We employ Magnetospheric Multiscale, Geostationary Operational Environmental and Los Alamos National Laboratory satellites, as well as the ground magnetometer networks over Greenland and North America to study a substorm on 9 August 2016 between 9 and 10 UT.We found that during the substorm two earthward flows, whose dipolarization-injection fronts exceeded 6.5 and 4 Earth’s radii (RE) in YGSM, impinged and rebounded from Earth’s dipolar field lines at L = 6–7 downtail, where L is the McIlwain number. The impingements and rebounds ended with a substorm current system of downward R1 and upward R2 currents which grew to azimuthally cover the whole North American continent. At the fronts, regions of enhanced negative j·E′ were formed and peaked toward the end of the impingements. These regions appeared to be conjugate with eastward moving aurora (along the growth phase arc and together with eastward drifting energetic electrons at geosynchronous equatorial orbit), which manifests ionospheric Ohmic losses.

The COVID-19 virus was first detected in Wuhan, China and its genesis was traced to infected bats, passed through an intermediate host to humans. COVID-19 or “Coronavirus” reached approximately 4 million confirmed cases in the United States by July 2020. The spread of this virus has affected both social and economic affairs on a global scale with more than 15 million confirmed cases worldwide in July 2020. The pandemic has proven a global public health and socio-economic crisis. In addition, the shelter-in-place orders provide an unprecedented opportunity for examining the resulting reduction in greenhouse gases and aerosols on the atmosphere. The Sentinel-5P satellite has shown distinct changes in atmospheric aerosols over the COVID-19 epicenter in Wuhan. After Texas, U.S. established similar shelter-in-place orders to prevent the spread of the virus, the state’s industrial activity experienced an economic slowdown. This paper quantifies the changes in atmospheric aerosols associated with the COVID-19 slowdown over major cities in Texas and compares it to similar changes in Wuhan, China using satellite imagery.

The effect of non-uniform solar radiation over opposing hillslopes leads to aspect-controlled vegetation patterns in semi-arid ecosystems. It creates a differentiation in soil properties and vegetation characteristics. In mid- to high-latitudes where available soil moisture is a limiting factor for vegetation growth, slopes with polewardfacing aspect tend to develop denser vegetation cover that provides more erosion protection than on the equatorward-facing hillslopes. The variation in erosion rates across opposing hillslopes causes the topographic asymmetry of hillslopes over long timescales. The magnitude of this asymmetry is measured by the hillslope asymmetry index (HAI), a metric given as the ratio of the median slope angles of opposite hillslopes. In this study, we present a novel approach to investigate the relationships of HAI with climatological, geomorphic, and ecologic variables at a global scale. Here, we analyzed these relationships using DEM data to compute HAI for 80 different catchments across the world, in which aspect-controlled vegetation has been reported in the literature. We used the CHILD landscape evolution model (LEM), which uses the continuity equation for water, sediment, and biomass, in order to investigate the control of climatological, geomorphic, and ecologic variables on the development of hillslope asymmetry. Preliminary results show that latitude and mean topographic gradient are the two dominant factors affecting hillslope asymmetry due to their vital role in controlling vegetation density through the modulation of incoming solar radiation. These results improve our understanding on how different climatic variables and geographic properties affect the magnitude of hillslope asymmetry and their implications on landform evolution modelling.

Annotation plays an essential and important role in the performance of machine learning approaches. However, manually labeling objects accurately is often time-consuming, especially when processing large image data. To improve this process, we developed a deep learning-based interactive annotation approach with application to hyperspectral imagery. Specifically, a deep network with a metric-based loss function will be pre-trained on a large training set. Encoding layers in the network are used for dimensionality reduction as well as boosting discriminability among pixels belonging to different classes in the embedding space. Then, when new images are provided, the user can provide (fast, easy, weak) annotations to indicate objects of interest and fine-tune the pre-trained network to the task at hand. In this way, the fine-tuned network can adapt for the new category and task. Annotations can be iteratively modified and corrected. Once the model is fine-tuned by quickly annotating a few images, it can then be generalized to a large image dataset of similar objects. Experiments on the MUUFL Gulfport hyperspectral imaging dataset show that our approach outperforms classical active learning methods based on support vector machine in average Intersect of Union (from around 65% to around 80%) with significant improvement on computing speeds (from 30-200 seconds to less than 10 seconds)

