Passive Satellite Research Articles

Drought Monitoring of Winter Wheat in Henan Province, China Based on Multi-Source Remote Sensing Data

Characterized by soil moisture content and plant growth, agricultural drought occurs when the soil moisture content is lower than the water requirement of plants. Microwave remote sensing observation has the advantages of all-weather application and sensitivity to soil moisture change. However, microwave remote sensing can only invert 0~5 cm of soil surface moisture, so it cannot effectively reflect the drought situation of farmland. Therefore, this study took Henan Province as the study area, used soil moisture active and passive (SMAP) satellite soil moisture data, employed NDVI, LST, and ET as the independent variables, and took the drought grade on the sample as the dependent variable. Using the 2017–2019 data as the training set and the 2020 data as the testing set, a random forest drought monitoring model with comprehensive influence of multiple factors was constructed based on the training set data. In the process of model training, the cross-validation method was employed to establish and verify the model. This involved allocating 80% of the sample data for model construction and reserving 20% for model verification. The results demonstrated an 85% accuracy on the training set and an 87% accuracy on the testing set. Additionally, two drought events occurring during the winter wheat growing period in Henan Province were monitored, and the validity of these droughts was confirmed using on-site soil moisture and the vegetation supply water index (VSWI). The findings indicated a high incidence of agricultural drought in the southwestern part of Henan Province, while the central and northern regions experienced a lower incidence during the jointing to heading and filling stages. Subsequently, leveraging the results from the random forest drought monitoring, this study conducted a time series analysis using the Mann–Kendall test and a spatial analysis employing Moran’s I index to examine the temporal and spatial distribution of agricultural drought in Henan Province. This analysis aimed to unveil trends in soil moisture changes affecting agricultural drought, as observed via the SMAP satellite (NASA). The results suggested a possible significant spatial auto-correlation in the occurrence of agricultural drought.

Modulation of Annual Rossby Waves on the Formation of Basin‐Wide Salinity Fronts

AbstractThis study reveals the formation mechanisms of a group of salinity fronts across the Arabian Sea (AS) and explores their associations with annual Rossby waves, utilizing Soil Moisture Active Passive (SMAP) satellite data and other relevant data sets. As low‐salinity water is transported westward and northward at varying rates between 6°N ∼ 20°N, substantial basin‐wide northeast‐southwest oriented salinity fronts emerge at its leading edge from March to May. Further analysis indicates a strong correlation between the transport of low‐salinity water and the westward‐propagating Rossby waves. The salinity fronts are primarily influenced by the geostrophic circulation, which significantly contributes to the transport of low‐salinity water. As a result, the low‐salinity water closely aligns with positive sea level anomaly (SLA), representing westward propagating downwelling Rossby waves. A continuously stratified linear ocean model (LOM) analysis suggests second mode baroclinic Rossby waves dominate seasonal sea level changes, surpassing the influence of the first mode. The spatial mean standard deviation of the first mode within the region of 56 ∼ 75°E, 9 ∼ 11°N is approximately 60% that of the second mode. Positive SLA is mainly caused by the annual downwelling Rossby waves radiating from the eastern boundary. Due to the latitudinal decrease in the phase speed of the annual Rossby waves, the sea level fronts manifest the northeast‐southwest direction, resembling the location of the sea surface salinity fronts. The salinity fronts highlight the crucial role of Rossby waves in shaping upper ocean water mass distribution in the AS, potentially impacting the broader Indian Ocean climate.

Preferential Hydrologic States and Tipping Characteristics of Global Surface Soil Moisture

