Earth and Space Science

Journal Information
ISSN / EISSN : 23335084 / 23335084
Current Publisher: American Geophysical Union (AGU) (10.1029)
Former Publisher: Wiley (10.1002)
Total articles ≅ 629
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Udit Asopa, Shashi Kumar
Earth and Space Science; doi:10.1029/2020ea001230

Polarimetric SAR remote sensing has been widely used for structural and biophysical parameters retrieval of forest vegetation. It has been found that the combination of polarimetric properties and interferometric characteristics of SAR remote sensing provides the capacity to retrieve forest height. The prime objective of this research was to investigate the potential of Polarimetric Synthetic Aperture Radar Tomography (PolTomSAR) for the forested and river region of Mondah National Park, Gabon. SAR Tomography is an improved method for acquiring the height of geographical features. UAVSAR L‐band fully polarimetric multibaseline data have been used in this research (1.275 GHz). SAR data and Ground data over the area have been collected in the year 2016. With the super‐resolution based capon algorithm, multiple scatterers located at a different vertical position in the same azimuth‐range cell has been resolved and reconstructed. This work provides a framework for capon based tomographic processing of multibaseline UAVSAR data for vertical profile retrieval of forest vegetation. The height profile of the forest patch having sparse, as well as dense vegetation, were retrieved. The vertical profile for a single azimuthal bin was obtained in range direction. The tomographic profile obtained was cross‐checked with the field‐measured forest height for the 16 locations in Mondah National Park, Gabon. To check the accuracy of the applied method, the statistical method of R2 and RMSE is employed. The obtained RMSE of the result is 4.21 m and R2 is 0.92. The obtained results were concluded to find the potential of the capon algorithm for the tomographic reconstruction of UAVSAR data.
S. Rehana, N. T. Monish
Earth and Space Science; doi:10.1029/2020ea001264

Understanding the drought characteristics is critical for water resources management in water stressed countries such as India. Previous studies evaluating drought assessments over India considered Precipitation (P) and Potential Evapotranspiration (PET) as drivers using Standardized Precipitation‐Evapotranspiration Index (SPEI). The suitability of Actual Evapotranspiration (AET), which accounts for both water and energy based evaporative demands, in drought characterization is limited. In this study, SPEI is restructured with AET to characterize the regional drought over water and energy limited regions as Standardized Precipitation Actual Evapotranspiration Index (SPAEI). For this, AET estimated based on Budyko framework and remote sensing‐based AET data has been used. The original formulation of SPEI is limited towards capturing the seasonality present in P and PET. The SPEI is restructured to account for the water availability deficit in the drought assessment rather than the actual atmospheric water demand in a given period to capture the strong seasonality of rainfall. The study compared the drought characteristics with both PET and AET for various meteorological homogeneous zones of India, which are characterized as water‐limited (Central, North, West, South, J&K) and energy‐limited (North‐East and North‐East hills) zones. Overall, the proposed new drought index based on AET can be promising towards drought intensity, extreme drought areal extents, shorter‐time scale drought frequencies and longer‐time scale drought durations for water‐limited zones. The proposed drought indices based on AET can be robust for the drought assessment under consideration of water‐energy along with land and vegetation variability and can provide more insights for water‐limited regions.
J. T. Emmert, D. P. Drob, J. M. Picone, D. E. Siskind, M. Jones, M. G. Mlynczak, P. F. Bernath, X. Chu, E. Doornbos, B. Funke, et al.
Earth and Space Science; doi:10.1029/2020ea001321

NRLMSIS® 2.0 is an empirical atmospheric model that extends from the ground to the exobase and describes the average observed behavior of temperature, 8 species densities, and mass density via a parametric analytic formulation. The model inputs are location, day of year, time of day, solar activity, and geomagnetic activity. NRLMSIS 2.0 is a major, reformulated upgrade of the previous version, NRLMSISE‐00. The model now couples thermospheric species densities to the entire column, via an effective mass profile that transitions each species from the fully mixed region below ~70 km altitude to the diffusively separated region above ~200 km. Other changes include the extension of atomic oxygen down to 50 km and the use of geopotential height as the internal vertical coordinate. We assimilated extensive new lower and middle atmosphere temperature, O, and H data, along with global average thermospheric mass density derived from satellite orbits, and we validated the model against independent samples of these data. In the mesosphere and below, residual biases and standard deviations are considerably lower than NRLMSISE‐00. The new model is warmer in the upper troposphere and cooler in the stratosphere and mesosphere. In the thermosphere, N2 and O densities are lower in NRLMSIS 2.0; otherwise, the NRLMSISE‐00 thermosphere is largely retained. Future advances in thermospheric specification will likely require new in situ mass spectrometer measurements, new techniques for species density measurement between 100 and 200 km, and the reconciliation of systematic biases among thermospheric temperature and composition datasets, including biases attributable to long‐term changes.
Shahid Shuja Shafai, Shashi Kumar
Earth and Space Science; doi:10.1029/2020ea001279