Scene understanding methodologies (e.g., classification, segmentation, and anomaly detection) can be costly when operating on a per-pixel basis. As remote sensing applications rely on imagery with greater resolution, pixels regularly contain similar information to their neighbors. In recent years, scene understanding approaches have leveraged superpixel algorithms to partition imagery into small homogeneous regions for a variety of tasks. Instead of performing operations on millions of pixels, thousands, or in some cases hundreds of superpixels can be used as inputs into various pipelines. Most superpixel algorithms rely solely on RGB color information to produce superpixel maps. However, when multiple co-registered modalities are available, as is the case with the National Ecological Observatory Network (NEON) tree crown dataset, it is possible to combine information from multiple sources to produce a single shared map. In this work we combine airborne hyperspectral imagery, LiDAR point clouds, and RGB imagery to obtain superpixel maps and compare the oversegmentation results to those obtained by fusing individual maps produced from each modality. Superpixels are computed using the Simple Non-Iterative Clustering (SNIC) algorithm. Oversegmentation maps are scored using standard evaluation approaches. We present results on a subset of the National Ecological Observatory Network imagery dataset.

Plant growth is the product of gene by environment (GxE) interactions during plant development. Effective characterization of plant growth under various conditions provides insight into genetic components of plant development and mechanisms of stress resilience. While the emergence of high-through phenotyping facilities provides new avenues to further understand plant development and stress responses, the large costs of such facilities are hindering the study of dynamic growth processes. To democratize high-throughput plant phenotyping, we developed three sets of image-based phenotyping devices utilizing Raspberry Pi computers and low-weight/low-cost materials to continuously monitor shoot and root growth. The process is further automated by our workflows including data collection and statistical analysis. Our devices and workflows are customizable to image a wide variety of plants and tissues. To validate our system, we measured growth of Arabidopsis rosettes, tomato roots, and characterized the relationship between cowpea growth and evapotranspiration. These results demonstrate the variety of applications for Raspberry Pi based phenotyping. Importantly, this low-cost system is ideal for studying the genetics of plant growth and identifying new components of abiotic stress tolerance in a wide variety of species.

Agronomic technological advancements provide more precise means to establish methodologies that can estimate yield response in many different ways. We are experimenting with a new image-based technique to predict the yield response of Canola using the rate of ground cover accumulation. Our trial was composed of row spacing and seeding rate as factors that influence the growth and the spatial distribution to evaluate its influence on the yield. Using the Visible Band Difference Vegetation Index (VDVI) from digital images, we estimated the ground cover and modelled the change over time. We regressed ground cover accumulation and integrated the function to calculate the area under the curve to regress against yield. Preliminary analysis indicates that the green ground cover accumulation overtime is sufficiently correlated with the yield (F=168.1, p=2.2e-16, R2=0.4694). Further, our results suggest the amount of green ground cover accumulation over time is dependent on the seeding density and row spacing. The analysis shows the higher seeding densities, 40 plants/m2 and above, acquire biomass rapidly, and the most stable yield predictions with ground cover are likely reached at similar plant densities. The most stable yield predictions in-relation to row spacing obtained from either 0.3m, 0.45m or 0.6m spacing (R-squares 0.94, 0.93, and 0.89, respectively). We are further experimenting to understand what growth period of the crop is most suitable for ground cover based yield predictions. Our primary target is to develop a high throughput image-based methodology to estimate the yield response using the ground cover accumulation rate for on-farm precision agronomy.