AbstractA dynamic transition in soil hydrologic states through meteorological variability and terrestrial feedback governs soil‐vegetation‐climate (SVC) interactions, constrained by critical soil moisture (SM) thresholds. However, observational and scaling constraints limit critical SM threshold estimation at the remote‐sensing (RS) footprint scale. Using global surface SM (θRS) from NASA’s Soil Moisture Active Passive (SMAP) satellite, we characterize the seasonal preferential hydrologic states of θRS and derive three tipping characteristics to estimate the intensity (Mean Tipping Depth, ), frequency (Tipping Count, η), and duration (Mean Tipped Time, ) of the excursion of θRS from wet‐ to dry‐average conditions. The preferential state provides the seasonally dominant hydrological states of θRS, while tipping characteristics capture the ecosystem linkages of the dynamic transition in θRS hydrologic states. Globally, θRS predominantly exhibits a (unimodal) dry‐preferential state, especially over arid/semi‐arid drylands and a unimodal wet‐preferential θRS state in high‐latitude boreal forests and tundra biomes. Prevalence of (bimodal) bistable θRS state overlaps with regions of strong positive SM‐precipitation coupling and monsoonal climate in semi‐arid/subhumid climates. Seasonal preferential hydrologic states co‐vary with the regional variability in plant water stress threshold and land‐atmospheric coupling strength. Tipping characteristics of θRS show sensitivity to intra‐biome variability in SVC coexistence patterns and display high skill in partitioning global ecoregions. While and η are climate‐controlled, is moderated by soil and vegetation through their influence over θRS drydown during water‐limited evapotranspiration. Preferential states and tipping characteristics find applications in quantifying SVC coexistence patterns, climate model diagnosis, and assessing ecosystem sensitivity to climate change.

Assessment of smoke plume height products derived from multisource satellite observations using lidar-derived height metrics for wildfires in the western US

Abstract. As wildfires intensify and fire seasons lengthen across the western US, the development of models that can predict smoke plume concentrations and track wildfire-induced air pollution exposures has become critical. Wildfire smoke plume height is a key indicator of the vertical placement of plume mass emitted from wildfire-related aerosol sources in climate and air quality models. With advancements in Earth observation (EO) satellites, spaceborne products for aerosol layer height or plume injection height have recently emerged with increased global-scale spatiotemporal resolution. However, to evaluate column radiative effects and refine satellite algorithms, vertical profiles of regionally representative aerosol properties from wildfires need to be measured directly. In this study, we conducted the first comprehensive evaluation of four passive satellite remote-sensing techniques specifically designed for retrieving plume height. We compared these satellite products with the airborne Wyoming Cloud Lidar (WCL) measurements during the 2018 Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign in the western US. Two definitions, namely, “plume top” and “extinction-weighted mean plume height”, were used to derive the representative heights of wildfire smoke plumes, based on the WCL-derived vertical aerosol extinction coefficient profiles. Using these two definitions, we performed a comparative analysis of multisource satellite-derived plume height products for wildfire smoke. We provide a discussion related to which satellite product is most appropriate for determining plume height characteristics near a fire event or estimating downwind plume rise equivalent height, under multiple aerosol loadings. Our findings highlight the importance of understanding the sensitivity of different passive remote-sensing techniques on space-based wildfire smoke plume height observations, in order to resolve ambiguity surrounding the concept of “effective smoke plume height”. As additional aerosol-observing satellites are planned in the coming years, our results will inform future remote-sensing missions and EO satellite algorithm development. This bridges the gap between satellite observations and plume rise modeling to further investigate the vertical distribution of wildfire smoke aerosols.

Eurasian-scale experimental satellite-based quantum key distribution with detector efficiency mismatch analysis.

The Micius satellite is the pioneering initiative to demonstrate quantum teleportation, entanglement distribution, quantum key distribution (QKD), and quantum-secured communications experiments at the global scale. In this work, we report on the results of the 600-mm-aperture ground station design which has enabled the establishment of a quantum-secured link between the Zvenigorod and Nanshan ground stations using the Micius satellite. As a result of a quantum communications session, an overall sifted key of 2.5 Mbits and a total final key length of 310 kbits have been obtained. We present an extension of the security analysis of the realization of satellite-based QKD decoy-state protocol by taking into account the effect of the detection-efficiency mismatch for four detectors. We also simulate the QKD protocol for the satellite passage and by that validate our semi-empirical model for a realistic receiver, which is in good agreement with the experimental data. Our results pave the way to the considerations of realistic imperfection of the QKD systems, which are important in the context of their practical security.

Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols

Abstract. From a passive satellite remote sensing point of view, the richest set of information on aerosol properties can be obtained from instruments that measure both intensity and polarization of backscattered sunlight at multiple wavelengths and multiple viewing angles for one ground pixel. However, it is challenging to exploit this information at a global scale because complex algorithms are needed with many fit parameters (aerosol and land/ocean reflection), based on online radiative transfer models. So far, two such algorithms have demonstrated this capability at a global scale: the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm and the Remote sensing of Trace gas and Aerosol Products (RemoTAP) algorithm. In this paper, we present a detailed comparison of the most recent versions of RemoTAP and GRASP. We evaluate both algorithms for synthetic observations, for real PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) observations against AERONET (Aerosol Robotic Network) for common pixels, and for global PARASOL retrievals for the year 2008. For the aerosol optical depth (AOD) over land, both algorithms show a root mean square error (RMSE) of 0.10 (at 550 nm). For single scattering albedo (SSA), both algorithms show a good performance in terms of RMSE (0.04), but RemoTAP has a smaller bias (0.002) compared to GRASP (0.021). For the Ångström exponent (AE), GRASP has a smaller RMSE (0.367) than RemoTAP (0.387), mainly caused by a small overestimate of AE at low values (large particles). Over ocean both algorithms perform very well. For AOD, RemoTAP has an RMSE of 0.057 and GRASP an even smaller RMSE of 0.047. For AE, the RMSEs of RemoTAP and GRASP are 0.285 and 0.224, respectively. Based on the AERONET comparison, we conclude that both algorithms show very similar overall performance, where both algorithms have stronger and weaker points. For the global data products, we find a root mean square difference (RMSD) between RemoTAP and GRASP AOD of 0.12 and 0.038 over land and ocean, respectively. The largest differences occur over the biomass burning region in equatorial Africa. The global mean values are virtually unbiased with respect to each other. For AE the RMSD between RemoTAP and GRASP is 0.33 over land and 0.23 over ocean. For SSA, we find much better agreement over land (bias = −0.01, RMSD = 0.043 for retrievals with AOD > 0.2) than over ocean (bias = 0.053, RMSD = 0.074). As expected, the differences increase towards low AOD, over both land and ocean. We also compared the GRASP and RemoTAP AOD and AE products against MODIS. For AOD over land, the agreement of either GRASP or RemoTAP with MODIS is worse than the agreement between the two PARASOL algorithms themselves. Over ocean, the agreement is very similar among the three products for AOD. For AE, the agreement between GRASP and RemoTAP is much better than the agreement of both products with MODIS. The agreement of the latest product versions with each other and with AERONET improved significantly compared to the previous version of the global products of GRASP and RemoTAP. The results demonstrate that the dedicated effort in algorithm development for multi-angle polarimetric (MAP) aerosol retrievals still leads to substantial improvement of the resulting aerosol products, and this is still an ongoing process.

Surface Displacements Monitoring in Cyprus via InSAR and Field Investigation: The Case Studies of Pyrgos-Parekklisia and Pedoulas Villages

The island of Cyprus is characterised by a complex geological environment as it overlies a boundary zone of three tectonic plates, leading to high seismicity and intensive tectonism. It consists highly of Neogene marls, exhibiting serious geotechnical problems due to their high content of clay minerals. Along with strong, destructive earthquakes, various geohazards have been identified in Cyprus, including landslides, swelling/shrinking phenomena and land subsidence etc. Pedoulas is a village in Cyprus experiencing ground deformation due to landslide phenomena. Conversely, Pyrgos and Parekklisia villages in Limassol, Cyprus are experiencing a long-term swelling/shrinking phenomenon. To further investigate this surface deformation, a time-series InSAR analysis of Sentinel-1 SLC images of ascending satellite passes was performed, with a parallelised version of PSI (Persistent Scatterers Interferometry), along with field investigation, for the time period of 2016 to 2021. Negative vertical displacements with maximum rates of −10 mm/y, were identified in Pedoulas village, while positive vertical displacements with a maximum rate of 10 mm/y, dominated in Pyrgos and Parekklisia villages. The analysis of precipitation data from 2017 to 2021, presented a correlation between annual fluctuations in precipitation in the affected areas and changes in the InSAR time-series deformation trends. In Pedoulas village, landslide movements sped up during spring and summer, when the infiltration of waste water in the ground intensified due to the increase in the tourist population. In Pyrgos-Parekklisia villages, higher positive deformation rates were identified in winter months, while during summer, when the formations dried out, uplifting phenomena stopped evolving. The integration of InSAR displacements with field investigation provided validation of the observed ground failures and added valuable insights into the driving mechanisms of the deformation phenomena. Finally, the assessment of the impact of the triggering factor in the evolution of the deformation phenomena, can serve as a valuable tool for risk mitigation.

