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During the past 35 years, scientists have developed a vast network of seismometers that record earthquakes, volcanic eruptions, and nuclear explosions throughout the world. Seismographic data support disaster response, scientific research, and global security. With this network, the United States maintains world leadership in monitoring the greatest natural and technological events that threaten our planet's population.
The primary ingredients on the hazard side of the equation include the rapid characterization of the earthquake source and quantifying the spatial distribution of the shaking, plus any secondary hazards an earthquake may have triggered. On the earthquake impact side, loss calculations require the aforementioned hazard assessments—and their uncertainties—as input, plus the quantification of the exposure and vulnerability of structures, infrastructure, and the affected inhabitants. Lastly, effectively communicating uncertain estimates of the resulting impacts on society requires careful consideration of its function and form. All these aspects of rapid earthquake information delivery entailed wide-ranging collaborative research and development among seismologists, earthquake engineers, geographers, social scientists, Information Technology professionals, and communication experts, leveraging diverse components and ingredients not achievable without extensive collaboration. I was very fortunate to be able to work on interesting and useful projects with many colleagues who got involved with them. Advances in content, its rapid delivery, and our ability to better communicate uncertain loss estimates greatly expanded the range of users and critical decision-makers who could directly benefit from rapid post-earthquake information. Moreover, in the critical user–developer feedback loop, we have intently followed requests from users to develop new ways of delivering the most-requested post-earthquake information within the limitations of the science and technology. Such new avenues and tools then motivated and prioritized additional research directions and developments.
In April 2020, the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center introduced a Level 2 provisional Aquatic Reflectance (AR) product for the Landsat 8 Operational Land Imager (OLI), marking the initial phase in developing a standardized global product for Landsat-derived surface water measurements. The goal of USGS EROS aquatic product research and development is to prepare for an operational processing architecture for Landsat Collection 3 in the late 2020s that will enable use of quality-controlled data for emerging Landsat aquatic science applications. To achieve this, we released a subset of the Landsat 8/9 provisional AR products (Crawford et al., 2025, https://doi.org/10.5066/P14MBBRM) and examined its general performance through the Science Algorithms to Operations (SATO) framework alongside quantitative assessment using community made inland water data records (GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments, GLORIA) and radiometric coastal validation platforms (NASA’s Ocean Color component of the Aerosol Robotic Network, AERONET-OC). Variability within the validation datasets indicate that the performance of the Landsat 8/9 provisional AR retrieval is highly context-dependent; errors are minimal in optically simple waters (e.g., clear to moderately turbid coastal waters) but increase considerably in optically complex waters where factors such as elevated levels of turbidity, chlorophyll (Chl a ) concentrations, or colored dissolved organic matter (CDOM) dominate the water column. Additionally, this paper examines key algorithmic considerations for atmospheric correction, highlighting factors that influence accuracy, scalability, and computational efficiency necessary for collection processing in the operational Landsat Product Generation System (LPGS). This paper is intended to communicate with aquatic scientists, satellite oceanographers, and the broader Earth observation community on the origins, requirements, challenges, successes, and future objectives for operationalizing global AR data products for Landsat satellite missions.
During the past 4 years, scientists of the Geological Survey, in cooperation with NASA, have been investigating the feasibility and practicality of using remote-sensor data collected during conventional aircraft flights. This work is aimed at developing techniques for gathering facts about the earth's natural resources from satellites carrying sophisticated remote-sensing instruments. These experiments have vividly demonstrated our ability to study and assess natural resources from remotely-sensored data. Discussion of a few of these experiments will illustrate this point.