Across geospatial domains and practices, there is a pressing educational need for engagement with and understanding of computational and technical advancements relevant to geospatial work. Computational skills and practices, like working with big data, creation and management of machine learning algorithms, work with automation in data analysis and management, and many others, are growing as fundamentals to conducting geospatial scientific research, and to learning in the geospatial classroom. The Hour of CyberInfrastructure (Hour of CI) project aims to create learning materials for use in diverse learning contexts to provide learners a base to build their practice in eight core areas of cyber literacy pertinent to geographic information science (GIS) and geospatial practice. Leveraging the affordances of Jupyter notebooks and cloud computing, we discuss the design, development, and deployment of introductory lessons in these areas. We report the educational design strategies behind development of these lessons, discuss the technical development and support required to use these in an interactive Jupyter notebook enabled space, and report on the deployment of these lessons in initial classroom testing environments. By sharing components of the entire process in developing Hour of CI lessons, we aim to outline our experiences, share best practices and pitfalls, and consider feedback from initial testing. These results will support other efforts in computer-based STEM education by providing feedback and results from an interwoven, cross-discipline geospatial, GIS, and computer science context.

Various recent studies have shown that basalt formations have the capacity for long-term secure CO2 storage through carbon mineralization. Many of these studies have demonstrated extremely rapid rates of mineralization, but the underlying mechanism enabling these elevated reaction rates, and their relation to the processes occurring in proposed basaltic reservoirs, remain poorly constrained. In this work, a 3D micro-continuum reactive transport model was designed to investigate the impact of alkalinity on basalt interactions with CO2-rich fluids. Reactive transport models were developed in PFLOTRAN based on 3D imaging data from high-temperature, high-pressure flow-through experiments (Luhmann et al. (2017) Chemical Geology, Water Resources Research). Mineral reactive surface areas in the model were adjusted to produce agreement with chemistry of output fluids sampled during the experiments. The benchmarked model showed that no considerable carbonate was formed during interaction with the relatively low alkalinity, low pH solutions, regardless of the enrichment of basalt-derived Na+, Mg2+, and Fe2+ ions in the reactant fluid. Increasing the alkalinity of the injected fluids consistently yielded higher rates of carbon mineralization. Similarly, introducing a small initial volume fraction of carbonate minerals into the system contributed to increased carbon mineralization, because of the increased fluid alkalinity. These results thus reinforce a conceptual understanding of carbonate mineralization in basalt-hosted CO2 storage reservoirs that emphasizes the importance of aquifer fluid alkalinity, and caution against extrapolating results from elevated-alkalinity CO2 storage reservoirs and experiments to others where this is less likely to be representative.

Brazil’s Amazon deforestation is one of the world’s great environmental problems, and the fate of the “TransPurus” region – the vast block of intact forest to the west of the Purus River in Brazil’s state of Amazonas – will be the deciding factor in maintaining Amazonia’s biogeochemical cycles and associated ecosystem services, including carbon storage and water cycling and biodiversity. The Purus River effectively divides Brazilian Amazonia between its eastern side (where the forest has been heavily affected by deforestation, fragmentation and degradation), and the western side (where the forest is largely intact due to lack of accessibility by road). This situation in western Amazonia is likely to change radically if the proposed AM-366 and associated roads are built, opening this block of forest with a connection to Highway BR-319 (Manaus-Porto Velho). The AM-366 would cross the Purus River at Tapauá and link BR-319 to Tefé, Coarí and Juruá. BR-319 is a highway that was-abandoned in 1988 but that has been “maintained” since 2015 while awaiting approval of a federal environmental impact assessment (EIA) before being “reconstructed.” Another threat comes from the “Solimões Sedimentary Basin” project that would open a substantial part of the TranPurus area to oil and gas drilling, potentially totaling thousands of wells. This would greatly increase the probability of roads being built to reduce the costs of servicing these wells. Roads are the primary drivers of deforestation in Brazil. The TransPurus area would be subject to invasion by actors ranging from landless farmers to large “land grabbers” (grileiros), and would be likely to attract oil palm and logging interests. Logging can potentially provoke large-scale forest degradation in conjunction with its increasing the vulnerability of forest to fire. Should the huge area of intact forest in this region be lost, potential release of greenhouse gases would be of a scale that could be a key factor in pushing global temperatures beyond a “tipping point” leading to global warming escaping from human control to begin a “runaway greenhouse” ending in a “hothouse Earth.” It would also threaten the source of water vapor for the “flying rivers” – winds that carry water vapor recycled through Amazonian trees to São Paulo and other parts of Brazil and neighboring countries.

