ALBERT

Description: This dataset includes annual mosaics of Antarctic ice velocity derived from Landsat 8 images between December, 2013 and April, 2019, which was updated in 2020 in order to produce multi-year annual ice velocity mosaics and improve the quality of products including non-local means (NLM) filter, and absolute calibration using rock outcrops data. The resulting Version 2 of the mosaics offer reduced local errors, improved spatial resolution as described in the README file.

Description: On February 6, 2023, a sequence of earthquakes with Mw 7.8 and Mw 7.5 occurred in southern central Turkey near the northern border of Syria about in 9 hours. The disastrous earthquake sequence and its other aftershocks caused heavy human casualties and devastating building collapses. We employ the Empirical Orthogonal Function (EOF) analysis to capture coherent spatiotemporal features of co-seismic deformation for three components (N, E, U), which is based on the time series of 1-Hz GPS solutions at 20 permanent stations spatially well-distributed around the ruptured Anatolian fault system. The solved EOF modes show patterns which would help to investigate co-seismic rupture of the seismogenic faults. We compare the EOF-derived co-seismic displacement to the modeling results, which is computed from the spherical, elastic dislocation theory and finite fault model inverted from teleseismic waves records. Both GPS-observed and the modeled displacements show high consistency except for that at station EKZ1 (Ekinözü) where ~4.7 m of westward motion was estimated from GPS which we believe does not entirely represent the crustal motion; some other phenomena such as a local co-seismic landslide or a relative motion of the pillar with respect to the ground might have occurred. Moreover, this sequence is a large typical strike-slip faulting, which can generate gravity change above the threshold proposed by some theoretical simulation based on the satellite gravimetry observations. We also compute forward-modeled coseismic gravity changes, and discuss the plausible detection by instrument onboard of GRACE Follow-On gravimetry mission.

Description: Satellite altimetry has evolved into a unique and operational geodetic remote sensing measurement system with multi-missions and multi-satellite constellations generating an unprecedented climate data record since 1993, which has fostered seminal research in interdisciplinary Earth sciences. Satellite altimetry is deemed to be operational and sustained, contributing to much geodesy and climate research into climate monitoring, meteorological and ocean circulation forecasting, vertical datum realization, maritime safety, ocean pollution tracking services, flood and water resources management, energy, and many others. The constellations of altimeter missions enable the generation of multi-decadal, continuous, and uniform geodetic and climate data records at unprecedented spatiotemporal resolution and accuracy. Innovative instrumentation has advanced from pulse-limited to Delay-Doppler (or SAR), KaRIn, ATLAS and spaceborne lidar altimeters, as well as to the recent exploitation of SoOP (Signals of Opportunity) satellite sources in bistatic radar enabled altimetry, including L-band GNSS-Reflectometry enabled altimetry, P-band and other radar band SoOP signal-enabled sensors/altimetry. Altimetry advances have offered significant opportunities and challenges to all scientific disciplines and applications, which could afford, more than ever, a need for the establishment of the International Altimetry Service (IAS) under the International Association of Geodesy (IAG).IAS aims to (1) establish IAG Service-Style Coordinating Board, Analysis and Data Centers, and (2) convene and establish formal dialogs with scientists and key technical curators of existing altimetry data product services.

Description: Groundwater is a key freshwater resource used for agricultural, industrial, and municipal water supply. Climate change and human activities have resulted in an increased dependence on groundwater in many aquifers around the world. Climate change extremes such as floods and droughts, population growth along with increasing dependence on using groundwater for irrigation and industrial activities pose challenge to the future availability of groundwater. Accurate and reliable methods of groundwater level (GWL) forecasting are a key tool which can inform the groundwater managers about the future quantitative availability of groundwater. Although a variety of numerical and statistical approaches have been applied for GWL forecasting of single wells, a global forecasting method utilizing the GWL time series from several well sites in an aquifer is applied in this study. The global forecasting approach leverages algorithms such as Deep Learning (DL), Long Short-Term Memory (LSTM), Nonlinear Autoregressive Neural Network (NARX) which can model complex non-linear time series relationships between input and output variables. We primarily use precipitation and temperature meteorological data along with GRACE derived Terrestrial Water Storage (TWS) as the input for models and GWL as the output. We implement the algorithm in several stressed aquifers around the world, such as Central Valley in California, High Plains in Kansas, Indo-Gangetic basin in northern India, and North China Plain in China. The data-driven approaches provide robust forecasting and can thus form the basis for effective management decisions and strategies.