Target characterization is an essential aspect of polarimetric decomposition. This technique is capable of categorizing polarimetric signatures for different types of targets based on the scattering mechanisms they follow, enabling straightforward physical interpretation of the targets. The geometric anomalies associated with human‐made targets escalate the degree of randomness in the scattering process, which causes scattering ambiguity for such targets. The second‐order model descriptors do not relate to the actual physical structure and yield predominant volume scattering power. Such urban targets are decomposed as natural targets leading to irrelevant decomposition results. The methods developed to curb the problem are unable to maintain the consistency in the decomposition modeling as they underestimate volume scattering powers for natural landcover. A hybrid decomposition model is proposed herein to solve the problem of predominant volume scattering observed from urban targets by preserving volume scattering powers for natural targets. The model uses eigenvalue‐based decomposition parameters and PolInSAR coherence to decompose ambiguous targets. The proposed model has been tested on NISAR UAVSAR PolInSAR data acquired over the Greenville region, MS, USA. The proposed model has increased the double‐bounce scattering from the urban targets and enhanced the volume scattering from natural landcover as well. By comparing the results with existing decomposition models, it is observed that the proposed model gives a more robust representation of the landcover than the compared decomposition models.
T. Kubota, T. Saito, N. Y. Chikasada, W. Suzuki
Earth and Space Science; doi:10.1029/2020ea001197

Recent developments of ocean‐bottom pressure gauges (PG) have enabled us to observe various waves including seismic and tsunami waves covering periods of T ~100–103 s. To investigate the quality for broadband observation, this study examined the broadband PG records (sampling rate of 1 Hz) around Japan associated with the 2010 Chile earthquake. We identified three distinct wave trains, attributed to seismic body waves, Rayleigh waves and tsunamis. Clear dispersive features in the Rayleigh waves and tsunamis were explained by theories of elastic waves and gravity waves. Quantitative comparison between pressure change and nearby seismograms demonstrated the validity of the theoretical relation between pressure p and vertical acceleration az for ~3h from the origin time. We also found a relationship between p and vertical velocity vz holds only at the first P wave arrival, but not for later arrivals. Similar results were confirmed for various earthquakes with different source‐station distances and magnitudes, suggesting the robustness of these relations. The results demonstrate that the high‐sampling rate (≥ 1 Hz) is necessary to observe seismic‐wave dispersion and PG can record both seismic waves and tsunamis with reasonable quality for waveform analyses, whereas conventional onshore and offshore seismometers or tide gauges can observe either of seismic waves and tsunamis. Utilizing the high‐sampling PG in combination with the seismic and tsunami propagation theory for estimating earthquake source process or analyzing wave propagation processes in the ocean, will deepen our geophysical understanding of the solid‐fluid coupled system in the earth and contribute towards disaster mitigation.
Published: 8 September 2020
by Wiley
Earth and Space Science, Volume 7; doi:10.1002/ess2.319

Congcong Yu, Jinpei Yan, Honghai Zhang, Qi Lin, Hongguo Zheng, Xinlin Zhong, Suli Zhao, Miming Zhang, Shuhui Zhao, Xiaojun Li
Earth and Space Science, Volume 7; doi:10.1029/2020ea001227

In the current study, the characteristics of aerosol water‐soluble ions (WSI) were investigated via high‐time‐resolution observations as part of the nineth Chinese National Arctic Research Expedition (CHINARE) in 2018. WSI, including Cl−, SO42−, NO3−, NO2−, F−, Br−, Na+, NH4+, K+, Ca2+, Mg2+, and Methanesulfonic acid (MSA−), were measured using an online ion chromatography system deployed on the Xuelong research vessel. Moreover, aerosol particle sources were analyzed in order to clarify the impacts of marine emissions and anthropogenic sources on atmospheric aerosols in the Arctic Ocean. Sea salt ions (Na+ and Cl−) were observed to be the most dominant compounds, accounting for 53.5% of the total WSI, followed by the secondary ions (SO42−, NH4+, and NO3−), which accounted for 36.0%. Furthermore, similar spatial distributions of MSA− and SO42− were observed during the measurement period. High levels of Na+ were observed in the areas close to the land and Central Arctic Ocean, attributed to wind speed. In the open ocean, NH4+ and NO3− concentrations were extremely low, however, high levels of NH4+ and NO3− were observed in the coastal area close to Alaska and Russia. This indicates that these aerosol particles were influenced by the anthropogenic sources in these regions. In addition, mean fluxes of NO3− and NH4+ were determined as 42 ± 41 and 347 ± 166 μg m−2 day−1 in the Arctic Ocean, respectively.
John B. RundleiD, A. Donnellan
Earth and Space Science; doi:10.1029/2020ea001097

