Observation Satellite Research Articles

Mining Partners in Trajectories

Spatiotemporal data is everywhere, being gathered from different devices such as Earth Observation and GPS satellites, sensor networks and mobile gadgets. Spatiotemporal data collected from moving objects is of particular interest for a broad range of applications. In the last years, such applications have motivated many pieces of research on moving object trajectory data mining. In this article, it is proposed an efficient method to discover partners in moving object trajectories. Such a method identifies pairs of trajectories whose objects stay together during certain periods, based on distance time series analysis. It presents two case studies using the proposed algorithm. This article also describes an R package, called TrajDataMining, that contains algorithms for trajectory data preparation, such as filtering, compressing and clustering, as well as the proposed method Partner.

Modelling the solar, geomagnetic and periodic variations of the ionosphere over Ethiopia

A careful and continuous ionospheric modelling can significantly influence the performance of activities such as Positioning, Navigation and Timing services related with the Global Navigation Satellite System applications as well as the Earth Observations System, satellite communication and Space weather forecasting applications. In this paper, the linear time-series modelling that consists of the solar, geomagnetic and periodic components has been carried out on the daily ionospheric vTEC at two different Ethiopian GPS locations, at Arbaminch, ARMI (geographic 6.06ºN, 37.56ºE) and Bahir Dar, BDMT (geographic 11.60ºN, 37.38ºE), for the year 2012, 2014 and 2016 in the 24th solar cycle. The variations of vTEC due to the solar activities, geomagnetic activities and periodic oscillations have been explicitly investigated. The results confirmed that the correlation coefficient of the linear model based estimated vTEC and the observed GPS-vTEC is around 80% in the year 2014. Besides, solar activity is identified as the key component for the 27 days period variations of vTEC whereas geomagnetic activity is identified as the key component that influences the short-period variations of the daily average vTEC. In addition to the correlation analysis, the accuracy of the model has been assessed by comparing the International Reference Ionosphere (IRI 2016) model based vTEC and GPS-vTEC measurements as well as with the quadratic model based vTEC. Consequently, the linear model formulated with the solar, geomagnetic and periodic components significantly captured the variations (78-80%) of the observed vTEC compared with both the IRI 2016 and the quadratic models during the years 2012, 2014 and 2016. The comparison of the observed and predicted vTEC variations has also been examined using the continuous wavelet transform. The decomposed waves from the wavelet analysis have revealed that the predicted and observed vTEC have had simultaneous periods of variations specifically with the period of 27 days whereas the IRI 2016 could capture the short-period variations of vTEC. Moreover, the analysis from the transformed data in the year 2014 over both Arbaminch and Bahir Dar has indicated that the linear model based vTEC and the observed GPS-vTEC have had common pattern of variations with the period of 27 days that had lasted for 150 days (from day of the year 100 to 250).

InSAR Time Series Analysis of L-Band Data for Understanding Tropical Peatland Degradation and Restoration

In this study, satellite radar observations are employed to reveal spatiotemporal changes in ground surface height of peatlands that have, and have not, undergone restoration in Central Kalimantan, Indonesia. Our time series analysis of 26 scenes of Advanced Land Observation Satellite-1 (ALOS-1) Phased-Array L-band Synthetic-Aperture Radar (PALSAR) images acquired between 2006 and 2010 suggests that peatland restoration was positively affected by the construction time of dams—the earlier the dam was constructed, the more significant the restoration appears. The results also suggest that the dams resulted in an increase of ground water level, which in turn stopped peat losing height. For peatland areas without restoration, the peatland continuously lost peat height by up to 7.7 cm/yr. InSAR-derived peat height changes allow the investigation of restoration effects over a wide area and can also be used to indirectly assess the relative magnitude and spatial pattern of peatland damage caused by drainage and fires. Such an assessment can provide key information for guiding future restoration activities.

A Swift view of X-ray and UV radiation in the planet-forming T Tauri system PDS 70

ABSTRACT PDS 70 is a ∼5-Myr-old star with a gas and dust disc in which several protoplanets have been discovered. We present the first ultraviolet (UV) detection of the system along with X-ray observations taken with the Neil Gehrels Swift Observatory satellite. PDS 70 has an X-ray flux of 3.4 × 10−13 erg cm−2 s−1 in the 0.3–10.0 keV range, and UV flux (U band) of 3.5 × 10−13 erg cm−2 s−1 . At the distance of 113.4 pc determined from Gaia Data Release 2, this gives luminosities of 5.2 × 1029 and 5.4 × 1029 erg s−1, respectively. The X-ray luminosity is consistent with coronal emission from a rapidly rotating star close to the log $\frac{L_{\mathrm{X}}}{L_{\mathrm{bol}}} \sim -3$ saturation limit. We find the UV luminosity is much lower than would be expected if the star were still accreting disc material and suggest that the observed UV emission is coronal in origin.