Description: The time-variable Earth’s gravity field is related to the mass transport and the physical processes within Earth’s system (including the atmosphere, oceans, hydrosphere, and cryosphere), such as melting of ice sheets and glaciers, ocean circulation and sea level variations, hydrological cycle, glacial isostatic adjustment, and earthquake-induced gravity change. The application of precise orbit determination (POD) provides valuable information about the Earth’s mass transport manifested in the temporal variations of the gravity field. This project uses the kinematics orbit-based acceleration approach, which has proven to be effective on the operational temporal gravity field data product generation using the high-low GNSS tracking data collected by the 3-satellite Swarm constellation. This project will utilize geodetic quality, dual-frequency high-low (GNSS-Spire CubeSats) tracking data from Spire Global, Inc.’s ~40 Lemur-2 3U CubeSat constellation. This low Earth orbiting (LEO) constellation is primarily dedicated to operational atmosphere data retrieval using radio occultation, bistatic GNSS-reflectometry forward scattering signals and grazing angle altimetry. The acceleration approach can provide solutions of the Earth’s temporal gravity field in long-wavelength (longer than 1,000 km), but plausibly at higher temporal sampling, weekly or finer with the global coverage of up to 40 simultaneous GNSS-Sensing of Spire CubeSats. This provides an additional observing architecture of gravity field solutions which can be used in conjunction with the more accurate and higher resolution temporal gravity solutions from GRACE-FO to further improve the temporal resolutions of monitoring abrupt-episode natural hazard evolution such as tropical cyclones, wildfires and floods, as well as weekly surface/ground water storage changes.

Description: An exponentially increasing number of scientific and commercial Earth orbiting satellites are delivering global and timely sensing of the Earth from space, on its surface or from inside the Earth. The onset of climate change and its dire consequences has been exacerbating the adverse impacts on Earth’s environments and its inhabitants. Timely satellite-based Big Earth observations at adequate spatiotemporal resolution provide a means to monitor the evolutions of more frequent and abrupt climate induced and enhanced hazards. These observations could contribute towards the elucidation of their respective governing climatic processes, and enable improved hazards forecasting, water resources monitoring, and informed hazards management and response. Example satellite geodetic and other observations include satellite gravimetry, altimetry, GNSS, GNSS bistatic altimetry, SAR/InSAR, and Planet PBC's high spatiotemporal (subdaily and 3-5 m) resolution multispectral imageries. We illustrate that the use of deep machine learning analytics can effectively integrating hydrometeorological model and other data, and downscaling the satellite geodetic observations, towards enabling timely monitoring of abrupt weather episode evolutions, including floods, groundwater depletions, cyclone landfall, snowstorms, and meteotsunamis.

Description: The knowledge gap of exact vertical motion at tide gauges limits the quantification of geocentric sea-level variations. Vertical motion, which is insensitive to satellite altimetry, plausibly exhibits transient or non-linear coastal land subsidence signals varying at spatial scales much finer than coastal sea-level rise. Alternate vertical motion determinations using GNSS receivers collocated with tide gauges, and GPS imaging technique for vertical motion estimates, arguably may not be exactly measuring the vertical motion exactly at tide gauge locations. The exact geocenter motion, defined as Earth’s Center of Mass change relative to Center of Figure, arguably remain elusive. The ongoing viscoelastic rebound of the solid Earth due primarily to the deglaciation of Late Pleistocene ice sheets, the GIA geophysical processes, are not well-known, including the bedrocks beneath polar ice sheets, and seafloor. The knowledge gap of steric sea level covering all depths of the ocean remain large. The plausible constraints using fingerprint approaches for sea-level adjustment have not been realized, presumably due to uncertainties of pattern stationarity. While statistically significant sea-level accelerations are detected using long-term tide gauges, altimeter-era sea-level acceleration estimates at local scales remains inconclusive. Finally, evidence of multi-decadal ocean oscillations presence would further diminish the more exact sea-level acceleration estimates at regional scales. Here, we report our sea-level research in progress, addressing some of the aforementioned limitations. We present an updated sea-level reconstruction and preliminary results, which jointly estimate for vertical motion at tide gauge locations, and geocentric sea-level trends at the local scale over the last seven decades.

