Knowing how many plants have germinated in a farmer’s or researcher’s field is central to crop management and research. Plants are commonly counted by walking the field and counting the plants in each row. This technique is labor-intensive, slow, and error-prone. Automating stand counts using RGB imagery from Unmanned Aerial Vehicles (UAV) is an obvious solution. We propose DeepMaizeCounter, a robust computational system that provides accurate stand counts for research and production fields from imagery captured by freely flown, inexpensive drones and processed with an inexpensive computer. DeepMaizeCounter exploits mosaics computed from RGB videos, using a YOLOv4 model that is trained to recognize seedling maize plants in the V2–V10 growth stages (approximately 10–40cm in height), singly or in groups of two and three plants, and determines its accuracy on these classes of seedlings. We evaluated DeepMaizeCounter against in-field and on-frame manual stand counts for a number of different maize lines in both nursery and production fields. DeepMaizeCounter can reliably distinguish corn from weeds and other grasses, counting only the maize. The network is light and able to run 175 test frames in 6 seconds, or 29 frames per second. This opens the prospect that DeepMaizeCounter can eventually be deployed on cheap platforms for real-time counting.

Forest and vegetation fires, used as tools for agriculture and deforestation, are a major source of air pollutants and can cause serious air quality issues in many parts of Asia. Actions to reduce fire may offer considerable, yet largely unrecognised, options for rapid improvements in air quality. In this study, we used a combination of regional and global air quality models and observations to examine the impact of forest and vegetation fires on air quality degradation and public health in Southeast Asia (including Mainland Southeast Asia and south-eastern China). We found that eliminating fire could substantially improve regional air quality across Southeast Asia by reducing the population exposure to fine particulate matter (PM2.5) concentrations by 7% and surface ozone concentrations by 5%. These reductions in PM2.5 exposures would yield a considerable public health benefit across the region; averting 59,000 (95% uncertainty interval (95UI): 55,200-62,900) premature deaths annually. Analysis of subnational infant mortality rate data and PM2.5 exposure suggested that PM2.5 from fires disproportionately impacts poorer populations across Southeast Asia. We identified two key regions in northern Laos and western Myanmar where particularly high levels of poverty coincide with exposure to relatively high levels of PM2.5 from fires. Our results show that reducing forest and vegetation fires should be a public health priority for the Southeast Asia region.

Plant growth and development is impacted by the ability to capture resources including sunlight, determined in part by the arrangement of plant parts throughout the canopy. This is a very complex trait to describe, but has a major impact on downstream traits such as biomass or grain yield per acre. Though some is known about genetic factors contributing to leaf angle, maturity, and leaf size and number, these discrete traits do not encompass the structural complexity of the canopy. In addition, modeling and prediction for plant developmental traits using genomics or phenomics are usually conducted separately. We have developed proof-of-concept models that incorporate spatio-temporal factors from drone-acquired LiDAR features in a maize diversity panel to predict plant growth and development over time to improve our understanding of the biology of canopy formation and development. Briefly, voxel models for probability of beam penetration into the foliage were generated from 3D LiDAR scans collected at seven dates throughout crop canopy development. From the same plots, key architectural features of the maize canopy were measured by hand: stand count; plant, tassel, and flag leaf height; anthesis and silking dates; ear leaf, total leaf, and largest leaf number; and largest leaf length and width. We develop a self-supervised autoencoding neural network architecture that separately encodes plant temporal growth patterns for individual genotypes and plant spatial distributions for each plot. Then, leveraging the resulting latent space encoding of the LiDAR scans, we train and demonstrate accurate prediction of hand-measured crop traits.

The compound-specific hydrogen isotopic composition (δ2H) of n‑alkanes is a valuable proxy to investigate hydrological conditions in lake sediments. While terrestrial n-alkanes reflect the isotopic signal of the local precipitation, aquatic n-alkanes incorporate the isotopic signal of the lake’s water, which can be strongly modulated by evaporative enrichment. So far, the spatial distribution of the terrestrial and aquatic δ2H signal within lakes have not systematically been investigated. We present compound-specific δ2H results of terrestrial (δ2HC31) and aquatic (δ2HC23) n‑alkanes of surface sediment samples from Lake Khar Nuur, a semi-arid, high-altitude lake in the Mongolian Altai, and additionally investigate the δ2H signal of the catchment plants and topsoils. Our results show only a small variability of δ2HC31 in the surface sediments which well-reflect δ2H values from catchment plants and topsoils, andsummer (growing season) precipitation. δ2HC23 in surface sediment samples from the central and deepest parts of the lake, i.e., the lake’s sediment accumulation zones, shows distinctly more positive δ2HC23 values due to evaporative lake water 2H-enrichment. By contrast, δ2HC23 in samples closer to the shore is likely biased by 2H-depleted meltwater. Consequently, Δaq‑terr, which is the isotopic offset between δ2HC23 and δ2HC31, indicates distinct lake water enrichment in the lake’s accumulation zones and is a valuable proxy to investigate past hydrological changes. Therefore, sediment retrieval for paleoenvironmental and -hydrological studies should hence carefully be figured out and be related to the sediment accumulation zones of the lake.

Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of entire, full sized root systems of crop plants, mesocosm growth containers were developed with an internal volume of 45 ft3 (1.27 m3). Mesocosms allow for unconstrained root growth, excavation and preservation of 3-dimensional RSA, and modularity that facilitates the use of a variety of sensors. Sensors arrays monitoring matric potential, temperature and CO2 levels are buried in a grid formation at depths of 1.25, 2.75, & 4.25 ft to assess environmental fluxes at regular intervals. Additionally, 3-dimensional water availability can be measured using ERT inside of root mesocosms. Methods of 3D data visualization of fluxes were developed to allow for comparison with root architectural traits. Following harvest, the recovered root system can be digitally reconstructed through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. Initial metrics inferred from the 3D models include root system biomass (occupied voxels), volume, flatness, convex hull volume and solidity with depth. Root systems are finally dissected and biomass measurements are made in a 3-dimensional matrix of the growth zone, while the crown is saved for X-ray CT analysis.

The American Geophysical Union (AGU) is pleased to submit this RFI response to 2021-13640 on improving federal scientific integrity policies. AGU is the largest global organization covering the Earth sciences with a mission “to support and inspire a global community of individuals and organizations interested in advancing discovery in Earth and space sciences and its benefit for humanity and the environment.” Fostering integrity is a key part of our new strategic plan and past activities and we are engaged in supporting integrity broadly, including with federal agencies. Although not a focus of these recommendations, AGU has often spoken up through position statements and letters related to scientific integrity. Several examples are listed in the references. With this perspective, we urge OSTP to consider two points that we elaborate below: • Fostering integrity–and in turn public trust in science and science policy–requires a broad, holistic view of practices that extend beyond the typical focus on transparency and ethics to include ensuring deeper public engagement, addressing diversity and inclusivity in science and supporting the backbone infrastructure that enables all of these. • The way science is supported, practiced and conducted is changing significantly, as is its dissemination and communication, and these changes have important implications for fostering integrity in the 21st century. Specifically, parts of the culture and reward system of science need improvement to align with these changes, and OSTP and federal policy can be a strong proactive force in enabling this change. This is particularly the case if these policies and practices provide leading examples and extend to federal grants. Many other organizations would then align.

In Alaska, pervasive irregularities of permafrost coverage and associated boreal forest heterogeneity within the North American Taiga-Tundra Ecological Transition Zone (TTE) are becoming more apparent as the climate warms. These anomalies correspond to extensive shifts in active layer thickness (ALT), carbon cycle disruption, and ecosystem response patterns. The feedback complexities associated with these climate-induced disturbances are evaluated with the integration of remote sensing, modeling, field observations, data assimilation and harmonization techniques, and artificial intelligence technology. In this study, to improve our understanding of shifting belowground dynamics and how they associate with aboveground vegetation patterns, we used the SIBBORK-TTE model to derive permafrost degradation and ecosystem transiency at high-resolution in this study. The intercomparison of model version output was first examined; then, multiple verification and validation methodologies revealed distinct historical and future implications resulting from ALT variability within four regions of the Alaska TTE domain (North Slope, Yukon Delta, Seward Peninsula, Interior). To quantify historical thaw variability and identify seasonality patterns across these regions of interest, in situ ALT point measurements were collected from two campaigns (CALM, SMALT) to cross-validate ALT-derived SAR data (AirMOSS, UAVSAR) and below-ground SIBBORK-TTE simulations between 1990-2020. Future conditions were then projected with a warming climate function and CMIP6 data from CNRM-CERFACS SSP126/585 scenarios. Initial results for derived and measured annual maximum ALT yield a mean-error performance metric of 0.2294. Paradoxically, future climate conditions advance the ubiquity of permafrost thaw and seasonality widening across the TTE. With this investigative approach, spatiotemporal variability in ALT provides a unique signal to enhance model precision and lower uncertainty through fine-tuning driver forcing and modular parameterization, forecast permafrost distribution, and identify the climatic and topographic mechanisms of earth system feedbacks and land cover change.