Ozone profile climatology for remote sensing retrieval algorithms

Abstract. New ozone (O3) profile climatologies are created from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) O3 record between 2005 and 2016, within the period of Aura Microwave Limb Sounder (MLS) and Aura Ozone Monitoring Instrument (OMI) assimilation. These two climatologies consist of monthly mean O3 profiles and the corresponding covariances dependent on the local solar time, longitude (15∘), and latitude (10∘), which are parameterized by tropopause pressure and total O3 column. They are validated through comparisons, which show good agreements with previous O3 profile climatologies. Compared to a monthly zonal mean climatology, both tropopause- and column-dependent climatologies provide improved a priori information for profile and total O3 retrievals from remote sensing measurements. Furthermore, parameterization of the O3 profile with total column O3 usually reduces the natural variability of the resulting climatological profile in the upper stratosphere further than the tropopause parameterization, which usually performs better in the upper troposphere and lower stratosphere (UTLS). Therefore tropopause-dependent climatology is more appropriate for profile O3 retrieval for complementing the vertical resolution of backscattered ultraviolet (UV) spectra, while the column-dependent climatology is more suited for use in total O3 retrieval algorithms, with an advantage of complete profile specification without requiring ancillary information. Compared to previous column-dependent climatologies, the new MERRA-2 column-dependent climatology better captures the diurnal, seasonal, and spatial variations and dynamical changes in O3 profiles with higher resolutions in O3, latitude, longitude, and season. The new MERRA-2 climatologies contain the first quantitative characterization of O3 profile covariances, which facilitate a new approach to improve O3 profiles using the most probable patterns of profile adjustments represented by the empirical orthogonal functions (EOFs) of the covariance matrices. The MERRA-2 daytime column-dependent climatology is used in the combo O3 and SO2 algorithm for retrieval from the Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) satellite, the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) on the Suomi National Polar Partnership (SNPP), and the Ozone Monitoring Instrument (OMI) on the Aura spacecraft.

Comparison of time–distance inversion methods applied to SDO/HMI Dopplergrams

Context. The Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO) satellite has been observing the Sun since 2010. The uninterrupted series of Dopplergrams are ideal for studying the dynamics of the upper solar convection zone. Within the Joint Science Operations Center (JSOC) the time–distance inversions for flows and sound-speed perturbations were introduced. The automatic pipeline has produced flow and sound-speed maps every 8 h. We verify the results of JSOC inversions by comparing the data products to equivalent results from inverse modelling obtained by an independent inversion pipeline. Aims. We compared the results from the JSOC pipeline for horizontal flow components and the perturbations of the speed of sound at set of depths with equivalent results from an independently implemented pipeline using a different time–distance inversion scheme. Our inversion pipeline allows inversion for all quantities at once while allowing minimisation of the crosstalk between them. This gives us an opportunity to discuss the possible biases present in the JSOC data products. Methods. For the tests we used the subtractive optimally localised averaging (SOLA) method with a minimisation of the cross-talk. We compared three test inversions for each quantity at each target depth. At first, we used the JSOC setup to reproduce the JSOC results. Subsequently, we used the extended pipeline to improve these results by incorporating more independent travel-time measurements but keeping the JSOC-indicated localisation in the Sun. Finally, we inverted for flow components and sound-speed perturbations using a localisation kernel with properties advertised in the JSOC metadata. Results. We successfully reproduced the horizontal flow components. The sound-speed perturbations are strongly affected by the high level of the cross-talk in JSOC products. This leads to larger amplitudes in the inversions for the sound-speed perturbations. Different results were obtained when a target function localised around the target depth was used. This is a consequence of non-localised JSOC averaging kernels. We add that our methodology also allows inversion for the vertical flow.

Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A Case Study in Kullu Valley, Himalayas

Landslides are one of the most damaging geological hazards in mountainous regions such as the Himalayas. The Himalayan region is, tectonically, the most active region in the world that is highly vulnerable to landslides and associated hazards. Landslide susceptibility mapping (LSM) is a useful tool for understanding the probability of the spatial distribution of future landslide regions. In this research, the landslide inventory datasets were collected during the field study of the Kullu valley in July 2018, and 149 landslide locations were collected as global positioning system (GPS) points. The present study evaluates the LSM using three different spatial resolution of the digital elevation model (DEM) derived from three different sources. The data-driven traditional frequency ratio (FR) model was used for this study. The FR model was used for this research to assess the impact of the different spatial resolution of DEMs on the LSM. DEM data was derived from Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) ALOS-PALSAR for 12.5 m, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global for 30 m, and the Shuttle Radar Topography Mission (SRTM) for 90 m. As an input, we used eight landslide conditioning factors based on the study area and topographic features of the Kullu valley in the Himalayas. The ASTER-Global 30m DEM showed higher accuracy of 0.910 compared to 0.839 for 12.5 m and 0.824 for 90 m DEM resolution. This study shows that that 30 m resolution is better suited for LSM for the Kullu valley region in the Himalayas. The LSM can be used for mitigation and future planning for spatial planners and developmental authorities in the region.

Satellite Estimation of Falling Snow: A Global Precipitation Measurement (GPM) Core Observatory Perspective.

Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth's atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global precipitation Measurement (GPM) Core Observatory satellite and CloudSat's Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow-rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow-rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM's Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)-snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR-DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z-S approach.

Kinematics and Energetics of A Solar Jet from NOAA Active Region 12297

In this article, we have presented the study of a solar jet on March 14, 2015 recorded from the Atmospheric Imaging Assembly (AIA) onboard Solar Dynamics Observatory (SDO) satellite. The jet was observed from the solar active region NOAA AR 12297. We have discussed the dynamics and calculated the various possible energy contents of the observed jet. The estimated total energy flux liberated during the jet ejection was 2.1 x 107 erg cm-2 s-1 .

Kinematics and Energetics of A Solar Jet from NOAA Active Region 12297

In this article, we have presented the study of a solar jet on March 14, 2015 recorded from the Atmospheric Imaging Assembly (AIA) onboard Solar Dynamics Observatory (SDO) satellite. The jet was observed from the solar active region NOAA AR 12297. We have discussed the dynamics and calculated the various possible energy contents of the observed jet. The estimated total energy flux liberated during the jet ejection was 2.1 x 107 erg cm-2 s-1 .

Estimates of the Precipitation Top Heights in Convective Systems Using Microwave Radiances

The absence of systematic estimates of vertical transport of water by convective storms is one of the main causes of uncertainties in weather forecasting in the tropics, in severe weather forecasting, and in the climate-scale analysis and prediction of global circulation. The difficulty of providing such estimates lies in that the only instruments capable of providing such estimates at present, radiometers onboard satellites, operate at short wavelengths, and that makes difficult the retrievals. This paper describes a method capable of providing robust estimates of the maximum height of precipitation and the 3-D structure of condensed water in convective systems using a constellation of radiometers. Using hurricane Winston and the Global Precipitation Measurement (GPM) mission Core Observatory satellite dual precipitation radar as a reference, it is shown that the method is suitable to improve the parameterization of convection in numerical models.

Physical Evaluation of GPM DPR Single- and Dual-Wavelength Algorithms.

A physical evaluation of the rain profiling retrieval algorithms for the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory satellite is carried out by applying them to the hydrometeor profiles generated from measured raindrop size distributions (DSD). The DSD-simulated radar profiles are used as input to the algorithms, and their estimates of hydrometeors' parameters are compared with the same quantities derived directly from the DSD data (or truth). The retrieval accuracy is assessed by the degree to which the estimates agree with the truth. To check the validity and robustness of the retrievals, the profiles are constructed for cases ranging from fully correlated (or uniform) to totally uncorrelated DSDs along the columns. Investigation into the sensitivity of the retrieval results to the model assumptions is made to characterize retrieval uncertainties and identify error sources. Comparisons between the single- and dual-wavelength algorithm performance are carried out with either a single- or dual-wavelength constraint of the path integral or differential path integral attenuation. The results suggest that the DPR dual-wavelength algorithm generally provides accurate range-profiled estimates of rainfall rate and mass-weighted diameter with the dual-wavelength estimates superior in accuracy to those from the single-wavelength retrievals.

