scholarly journals On the Efficacy of Compact Radar Transponders for InSAR Geodesy: Results of Multi-year Field Tests

2021 ◽  
Author(s):  
Richard Czikhardt ◽  
Hans van der Marel ◽  
Juraj Papco ◽  
Ramon Hanssen
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
High Precision ◽  
Deformation Monitoring ◽  
Seasonal Effects ◽  
Attractive Alternative ◽  
Leg Length ◽  
Short Baseline ◽  
Wide Range ◽  
The Impact

Compact and low-cost radar transponders are an attractive alternative to corner reflectors (CR) for SAR interferometric (InSAR) deformation monitoring, datum connection, and geodetic data integration.Recently, such transponders have become commercially available for C-band sensors, which poses relevant questions on their characteristics in terms of radiometric, geometric, and phase stability. Especially for extended time series and for high-precision geodetic applications, the impact of secular or seasonal effects, such as variations in temperature and humidity, has yet to be proven.Here we address these challenges using a multitude of short baseline experiments with four transponders and six corner reflectors deployed at test sites in the Netherlands and Slovakia. Combined together, we analyzed 980 transponder measurements in Sentinel-1 time series to a maximum extent of 21 months.We find an average Radar Cross Section (RCS) of over 42 dBm2 within a range of up to 15 degrees of elevation misalignment, which is comparable to a triangular trihedral corner reflector with a leg length of 2.0 m. Its RCS shows temporal variations of 0.3--0.7~dBm2 (standard deviation) which is partially correlated with surface temperature changes.The precision of the InSAR phase double-differences over short baselines between a transponder and a stable reference corner reflectors is found to be 0.5-1.2 mm (one sigma). We observe a correlation with surface temperature, leading to seasonal variations of up to +/-3 mm, which should be modeled and corrected for in high precision InSAR applications. For precise SAR positioning, we observe antenna-specific constant internal electronic delays of 1.2-2.1 m in slant-range, i.e., within the range resolution of the Sentinel-1 Interferometric Wide Swath (IW) product, with a temporal variability of less than 20~cm.Comparing similar transponders from the same series, we observe distinctdifferences in performance. Our main conclusion is that these characteristics are favorable for a wide range of geodetic applications. For particular demanding applications, individual calibration of single devices is strongly recommended.

scholarly journals InSAR Greece with Parallelized Persistent Scatterer Interferometry: A National Ground Motion Service for Big Copernicus Sentinel-1 Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3207
Author(s):  
Ioannis Papoutsis ◽  
Charalampos Kontoes ◽  
Stavroula Alatza ◽  
Alexis Apostolakis ◽  
Constantinos Loupasakis
Keyword(s):  
Time Series ◽  
Ground Motion ◽  
Large Scale ◽  
Persistent Scatterer Interferometry ◽  
Persistent Scatterers ◽  
Processing Chain ◽  
Persistent Scatterer ◽  
Basin Scale ◽  
Wide Range ◽  
The Impact

