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NASA NTRS2018

Remote Sensing of the Ocean Surface Refractive Index via Short-Wave Infrared Polarimetry

The linear polarization of sunlight reflected by ocean surfaces in the shortwave infrared (SWIR), at geometries where specular reflection dominates the signal, is a direct function of the refractive index of the surface microlayer(SML). This simple physical concept is at the base of a novel technique presented in this study. We invert observations obtained by the airborne Research Scanning Polarimeter (RSP) in the sun glint region, where each pixel's radiance is dominated by the signal originating from the wave slopes oriented precisely to cause specular reflection. The SWIR wavelength ensures minimization of aerosol interference when radiance travels through the atmosphere; strong absorption by the water body then limits the penetration depth to the first micrometer or so, effectively probing the SML. The resulting Degree of Linear Polarization (DoLP) is then governed by the refractive index via the Fresnel law for the specfic pixel geometry, independently of the wind speed. The selected dataset concerns several field deployments from both low- and high-altitude aircraft, including total reflectance measurements with the sole purpose of accounting for the residual aerosol effect.Stable retrievals from transects above pure seawater yielded values of refractive index that match the values published in the literature within an accuracy of 5 x 10 [superscript 4]. Flying over the oil spill caused by the explosion of the Deepwater Horizon platform, detected variations were found compatible with the presence of an oil slick. The robustness of the results, guaranteedby the high RSP polarimetric accuracy (less than or equal to 0:2%), opens the possibility for remote-sensing detection of other entities that similarly affect the refractive index including whitecaps, microplastics, biological gels, seaweed and grass mats.

Surface Microlayer↗
NASA NTRS2019

Modeling Phenological Controls on Carbon Dynamics in Dryland Sagebrush Ecosystems

Dryland ecosystems play an important role in determining how precipitation anomalies affect terrestrial carbonfluxes at regional to global scales. Thus, to understand how climate change may affect the global carbon cycle,we must also be able to understand and model its effects on dryland vegetation. Dynamic Global VegetationModels (DGVMs) are an important tool for modeling ecosystem dynamics, but they often struggle to reproduceseasonal patterns of plant productivity. Because the phenological niche of many plant species is linked to bothtotal productivity and competitive interactions with other plants, errors in how process-based models representphenology hinder our ability to predict climate change impacts. This may be particularly problematic in drylandecosystems where many species have developed a complex phenology in response to seasonal variability in bothmoisture and temperature. Here, we examine how uncertainty in key parameters as well as the structure ofexisting phenology routines affect the ability of a DGVM to match seasonal patterns of leaf area index (LAI) andgross primary productivity (GPP) across a temperature and precipitation gradient. First, we optimized modelparameters using a combination of site-level eddy covariance data and remotely-sensed LAI data. Second, wemodified the model to include a semi-deciduous phenology type and added flexibility to the representation ofgrass phenology. While optimizing parameters reduced model bias, the largest gains in model performance wereassociated with the development of our new representation of phenology. This modified model was able to bettercapture seasonal patterns of both leaf area index (R2=0.75) and gross primary productivity (R2=0.84), thoughits ability to estimate total annual GPP depended on using eddy covariance data for optimization. The new modelalso resulted in a more realistic outcome of modeled competition between grass and shrubs. These findingsdemonstrate the importance of improving how DGVMs represent phenology in order to accurately forecastclimate change impacts in dryland ecosystems.