The Tibetan Plateau space-based tropospheric aerosol climatology: 2007–2020

Abstract. A comprehensive and robust dataset of tropospheric aerosol properties is important for understanding the effects of aerosol–radiation feedback on the climate system and reducing the uncertainties of climate models. The “Third Pole” of Earth (Tibetan Plateau, TP) is highly challenging for obtaining long-term in situ aerosol data due to its harsh environmental conditions. Here, we provide the more reliable new vertical aerosol index (AI) parameter from the spaceborne-based lidar CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) on board CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) for daytime and nighttime to investigate the aerosol's climatology over the TP region during 2007–2020. The calculated vertical AI was derived from the aerosol extinction coefficient (EC), which was rigorously quality-checked and validated for passive satellite sensors (MODIS) and ground-based lidar measurements. Generally, our results demonstrated that there was agreement of the AI dataset with the CALIOP and ground-based lidar. In addition, the results showed that, after removing the low-reliability aerosol target signal, the optimized data can obtain the aerosol characteristics with higher reliability. The data also reveal the patterns and concentrations of high-altitude vertical structure characteristics of the tropospheric aerosol over the TP. They will also help to update and make up the observational aerosol data in the TP. We encourage climate modelling groups to consider new analyses of the AI vertical patterns, comparing the more accurate datasets, with the potential to increase our understanding of the aerosol–cloud interaction (ACI) and aerosol–radiation interaction (ARI) and their climate effects. Data described in this work are available at https://doi.org/10.11888/Atmos.tpdc.300614 (Huang, 2023).

Generating Wall-to-Wall Canopy Height Information from Discrete Data Provided by Spaceborne LiDAR System

Provision of multi-temporal wall-to-wall canopy height information is one of the initiatives to combat deforestation and is necessary in strategizing forest conversion and reforestation initiatives. This study generated wall-to-wall canopy height information of the subtropical forest of Lishan, Taiwan, using discrete data provided by spaceborne LiDARs, wall-to-wall passive and active remote sensing imageries, topographic data, and machine learning (ML) regression models such as gradient boosting (GB), k-nearest neighbor (k-NN), and random forest (RF). ICESat-2- and GEDI-based canopy height data were used as training data, and medium-resolution passive satellite image (Sentinel-2) data, active remote sensing data such as synthetic aperture radar (SAR), and topographic data were used as regressors. The ALS-based canopy height was used to validate the models’ performance using root mean square error (RMSE) and percentage RMSE (PRMSE) as validation criteria. Notably, GB displayed the highest accuracy among the regression models, followed by k-NN and then RF. Using the GEDI-based canopy height as training data, the GB model can achieve optimum accuracy with an RMSE/PRMSE of 8.00 m/31.59%, k-NN can achieve an RMSE/PRMSE of as low as 8.05 m/31.78%, and RF can achieve optimum RMSE/PRMSE of 8.16 m/32.24%. If using ICESat-2 data, GB can have an optimum RMSE/PRMSE of 13.89 m/54.86%; k-NN can have an optimum RMSE/PRMSE of 14.32 m/56.56%, while RF can achieve an RMSE/PRMSE of 14.72 m/58.14%. Additionally, integrating Sentinel-1 with Sentinel-2 data improves the accuracy of canopy height modeling. Finally, the study underlined the crucial relevance of correct canopy height estimation for sustainable forest management, as well as the potential ramifications of poor-quality projections on a variety of biological and environmental factors.

