NASA designated Reiner Gamma (RG) as the landing site for the first Payloads and Research Investigations on the Surface of the Moon (PRISM) delivery (dubbed PRISM-1a). Reiner Gamma is home to a magnetic anomaly, a region of magnetized crustal rocks. The RG magnetic anomaly is co-located with the type example of a class of irregular high-reflectance markings known as lunar swirls. RG is an ideal location to study how local magnetic fields change the interaction of an airless body with the solar wind, producing stand-off regions that are described as mini-magnetospheres. The Lunar Vertex mission, selected by NASA for PRISM-1a, has the following major goals: 1) Investigate the origin of lunar magnetic anomalies; 2) Determine the structure of the mini-magnetosphere that forms over the RG magnetic anomaly; 3) Investigate the origin of lunar swirls; and 4) Evaluate the importance of micrometeoroid bombardment vs. ion/electron exposure in the space weathering of silicate regolith. The mission goals will be accomplished by the following payload elements. The lander suite includes: The Vertex Camera Array (VCA), a set of fixed-mounted cameras. VCA images will be used to (a) survey landing site geology, and (b) perform photometric modeling to yield information on regolith characteristics. The Vector Magnetometer-Lander (VML) is a fluxgate magnetometer. VML will operate during descent and once on the surface to measure the in-situ magnetic field. Sophisticated gradiometry allows for separation of the natural field from that of the lander. The Magnetic Anomaly Plasma Spectrometer (MAPS) is a plasma analyzer that measures the energy, flux, and direction of ions and electrons. The lander will deploy a rover that conducts a traverse reaching ≥500 m distance, obtaining spatially distributed measurements at locations outside the zone disturbed by the lander rocket exhaust. The rover will carry two instruments: The Vector Magnetometer-Rover (VMR) is an array of miniature COTS magnetometers to measure the surface field. The Rover Multispectral Microscope (RMM) will collect images in the wavelength range ~0.34–1.0 um. RMM will reveal the composition, texture, and particle-size distribution of the regolith.

A NASA sponsored study conducted at John Hopkins University Applied Physics Lab culminated in a community-inspired heliospheric mission concept called the Interstellar Probe (ISP). The ISP’s science goals include understanding our habitable astrosphere by investigating its interactions with the interstellar medium, and determining the structure, composition, and variability of its constituents. A suite of instruments were proposed to achieve these and other science objectives. The instruments include a Lyman-a spectrograph for velocity-resolved measurements of neutral H atoms. The capability to address key components of the ISP’s science objectives by utilizing high spectral resolution Lyman-a measurements are described in this presentation. These findings have been submitted as a community White Paper to the recent Heliophysics decadal survey.

Lava flows are one of the main hazards related to effusive basaltic volcanism. To minimize their impact during emplacement, we use lava flow potential distance-to-run predicted by propagation models. These models are partly based on infrared (IR) measurements of lava radiative heat fluxes by remote sensing (RS) methods (ground-based or satellite-based detectors) [1]. These results are however subjected to important errors related to the poor knowledge of spectral emissivity (ε), commonly considered constant by these well-established techniques[2, 3]. This oversimplification is an important source of uncertainties in derived temperatures, which restrain our capacity to accurately model active lava flows. In this study, we developed new algorithms that take into account the effect of spectral emissivity for calculating radiative heat fluxes. We describe the temperature-emissivity relationship with equations established at two wavelengths of interest for RS (10.9 μm and 1.6 μm) that are retrieved from in situ measurements of spectral emissivity for basaltic magma from the 2014–2015 Holuhraun eruption. Spectral emissivity data were systematically acquired over a wide spectral range (400–8000 cm−1) covering TIR, MIR and SWIR, and up to 1473 K [4]. Our results show that spectral emissivity varies linearly with temperature in TIR (10.9 μm), and nonlinearly in SWIR (1.6 μm). We confronted our lab-based results to the field IR data retrieved by [5] and found that temperature precision increases compared to data using constant emissivity value. These new insights will ultimately improve the thermo-rheological models of lava flows [6] in order to support hazard assessment in volcanic systems. References: [1] Kolzenburg et al. 2017. Bull. Volc. 79:45. [2] Harris, A. 2013: Cambridge University press. 728. [3] Rogic et al. 2019 Remote Sens., 11, 662 [4] De Sousa Meneses et al. 2015. Infrared Physics & Technology 69. [5] Aufaristama et al. 2018, Remote Sens, 10,151. [6] Thompson and Ramsey, 2021, Bulletin of Volcanology, 83:41. Keywords: Spectral emissivity, temperature, IR spectroscopy, remote sensing, basalt