Transpolar arcs (TPAs) are auroral features that occur polewards of the main auroral oval suggesting that the magnetosphere has acquired a complicated magnetic topology. They are primarily a northward interplanetary magnetic field (IMF) auroral phenomenon, and their formation and evolution have no single explanation that is unanimously agreed upon. An automated detection algorithm has been developed to detect the occurrence of TPAs in UV images captured from the Special Sensor Ultraviolet Spectrographic Imager (SSUSI) instrument onboard the Defense Meteorological Satellite Program (DMSP) spacecraft, in order to further study their occurrence. Via this detection algorithm TPAs are identified as a peak in the average radiance intensity poleward of 12.5 degrees colatitude, in two or more of the wavelengths/bands sensed by SSUSI. Orbital biases in the data have been investigated and these differ from spacecraft to spacecraft. For the spacecraft of interest (F16, F17 and F18) this leads to a preferential observation of the northern hemisphere with the detection algorithm missing TPAs in the southern hemisphere between approximately 01 - 06 UT. No seasonal bias has been found for these spacecraft. Using the detection algorithm on observations from the years 2010 to 2016, over 5000 images containing TPAs are identified. The occurrence of these TPA images shows a seasonal dependence, with more arcs being visible in the winter hemisphere. We discuss the ramifications of these findings in terms of proposed TPA generation mechanisms.

Estimates of global carbon stocks in coastal wetlands reveal that these are some of the most efficient carbon-sequestering environments in the world, which has prompted a renewed interest in conservation and restoration programs as an opportunity for greenhouse gas abatement. Accumulation of carbon in coastal wetlands is linked to diverse factors such as the type of vegetation, geomorphic setting, and sediment supply. Feedbacks between these factors and the tidal flow conditions drive the dynamics of carbon accumulation rates. Climate change-induced sea-level rise has been shown to increase the vulnerability to submergence of saltmarsh and mangroves in coastal wetlands, even if accommodation and landward colonization are possible. These potential losses of wetland vegetation combined with the reduced productivity of newly colonized areas will directly affect the capacity of the wetlands to sequester carbon from sediments and root growth. Here, we implement an eco-geomorphic model to simulate vegetation dynamics, soil carbon accumulation, and changes in soil carbon stock for a restored mangrove-saltmarsh wetland experiencing accelerated sea-level rise. We evaluate model outcomes for existing conditions and two different management scenarios aimed at mitigating sea-level rise effects and conserve wetland vegetation. Even though some management measures can result in partial conservation of wetland vegetation, they do not necessarily result in the best option for soil carbon capture. Our results suggest that accelerated sea-level can trigger accelerated wetland colonization resulting in wetland areas with limited opportunities for soil carbon capture from sediment and root mineralization, an issue that has not been considered in previous studies.

In both natural and built environments, microbes on occasions manifest in spherical aggregates instead of solid-affixed biofilms. These microbial aggregates are conventionally referred to as granules. Cryoconites are mineral rich granules that appear on glacier surfaces and are linked with expanding surface darkening, thus decreasing albedo, and enhanced melt. The oxygenic photogranules (OPGs) are organic rich granules that grow in wastewater with photosynthetic aeration and present potential for net autotrophic wastewater treatment in a compact system. Despite obvious differences inherent in the two, cryoconite and OPG pose striking resemblance. In both, the order Oscillatoriales in Cyanobacteria envelope inner materials and develop dense spheroidal aggregates. We explore the mechanism of photogranulation on account of high similarity between cryoconites and OPGs. We contend that there is no universal external cause for photogranulation. However, cryoconites and OPGs, as well as their intra variations, which are all are under different stress fields, are the outcome of universal physiological processes of the Oscillatoriales interfacing goldilocks interactions of stresses, which select for their manifestation as granules. Finding the rules of photogranulation may enhance engineering of glacier and wastewater systems to manipulate their ecosystem impacts.

Alluvial fans form through primary and secondary geomorphic processes. Primary processes act to transport sediment from the watershed to the fan while secondary processes re-mobilize and rework the fan surface. While primary processes on alluvial fans are well studied, secondary processes and their relationship to fan flood hazards have received little attention. The experiments described herein isolate the role of secondary processes in determining alluvial fan behaviour and morphology. We conducted four experiments, in which alluvial fans were allowed to evolve under alternating primary and secondary process periods, with different durations of secondary processes. While the secondary process duration changed, the total primary process duration remained constant keeping the total volume of sediment constant for each experimental fan. Experiments with longer durations of secondary processes generated fans with larger areas and gentler gradients. In addition, longer secondary process durations led to increased flow channelization and centralization between flood periods. These morphologic changes resulted in fewer avulsions, that occurred later during primary process periods. These results indicate that changes to the relative duration of primary and secondary process periods caused by climate change can affect fan morphology and flow behaviour.