Galileo has now completed 7 years exploring Jupiter. The spacecraft obtained breathtaking views of the four major satellites, and studied Jupiter's clouds and atmospheric composition, rings, small satellites, and magnetic field. It had five successful close flybys and many distant observations of Io. Scientists already knew from Voyager and Earth‐based astronomy that Io is by far the most volcanically active object in the solar system. Galileo has given us stunning color panoramas of Io's surface and unprecedented close views of erupting volcanoes (Figure 1) and the largest active flows observed anywhere. Among recent discoveries about Io, perhaps most astonishing since Voyager, is that some lavas possess emission temperatures greater than any lavas erupted on Earth today and possibly since the start of Earth's geologic history. The Io science community has identified three alternative interpretations of Io's hottest lavas: (1) ultramafic material similar to komatiite; (2) superheated lava; or (3) an ultra‐refractory substance deficient in silica and rich in Ca‐Al oxides.
Landslide dynamics is the branch of science that seeks to understand the motion of landslides by applying Newton's laws. This memoir focusses on a 40‐year effort to understand motion of highly mobile—and highly lethal—landslides such as debris avalanches and debris flows. A major component of this work entailed development and operation of the U.S. Geological Survey debris flow flume, a unique, large‐scale experimental facility in Oregon. Experiments there yielded new insights that informed development of mathematical models that were aimed not only at explaining landslide dynamics but also at evaluating landslide and debris flow hazards. The most sophisticated of these models, called D‐Claw, found its first practical application during investigations of the 2014 Oso, Washington, landslide disaster. That event provided indelible lessons about the utility and sociology of science in the real world.
Seismic tomography is the most abundant source of information about the internal structure of the Earth at scales ranging from a few meters to thousands of kilometers. It constrains the properties of active volcanoes, earthquake fault zones, deep reservoirs and storage sites, glaciers and ice sheets, or the entire globe. It contributes to outstanding societal problems related to natural hazards, resource exploration, underground storage, and many more. The recent advances in seismic tomography are being translated to nondestructive testing, medical ultrasound, and helioseismology. Nearly 50 yr after its first successful applications, this article offers a snapshot of modern seismic tomography. Focused on major challenges and particularly promising research directions, it is intended to guide both Earth science professionals and early‐career scientists. The individual contributions by the coauthors provide diverse perspectives on topics that may at first seem disconnected but are closely tied together by a few coherent threads: multiparameter inversion for properties related to dynamic processes, data quality, and geographic coverage, uncertainty quantification that is useful for geologic interpretation, new formulations of tomographic inverse problems that address concrete geologic questions more directly, and the presentation and quantitative comparison of tomographic models. It remains to be seen which of these problems will be considered solved, solved to some extent, or practically unsolvable over the next decade.
The U.S. Geological Survey (USGS) Rocky Mountain Region (RMR) hosted USGS scientists, managers, program coordinators, and leadership team members for a virtual Science Exchange during September 15–17, 2020. The Science Exchange had 216 registered participants and included 48 talks over the 3-day period. Invited speakers presented information about the novel USGS Earth Monitoring, Analysis, and Prediction (EarthMAP) concept. Scientists in the RMR and other regions showcased their research and participated in discussions related to the EarthMAP concept and EarthMAP applications. In addition, the Colorado River Basin Pilot Project, the first formal EarthMAP pilot project, was unveiled during the Science Exchange. Many of the products designed during the RMR Science Exchange were done so with the EarthMAP – Colorado River Basin Pilot Project in mind. This report summarizes the organization and objectives of the Science Exchange, highlights key points from session presentations, panel discussions, and breakout sessions, and, most importantly, discusses momentum generated for the EarthMAP – Colorado River Basin Pilot Project.
Estimating precipitation variations in space and time is an important aspect of drought early warning and environmental monitoring. An evolving drier-than-normal season must be placed in historical context so that the severity of rainfall deficits may quickly be evaluated. To this end, scientists at the U.S. Geological Survey Earth Resources Observation and Science Center, working closely with collaborators at the University of California, Santa Barbara Climate Hazards Group, have developed a quasi-global (50°S–50°N, 180°E–180°W), 0.05° resolution, 1981 to near-present gridded precipitation time series: the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data archive.