Seismic bursts in Southern California are sequences of small earthquakes strongly clustered in space and time, and include seismic swarms and aftershock sequences. A readily observable property of these events, the radius of gyration (RG), allows us connect the bursts to the temporal occurrence of the largest M≥7 earthquakes in California since 1984. In the Southern California earthquake catalog, we identify hundreds of these potentially coherent space‐time structures in a region defined by a circle of radius 600 km around Los Angeles. We compute RG for each cluster, then filter them to identify those bursts with large numbers of events closely clustered in space, which we call "compact" bursts. Our basic assumption is that these compact bursts reflect the dynamics associated with large earthquakes. Once we have filtered the burst catalog, we apply an exponential moving average to construct a time series for the Southern California region. We observe that the RG of these bursts systematically decreases prior to large earthquakes, in a process that we might term "radial localization." The RG then rapidly increases during an aftershock sequence, and a new cycle of "radial localization" then begins. These time series display cycles of recharge and discharge reminiscent of seismic stress accumulation and release in the elastic rebound process. The complex burst dynamics we observe are evidently a property of the region as a whole, rather than being associated with individual faults. This new method allows us to improve earthquake nowcasting, which is a technique to evaluate the current state of hazard in a seismically active region.
Yen‐Jung J. Wu, Brian J. Harding, Colin C. Triplett, Jonathan J. Makela, Kenneth D. Marr, Christoph R. Englert, John M. Harlander, Thomas J. Immel
Earth and Space Science; doi:10.1029/2020ea001164

The Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) on NASA's Ionospheric Connection Explorer (ICON) mission is designed to measure the neutral wind and temperature between 90 km and ~300 km altitude. Using the Doppler Asymmetric Spatial Heterodyne (DASH) spectroscopy technique, observations from MIGHTI can be used to derive thermospheric winds by measuring Doppler shifts of the atomic oxygen red line (630.0 nm) and green line (557.7 nm). Harding et al. (2017) (Harding17) describe the wind retrieval algorithm in detail, and point out the large uncertainties that result near the solar terminators and equatorial arcs, regions of large spatial gradients in airglow volume emission rates (VER). The uncertainties originate from the assumption of a constant VER at every given altitude, resulting in errors where the assumption is not valid when limb sounders, such as MIGHTI, observe regions with significant VER gradients. In this work, we introduce a new wind retrieval algorithm (Wu20) with the ability to account for VER that is asymmetric along the line of sight with respect to the tangent point. Using the predicted ICON orbit and simulated global VER variation, the greatest impact of the symmetric airglow assumption to the ICON vector wind product is found within 30° from the terminator when the spacecraft is in the dayside, causing an error of at least 10 m/s. The new algorithm developed in this study reduces the error near the terminator by a factor of 10. Although Wu20 improves the accuracy of the retrievals, it loses precision by 75% compared to Harding17.
Nozomi Sugiura, Shigeki Hosoda
Earth and Space Science; doi:10.1029/2019ea001019

A profile from the Argo ocean observation array is a sequence of three‐dimensional vectors composed of pressure, salinity, and temperature, appearing as a continuous curve in three‐dimensional space. The shape of this curve is faithfully represented by a path signature, which is a collection of all the iterated integrals. Moreover, the product of two terms of the signature of a path can be expressed as the sum of higher‐order terms. As a result of this algebraic property, a nonlinear function of the profile shape can always be represented by a weighted linear combination of the iterated integrals, which enables machine learning of a complicated function of the profile shape. In this study, we performed supervised learning for existing Argo data with quality control flags by using the signature method, and demonstrated the estimation performance by cross‐validation. Unlike rule‐based approaches, which require several complicated and possibly subjective rules, this method is simple and objective in nature because it relies only on past knowledge regarding the shape of profiles. This technique is critical for realizing automatic quality control for Argo profile data.
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