The knowledge of the radioactive 14C yield under atmosphere thunderstorm flash conditions (the additional discovered channel of 14C production relative to the main - cosmogenic one) is important for radiocarbon analysis. It is proposed the gross model for evaluation of the thunderstorm 14C yield simulated for the altitudes up to 15 km. It was obtained that yield from the thunderstorm mechanisms of 14C creation cannot compete with cosmogenic production which is six orders of value larger. The obtained result allows to take off the problematic issue on thunderstorm radiocarbon generation in the atmosphere as the additional significant source.

With a warming climate, solar activity including Sunspot Number (SSN) and large-scale climate phenomena including EI Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Arctic Oscillation (AO) have induced changes in climate extremes and changes in the hydrological cycle in arid-semiarid regions of the world, thus a detailed investigation of climate variability can play a key role in water resources management, drought monitoring, ecological restoration and sustainable development. In this study, we used wavelet coherence (WTC) based on continuous wavelet transform (CWT) to assess the impacts of SSN, ENSO, PDO and AO on multiple interacting hydrological processes and identify the teleconnection patterns and lead-lag relationships between the four principal modes and changes in extreme temperature and precipitation events, meteorological drought, and streamflow variability in Xinjiang, an arid-semiarid region of China. The results indicated that solar activity and climatic oscillations were viewed as the primary drivers for periodic variation of extreme temperature events and the evolution of drought in Xinjiang. For instance, the ENSO positively affected warm extremes with intermittent coherence in the 2–6-year band during 1984–2000 and had negative correlations with cold extremes in the 2–6-year band at the interannual scale. Compared with warm extremes, variability in cold extremes was much more sensitive to the activity pattern of AO. It was clear that the coherence of temperature variables in Xinjiang with PDO was weaker than that with ENSO and AO, and there was a nonsignificant covariance between PDO and extreme temperature events. In addition, the WTC spectra showed that teleconnection factors including solar activity and three large-scale climate phenomena had significant impacts on annual and monthly drought evolution, and AO had the strongest influence on annual standardized precipitation evapotranspiration index (SPEI) values. In general, compared with SSN, ENSO, PDO and AO all showed clear leading effects on precipitation extremes variability and annual streamflow variability for a specific time and frequency, and solar activity’s influences might be transferred by ENSO to precipitation extremes or streamflow variability at the 2–7-year band. Overall, the warming and wetting trend in Xinjiang may be a local manifestation of global multivariate climate change. Thus, our findings will have important implications for designing best practice strategies for water resource management and ecological restoration in similar arid-semiarid basins around the world.

Man-made terraces for agricultural purposes are a quite diffuse and ancient anthropogenic landscape modification in mountainous areas. The original slope alteration, obtained through a sequence of sub-vertical and sub-planar surfaces, represents a human interference with the geomorphic system, altering the original balance of geomorphological and geo-hydrological factors. Stone walls and soil formed by human activity have been artificially immobilized on the slopes and are available again to gravitative processes once in abandonment and may be subject to deep degradation in case of intense rain events. However, socio-economic conditions play often a crucial role in the abandonment of terraces, indirectly contributing to increase gully erosion and walls failure. The modification of the original slope profile, due to its regular geometry in respect to the typical more complex natural surface, is rather suitable to be detected through remote sensing, particularly LIDAR, as many authors have recently demonstrated. In the present research the attention has been focused on the assessment of terraces and of the volume of stones and soil that have been involved by human activity. The research area is among the most deeply modified by terraces in the Mediterranean area and internationally famous for this landscape anthropogenic alteration: The Cinque Terre in Italy is a National Park intensively visited by tourists all over the year. Then terraces represent an important economic asset that need to be preserved from degradation and collapse as partially occurred in 2011 after the intense rain event that caused flood, hundreds of landslides and consequently damage. During the 2011 event many terraced slopes have collapsed with significant loss of soil and stone walls: the research allowed to evaluate the lost volumes and to estimate the remaining ones in the Vernazza catchment.