The surface is the interface through which the bedrock interacts with the boundary-layer. Here, living organisms of various shapes, sizes, and function intermingle with the mineral soil, organic residue, and canopy air space that reside there. Together, they breath, absorb momentum, exchange water and gases, and bask in the heat from the sun and clouds above and thermal reservoirs below. While many of these functions are well understood, we knew less about how those functions operate and behave at different spatial and time scales. More intriguing, surface variance with scale influences scales of motion in the atmosphere. Here, I present a generalized look at how land and atmosphere scales interact, focusing on the lens of the surface energy budget. These processes are investigated through intensive measurements and high-resolution models conducted at the CHEESEHEAD19 field experiment in Wisconsin. A combination of airborne and tower eddy covariance networks, drone and airborne canopy imaging, and turbulence resolving simulations reveal persistent mesoscale contributions in the atmosphere enabled by surface heterogeneity.

Simple models of fluid and solid mechanics predict that the depressurization of a shallow reservoir that occurs during large effusive eruptions produces exponential trends in time series of both pressure drop and extruded volume. These models are attractive due to their simplicity and because they can explain geodetic and extruded volume data recorded at several volcanoes like at St. Helens and Cordón Caulle, regardless of their magma compositions. However, several lava and dome-forming eruptions like at Redoubt, Hekla and Santiaguito volcanoes do not show clear ground deformation coeval to lava and dome effusion despite the extrusion of at least 0.1 km3 DRE of magma. This apparent paradox can be explained by a variety of factors including deep magma sources and highly compressible magmas that leave no geodetic footprint. Here we explore the role of magma buoyancy with a reanalysis of ALOS-1, TerraSAR-X and RADARSAT-2 InSAR ground deformation and Pleiades DEM data of the VEI 5 Plinian and dome forming rhyolitic eruption of Chaitén volcano in 2008-2009. We show that almost all of the recorded ground deformation occurred during the first three weeks of the eruption, which implies that the extrusion of a rhyolitic dome (~0.8 km3 DRE) did not result in significant depressurization of a magma reservoir, despite the clear exponential trends in the extrusion data. Instead, we show that the exponential trend in the time series of extruded volume can be explained by magma ascending due its buoyancy instead of its overpressure. These results imply that ground deformation alone is not always indicative of the temporal evolution of an eruption and urges for the acquisition of denser time series of DEM data to calculate time-lapse extrusion rates.

Structural and lithological controls in mountain valleys have been shown to affect the erosional connectivity of hillslopes, tributaries and alluvial fans, as well as the formation and preservation of strath terraces. In this study we explore the synchronicity of fluvial bedload aggradation and bedrock incision in valleys of one river draining a collisional mountain belt through geomorphological, sedimentological and chronological datasets of strath terraces. Along the M’Goun River on the southern flank of the central High Atlas Mountains strath surfaces and bedload sediments are preserved in valleys with differing structure and lithology throughout the thrust front and wedge-top basin. We (1) extract terrace surface and river channel elevations from a digital elevation model and field mapping to reconstruct river long profiles; (2) collect grain size and lithology data from terraces to derive information on sediment source and transport; (3) constrain the timing of bedload aggradation within the two latest strath levels using a new approach to OSL dose rate correction of gravels. Strath terraces form in weak sedimentary bedrock in valleys confined by limestone and conglomerate units, and are capped by up to 3 to 10 m of gravel bedload and overbank sands. The record of strath burial and incision extends to 180 ka, but only includes one synchronous bedload aggradation event during the last interglacial maximum MIS 5a (~130-110 ka). Reconstructed paleo-river long profiles and chronology demonstrate time-transgressive bedrock incision through the thrust front and wedge-top basin on the order of 10^4 – 10^5 yrs, representing strath incision of 12 to 40 m. A lack of a downstream bedload grain-size trend and presence of locally derived clast lithologies indicate lateral input of gravel from valleys in addition to downstream transport. These results allow for the development of a conceptual model of asynchronous strath terrace formation in a collisional mountain belt.