Ecosystem model Phenology Parameter optimization E↗
NASA NTRS2018

Evapotranspiration-Based Irrigation Scheduling in Cool-Season Vegetables

Crop evapotranspiration (ETc) is strongly linked with photosynthetically active vegetation fraction (Fc). Estimation of ETc may support efficiency gains in irrigation water management, which in turn can mitigate nitrate leaching, promote water supply sustainability, and reduce energy costs associated with water pumping or transport. The University of California Cooperative Extension operates the CropManage (CM) model as a freely-available web-application for growers and consultants to support irrigation and nitrogen scheduling decisions. CM accounts for the rapid growth and typically brief cycle of cool-season vegetables, where Fc and crop coefficient (fraction of reference ET) can change daily during canopy development. Daily weather conditions are inherently accounted for by use of grass reference ETo data imported from the California Dept. Water Resources. Crop water requirement calculations are output in terms of irrigation system runtime. Empirical equations are used to estimate daily Fc time-series for a given crop type, primarily as a function of planting date and expected harvest. An applications programming interface (API) enables CM to import satellite-based Fc observations from NASA's Satellite Irrigation Management Support, which uses Landsat imagery to monitor about eight million irrigation acres statewide. The API is intended to provide a check on internal CM predictions of Fc and to facilitate expansion of the web-app to new crops and regions. A replicated irrigation trial was performed on cauliflower during spring/summer 2018 at the USDA Agricultural Research Station in Salinas, CA. The crop was established by sprinkler irrigation, and CropManage was then used to guide a series of drip irrigation treatments at 50%, 75%, 100%, and 150% of ETc replacement levels. Results will be presented with respect to water use efficiency, nitrogen use efficiency, biomass yield, and marketable yield. Additional findings will be presented for a celery trial harvested during autumn 2018.

Johnson, Lee↗
NASA NTRS2019

Evapotranspiration-Based Irrigation Scheduling in Salinas Valley Vegetable Crops

Crop evapotranspiration (ETc) monitoring may support improved irrigation water management in California's Central Coast agricultural region. The CropManage (CM) web-application, developed and operated by the University of California, informs irrigation and nitrogen scheduling decisions made by growers and crop consultants. Daily weather conditions are accounted for by use of grass reference ET data imported from the California Dept. Water Resources. Satellite observations of crop development from NASA's Satellite Irrigation Management Support can be imported and used by CM to adjust crop phenology. A replicated irrigation trial was performed July-October 2018 to investigate yield response of drip-irrigated celery (Apium graveolens) to applied water volume, and to further calibrate CM for use in that crop. Water was uniformly applied by sprinklers to establish the crop, and CM was then used to guide surface drip irrigation treatments applied three times weekly at 50%, 75%, 100%, 125% and 150% of ETc replacement level. Nitrogen fertilizer totaling 380 kg/ha was applied through the drip system generally once per week. Treatments were evaluated for commercial yield 85 and 93 days after transplanting (DAT). The 100% ETc treatment yielded 83 Mg/ha 93 DAT, which was 10 Mg/ha higher than the reported regional average yield in 2017. The 100% treatment received a seasonal total of 34 cm of water, compared with reported grower average exceeding 48 cm. Higher yields of 102 and 108 Mg/ha were observed in the 125% and 150% ETc treatments, respectively. Regarding crop quality, however, the 150% ETc treatment suffered greater incidence of pith breakdown and basal rot than the 100% treatment. Yields from the 50% and 75% treatments were below regional average. Aboveground biomass was evaluated 87 DAT. Fresh and dry biomass accumulations were positively related to applied water volume. Additional findings will be presented for a cauliflower trial conducted spring/summer 2019.

Johnson, Lee F.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-35727-DND

Concerns: Beetles killed 5 million acres of forest in Alaska in the 1990s, flammable grasses take over dead forest, beetle outbreaks becoming more frequent.

Katherine Hess↗
NASA NTRSDate not supplied

Derivation and Testing of Consumptive Water Use Fraction for Specialty Crops

The Crop Consumptive Use Fraction (CCUF) expresses beneficial water use in the form of seasonal evapotranspiration of applied water (ETAW), relative to total irrigation volume. The metric is an indicator of the efficiency of agricultural water use and is a recommended component for preparation of agricultural water management plans in California. An FAO-56 based web application has been developed to facilitate retrospective evaluation of ETAW, and hence CCUF, at field level. The current application is optimized for prevailing climate in four of the state’s main growing regions: San Joaquin Valley, Sacramento Valley, Central Coast, and North Coast. User inputs include crop type, soil texture, irrigation method, daily irrigation volume, daily rainfall, and seasonal start/stop dates to define the analysis period. Time series of fractional green canopy cover (Fc), based on Landsat and Sentinel-2 Earth-resource satellite observations, are imported from NASA’s Satellite Irrigation Management Support (SIMS) system. Grass reference evapotranspiration (ETo) time series are accessed from Spatial CIMIS (California Irrigation Management Information System). Daily crop height (h) is estimated as a simple function of typical maximum height for the given crop type (from FAO-56) and Fc. A vegetation density coefficient (Kd) is derived from Fc and h. Stomatal control factors during mid-and late-season are applied to tree and vine crops. Typical values for minimum daily relative humidity and mean daily windspeed, derived from historical CIMIS weather data, are used to correct for regional deviations from standard climate (defined as RHmin=45%, windspeed =2m/s). The resulting daily basal crop coefficient (Kcb) represents the ET of a well-watered crop with minimal soil evaporation, relative to ETo. A soil water balance sub-model for the top 0.1 m is used to calculate daily evaporation coefficients (Ke). A second sub-model applies to the root zone to calculate crop water stress coefficients (Ks) and effective precipitation (Pe), which is the fraction of rainfall that is available for crop use. Those coefficients are combined with Kcb and ETo to calculate daily ETc. ETAW is then derived as cumulative ETc less cumulative Pe. For verification purposes, sensor installations were used to measure seasonal ETc in commercial fields for several annual and perennial specialty crops by soil water balance and energy balance methods. Model estimates of seasonal ETc show mean absolute error of <10% compared to the ground measurements.