A constellation design for orbiting solar reflectors to enhance terrestrial solar energy

Orbiting solar reflectors (OSRs) are flat, thin and lightweight reflective structures that are proposed to enhance terrestrial solar energy generation by illuminating large terrestrial solar power plants locally around dawn/dusk and during night hours. The incorporation of OSRs into terrestrial energy systems may offset the daylight-only limitation of terrestrial solar energy. However, the quantity of solar energy delivered to the Earth’s surface remains low due to short duration of orbital passes and the low density of the reflected solar power due to large slant ranges. To compensate for these, this paper proposes a constellation of multiple reflectors in low-Earth orbit for scalable enhancement of the quantity of energy delivered. Circular near-polar orbits of 1000 km altitude in the terminator region are considered in a Walker-type constellation for a preliminary analysis. Starting from a simplified approach, the equations of Walker constellations describing the distribution of the reflectors are first modified by introducing a phasing parameter to ensure repeating pass-geometry over solar power farms. This approach allows for a single groundtrack optimisation to define the constellation, which was carried out by a genetic algorithm for single and two reflectors per orbit with an objective function defined as the total quantity of energy delivered per day, to existing and hypothetical solar power projects around the Earth. When full-scale constellations are considered with a number of reflectors, the quantity of solar energy delivered is substantial in the broader context of global terrestrial solar energy generation.

The Spatial Ecology of Nuisance Crocodiles: Movement Patterns of Relocated American Crocodiles (Crocodylus acutus) in Guanacaste, Costa Rica.

Anthropogenic alterations of the environment have increased, highlighting the need for human-wildlife coexistence and conflict mitigation. Spatial ecology, and the use of passive satellite movement technology in particular, has been used to identify patterns in human-wildlife conflict as a function of shared resources that present potential for dangerous situations. Here, we aim to remotely identify patterns indicative of human-crocodile conflict in Guanacaste, Costa Rica by exploring site fidelity and diverse modes of movement (i.e., land and water) across space between nuisance (relocated) and non-nuisance (wild) crocodiles. Advanced satellite remote sensing technology provided near-constant movement data on individuals at the regional scale. Telonics Iridium SeaTrkr-4370-4 transmitters were used with modified crocodilian fitting. Results indicate that relocated crocodiles exhibited large-scale movements relative to wild crocodiles. Nuisance relocated crocodiles either returned to the area of nuisance or potentially attempted to in short time frames. The results presented here highlight the need for alternative management strategies that facilitate relocation efficacy.

Temporal Stability of Grassland Soil Moisture Utilising Sentinel-2 Satellites and Sparse Ground-Based Sensor Networks

Soil moisture is important for understanding climate, water resources, water storage, and land use management. This study used Sentinel-2 (S-2) satellite optical data to retrieve surface soil moisture at a 10 m scale on grassland sites with low hydraulic conductivity soil in a climate dominated by heavy rainfall. Soil moisture was estimated after modifying the Optical Trapezoidal Model to account for mixed land cover in such conditions. The method uses data from a short-wave infra-red band, which is sensitive to soil moisture, and four vegetation indices from optical bands, which are sensitive to overlying vegetation. Scatter plots of these data from multiple, infrequent satellite passes are used to define the range of surface moisture conditions. The saturated and dry edges are clearly non-linear, regardless of the choice of vegetation index. Land cover masks are used to generate scatter plots from data only over grassland sites. The Enhanced Vegetation Index demonstrated advantages over other vegetation indices for surface moisture estimation over the entire range of grassland conditions. In poorly drained soils, the time lag between satellite surface moisture retrievals and in situ sensor soil moisture at depth must be part of the validation process. This was achieved by combining an approximate solution to the Richards’ Equation, along with measurements of saturated and residual moisture from soil samples, to optimise the correlations between measurements from satellites and sensors at a 15 cm depth. Time lags of 2–4 days resulted in a reduction of the root mean square errors between volumetric soil moisture predicted from S-2 data and that measured by in situ sensors, from ~0.1 m3/m3 to <0.06 m3/m3. The surface moisture results for two grassland sites were analysed using statistical concepts based upon the temporal stability of soil water content, an ideal framework for the intermittent Sentinel-2 data in conditions of persistent cloud cover. The analysis could discriminate between different natural drainages and surface soil textures in grassland areas and could identify sub-surface artificial drainage channels. The techniques are transferable for land-use and agricultural management in diverse environmental conditions without the need for extensive and expensive in situ sensor networks.