The effects of climate on eroding landscapes and the delivery of sediment from these remain poorly understood. The sampling and dating of river terraces provide one way to address this question, because these embed information about the interconnected dynamics of mountain valley widening, river incision, sediment delivery, and their sensitivity to external forcing. We developed a new approach to OSL dose rate correction of gravels to derive the most detailed chronology of river terrace stratigraphy in NW Africa to date. We sampled river terraces 10-20 m above the modern river plain for dating in a 1200 km2 river catchment, the River M’Goun, eroding the High Atlas Mountains hinterland of the continental river Draa. We applied OSL and IRSL dating to determine the age of 23 samples, using Bayesian methods to derive a robust chronology. We show terrace strath planation starts at ~180 ka in the MIS 6 glacial maximum, followed by aggradation from 140 – 57 ka in MIS 5 to MIS 4 which deposited the up to 10 m stratigraphy of fluvial conglomerate (imbricated rounded cobbles). Incision and abandonment of river terraces occur in MIS 3 to 2 during the transition to the last glacial maximum. Our results compared with an Atlantic record of aridity in the Northern Sahara over the last 120 ka show that aggradation and valley widening occur in response to periods of northward penetration of the African summer monsoon into the High Atlas. We note that these signals persist across the different tectonic zones, from the fold and thrust belts into the sedimentary basins. More widely, our data demonstrate how changes in monsoon patterns can cause changes in the erosion of mountains and transport of sediment in arid continental interiors and these form new inputs for numerical landscape evolution models.

Existing global FT records, derived from the Soil Moisture Active Passive (SMAP), the Advanced Scanning Microwave Radiometer (AMSR), and the Special Sensor Microwave Imager (SSM/I) produce relatively course spatial resolution (25-36km) binary FT classifications. These classifications can vary widely depending on the microwave bands used, topography, and land cover, leading to a somewhat ambiguous definition of ‘frozen’ and ‘thawed’ states. In this study, we assess the relationship between satellite observation derived FT products over North America compared to modeled near-surface temperatures and land surface temperature (LST) from the Geostationary Operational Environmental Satellite system (GOES). Utilizing the higher spatial resolution of these products (~4.5km), sub-grid scale variability and its relationship to courser microwave FT classifications was assessed. Through an analysis of spatial variability and uncertainty across North America, five focus study pixels each representing unique FT profiles were examined. These included pixels in: (1) Southern Plains (36, -97), (2) Tundra (61, -76), (3) Northern Forest (47, -74), (4) Northern Plains (52, -103), and Mountainous (38.9, -107.9). The model ensemble adequately captured near surface temperatures as they relate to FT classifications in Tundra, the Northern Plains, and Northern Forest regions. On average, 85.3% to 99.6% of sub-grid cells were below freezing when FT products classified the associated pixels as frozen. GOES - LST observations were shown to have the highest proportion of sub-grid cells below freezing on average, when classified as frozen by FT products (97.3% - 100%) across the same 3 focus locations. However, we also find that fractional FT products utilizing higher resolution data inputs, such as LST, would provide a considerable improvement in mountainous regions with high inter-grid cell heterogeneity, in regions characterized by ephemeral FT events (Southern Plains), as well as during freeze and thaw onset periods. These locations showed a significant reduction in the average temperature product frozen proportion associated with frozen classifications (as low as 5.8%). This study provides insight to improving representation of FT state and providing a clearer meaning of what constitutes a ‘frozen’ classification.