Derivation↗
NASA NTRS2022

Leaf-level chlorophyll fluorescence and reflectance spectra of high latitude plants

Little is known about the chlorophyll fluorescence spectra for high latitude plants. A FluoWat leaf clip was used to measure leaf-level reflectance and chlorophyll fluorescence spectra of leaves of common high latitude plants to examine general spectral characteristics of these species. Fluorescence yield (Fyield) was calculated as the ratio of the emitted fluorescence divided by the absorbed radiation for the wavelengths from 400 nm up to the wavelength of the cut-off for the FluoWat low pass filter (either 650 or 700 nm). The Fyield spectra grouped into distinctly different patterns among different plant functional types. Black spruce (Picea mariana) Fyield spectra had little red fluorescence, which was reabsorbed in the shoot, but displayed a distinct far-red peak. Quaking aspen (Populus tremuloides) had both high red and far-red Fyield peaks, as did sweet coltsfoot (Petasites frigidus). Cotton grass (Eriophorum spp.) had both red and far-red Fyield peaks, but these peaks were much lower than for aspen or coltsfoot. Sphagnum moss (Sphagnum spp.) had a distinct Fyield red peak but low far-red fluorescence. Reindeer moss lichen (Cladonia rangiferina) had very low fluorescence levels, although when damp displayed a small red Fyield peak. These high latitude vegetation samples showed wide variations in Fyield spectral shapes. The Fyield values for the individual red or far-red peaks were poorly correlated to chlorophyll content, however the ratio of far-red to red Fyield showed a strong correlation with chlorophyll content. The spectral variability of these plants may provide information for remote sensing of vegetation type but may also confound attempts to measure high latitude vegetation biophysical characteristics and function using solar induced fluorescence (SIF).

K Fred Huemmrich↗
NASA NTRSDate not supplied

X-57 Ground Dynamics Modeling and Analysis

This paper presents simulation work to assess the ground handling behavior of the X-57 airplane, prior to its first flight under electric propulsion. A tire deformation model was fit to manufacturer ground testing data to develop a detailed simulation model of previously unmodeled rolling drag forces on the tires. A special modeling effort corrected for a difference between the runway surface in the manufacturer data (grass) and in intended X-57 surface operations (asphalt/concrete). Analysis based on variation in the test conditions of the manufacturer test data showed that uncertainty in the new estimated rolling drag model should lead to no more than 1.5-percent uncertainty in the takeoff acceleration. Another batch simulation series swept across ground operating conditions and airplane configurations, characterizing turning radii and roll angles experienced for different open-loop steering commands in these various situations. This simulation allowed the identification of ground handling characteristics even beyond the regime of normal operations. As part of this task, a new, modified variant of the Fibonacci search algorithm identified the correct steering inputs for maintaining a straight path as a function of the operating condition. Finally, this paper describes a fixed-base piloted simulator assessment. This assessment supplements the previous simulated ground dynamics data with pilot-in-the-loop data and pilot feedback and confirms the overall acceptability of the ground handling qualities of the X-57 airplane prior to first flight.