We report fallout radionuclide (FRN) and major/trace element (MTE) contributions to bulk atmospheric deposition in Hanover, NH USA (43.7022° N, 72.2896° W). Deposition of 7Be, 210Pb and SO4 covary [R2>0.4, n=461] but are discriminated by production sources, depositional mechanisms, meteorological controls, MTE associations, and seasonal biases. 7Be is dominated by rainout (78% of total deposition), recharged by long-range transport (+23% over mean, o.m.), influenced by stratosphere-troposphere-exchange (+9% o.m.) and solar activity (-2% per doubling of sunspot count). Correlation with particulate nitrogen (+9% per doubling of N) indicates 7Be affinity for biogenic aerosols. 210Pb is dominated by dry+washout deposition (54% of total) and convective storms (+107% o.m), is depleted in marine moisture sources (-133% o.m.), correlated with S (+9% per doubling of S) and biased to autumn with Mn, Hg, and V (+7% o.m.). Coincident long-term declines in S and 210Pb (-14%, -4% per year) suggest co-scavenging by PbSO4. 7Be:210Pb ratios increase asymptotically with precipitation through the dry-washout-rainout transition and recharge of 7Be. At the global scale, 7Be:210Pb increases with precipitation for North American/European sites due to recharge of 7Be in mid-latitude storm belts [R2=0.64, n=31]. Conversely, 7Be:210Pb is independent of precipitation for Southeast/East Asian sites where 7Be recharge is low [R2=0.01, n=40]. Globally, 7Be:210Pb ratios in dry deposition reflect resuspended aerosols with mean age of ca. 200 days, contributing <5% of 210Pb deposition. Different aerosol populations contributing to FRN deposition across spatial and temporal scales should emerge as a focus in terrestrial 7Be, 210Pb and 10Be tracer applications.

We use machine learning methods to predict whether an active region (AR) which produces flares will lead to a solar energetic particle (SEP) event using Space-Weather Michelson Doppler Imager (MDI) Active Region Patches (SMARPs). This new data product is derived from maps of the solar surface magnetic field taken by the Michelson Doppler Imager (MDI) aboard the Solar and Heliospheric Observatory (SOHO). We survey the SMARP active regions associated with flares that appear on the solar disk between June 5, 1996 and August 14, 2010, label those that produced SEPs as positive and the rest as negative. The AR SMARP features that correspond to each flare are used to train two different types of machine learning methods, the support vector machines (SVMs) and the regression models. The results show that the SMARP data can predict whether a flare will lead to an SEP with accuracy (ACC) {less than or equal to}0.72{plus minus}0.12 while allowing for a competitive leading time of 55.3{plus minus}28.6 minutes for forecasting the SEP events.

Some aspects of the dynamics of aeolian transport over a flat sediment bed have been thoroughly investigated and are relatively well understood. The interactions between grains in transport and the wind give rise to well-known dynamical scaling laws for the fluxes and concentrations of grains in most of the transport layer. However, recent work has revealed a sudden shift in these scaling laws near the granular surface and below. While the vertical flux of grains in the transport layer scale linearly with excess wind shear stress, the vertical flux near the granular surface—the ‘erosion rate’—scales linearly with wind speed. Analysis of numerical modeling results reveal that near-surface horizontal and vertical fluxes are important for the instability that leads to wind ripple growth and stabilization as well as ripple propagation. A few main open questions are: What are the physical mechanisms behind the scaling of the erosion rate with wind speed? Could they arise from the small subpopulation of high-energy grains, who’s characteristics scale differently than the average grain in transport? As these grains move downward from the free-wind layer, do they tend to retain their properties as they pass through the feedback layer, delivering their energy, momentum and scaling directly to the bed? Do collisions between grains near and within the bed, which redistribute energy and momentum from high-energy impacts, play a key role in determining the scaling of near-bed fluxes? How important are potential collective effects that can occur when impacts with sufficient energy to excite the bed occur close together in time and space? An answer to these questions would help complete our understanding of the physics of aeolian transport, with repercussions that shed light onto the emergence and propagation of wind ripples. Using a detailed grain scale numerical model, we are investigating the dynamics of grains near the granular bed, and what saltation properties drive these dynamics. Preliminary results, including velocity distributions near the bed, indicate that the signal from high-energy grains that traverse the feedback layer from above reaches the bed surface, consistent with the hypothesis that the surface erosion rate is related to this small population of grains who’s characteristics scale with the free-wind speed.

Seismologists working with fiber-optic sensing, commonly referred to as Distributed Acoustic Sensing (DAS), have yet to find an established way of automatically detecting signals of interest within its recordings. We propose a new research perspective within the field by examining the output of a DAS array as an image and processing the image to find signals of interest. In this manuscript, we show an example of such a method, where we automatically detect seismic events of interest within two different DAS datasets, finding, respectively 99 % and 96 % of the local earthquakes previously identified within the data by manual analysis. The method is based on simple image processing and computer vision techniques, which clean the image, and, ideally, leave nothing but the signal of interest. These simple image processing steps yield promising results, indicating that computer vision and image processing might have an immediate impact in geophysical applications of fiber-optic sensing.