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The National Innovation Center partnership development and science support has generated numerous publications over the last decade.

Filter Total Items: 25

From data to interpretable models: Machine learning for soil moisture forecasting

Soil moisture is critical to agricultural business, ecosystem health, and certain hydrologically driven natural disasters. Monitoring data, though, is prone to instrumental noise, wide ranging extrema, and nonstationary response to rainfall where ground conditions change. Furthermore, existing soil moisture models generally forecast poorly for time periods greater than a few hours. To improve such

In situ enhancement and isotopic labeling of biogenic coalbed methane

Subsurface microbial (biogenic) methane production is an important part of the global carbon cycle that has resulted in natural gas accumulations in many coal beds worldwide. Laboratory studies suggest that complex carbon-containing nutrients (e.g., yeast or algae extract) can stimulate methane production, yet the effectiveness of these nutrients within coal beds is unknown. Here, we use downhole

Poor relationships between NEON Airborne Observation Platform data and field-based vegetation traits at a mesic grassland

Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Observatory Ecological Network (NEON) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high-resolution trait mappi

Representing plant diversity in land models: An evolutionary approach to make ‘Functional Types’ more functional

Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into

Experiences in LP-IoT: EnviSense deployment of remotely reprogrammable environmental sensors

The advent of Low Power Wide Area Networks (LPWAN) has improved the feasibility of wireless sensor networks for environmental sensing across wide areas. We have built EnviSense, an ultra-low power environmental sensing system, and deployed over a dozen of them across two locations in Northern California for hydrological monitoring applications with the U.S. Geological Survey (USGS). This paper det

Activity-based, genome-resolved metagenomics uncovers key populations and pathways involved in subsurface conversions of coal to methane

Microbial metabolisms and interactions that facilitate subsurface conversions of recalcitrant carbon to methane are poorly understood. We deployed an in situ enrichment device in a subsurface coal seam in the Powder River Basin (PRB), USA, and used BONCAT-FACS-Metagenomics to identify translationally active populations involved in methane generation from a variety of coal-derived aromatic hydrocar

Snow depth retrieval with an autonomous UAV-mounted software-defined radar

We present results from a field campaign to measure seasonal snow depth at Cameron Pass, Colorado, using a synthetic ultrawideband software-defined radar (SDRadar) implemented in commercially available Universal Software Radio Peripheral (USRP) software-defined radio hardware and flown on a small hexacopter unmanned aerial vehicle (UAV). We coherently synthesize an ultrawideband signal from steppe

Influence of permafrost type and site history on losses of permafrost carbon after thaw

We quantified permafrost peat plateau and post-thaw carbon (C) stocks across a chronosequence in Interior Alaska to evaluate the amount of C lost with thaw. Macrofossil reconstructions revealed three stratigraphic layers of peat: (1) a base layer of fen/marsh peat, (2) peat from a forested peat plateau (with permafrost) and, (3) collapse-scar bog peat (at sites where permafrost thaw has occurred).

QCam: sUAS-based doppler radar for measuring river discharge

The U.S. Geological Survey is actively investigating remote sensing of surface velocity and river discharge (discharge) from satellite-, high altitude-, small, unmanned aircraft systems- (sUAS or drone), and permanent (fixed) deployments. This initiative is important in ungaged basins and river reaches that lack the infrastructure to deploy conventional streamgaging equipment. By coupling alternat

Profiling lunar dust dissolution in aqueous environments: The design concept

Published studies and internal NASA reports indicate that when native lunar dust is suspended in an aqueous solution a variety of metal and other ions are released. This release has implications for future lunar missions, ranging from effects on mission hardware, effects on life support systems, possible direct effects on human health, and effects on research experiments such as plant growth exper

Robotic environmental DNA bio-surveillance of freshwater health

Autonomous water sampling technologies may help to overcome the human resource challenges of monitoring biological threats to rivers over long time periods and large geographic areas. The Monterey Bay Aquarium Research Institute has pioneered a robotic Environmental Sample Processor (ESP) that overcomes some of the constraints associated with traditional sampling since it can automate water sample

Near-field remote sensing of surface velocity and river discharge using radars and the probability concept at 10 USGS streamgages

Near-field remote sensing of surface velocity and river discharge (discharge) were measured using coherent, continuous wave Doppler and pulsed radars. Traditional streamgaging requires sensors be deployed in the water column; however, near-field remote sensing has the potential to transform streamgaging operations through non-contact methods in the U.S. Geological Survey (USGS) and other agencies