Loren J. Newton↗
NASA NTRS2022

Modeling Demographic-Driven Vegetation Dynamics and Ecosystem Biogeochemical Cycling in NASA GISS’s Earth System Model (ModelE-BiomeE v.1.0)

We developed a demographic vegetation model, BiomeE, to improve the modeling of vegetation dynamics and ecosystem biogeochemical cycles in the NASA Goddard Institute of Space Studies' ModelE Earth system model. This model includes the processes of plant growth, mortality, reproduction, vegetation structural dynamics, and soil carbon and nitrogen storage and transformations. The model combines the plant physiological processes of ModelE's original vegetation model, Ent, with the plant demographic and ecosystem nitrogen processes that have been represented in the Geophysical Fluid Dynamics Laboratory's LM3-PPA. We used nine plant functional types to represent global natural vegetation functional diversity, including trees, shrubs, and grasses, and a new phenology model to simulate vegetation seasonal changes with temperature and precipitation fluctuations. Competition for light and soil resources is individual based, which makes the modeling of transient compositional dynamics and vegetation succession possible. Overall, the BiomeE model simulates, with fidelity comparable to other models, the dynamics of vegetation and soil biogeochemistry, including leaf area index, vegetation structure (e.g., height, tree density, size distribution, and crown organization), and ecosystem carbon and nitrogen storage and fluxes. This model allows ModelE to simulate transient and long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems. Furthermore, BiomeE also allows for the eco-evolutionary modeling of community assemblage in response to past and future climate changes with its individual-based competition and demographic processes.

Biogeochemical cycles↗
NASA NTRSDate not supplied

Cropland and Land-Clearing Emission Factors From Firex-AQ

The Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign is a NOAA/NASA interagency intensive study of North American fires that took place from July to September 2019. Agricultural fires were sampled on 7 flights from August 21st to September 3rd, 2019. We present emission factors for gas-phase and aerosol-phase species for the different fuel types identified during the campaign. We discuss the dependence of these emission factors on both fuel type and burning characteristics such as modified combustion efficiency (MCE). We analyze 73 fires encompassing four different agricultural fuels (corn, rice, soybean, wheat) and four different types of land-clearing fuels (grass, slash, pile, shrub). Approximately half of all sampled fires were burning corn agricultural residue. We report emission factors for 118 VOCs, where two thirds of emissions are made up of species with lifetimes against oxidation by OH of less than 12 hours (near-field impacts) and the remainder have longer-lifetimes (far-field impacts). We also include an additional 9 aerosol species, 12 halogenated species, 5 sulfur-containing species, and 22 nitrogen-containing species, including NH3 and NOx. There is a statistically significant difference in MCE between agricultural and land-clearing fires that accounts for some of the differences in emission factors across these fuels. VOCs generally have a strong negative correlation with MCE but with different slopes across fuel types. There is a significant positive relationship between MCE and three gas-phase (NO, NO2, SO2) and two aerosol-phase (pCl, pNH4) species. Many emission factors show additional dependence factors beyond MCE that could be related to fuel composition, fire temperature, soil moisture, or other possible differences in burning conditions. Therefore, important differences in emission factors may result when averaging together different types of agricultural and land-clearing fuels in compilations for use in modeling efforts.