A Spatial Downscaling Framework for SMAP Soil Moisture Based on Stacking Strategy

Soil moisture (SM) data can provide guidance for decision-makers in fields such as drought monitoring and irrigation management. Soil Moisture Active Passive (SMAP) satellite offers sufficient spatial resolution for global-scale applications, but its utility is limited in regional areas due to its lower spatial resolution. To address this issue, this study proposed a downscaling framework based on the Stacking strategy. The framework integrated extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) to generate 1 km resolution SM data using 15 high-resolution factors derived from multi-source datasets. In particular, to test the influence of terrain partitioning on downscaling results, Anhui Province, which has diverse terrain features, was selected as the study area. The results indicated that the performance of the three base models varied, and the developed Stacking strategy maximized the potential of each model with encouraging downscaling results. Specifically, we found that: (1) The Stacking model achieved the highest accuracy in all regions, and the performance order of the base models was: XGBoost > CatBoost > LightGBM. (2) Compared with the measured SM at 87 sites, the downscaled SM outperformed other 1 km SM products as well as the downscaled SM without partitioning, with an average ubRMSE of 0.040 m3/m3. (3) The downscaled SM responded positively to rainfall events and mitigated the systematic bias of SMAP. It also preserved the spatial trend of the original SMAP, with higher levels in the humid region and relatively lower levels in the semi-humid region. Overall, this study provided a new strategy for soil moisture downscaling and revealed some interesting findings related to the effectiveness of the Stacking model and the impact of terrain partitioning on downscaling accuracy.

Facilitating Spectrum Sharing with Passive Satellite Incumbents

Facilitating Spectrum Sharing with Passive Satellite Incumbents

A Radiation Source Positioning System Based on GEO Satellites TDOA/FDOA Measurements

Traditional passive satellite positioning systems are facing problems such as construction difficulty, high operation and maintenance costs, and positioning range limits caused by quantity of launched satellites, due to gradually scarce earth’s orbit resources. Therefore, a radiation source positioning system design scheme based on quilted GEO communication satellites is proposed to meet with those problems. The proposed positioning system is able to intelligently choose one of three modes (three-satellites TDOA mode, dual-satellites FDOA/TDOA mode, or single-satellite mode), on the basis of searching satellites database, to position unknown radiation source. The positioning result is corrected using a known reference radiation station. To improve signal detection performance, a technology of co-channel interference cancellation is proposed when unknown radiation source signal forwarded by neighboring satellite is interference badly by its communication signal. Results of emulation shows， the proposed system can effectively locate unknown radiation sources within a range of 1000 kilometers, its positioning accuracy can reach 10 kilometers.

Validating GEDI tree canopy cover product across forest types using co-registered aerial LiDAR data

Reliable tree canopy cover (TCC) products are vital for national forest inventory, land process modeling and forest dynamic monitoring. The new generation of space-based laser altimeter, GEDI, offers a three-dimensional (3D) insight on the forest structure, shaping the paradigm of structural variable estimation. However, the generality of newly released GEDI level-2 TCC product version 2 was less investigated across various forest types. Additionally, satellite-derived product validation usually suffers from the geolocation mismatch between satellite and reference data. In this study, we comprehensively validated the GEDI TCC product across seven forest types using the reference TCC derived from several public and private aerial LiDAR datasets after geographical registration, and crossly compared with a commonly-used passive satellite product (i.e., GFCC TCC). As the reference aerial TCC maps were derived using various aerial LiDAR instruments, we investigated the consistency of TCC estimation among them using simulation datasets and found that the distributions of TCC relative bias (biasR, %) were almost identical and the differences of relative RMSE (rRMSE, %) was less than 0.2%. Through the registration process, we found that the geolocation offsets of GEDI footprints tended to be independent of azimuth directions and their average was about 10 m, verifying the necessity of registration during the validation process. Importantly, the post-registration validation of GEDI TCC showed an average RMSE of 0.10 and an average R2 of 0.85 for all forest types, resulting in a decrease of RMSE of up to 0.15 and an increase of R2 of up to 0.33 compared to the pre-registration validation. The inter-comparison also exhibited improved consistency between GEDI and GFCC TCC products after registration. Further, we found a non-negligible dependence of GEDI TCC on the slope factor but almost independence on forest type, encouraging the spread of GEDI TCC product.