Advances in synthetic aperture radar (SAR) interferometry have enabled the seamless monitoring of the Earth’s crust deformation. The dense archive of the Sentinel-1 Copernicus mission provides unprecedented spatial and temporal coverage; however, time-series analysis of such big data volumes requires high computational efficiency. We present a parallelized-PSI (P-PSI), a novel, parallelized, and end-to-end processing chain for the fully automated assessment of line-of-sight ground velocities through persistent scatterer interferometry (PSI), tailored to scale to the vast multitemporal archive of Sentinel-1 data. P-PSI is designed to transparently access different and complementary Sentinel-1 repositories, and download the appropriate datasets for PSI. To make it efficient for large-scale applications, we re-engineered and parallelized interferogram creation and multitemporal interferometric processing, and introduced distributed implementations to best use computing cores and provide resourceful storage management. We propose a new algorithm to further enhance the processing efficiency, which establishes a non-uniform patch grid considering land use, based on the expected number of persistent scatterers. P-PSI achieves an overall speed-up by a factor of five for a full Sentinel-1 frame for processing in a 20-core server. The processing chain is tested on a large-scale project to calculate and monitor deformation patterns over the entire extent of the Greek territory—our own Interferometric SAR (InSAR) Greece project. Time-series InSAR analysis was performed on volumes of about 12 TB input data corresponding to more than 760 Single Look Complex Sentinel-1A and B images mostly covering mainland Greece in the period of 2015–2019. InSAR Greece provides detailed ground motion information on more than 12 million distinct locations, providing completely new insights into the impact of geophysical and anthropogenic activities at this geographic scale. This new information is critical to enhancing our understanding of the underlying mechanisms, providing valuable input into risk assessment models. We showcase this through the identification of various characteristic geohazard locations in Greece and discuss their criticality. The selected geohazard locations, among a thousand, cover a wide range of catastrophic events including landslides, land subsidence, and structural failures of various scales, ranging from a few hundredths of square meters up to the basin scale. The study enriches the large catalog of geophysical related phenomena maintained by the GeObservatory portal of the Center of Earth Observation Research and Satellite Remote Sensing BEYOND of the National Observatory of Athens for the opening of new knowledge to the wider scientific community.

scholarly journals Modeling and Short-Term Forecasts of Indicators for COVID-19 Outbreak in 25 Countries at the end of March

2020 ◽  
pp. 6-20
Author(s):  
Handan Ankaralı ◽  
Nadire Erarslan ◽  
Özge Pasin ◽  
Abu Kholdun Al Mahmood
Keyword(s):  
Time Series ◽  
Medical Science ◽  
Exponential Smoothing ◽  
Seasonal Effects ◽  
Time Series Models ◽  
Double Exponential ◽  
Wide Range ◽  
Simple Exponential Smoothing ◽  
Double Exponential Smoothing ◽  
Prediction And Forecasting

Objective: The coronavirus, which originated in Wuhan, causing the disease called COVID-19, spread more than 200 countries and continents end of the March. In this study, it was aimed to model the outbreak with different time series models and also predict the indicators. Materials and Methods: The data was collected from 25 countries which have different process at least 20 days. ARIMA(p,d,q), Simple Exponential Smoothing, Holt’s Two Parameter, Brown’s Double Exponential Smoothing Models were used. The prediction and forecasting values were obtained for the countries. Trends and seasonal effects were also evaluated. Results and Discussion: China has almost under control according to forecasting. The cumulative death prevalence in Italy and Spain will be the highest, followed by the Netherlands, France, England, China, Denmark, Belgium, Brazil and Sweden respectively as of the first week of April. The highest daily case prevalence was observed in Belgium, America, Canada, Poland, Ireland, Netherlands, France and Israel between 10% and 12%.The lowest rate was observed in China and South Korea. Turkey was one of the leading countries in terms of ranking these criteria. The prevalence of the new case and the recovered were higher in Spain than Italy. Conclusion: More accurate predictions for the future can be obtained using time series models with a wide range of data from different countries by modelling real time and retrospective data. Bangladesh Journal of Medical Science Vol.19(0) 2020 p.06-20

scholarly journals Analysis of Climate Change Effects on Surface Temperature in Central-Italy Lakes Using Satellite Data Time-Series

2021 ◽  
Vol 14 (1) ◽  
pp. 117
Author(s):  
Davide De Santis ◽  
Fabio Del Frate ◽  
Giovanni Schiavon
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Surface Temperature ◽  
Satellite Data ◽  
Water Surface ◽  
Central Italy ◽  
Large Lakes ◽  
Climate Change Effects ◽  
Surface Temperatures ◽  
The Impact