biomass burning↗
NASA NTRSDate not supplied

The Payload and Operations of the Hera Mission

On 26 September 2022, NASA’s DART mission successfully impacted on Dimorphos, the secondary of the binary asteroid Didymos. DART released the Light Italian Cubesat for Imaging of Asteroids (LICIACube) two weeks before the impact, and LICIACube flew by the asteroids three minutes after the impact. On approach, DART took images of both asteroids, which, supported by additional imagery from LICIACube, characterized the pre-impact state of the Didymos system. DART changed the orbit of Dimorphos around Didymos, reducing its orbital period by 33 minutes. DART will be followed by a detailed investigation of the Didymos system and the outcome of the impact by ESA’s Hera mission. Hera will be launched in October 2024 and arrive at Didymos in early 2027. The presentation will focus on the payload of the Hera mission and the operations at the asteroid system. The main objectives of the Hera mission are to: - Measure the mass of Dimorphos to accurately measure the momentum transfer efficiency of the DART impact. - Characterise the change of the surface of Dimorphos by DART, including the properties (and existence!) of the DART crater, to improve our understanding of impact physics and to observe unweathered material, recently exposed at the surface. - Determine the physical properties of Dimorphos, including its internal structure, to allow scaling of the impact to different types of asteroids. - Measure the dynamical and physical state of the Didymos and Dimorphos system to constrain binary formation scenarios. Hera is equipped with the following payload: Two Asteroid Framing Cameras (AFCs) for both science and navigation. There resolution is ~10-4 rad/pixel, or 40 cm/pixel for close observations from 4 km. They will provide the target global properties as well as local geomorphology and will investigate the crater and impact site. They will also measure the mass of Dimorphos through the “wobble” motion of Didymos. The Planetary ALTimeter (PALT) will measure the distance to the target and shape and topography information complementary to that from AFC images. The Thermal InfraRed Imager (TIRI) will provide information about the thermal properties of the Didymos system and spectral information in the mid-infrared (6 bands from 7 – 14 µm). The Hyperscout-H hyperspectral imager will provide mineralogical information from 25 spectral bands between 665 and 975 nm. Milani, a 6 unit cubesat, will carry a visible to near-IR imaging spectrometer (ASPECT, 500 – 2500 nm) to derive mineralogical information on the composition of the asteroids, and a thermogravimeter (VISTA) to measure the abundance and constrain the composition of ambient dust particles. Juventas, a 6 unit cubesat, will carry a monostatic low-frequency radar (JuRa), and a gravimeter (GRASS) to derive interior and surface properties of the asteroids. The Radio Science experiment will measure the gravity field of the Didymos system. Measurements of the acceleration of the Hera spacecraft by the asteroid pair through the radio link between Earth and Hera will be used as well as the inter-satellite link between Hera and the two cubesats. We will describe how the goals of Hera will be achieved with the different payload elements.

Michael Küppers↗
NASA NTRS2023

Stand Alone Electrical Equipment PRL 227a (Formerly PRL 227 LOC3) Confirmatory Sampling Report Addendum

HydroGeoLogic, Inc. (HGL) prepared this Confirmatory Sampling Report Addendum (CSRA) for the National Aeronautics and Space Administration (NASA), Kennedy Space Center (KSC), Florida. PRL 227a Location of Concern (LOC) 3 is located on 10th Street, west of the former Vertical Processing Facility (M7-1469), SWMU 077. The Load Break Switches (LBS) were installed in 1986 and are suspected to have formerly contained dielectric fluid that contained polychlorinated biphenyls (PCBs). The LBS were replaced in 1993 with new LBSs that do not contain PCBs. The LBS are located on a concrete pad surrounded by grass that is mowed and maintained. A total of 330 soil samples were collected between June 2020 and December 2022 from 88 locations at depths ranging from 0-0.5 to 19-20 ft bls. In general, soil samples collected from depths less than 5 ft were collected via hand auger and samples collected beyond 5 ft bls were collected using a direct-push technology drill rig. Soil samples collected from greater than 5 ft bls to 20 ft bls were collected to confirm PCBs were not present at depth in the aquifer matrix. Delineation samples were collected using an approximately 20 ft horizontal spacing. PCB impacted soil has been completely delineated to the State of Florida residential soil cleanup target level (SCTL) at PRL 227a horizontally and vertically. PCB concentrations have been identified above the residential and industrial SCTL as well as concentrations greater than or equal to 50 ppm requiring coordination with EPA and disposal at a facility authorized under TSCA. Based upon the CSRA sampling, an interim measure is recommended to remove PCB impacted soil above the residential SCTL. The total volume of PCB impacted soil to be removed is 591.6 cubic yards. This includes approximately 9.3 cubic yards of soil with PCB concentrations greater than or equal to 50 ppm. An interim measure work plan is included as an attachment to this report.

electrical equipment↗
NASA NTRS2023

Accounting for Herbaceous Communities in Process-Based Models Will Advance Our Understanding of “Grassy” Ecosystems

Grassland and other herbaceous communities cover significant portions of Earth's terrestrial surface and provide many critical services, such as carbon sequestration, wildlife habitat, and food production. Forecasts of global change impacts on these services will require predictive tools, such as process-based dynamic vegetation models. Yet, model representation of herbaceous communities and ecosystems lags substantially behind that of tree communities and forests. The limited representation of herbaceous communities within models arises from two important knowledge gaps: first, our empirical understanding of the principles governing herbaceous vegetation dynamics is either incomplete or does not provide mechanistic information necessary to drive herbaceous community processes with models; second, current model structure and parameterization of grass and other herbaceous plant functional types limits the ability of models to predict outcomes of competition and growth for herbaceous vegetation. In this review, we provide direction for addressing these gaps by: (1) presenting a brief history of how vegetation dynamics have been developed and incorporated into earth system models, (2) reporting on a model simulation activity to evaluate current model capability to represent herbaceous vegetation dynamics and ecosystem function, and (3) detailing several ecological properties and phenomena that should be a focus for both empiricists and modelers to improve representation of herbaceous vegetation in models. Together, empiricists and modelers can improve representation of herbaceous ecosystem processes within models. In so doing, we will greatly enhance our ability to forecast future states of the earth system, which is of high importance given the rapid rate of environmental change on our planet.