Description: After a weak decreasing trend from 2000 to 2013, the water levels of the Laurentide Great Lakes increased by about 0.9-1.9 m from 2013 to 2020. Apart from such long-term change, water levels of the Great Lakes also exhibit significant annual and interannual variabilities. Investigating such multitemporal signals is pivotal to improve our understanding of the continental-scale water cycle and provides insights for elucidating short-term climate variations. Here, we use the variational mode decomposition (VMD) method to study the multitemporal water level variations of the Great Lakes. Specifically, the VMD method is applied to twenty water level gauges, and then the decomposed signals with the identical temporal resolution are averaged to obtain corresponding water level changes. Changes in water levels results in a variation in terrestrial water storage (TWS) and causes elastic loading deformation. Here, the GRACE/GRACE-FO gravimetry Level 2 data are used to reveal the TWS change, which is computed using the Slepian basis function which mitigates the signal leakage effect. The loading-induced deformation is calculated with a forward model and validated with GNSS coordinate time series. In addition, to further explore the climatologic mechanism for water level change, precipitation data from National Weather Service is also used. All of the geodetic observations mentioned are processed with the VMD method to acquire resolutions suitable to model multitemporal water levels, and the joint analysis aims to improve our knowledge of the causes and extent of water level change of the Great Lakes during the recent decades.

Description: Although the world’s land digital elevation/surface model (DEM/DSM) has been well established through spaceborne and airborne sensors, high-resolution shallow clear water bathymetry remains poorly charted. Knowing the bathymetry in coastal zones is critical, especially for ocean navigation, environmental protection, mineral resources mining, and coastal management. However, surveys of bathymetry data in a traditional way relies heavily on humanpower and cost of equipment/vessels. National Aeronautics and Space Administration’s (NASA’s) Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), launched in 2018, provides multi-beam georeferenced photon data every 70 centimeter along its ground tracks with a 91-day repeat orbit. The georeferenced photons can be utilized to derive water depth on coastal islands even without any on-site data. Here, we examine a synthesis of ICESat-2 ATL03 photon data and Sentinel-2 optical imagery to derive a bathymetry model based on Beer-Lambert law. Once the model is trained, the prediction of accuracy using goodness-of-fit (GoF) helps to select the most appropriate images. We select multiple islands in the South China Sea as testing sites, where the availability of coastal bathymetry models is scarce because it is costly and difficult to survey. The final bathymetry model is derived with a composite of multiple images, and the bathymetry accuracy via cross validation meets the requirement of category C in Zones of Confidence (ZOC) of the Electronic Navigational Chart (ENC) in 0-15m.

Description: The rapid rising sea levels and their associated adverse impacts on world’s coastal zones, where approximately 40% of the world's population resides within a 100-kilometer proximity to the coast, have become a subject of increasing global concern as sea level rises are projected to be worsen over time. One of the key conclusions from the latest Intergovernmental Panel on Climate Change (IPCC) Reports is that global sea level changes are experiencing marked acceleration and significant regional variability as a result of both climate change and local factors. Many countries located in the vicinity of the Arabian Peninsula are with low-lying coastal regions, making them highly vulnerable to the adverse effects of sea level rise, including saltwater intrusion and the threat of coastal flooding. It is imperative to assess the rate of sea level rise and its underlying determinants in the region. This study aimed to comprehend the sea level trend in the Arabian Peninsula by analyzing five tide gauge data obtained from the Permanent Service for Mean Sea Level along the coasts of the region and altimetry data from the Archiving, Validation, and Interpretation of Satellite Oceanographic and the Copernicus Climate Change Service for the period 1993-2020 and estimate the vertical land motions at the tide gauge station locations. An accurate determination of the regional relative sea level change allows allows evaluating potential flood patterns and developing long-term strategies for coastal management, and adaptation to the impacts of climate change.