Updates of HITRAN spectroscopic database from 2008 to 2016 and implications for near‐infrared radiative transfer calculations

Spectroscopic information on atmospheric gases, such as absorption line positions, intensities and widths, is important for radiative transfer calculations in climate and weather models. The widely used HIgh resolution TRANsmission (HITRAN) spectroscopic database is updated regularly. A line‐by‐line radiative transfer model has been used to investigate the impacts of the updates of HITRAN from 2008 to 2016 on clear‐sky calculations of absorption and solar heating rates in the near‐infrared region from 4,000 to 10,000 cm−1 (1.0–2.5 µm). These calculations were carried out for the tropical and sub‐arctic atmospheres at solar zenith angles of 30° and 75° using HITRAN absorption line parameters for H2O, CO2, O3, N2O, CO, CH4 and O2 as well as the MT_CKD 2.5 (Mlawer‐Tobin‐Clough‐Kneizys‐Davies) continua for H2O, CO2, O3 and O2. It was found that the improvements in HITRAN absorption line parameters led to modest increases in the spectrally integrated absorbed downward solar fluxes and heating rates. The differences in absorbed fluxes are larger between HITRAN2016 and HITRAN2008 (maximum difference ∼1.3%) than between HITRAN2016 and HITRAN2012 (largest difference ∼0.3%). The corresponding maximum differences in heating rates are less than 2%. In order to improve accuracy, HITRAN2016 should be used in preference to earlier line‐lists for calculating near‐infrared absorbed solar fluxes and heating rates; but for calculations of clear‐sky heating rates in the near‐infrared region that do not require better than 2% accuracy, line parameters from HITRAN2012 and HITRAN2008 may still be used. Although the revisions of HITRAN led to relatively small differences in the spectrally integrated quantities, the improved quality of near‐infrared line parameters will have a significant impact on other atmospheric applications such as the measurements of CO2 concentrations at a precision of less than 1% in Greenhouse Gases Observatory Satellite (GOSAT) and Orbiting Carbon Observatory‐2 (OCO‐2) missions.

Remote Sensing of Daytime Water Leaving Reflectances of Oceans and Large Inland Lakes from EPIC onboard the DSCOVR Spacecraft at Lagrange-1 Point.

The NASA’s Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) satellite has been making multiple observations of the entire sunlit Earth in a given day from the Sun-Earth Largangian L1 point since the summer of 2015. EPIC contains 10 narrow channels in the 317–780 nm solar spectral range. The data acquired with EPIC have already been used in a variety of scientific investigations, including the study of the global ozone levels, aerosol index and aerosol optical depth, UV reflectivity of clouds over land and ocean, cloud height over land and ocean, and vegetation indices. In this article, we report that EPIC data, particularly for the data measured with narrow channels centered near 443, 551, and 680 nm, can also have important applications in remote sensing of ocean color in different geographical regions. We have modified a version of a multi-channel atmospheric correction algorithm for Moderate Resolution Imaging SpectroRadiometer (MODIS) ocean color applications and adapted the algorithm for processing EPIC data. We present three case studies on water leaving reflectance retrievals from EPIC data acquired over a large turbid river, inland lakes, and oceans. We conclude that a future ocean color instrument on board a satellite at the L1 point, which provides continuous view of the full sunlit disk of the Earth, will complement and extend ocean color observations with the low Earth observing polar orbital and geostationary satellite instruments in both the spatial and time domains.

Source Parameter Estimation of the 2009 Ms6.0 Yao’an Earthquake, Southern China, Using InSAR Observations

On 9 July 2009, an Ms6.0 earthquake occurred in mountainous area of Yao’an in Yunnan province of Southern China. Although the magnitude of the earthquake was moderate, it attracted the attention of many Earth scientists because of its threat to the safety of the population and its harm to the local economy. However, the source parameters remain poorly understood due to the sparse distribution of seismic and GNSS (Global Navigation Satellite System) stations in this mountainous region. Therefore, in this study, the two L-band ALOS (Advanced Land Observing Satellite-1) PALSAR (Phased Array type L-band Synthetic Aperture Radar) images from an ascending track is used to investigate the coseismic deformation field, and further determine the location, fault geometry and slip distribution of the earthquake. The results show that the Yao’an earthquake was a strike-slip event with a down-dip slip component. The slip mainly occurred at depths of 3–8 km, with a maximum slip of approximately 70 cm at a depth of 6 km, which is shallower than the reported focal depth of ~10 km. An analysis of the seismic activity and tectonics of the Yao’an area reveals that the 9 July 2009 Yao’an earthquake was the result of regional stress accumulation, which eventually led to the rupture of the northwestern most part of the Maweijing fault.

