite missions that could highly increase the capability to monitor landslides’ pre-failure behaviour.