The Intergovernmental Panel on Climate Change (IPCC) currently prepares its Sixth Assessment Report (AR6). Its authors assess peer-reviewed scientific literature and recent climate datasets to inform policy-makers about the current state of the science regarding climate change and its impacts, as well as adaptation and mitigation options. For AR6, efforts are underway to make its main results FAIR and preserve them in the TRUSTworthy repositories of the IPCC Data Distribution Centre (DDC), jointly managed by CEDA, DKRZ, and CIESIN. The AR6 FAIR initiative was kickstarted by the IPCC DDC and Working Group I (WGI) [Stockhause et al., 2019], then adopted by IPCC TG-Data (Task Group on Data Support for Climate Change Assessments) shortly after its creation. All three WGs have adopted the FAIR data guidelines. IPCC assessments are large and diverse in in terms of scientists involved as well as included scientific objects. Challenges for digital data curation are related to the scale and diversity of papers, reports, datasets, the variety of software, and the different familiarity of the scientists with these technical aspects. The following priority areas for improved data stewardship were selected based on the aims to enhance the traceability of AR6 key findings and their reusability: preserve figure datasets in the DDC; - preserve analysis software; . preserve main input datasets in the DDC; . assemble datasets and provenance information on the figure creation from IPCC authors; and . interlink datasets to the IPCC report. Datasets are transferred to the DDC at the end of AR6. The DDC partners are responsible to preserve the data for future reuse by different stakeholders and under a variety of current and future scientific and policy-related questions. As the role for the DDC expands within the IPCC, new partners are sought. The TRUST principles provide a framework for the communication of DDC tasks to different stakeholders, e.g. to countries interested to host a DDC. The presentation will give an overview over the IPCC AR6 approaches towards FAIR data maintained in TRUSTworthy repositories, their challenges, their approach to meet these challenges and open questions, e.g. the integration of digital data into the IPCC Error Protocol, targeted within TG-Data.

Soil moisture is an essential metric to understand crop condition throughout the growing season. Collecting soil moisture data by field observation is time-consuming and labor-intensive, especially for a CONUS geographic coverage. NASA’s SMAP Mission has been providing global mapping of soil moisture and freeze/thaw state at high spatial and temporal resolutions since 2015. However, handling SMAP data could be difficult for users who do not have technical background. Creating a soil moisture map with SMAP data contains a series of steps, including data retrieval, reformatting, reprojection, mosaicking, and clipping. Moreover, users need to install special software and configure the system environment to further perform geospatial analysis for SMAP data. To facilitate using of SMAP data, this paper presents a cloud-based web geoprocessing service system for disseminating and analyzing SMAP data products. This web service system serves a variety of data products, including 9-km SMAP Level-4 data, 1-km SMAP Hybrid data, and MODIS-based vegetation index data. The development of the geoprocessing services is based on the three-layer system architecture composed of an infrastructure layer, a data layer, and a service layer. The infrastructure layer manages the fundamental computing resources and offers the Platform as a Service (PaaS) to the data layer and service layer. The data layer stores all raw raster and vector data offered by the web service. The service layer handles all geospatial web services including map services and geoprocessing services. Both the data layer and service layer are deployed as instances of virtual machines on the top of the infrastructure layer. The system offers a variety of geoprocessing operations in the OGC Web Processing Service (WPS) interface standard. These operations are grouped into get file processes, geospatial statistics processes, map production processes, time-series profile processes, image composite processes, and miscellaneous processes. The proposed geoprocessing services have been implemented as a part of the Crop Condition and Soil Moisture Analytics (Crop-CAMSA) web service system and are interoperable with other web applications. The experiment result shows the geoprocessing service can significantly simplify the procedures of dissemination and analysis of SMAP data.

Seasonally dry tropical forests (SDTFs) are well adapted to seasonal and interannual rainfall variability and have mechanisms to control growth dynamics under water limitation. The forest starts flushing the leaves following the first rainfall events of the wet season, after which it grows fast and remains green until litterfall starts at the end of the wet season. Although ecosystem adaptation is known to buffer seasonal rainfall variability, yearly fluctuations in biomass productivity are still observed. In order to understand the origin of these productivity fluctuations, here we analyze the effects of rainfall variability (seasonal, interannual, and spatial) on the growth dynamics of an SDTF located in the semi-arid region of Brazil. We used a coupled soil water and vegetation model to simulate soil moisture and NDVI dynamics in three sites across a rainfall gradient (from 400 to 800 mm year-1) to compute specific assimilation (kA) and respiration (kR) rates for 19 hydrological years. Based on the time-series of the rainfall and kA, the analysis shows that in all three sites kA tends to increase following dry years and to decrease following wet ones, whereas kR is about ten times smaller and does not vary particularly over time. The observed patterns of kA and kR suggest an interannual mechanism to buffer long-term rainfall variability and mitigate water stress by tuning assimilation rates.