For more than 100 years, the United States Geological Survey (USGS) has provided earth-science information on which managers, scientists, and other interested citizens base decisions regarding Michigan’s natural resources and natural hazards. The non-regulatory and scientific nature of the USGS work ensures that our products are technically sound, unbiased, and equally accessible and available to all interested parties. The various programs of the USGS in Michigan reflect a response to the citizens of Michigan and their need for geologic, topographic, biologic, and hydrologic information. Much of the work of the USGS in Michigan is part of cooperative programs in which the diversity of interests among local, regional, State, Tribal, and Federal agencies is accommodated through joint planning and funding.
The monitoring of wide-area hydrologic events requires the use of geospatial and time series data available in near-real time. These data sets must be manipulated into information products that speak to the location and magnitude of the event. Scientists at the U.S. Geological Survey Earth Resources Observation and Science (USGS EROS) Center have implemented a hydrologic modeling system which consists of an operational data processing system and the Geospatial Stream Flow Model (GeoSFM). The data processing system generates daily forcing evapotranspiration and precipitation data from various remotely sensed and ground-based data sources. To allow for rapid implementation in data scarce environments, widely available terrain, soil, and land cover data sets are used for model setup and initial parameter estimation. GeoSFM performs geospatial preprocessing and postprocessing tasks as well as hydrologic modeling tasks within an ArcView GIS environment. The integration of GIS routines and time series processing routines is achieved seamlessly through the use of dynamically linked libraries (DLLs) embedded within Avenue scripts. GeoSFM is run operationally to identify and map wide-area streamflow anomalies. Daily model results including daily streamflow and soil water maps are disseminated through Internet map servers, flood hazard bulletins and other media.
The Tree Cover Mapping (TCM) tool was developed by scientists at the U.S. Geological Survey Earth Resources Observation and Science Center to allow a user to quickly map tree cover density over large areas using visual interpretation of high resolution imagery within a geographic information system interface. The TCM tool uses a systematic sample grid to produce maps of tree cover. The TCM tool allows the user to define sampling parameters to estimate tree cover within each sample unit. This mapping method generated the first on-farm tree cover maps of vast regions of Niger and Burkina Faso. The approach contributes to implementing integrated landscape management to scale up re-greening and restore degraded land in the drylands of Africa. The TCM tool is easy to operate, practical, and can be adapted to many other applications such as crop mapping, settlements mapping, or other features. This user manual provides step-by-step instructions for installing and using the tool, and creating tree cover maps. Familiarity with ArcMap tools and concepts is helpful for using the tool.
Alaska is one of the most vigorously volcanic regions on the planet, and Alaska’s national parks are home to many of the state’s most active volcanoes. These pose both local and more distant hazards in the form of lava and pyroclastic flows, lahars (mudflows), ash clouds, and ash fall. Alaska’s volcanoes lie along the arc of the Aleutian-Alaskan subduction zone, caused as the oceanic Pacific plate moves northward and dips below the North American plate. These volcanoes form as water-rich fluid from the down-going Pacific plate is released, lowering the melting temperature of rock in the overlying mantle and enabling it to partially melt. The melted rock (magma) migrates upward, collecting at the base of the approximately 25 mile (40 km) thick crust, occasionally ascending into the shallow crust, and sometimes erupting at the earth’s surface. During volcanic unrest, scientists use geophysical signals to remotely visualize volcanic processes, such as movement of magma in the upper crust. In addition, erupted volcanic rocks, which are quenched samples of magmas, can tell us about subsurface magma characteris-tics, history, and the processes that drive eruptions. The chemical compositions of and the minerals present in the erupted magmas can reveal conditions under which these magmas were stored in crustal “chambers”. Studies of the products of recent eruptions of Novarupta (1912), Aniakchak (1931), Trident (1953-74), and Redoubt (2009) volcanoes reveal the depths and temperatures of magma storage, and tell of complex interactions between magmas of different compositions. One goal of volcanology is to determine the processes that drive or trigger eruptions. Information recorded in the rocks tells us about these processes. Here, we demonstrate how geologists gain these insights through case studies from four recent eruptions of volcanoes in Alaska national parks.