UCSC GEOPATHS is an NSF-supported initiative to improve undergraduate success in the geosciences, driven by a desire to broaden academic engagement. One component of the program is a funded undergraduate summer program that provides authentic, professional experiences – across all employment sectors – to increase commitment in the geoscience pipeline. Many hydrologic basins rely on groundwater to supply domestic, municipal, and agricultural demand, but resources are increasingly stressed by rising demand, changes in land use, and a shifting climate. Consequences of groundwater overdraft include drying surface water systems, land subsidence, and seawater intrusion. Managed aquifer recharge (MAR) can help improve groundwater resources by increasing infiltration of excess surface water. We are part of a research team assessing hydrologic conditions during MAR on an active vineyard in Central California, through diversion of high flows from an adjacent river, a strategy known as “flood-MAR.” Our team collected soil samples from the upper 100 cm below ground surface at 24 locations across the 785-acre field site. We analyzed samples for soil texture at 10-cm spacing using a particle size analyzer based on laser light scattering. Preliminary analysis of fractions of sand, silt, and clay-sized particles indicate some lateral continuity from site to site. The northern part of the field area appears to be finer grained, on average, consistent with regional soil maps, but there is also considerable variability with depth. These data will be used to assess variations in expected infiltration rates by combining soil texture (to estimate infiltration capacity) and potential flood and saturation depths (to bracket vertical head gradients). Studies of this kind are helpful for assessing the efficacy of flood-MAR as a strategy to improve groundwater supplies and quality.

A cloud event in the altitude range of 53-65 km was observed with lidars over Yanqing (40.5°N, 116°E) and Pingquan (41°N, 118.7°E) on 30 October 2018. Clouds with a multilayer structure first occurred within the line view of lidar at dawn (03:40-06:00LT). They were faint and tenuous, and the maximum volume backscatter coefficient (VBSC) was 1.4×10-10m-1sr-1. At twilight, clouds with multilayer structures were reobserved via lidars, but they became much thicker, with a maximum VBSC of 11.2×10-10m-1sr-1. The structure of the cloud layers varied with time, and they faded completely at approximately ~00:30 LT (+1 day). Measurements from SABER/TIMED were utilized for analysis, and it was found that before the onset of cloud event, a temperature anomaly occurred in the mesosphere over Beijing, and water vapor was also very abundant. The frost point temperature profile of water vapor was estimated, and lidar measurements showed that the atmospheric temperature was close to the frost point of water vapor in the vicinity of the stratopause when the mesosphere was undergoing a low-temperature phase. It was a rare mesospheric cloud event observed with lidars at rather low latitudes, and the clouds probably resulted from the nucleation of saturated water vapor due to the occurrence of a temperature anomaly in the mesosphere.

Despite significant advances in monitoring of the development of active lava flow fields, many challenges remain. Timely field surveys of active lava flows could improve our understanding of the development of flow fields, but data of sufficient accuracy, spatial extent and repeat frequency have yet to be acquired. Satellite remote sensing of volcanoes is very useful because it can provide data for large areas with a variety of modalities ranging from visible to infra-red and radar. Satellite sensing can also access remote locations and hazardous regions without difficulty. Radar and multispectral satellite sensing data have been shown that can be combined to map heterogeneous lava flows using machine learning techniques, but a robust general model trained with several different lava compositions has to be developed. Here, we propose a robust, automatic approach based on machine learning techniques for analysing open-access satellite data in order to map lava flows in near-real time applicable to different kind of lava with different thermal components (i.e., incandescent, cooling and cooled lava component). We built a neural network model and trained it with a set of satellite images (e.g., Sentinel-1 SAR, Sentinel-2 MSI and Landsat 8 OLI/TIRS) of recent lava flows, and the relative labels of the lava and background regions. In this way, the trained model becomes capable to detect and map lava flows and to classify any new image, when available. The relative output is a segmented image with lava and background classes, obtained without an analysis made by a human operator. This approach allows to segment lava flows with both hot spot and cooling parts, and to recognize lava flows with different characteristics in near-real time. The results obtained during the long sequence of short-lived eruptive events occurred at Mt. Etna (Italy) between 2020 and 2021 are shown.