Sunspot proper motion of individual sunspot in active region 11429 during X5.4 solar flare on March 7, 2012

Based on high resolution images obtained from Helioseismic and Magnetic Imager (HMI) instrument onboard Solar Dynamic Observatory (SDO) satellite at visual wavelength, we traced several individual umbral sunspots to find drift motion during flare occurrences within Active Region (AR) 11429 (N17, E15) at 00:00 to 05:00 UT on March 7, 2012. AR11429 was a large sunspot group with 1270 millionth solar hemisphere in area and complex magnetic morphology with beta-gamma-delta class. We measured relative motion of 22 umbral sunspots positions during eruptive and post-flare phases for about 5 hours. Some sunspots showed faster motion during eruptive phase at about 10% than decay phase and they moved nearly along the magnetic field polarity inversion line that showed shear motion. Flare outburst which is occurred in corona layer affected sunspot motion and area, as seen for their changing configuration and morphology, before and after peaked X-ray intensity. This result indicates that there is a strong connection between X-ray solar flare at corona layer and its foot points motion at photosphere.

Cluster-Keeping Algorithms for the Satellite Swarm Sensor Network Project

This paper develops cluster control algorithms for the Satellite Swarm Sensor Network project, for which the main aim is to enable disaggregation of space-based remote sensing, imaging, and observation satellites. A methodological development of orbit control algorithms is provided, supporting the various use cases of the mission. Emphasis is given on outlining the algorithm’s structure, information flow, and implementation. The methodology presented herein enables operation of multiple satellites in coordination to facilitate disaggregation of space sensors and augmentation of data provided therefrom.

EARTHQUAKE-INDUCED BUILDING DETECTION BASED ON OBJECT-LEVEL TEXTURE FEATURE CHANGE DETECTION OF MULTI-TEMPORAL SAR IMAGES

Abstract The damage of buildings is the major cause of casualties of from earthquakes. The traditional pixel-based earthquake damaged building detection method is prone to be affected by speckle noise. In this paper, an object-based change detection method is presented for the detection of earthquake damage using the synthetic aperture radar (SAR) data. The method is based on object-level texture features of SAR data. Firstly, the principal component analysis is used to transform the optimal texture features into a suitable feature space for extracting the key change. And then, the feature space is clustered by the watershed segmentation algorithm, which introduces the concept of object orientation and carries out the calculation of the difference map at the object level. Having training samples, the classification threshold values for different grade of earthquake damage can be trained, and the detection of damaged building is achieved. The proposed method could visualize the earthquake damage efficiently using the Advanced Land Observing Satellite-1 (ALOS-1) images. Its performance is evaluated in the town of jiegu, which was hit severely by the Yushu Earthquake. The cross-validation results shows that the overall accuracy is significantly higher than TDCD and IDCD.

Creating Synthetic Radar Imagery Using Convolutional Neural Networks

AbstractIn this work deep convolutional neural networks (CNNs) are shown to be an effective model for fusing heterogeneous geospatial data to create radar-like analyses of precipitation intensity (i.e., synthetic radar). The CNN trained in this work has a directed acyclic graph (DAG) structure that takes inputs from multiple data sources with varying spatial resolutions. These data sources include geostationary satellite (1-km visible and four 4-km infrared bands), lightning flash density from Earth Network’s Total Lightning Network, and numerical model data from NOAA’s 13-km Rapid Refresh model. A regression is performed in the final layer of the network using NEXRAD-derived data mapped onto a 1-km grid as a target variable. The outputs of the CNN are fused with analyses from NEXRAD to create seamless radar mosaics that extend to offshore sectors and beyond. The model is calibrated and validated using both NEXRAD and spaceborne radar from NASA’s Global Precipitation Measurement (GPM) Mission’s Core Observatory satellite. The advantages over a random forest–based approach used in previous works are discussed.

STUDY OF GPM-IMERG RAINFALL DATA PRODUCT FOR GANGOTRI GLACIER

Abstract. This study presents a comparison of new generation weather observatory satellites Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) rainfall products with field data collected for Gangotri glacier in India. The meteorological analysis of rainfall estimates has been performed on GPM IMERG Final, Late and Early precipitation products available at daily scale with a spatial resolution of 0.1° × 0.1° for melting season from May to September for the year 2014 and 2015 respectively. The comparison of satellite products with field data was done using correlation coefficient and standard anomaly. The Late run curve showed a high degree of similarity with final run curve while early run showed variation from them. The satellite meteorological data correctly identified non-rainy days with an average of ∼86.7%, ∼67.5% and ∼95% for pre-monsoon, monsoon and post-monsoon season respectively. The rmse for final run data product for 2014 and 2015 are 4.5, 1.23, 1.55, 1.24, 0.8 and 1.14, 7.1, 1.82, 1.15, 1.52 from May to September respectively. Overall, it has been observed that for medium to heavy rainfall final run estimates are close to field data and for light to medium rainfall late run estimates are close. Similar results have been obtained from both datasets for non-rainy days in the study area.