The first Earth Resources Technology Satellite (ERTS-1), launched by the National Aeronautics and Space Administration in. July 1972, is providing valuable data for investigations of the most inaccessible and hostile regions of the Earth the Arctic and Antarctic. ERTS images and map products derived from them offer a whole new dimension in source material for multidiscipline investigations in the earth sciences. For the first time scientists can view synoptic, repetitive scenes of the polar regions in four spectral bands. Ongoing experiments funded by NASA and conducted in the U.S. Geological Survey have demonstrated the feasibility of revising coastlines on maps of Antarctica, detected gross changes in the northern limits of the three largest ice shelves in the world, and led to the discovery of uncharted mountain ranges.
The first Earth Resources Technology Satellite (ERTS-1), launched by the National Aeronautics and Space Administration in.July 1972, is providing valuable data for investigations of the most inaccessible and hostile regions of the Earth the Arctic and Antarctic. ERTS images and map products derived from them offer a whole new dimension in source material for multidiscipline investigations in the earth sciences. For the first time scientists can view synoptic, repetitive scenes of the polar regions in four spectral bands. Ongoing experiments funded by NASA and conducted in the U.S. Geological Survey have demonstrated the feasibility of revising coastlines on maps of Antarctica, detected gross changes in the northern limits of the three largest ice shelves in the world, and led to the discovery of uncharted mountain ranges.
The U.S. Geological Survey Rocky Mountain Region hosted scientists, managers, program coordinators, and leadership team members for a virtual Science Exchange during September 15–17, 2020. The Science Exchange had 216 registered participants and included 48 talks over the 3-day period. Invited speakers presented information about the novel U.S. Geological Survey Earth Monitoring, Analysis, and Prediction (EarthMAP) concept. Scientists showcased their research and participated in discussions related to the EarthMAP concept and EarthMAP applications. In addition, the Colorado River Basin Pilot Project, one of the first EarthMAP Pilot Projects, was unveiled during the Science Exchange. This report provides synopses of session objectives and corresponding abstracts that were presented along with author affiliations and email address of the lead author. In addition, web links are provided for related programs and projects, and associated publications are referenced.
The core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are being transformed into new formats optimized for cloud storage that enable rapid analysis of multi-petabyte data sets. Data sets are moving from archive centers to vast cloud data storage, adjacent to massive server farms. Open source cloud-based data science platforms, accessed through a web-browser window, are enabling advanced, collaborative, interdisciplinary science to be performed wherever scientists can connect to the internet. Specialized software and hardware for machine learning and artificial intelligence are being integrated into data science platforms, making them more accessible to average scientists. Increasing amounts of data and computational power in the cloud are unlocking new approaches for data-driven discovery. For the first time, it is truly feasible for scientists to bring their analysis to data in the cloud without specialized cloud computing knowledge. This shift in paradigm has the potential to lower the threshold for entry, expand the science community, and increase opportunities for collaboration while promoting scientific innovation, transparency, and reproducibility. Yet, we have all witnessed promising new tools which seem harmless and beneficial at the outset become damaging or limiting. What do we need to consider as this new way of doing science is evolving?
The application of digital remote sensing to rangelands is as long as the history of digital remote sensing itself. Before the launch of the Earth Resources Technology Satellite (ERTS) – later renamed Landsat, scientists were evaluating the use of multispectral aerial imagery to map soils and range vegetation (Yost and Wenderoth 1969). During the late 1960’s, the promise of ERTS, designed to drastically improve our ability to update maps and study earth resources, particularly in developing countries, was eagerly anticipated by a number of government agencies (Carter 1969). With the ERTS launch on July 23, 1972, a flurry of research activity aimed at the application of this new data source to map earth resources began. Practitioners who pioneered the use of satellite based digital remote sensing found the new data source a significant value for rangeland assessments (e.g., Rouse et al., 1973, Rouse et al., 1974, Bauer 1976). This early work established many of the basic techniques still in use today to assess and monitor global rangelands. The following sub-sections discuss the evolution of remote sensing data, methods, and approaches in various decades.