Evaluation of the impact of climate change on water bodies has been one of the most discussed open issues of recent years. The exploitation of satellite data for the monitoring of water surface temperatures, combined with ground measurements where available, has already been shown in several previous studies, but these studies mainly focused on large lakes around the world. In this work the water surface temperature characterization during the last few decades of two small–medium Italian lakes, Lake Bracciano and Lake Martignano, using satellite data is addressed. The study also takes advantage of the last space-borne platforms, such as Sentinel-3. Long time series of clear sky conditions and atmospherically calibrated (using a simplified Planck’s Law-based algorithm) images were processed in order to derive the lakes surface temperature trends from 1984 to 2019. The results show an overall increase in water surface temperatures which is more evident on the smallest and shallowest of the two test sites. In particular, it was observed that, since the year 2000, the surface temperature of both lakes has risen by about 0.106 °C/year on average, which doubles the rate that can be retrieved by considering the whole period 1984–2019 (0.053 °C/year on average).

scholarly journals Modelling seasonality in Australian building approvals

2012 ◽  
Vol 12 (1) ◽  
pp. 26-36 ◽  
Author(s):  
Harry M Karamujic
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
South Australia ◽  
Residential Building ◽  
Seasonal Effects ◽  
General Level ◽  
Stochastic Trend ◽  
Out Of Sample ◽  
The Impact ◽  
Modelling Approach

The paper examines the impact of seasonal influences on Australian housing approvals, represented by the State of Victoria[1] building approvals for new houses (BANHs). The prime objective of BANHs is to provide timely estimates of future residential building work. Due to the relevance of the residential property sector to the property sector as whole, BANHs are viewed by economic analysts and commentators as a leading indicator of property sector investment and as such the general level of economic activity and employment. The generic objective of the study is to enhance the practice of modelling housing variables. In particular, the study seeks to cast some additional light on modelling the seasonal behaviour of BANHs by: (i) establishing the presence, or otherwise, of seasonality in Victorian BANHs; (ii) if present, ascertaining is it deterministic or stochastic; (iii) determining out of sample forecasting capabilities of the considered modelling specifications; and (iv) speculating on possible interpretation of the results. To do so the study utilises a structural time series model of Harwey (1989). The modelling results confirm that the modelling specification allowing for stochastic trend and deterministic seasonality performs best in terms of diagnostic tests and goodness of fit measures. This is corroborated with the analysis of out of sample forecasting capabilities of the considered modelling specifications, which showed that the models with deterministic seasonal specification exhibit superior forecasting capabilities. The paper also demonstrates that if time series are characterized by either stochastic trend or seasonality, the conventional modelling approach[2] is bound to be mis-specified i.e. would not be able to identify statistically significant seasonality in time series.According to the selected modeling specification, factors corresponding to June, April, December and November are found to be significant at five per cent level. The observed seasonality could be attributed to the ‘summer holidays’ and ‘the end of financial year’ seasonal effects. [1] Victoria is geographically the second smallest state in Australia. It is also the second most populous state in Australia. Australia has six states (New South Wales, Queensland, South Australia, Tasmania, Victoria, and Western Australia), and two territories (the Northern Territory and the Australian Capital Territory).[2] A modelling approach based on the assumption of deterministic trend and deterministic seasonality.

Precipitation and Temperature Variations on the Interannual Time Scale: Assessing the Impact of ENSO and Volcanic Eruptions

2011 ◽  
Vol 24 (9) ◽  
pp. 2258-2270 ◽  
Author(s):  
Guojun Gu ◽  
Robert F. Adler
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Surface Temperature ◽  
Volcanic Eruptions ◽  
Time Lags ◽  
Tropospheric Temperature ◽  
Precipitation Temperature ◽  
The Impact ◽  
Global Mean ◽  
Temperature Surface