Methane’s high heat trapping potential has made it a priority for quantification and mitigation efforts worldwide. Ground-based surveys and in-situ measurement techniques to quantify natural and fugitive methane emission sources are time-consuming, expensive, and often lead to sparse measurements. Failure to accurately quantify emissions at the point-source scale have thus led to poorly constrained emission estimates. Airborne imaging spectrometers such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and the Global Airborne Observatory (GAO) have been employed to map the often stochastic and intermittent point-source emissions from a diverse set of source types including oil and gas, dairy, etc. A matched filter is applied to the methane-absorption relevant spectral features of the instrument’s radiance cube. Machine learning models are then trained to recognize methane plumes from these column-matched filter methane maps. However, current Convolutional Neural Network (CNN) models suffer from a high false-positive rate and poorly generalize to new scenes. False-positive detections are primarily due to methane absorption-mimicking surface spectroscopic features, as well as a lack of training data. To supplement the available training data, we utilize Large Eddy Simulations (LES) of methane point-source emissions to train a Convolutional Neural Network (CNN) on a plume-classification task. We observe a significant distribution shift between LES and AVIRIS-NG plumes, primarily caused by high LES plume enhancements. Through a series of image transforms verified through an adversarial approach using a discriminator network, we minimize the distribution shift between synthetic LES plumes and plumes observed by AVIRIS-NG and GAO. CNNs trained on a mixture of LES and real-world plumes, and tested on flightlines from multiple campaigns exhibit an error reduction compared to previous models. The reduction in false-positive plume detections demonstrates that supplementing the limited training data of real methane plumes with LES provides an avenue to make automatic detection more robust for future airborne and spaceborne missions such as SBG, EMIT, and Carbon Mapper.

Continental rifting is a critical component of the plate tectonic paradigm, and occurs in more than one mode, phase, or stage. While rifting is typically facilitated by abundant magmatism, some rifting is not. We aim to develop a better understanding of the fundamental processes associated with magma-poor (dry) rifting. Here, we provide an overview of the NSF-funded Dry Rifting In the Albertine-Rhino graben (DRIAR) project, Uganda. The project goal is to apply geophysical, geological, geochemical, and geodynamic techniques to investigate the Northern Western Branch of the East African Rift System in Uganda. We test three hypotheses: (1) in magma-rich rifts, strain is accommodated through lithospheric weakening from melt, (2) in magma-poor rifts, melt is present below the surface and weakens the lithosphere such that strain is accommodated during upper crustal extension, and (3) in magma-poor rifts, there is no melt at depth and strain is accommodated along pre-existing structures such as inherited compositional, structural, and rheological lithospheric heterogeneities. Observational methods in this project include: passive seismic to constrain lithospheric structure and asthenospheric flow patterns; gravity to constrain variations in crustal and lithospheric thickness; magnetics to constrain the thermal structure of the upper crust; magnetotellurics to constrain lithospheric thickness and the presence of melt; GNSS to constrain surface motions, extension rates, and help characterize mantle flow; geologic mapping to document the geometry and kinematics of active faults; seismic reflection analyses of intra-rift faults to document temporal strain migration; geochemistry to identify and quantify mantle-derived fluids in hot springs and soil gases; and geodynamic modeling to develop new models of magma-poor rifting processes. Fieldwork will begin in January 2022 and the first DRIAR field school is planned for summer 2022. Geodynamic modeling work and morphometric analyses are already underway.