Research on anthropogenic influence on satellite positioning accuracy

This paper presents the results of research into the accuracy of satellite-based positioning methods in the vicinity of operating industrial and energy facilities. Modern dual-frequency multi-system satellite receivers allow for high accuracy and productivity in compliance with the basic principles of work related to ensuring the criterion of unity of measurements and the mandatory implementation of the rules of localisation of the object and the receiver(s), as well as the study of geometric and temporal factors of satellite passage, with their subsequent application to perform quality measurements. This technique is widely and effectively used on large area and linear objects by qualified specialists in the field of geodesy. The accuracy of determinations is guaranteed by the openness of the sky and the absence of all kinds of man-made factors that potentially interfere with the reception of radio signals. This paper does not consider the extreme operation of receivers in areas of special signal suppression. It is of great interest to research the accuracy of satellite positioning methods in the vicinity of operating industrial and energy objects.

Online system for analyzing currents in the upper ionosphere according to Swarm satellite data

In this paper, we describe the TeslaSwarm online system [http://aleph.gcras.ru/teslaswarm] for visualizing field-aligned currents in the upper ionosphere, using data from Swarm low-orbit satellites. The system provides researchers with a simple and convenient tool for event selection and detailed analysis of currents and electromagnetic fields in the upper ionosphere. The system user can select satellite passages over a given region, visualize the geomagnetic field structure and field-aligned currents, compare the pattern of field-aligned currents with the auroral particle precipitation map, using the OVATION-Prime model, and save the selected parameters in a file in text format. We demonstrate advantages of the developed system over its foreign analogues. In practice, the collection and pre-processing of raw data for experiments make up about 80 % of all work with data. The proposed online system largely saves the user from the most time-consuming work of choosing the required satellite passage segments and calculating the characteristics of interest from raw measurements.

Онлайн система для анализа токов в верхней ионосфере по данным спутников Swarm

In this paper, we describe the TeslaSwarm online system [http://aleph.gcras.ru/teslaswarm] for visualizing field-aligned currents in the upper ionosphere, using data from Swarm low-orbit satellites. The system provides researchers with a simple and convenient tool for event selection and detailed analysis of currents and electromagnetic fields in the upper ionosphere. The system user can select satellite passages over a given region, visualize the geomagnetic field structure and field-aligned currents, compare the pattern of field-aligned currents with the auroral particle precipitation map, using the OVATION-Prime model, and save the selected parameters in a file in text format. We demonstrate advantages of the developed system over its foreign analogues. In practice, the collection and pre-processing of raw data for experiments make up about 80 % of all work with data. The proposed online system largely saves the user from the most time-consuming work of choosing the required satellite passage segments and calculating the characteristics of interest from raw measurements.

SYNERGY OF ADVANCED PROCESSING TECHNIQUES USING COPERNICUS SAR AND OPTICAL SATELLITE IMAGERY TO DETECT GROUND DISPLACEMENTS: THE CASE STUDIES OF PYRGOS AND PAREKKLISIA VILLAGES IN CYPRUS

Abstract. Cyprus has a long history of destructive tectonic activity and related geohazards. This is mainly due to its location on the Mediterranean fault zone and the interaction of the Eurasian and African plates. To study the occurrence of land displacements on a national scale, various space-based monitoring techniques exploiting the Copernicus program’s datasets were studied using Sentinel-1 and Sentinel-2 satellite images for the period of 2016–2021. More specifically, differential spectral indices were calculated by subtracting the reference 2016 Sentinel-2 image from an image for every year until 2021, and a parallelised version of Persistent Scatterer Interferometry (PSI) was conducted using a time series of Sentinel-1 data over the same period, to identify ground displacements over time. The PSI analysis in the area around Pyrgos and Parekklisia villages, detected Line-of-Sight (LoS) uplift phenomena of a maximum rate of 8 mm/y, in ascending and descending Sentinel-1 satellite passes. Similarly, deformation was identified from optical image processing. The integration of both techniques provided qualitative and quantitative information about ground deformation events in both areas, showcasing a complementarity of results obtained, with PSI detecting displacements in urban/built-up areas, and multispectral change detection in rural environment. In terms of correlation, the two techniques showed a fit of 31.34% in ascending and 26% in descending pass, providing additional information on the size of affected areas. This synergistic approach facilitates the systematic monitoring of areas of interest, providing significant and timely information to decision-makers, to take actions and adopt protective measures when and where it is required.