Abstract The effects of ENSO and two large tropical volcanic eruptions (El Chichón, March 1982; Mt. Pinatubo, June 1991) are examined for the period of 1979–2008 using various satellite- and station-based observations of precipitation, temperature (surface and atmospheric), and tropospheric water vapor content. By focusing on the responses in the time series of tropical and global means over land, ocean, and land and ocean combined, the authors intend to provide an observational comparison of how these two phenomena, represented by Niño-3.4 and the tropical mean stratospheric aerosol optical thickness (τ), respectively, influence precipitation, temperature, and water vapor variations. As discovered in past studies, strong same-sign ENSO signals appear in tropical and global mean temperature (surface and tropospheric) over both land and ocean. However, ENSO only has very weak impact on tropical and global mean (land + ocean) precipitation, though intense anomalies are readily seen in the time series of precipitation averaged over either land or ocean. In contrast, the two volcanoes decreased not only tropical and global mean surface and tropospheric temperature but also tropical and global mean (land + ocean) precipitation. The differences between the responses to ENSO and volcanic eruptions are thus further examined by means of lag-correlation analyses. The ENSO-related peak responses in oceanic precipitation and sea surface temperature (SST) have the same time lags with Niño-3.4, 2 (4) months for the tropical (global) means. Tropical and global mean tropospheric water vapor over ocean (and land) generally follows surface temperature. However, land precipitation responds to ENSO much faster than temperature, suggesting a certain time needed for surface energy adjustment there following ENSO-related circulation and precipitation anomalies. Weak ENSO signals in the tropical and global mean mid- to lower-tropospheric atmospheric (dry) static instability are further discovered, which tend to be consistent with weak ENSO responses in the tropical and global mean (land + ocean) precipitation. For volcanic eruptions, tropical and global mean precipitation over either ocean or land responds faster than temperature (surface and atmospheric) and tropospheric water vapor averaged over the same areas, suggesting that precipitation tends to be more sensitive to volcanic-related solar forcing. The volcanic-related precipitation variations are further shown to be related to the changes in the mid- to lower-tropospheric atmospheric (dry) instability.

scholarly journals INSAR COLLABORATIVE MONITORING MODE AND MULTI-MODE COMPUTING SERVICES FOR GEOHAZARDS IDENTIFICATION IN OPEN-PIT MINING AREA

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 241-247
Author(s):  
J. Zhang
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Underground Mining ◽  
Mining Area ◽  
Deformation Monitoring ◽  
High Density ◽  
Open Pit ◽  
Open Pit Mining ◽  
Mining Areas ◽  
Wide Range

Abstract. InSAR has developed a variety of methods, such as D-InSAR, PS-InSAR, MBAS, CT, SqueeSAR, POT, etc., which have been widely used in land subsidence monitoring. For open pit mining areas, there are usually mining activity, complex terrain features, low coherence, and local large deformation gradients, which makes it difficult for time series InSAR technology to obtain high-density surface deformation information in open pit mining areas. Traditional methods usually only monitor the linear deformation of the surface caused by the mining of a few working zone above the underground mining area, and the temporal and spatial resolution is lower. How to obtain high-precision, high-density, and time-sensitive deformation information is the main difficulty of InSAR monitoring in open pit mining areas. Make full use of the geosensor network monitoring system, optimize monitoring mode of collaborated satellite-to-ground based InSAR, further realize whole calculation and geographic information services, to achieve early identification and discovery of abnormal in large-area macro-monitoring, and accurate monitoring of local areas in real-time early warning, which is the development direction of ground deformation monitoring of mining areas. The study area is Pingshuo open pit mining area. we fully study the application mode and services of InSAR monitoring for geohazards in open-pit mining area, through the establishment of satellite InSAR technology system for large-scale macro-monitoring and forecasting, and GBSAR and GSN for local precision monitoring. The effective mode of InSAR monitoring of geohazard in open-pit mines is summarized. A combination of D-InSAR, POT (Pixel offset tracking), Time Series-InSAR and GB-SAR is used in a wide range, and high-resolution optical images are used to identify localized changes in subsidence areas and open-pit mining areas.