Nitrous oxide (N2O) is one of the important greenhouse gases (GHGs), with its global warming potential 265 times greater than that of carbon dioxide (CO2). About 60% of the anthropogenic N2O emission is from agriculture production. To date, estimating N2O emissions from cropland remains a challenging task because the related microbial origin processes (e.g. incomplete nitrification and denitrification) are controlled by a diverse factors of climate, soil, plant and human activities. In this study, we developed a ML model with physical/biogeochemical domain knowledge, namely knowledge guided machine learning (KGML), for simulating daily N2O fluxes from the agriculture ecosystem. The Gated Recurrent Unit (GRU) was used as the basis to build the model structure. A range of ideas have been implemented to optimize the model performance, including 1) hierarchical structure based on variable causal relations, 2) intermediate variable (IMV) prediction and transfer, 3) inputting IMV initials for constraints, 4) model pretrain/retrain, and 5) multitask learning. The developed KGML was pre-trained by millions of synthetic data generated by an advanced PB model, ecosys, and then re-trained by observations from six mesocosm chambers during three growing seasons. Six other pure ML models were developed using the same data from mesocosm chambers to serve as the benchmark for the KGML model. The results show that KGML can always outperform the PB model in efficiency and ML models in prediction accuracy of capturing N2O flux magnitude and dynamics. Besides, the reasonable predictions of IMVs increase the interpretability of KGML. We believe the footprint of KGML development in this study will stimulate a new body of research on interpretable machine learning for biogeochemistry and other related geoscience processes.

High-resolution mapping and monitoring of global inland surface water bodies are critical to address challenges in sustainable water management practices. Planet currently operates the largest constellation of Earth Observation satellites and collects images at very high spatial (0.5 m - 5 m) and temporal (near-daily) resolutions. Here, we use PlanetScope data (resampled to 3 m) to develop a holistic and fully automated pipeline running on the Google Cloud Platform for monitoring global inland surface water. We incorporate the openly-available Global Reservoir and Dam (GRanD) data set into a three-stage supervised learning approach which initiates with an unsupervised label-generation step consisting of k-means clustering and NIR-based thresholding. We then rank the labels generated from these steps and the water labels extracted from the latest ESRI 10 m land cover data based on image contours. The best (noisy) labels having the least number of contours from this unsupervised learning stage are bootstrapped to train a deep-learning based semantic segmentation model (U-Net) on a KubeFlow pipeline. We subsequently create a new refined dataset by using these model predictions as labels which are passed to a Stochastic Gradient Descent (SGD)-based multi-temporal supervised label refinement stage (SGD classifier running on the same label for multiple input scenes). Finally, we iterate over the SGD based-supervised and U-Net-based label refinement steps to successively denoise the bootstrapped data until we obtain an acceptable test accuracy (F1 score > 0.9). Visual inspection of the results obtained over different climatic regions, terrains, and seasons across the globe shows that our approach works quite well. We also aggregate these predictions to detect temporal changes in surface water area. However, the model predictions exhibit high uncertainty in agricultural areas and complex terrains characterized by hill shadows and clouds. This issue could potentially be mitigated using hard-negative mining. Nevertheless, with the nearly-daily imaging capability of Planet, the high-fidelity surface water maps developed using this proposed supervised learning approach could be beneficial to the global water community for dealing with water security issues as part of the UN sustainable development goals.

Accurate loss assessment plays a vital role in understanding the economic risk of natural hazards, for planning, mitigation, and actuarial purposes. Because of its juggernaut status as the most widespread and costly hazard, both nationally and around the world, loss assessment due to flood is particularly important. One of the shortcomings in existing flood loss models is to partition the structure (or building) economic value of loss into that borne by the homeowner and that covered by flood insurance. The goal of this research is to model the loss incurred by the homeowner and that incurred by the National Flood Insurance Program, considering flood damage, building replacement value, flood insurance coverage amount, deductible, and flood characteristics (slope and y-intercept of the loss vs. return period curve). A Monte Carlo approach is used to calculate the annual average loss due to flood at the individual homeowner scale. Multiple linear regression (MLR) and Classification and Regression Tree (CART) models are trained to provide the output of the owner’s share of the loss. The CART model outperformed the MLR model with lower RMSE and MSE values and a higher R² value (0.95) on the test data set. Because out-of-pocket expenses due to flood can be devastating to financial security, the results of this study support and inform the proactive decision-making process that homeowners can use to self-assess their degree of preparation and vulnerability to the flood hazard.

We present evidence of damping of equatorial noise due to Finite-Larmor-radius (FLR) effect in the inner Van Allen belt. Detail observation of the FLR phenomenon in the inner belt region has not been reported until now. Waves primarily damped by the FLR mechanism can influence the energy dependent proton density structure. We analyze a typical case recorded by the Van Allen probe that involves FLR damping of equatorial noise, which was propagating radially towards the Earth, at L-shell ~1.7. As a result of this damping, protons in the energy range of ~18 – 21 MeV at L-shell ~1.7 – 2 get energized. This kind of wave-particle interaction should be included in the future models of the inner Van Allen belt. This phenomenon may also account for the unknown proton loss mechanism reported in Selesnick and Albert (2019).

The study aims to investigate the relationship between dominant climate variabilities namely El Niño-Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD) versus rainfall, and vertical humidity in Chiangmai, Thailand. The Oceanic Niño Index (ONI) which represents ENSO status and Dipole Mode Index (DMI) which represent IOD indicator were collected for the period 1986-2020 (35 years). Subsequently, the 12-month lag correlations were analyzed between both indicators against rainfall and vertical moisture at cloud-base and cloud-top height levels for the same time periods. The results indicate that the impacts of both climate variabilities may not occur during the same period of the phenomenon. The effects of ENSO to weather pattern in Chiang Mai might be lagged by 0 to 12 months. Even though the correlation of the humidity versus ONI and DMI is fluctuated. The correlation results of ONI and cloud-base humidity level has moderate values and consistent with La Niña events. However, the DMI correlations against rainfall and mixing ratio are inconclusive due to insufficient data. The relationship between ONI index, rainfall, and mixing ratio can be used to predict the future rainfall and atmospheric moisture once they are validated. Keyword: ENSO, ONI, DMI, IOD, rainfall, mixing ratio, vertical humidity

Access to groundwater leaves riparian plants in drylands resistant to atmospheric drought but vulnerable to changes in climate or water use that reduce streamflow and groundwater tables. Despite the vulnerability of riparian vegetation to water balance changes few extensible methods have been developed to automatically map riparian plants at the scale of individual stands or stream reaches, to assess their response to changes in moisture due to drought and climate change, and to contrast those responses across plant functional types. We used LiDAR and a sub-annual timeseries of NDVI to map vegetation and then assessed drought response by comparing a drought index to variation in a remotely sensed metric of plant health. First, a random forest model was built to classify vegetation communities based on phenological changes in Sentinel-2 NDVI. This model produced community classes with an overall accuracy of 97.9%; accuracy for the riparian vegetation class was 98.9%. Following this initial classification, LiDAR measurements of vegetation height were used to split the riparian class into structural subclasses. Multiple Endmember Spectral Mixture Analysis was applied to a timeseries of Landsat imagery from 1984 to 2018, producing annual sub-pixel fractions of green vegetation, non-photosynthetic vegetation, and soil. Relationships were assessed within structural subclasses between mid-summer green vegetation fraction (GV) and the Standardized Precipitation-Evapotranspiration Index (SPEI), a measure of soil moisture drought. Among riparian vegetation subclasses, all groups showed significant positive correlations between SPEI and GV, indicating an increase in healthy plant material during wetter years. However, the relationship was strongest for herbaceous plants (R^2=0.509, m=0.0278), intermediate for shrubs (R^2=0.339, m=0.0262), and weakest for the largest trees (R^2=0.1373, m=0.0145). This implies decoupling of larger riparian plants from the impacts of atmospheric drought due to subsidies provided by groundwater resources. Our method was extended successfully to multiple climatically-dissimilar dryland systems in the American Southwest, and the results provide a basis for ongoing studies on the fine-scale drought response and climatic vulnerability of riparian woodlands.