scholarly journals Modeling and Short-Term Forecasts of Indicators for COVID-19 Outbreak in 25 Countries at the end of March

2020 ◽  
Author(s):  
Handan Ankaralı ◽  
Nadire Erarslan ◽  
Özge Pasin
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Seasonal Effects ◽  
Time Series Models ◽  
Double Exponential ◽  
Wide Range ◽  
Simple Exponential Smoothing ◽  
Double Exponential Smoothing ◽  
Prediction And Forecasting ◽  
Made In

ABSTRACTBackgroundThe coronavirus, which originated in Wuhan, causing the disease called COVID-19, spread more than 200 countries and continents end of the March. There is a lot of data since the virus started. However, these data will be explanatory when accurate analyzes are made and will allow future predictions to be made. In this study, it was aimed to model the outbreak with different time series models and also predict the indicators.MethodsThe data was collected from 25 countries which have different process at least 20 days. ARIMA(p,d,q), Simple Exponential Smoothing, Holt’s Two Parameter, Brown’s Double Exponential Smoothing Models were used. The prediction and forecasting values were obtained for the countries. Trends and seasonal effects were also evaluated.ResultsChina has almost under control according to forecasting. The cumulative death prevalence in Italy and Spain will be the highest, followed by the Netherlands, France, England, China, Denmark, Belgium, Brazil and Sweden respectively as of the first week of April. The highest daily case prevalence was observed in Belgium, America, Canada, Poland, Ireland, Netherlands, France and Israel between 10% and 12%.The lowest rate was observed in China and South Korea. Turkey was one of the leading countries in terms of ranking these criteria. The prevalence of the new case and the recovered were higher in Spain than Italy.ConclusionsMore accurate predictions for the future can be obtained using time series models with a wide range of data from different countries by modelling real time and retrospective data.

scholarly journals Validation of AVHRR Land Surface Temperature with MODIS and In Situ LST—A TIMELINE Thematic Processor

2021 ◽  
Vol 13 (17) ◽  
pp. 3473
Author(s):  
Philipp Reiners ◽  
Sarah Asam ◽  
Corinne Frey ◽  
Stefanie Holzwarth ◽  
Martin Bachmann ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Shortwave Radiation ◽  
Turbulent Heat ◽  
Ground Heat Flux ◽  
The Impact

Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed.

scholarly journals Sea spray aerosol concentration modulated by sea surface temperature

2021 ◽  
Vol 118 (9) ◽  
pp. e2020583118
Author(s):  
Shang Liu ◽  
Cheng-Cheng Liu ◽  
Karl D. Froyd ◽  
Gregory P. Schill ◽  
Daniel M. Murphy ◽  
...  
Keyword(s):  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Sea Spray ◽  
Anthropogenic Aerosols ◽  
Wide Range ◽  
Sea Spray Aerosol ◽  
Natural Aerosols ◽  
The Impact

Natural aerosols in pristine regions form the baseline used to evaluate the impact of anthropogenic aerosols on climate. Sea spray aerosol (SSA) is a major component of natural aerosols. Despite its importance, the abundance of SSA is poorly constrained. It is generally accepted that wind-driven wave breaking is the principle governing SSA production. This mechanism alone, however, is insufficient to explain the variability of SSA concentration at given wind speed. The role of other parameters, such as sea surface temperature (SST), remains controversial. Here, we show that higher SST promotes SSA mass generation at a wide range of wind speed levels over the remote Pacific and Atlantic Oceans, in addition to demonstrating the wind-driven SSA production mechanism. The results are from a global scale dataset of airborne SSA measurements at 150 to 200 m above the ocean surface during the NASA Atmospheric Tomography Mission. Statistical analysis suggests that accounting for SST greatly enhances the predictability of the observed SSA concentration compared to using wind speed alone. Our results support implementing SST into SSA source functions in global models to better understand the atmospheric burdens of SSA.

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