Mosquitoes are major vectors of disease and thus a key public health concern. Some cities have programs to track mosquito abundance and vector competence, but such fieldwork is expensive, time-consuming, and retrospective. We present a comparative analysis of two machine-learning-based regression techniques for forecasting the rate at which mosquito abundance changes and the rate at which mosquitoes test positive for West Nile Virus (WNV) in our AOI, the City of Chicago, three weeks in advance. We selected an initial pool of climatic inputs based on the findings of prior work. Ordinary least squares regression was run on each input individually and then in various groups. A p-value cutoff of 0.05 was used to determine which were best suited for predicting the derivatives of mosquito abundance and WNV positivity rate. Using these inputs, we trained four machine learning models using two types of regression: a Random Forest Regressor (RFR) and Backward Elimination Linear Regression (BELR). We optimized our RFR’s hyperparameters using Randomized Search Cross Validation and further reduced our BELR inputs using a p-value of 0.05. The enhanced vegetation index and temperature, described in various metrics, emerged as common inputs across the four models. In three of the four models, the respective temperature metric was the most important feature while EVI varied between second and last place. Our root mean square error largely resided within the hundredths place or less, but spiked at novel, week-to-week extremes in the testing data. Our methodology and results indicate valuable directions for future research into forecasting mosquito population abundance and vector competence. This work is particularly applicable to public health programs, as our models’ use of open-source, remote sensing data to predict how quickly the mosquito population and their vector competence will change three weeks in advance streamlines disease monitoring and prevention.

In closed-canopy forests, the availability of photosynthetically active light has been a focal point of research, emphasizing the role of light as a resource in limiting carbon assimilation and individual tree growth. However, light shapes the functioning of forest ecosystems through multiple mechanisms. Here, using a series of studies from a network of tree diversity experiments, we explore the multifaceted ways in which light---in terms of both quantity and quality---shapes productivity in mixed-species forests. Spectral reflectance from remote sensing of forest canopies is being increasingly used to detect how tree diversity influences productivity. We demonstrate that airborne imaging spectroscopy captures functionally important differences among canopies related to their structure, chemistry, and underlying biological interactions. Ground-based analyses can show in detail how photosynthetically active light is partitioned among species in mixed-species communities. We show that greater interception of light and greater efficiency of light use, generated by inter- and intra-specific differences, combine to enhance productivity in mixed-species forests. Light may shape forest function not only as a resource but also as a stressor and cue. Plants can perceive light at various wavelengths, use this information to assess their neighborhoods, and subsequently adjust their physiology and allocation. We characterize how light quality---from the ultraviolet to shortwave infrared---varies among and within canopies of differing diversity. We explore how these diversity-light quality relationships arise and connect across levels of biological organization from leaf-level trait expression to forest function. Together these studies lend insight into light-mediated mechanisms that drive relationships between biodiversity and productivity in forest ecosystems---insights that are crucial to predict how biodiversity change will affect future forest function.

Continuous observations from geostationary satellites have been utilized to understand land surface seasonal dynamics and fill data gaps caused by clouds. However, the limited spatial resolution of geostationary satellite products constrained the processes of detecting the terrestrial changes in landscape scale, particularly over heterogeneous areas. Moreover, the variation of sun-target-sensor geometry in geostationary satellites results in diurnal changes in surface reflectance products. To overcome the limitations of geostationary satellite products over heterogeneous areas, we conducted the series of processes: 1) characterizing the effect of the solar and viewing geometry in surface reflectance using the bidirectional reflectance distribution function (BRDF), 2) harmonizing the satellite products from different platforms into a seamless product, and 3) fusing the different satellite products to enhance the spatial resolution of geostationary satellite products. Finally, we adopted spatial and temporal gap-filling methods to achieve daily gap-filled surface reflectance products. For robust application, the results from the integrated process were evaluated in both space and time using hyperspectral maps derived from unmanned aerial vehicle (UAV) and in situ tower-based continuous spectral measurements. We expect the geostationary satellite products with high spatial resolution to uncover the cloud-bound areas towards sensing the changes of the Earth in space and time.

Coastal regions are continuously under the threat of flooding induced by tropical cyclones worldwide. These threats continue to increase due to the effects of climate change such as sea-level rise. A number of available protective or mitigation strategies have been examined to address this threat and protect coastlines around the world. However, identifying the most effective strategy given limited resources is a complex question. Optimization methodologies as we have proposed integrate physical analysis and stakeholder feedback to come to a set of best mitigation strategies. This study examines physical and socio-economical aspects of flooding impacts to optimize these strategies. These are then examined including seawalls, elevated promenades, and strategic retreats.