biogeochemistry↗
NASA NTRS2024

Soil Interim Measures Implementation Report Q6 Radar Station – Solid Waste Management Unit 112 Locations of Concern 3 and 10 Kennedy Space Center, Florida

This report presents a summary of the soil interim measures (IM) implementation activities that occurred from January 23 through June 6, 2023, at the Q6 Radar Station (Q6RS) Solid Waste Management Unit (SWMU) 112, located at the John F. Kennedy Space Center (KSC), Florida. The site is monitored under KSC’s Resource Conservation and Recovery Act Corrective Action Program, which also meets the requirements of Chapter 62-780, Florida Administrative Code. Assessment activities have previously been performed at 10 Q6RS Locations of Concern (LOCs), identified during a SWMU assessment in 2013 (NASA 2013). Since the SWMU assessment, further confirmatory sampling and assessment was conducted for each LOC, and based on the data collected during these assessments, it was determined that Resource Conservation and Recovery Act Facility Investigation to further evaluate soil and groundwater was to be conducted at two LOCs: LOC 1 – Former Underground Storage Tanks (groundwater) and LOC 3 – Radar Antenna Refurbishment Area (soil). The LOC 1 groundwater assessment is ongoing, and the results will be presented under a separate cover. It was also determined that soil IM would be completed at two of the LOCs, LOC 3 and LOC 10 – Compressor Building Condensate Discharge. A Soil IM Work Plan was prepared and submitted in October 2021 (NASA 2021). The plan proposed excavation and disposal of barium and copper impacted soil in the LOC 3 and 10 areas. This report presents a summary and results of the soil IM implementation activities conducted at LOCs 3 and 10. The site contaminates of concern for this soil IM are barium and copper. The activities presented in this report includes soil IM implementation and groundwater sampling. From January 23 through March 24, 2023, AECOM Technical Services, Inc. (AECOM) field personnel conducted IM implementation activities in the LOC 3 and 10 areas. As presented in the IM Work Plan, shallow soils, impacted with metals, were excavated to a depth of 2 feet below land surface in the central area of the site around the radar antenna and associated equipment. AECOM personnel excavated 77.43 tons of soil using hand tools and, when able, heavy equipment. The soils were placed into lined roll-off containers, which were covered and transported under manifest to the Cedar Trail Landfill in Bartow, Florida. The site was restored to original condition using certified clean backfill material (sand), with filter fabric, and gravel. Areas outside of the excavation were restored with grass seed. On June 6, 2023, AECOM field personnel conducted groundwater sampling activities within the soil IM excavation area. AECOM personnel collected one groundwater sample from monitoring well Q6RS-MW0003. The sample was submitted to a fixed-based laboratory for analysis by United States Environmental Protection Agency Method 6020B for metals (barium and copper). Barium and copper were detected at 18 micrograms per liter (µg/L) and 14 µg/L, respectively, and were below the State of Florida Groundwater Cleanup Target Levels (GCTLs) for barium at 2,000 µg/L and copper at 1,000 µg/L.

Chadwick Sloan Lee↗
NASA NTRS2024

Extending the Utility of Space-Borne Snow Water Equivalent Observations Over Vegetated Areas With Data Assimilation

Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24° x 37° domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.

Justin Pflug↗
NASA NTRS2024

Big Bend Ecological Forecasting: Integrating Earth Observations Into Invasive Species Management Decisions in Big Bend National Park in Texas

Located in Brewster County Texas, U.S., along the Texas-Mexico border, Big Bend National Park is 3,243 square kilometers of desert, mountains, and rivers. NASA’s MSFC Spring 2024 DEVELOP Team partnered with the National Park Service (NPS), to address the environmental concern of perineal invasive grasses in Big Bend National Park. Buffelgrass (Cenchrus ciliaris), introduced to the park in the 1940’s, poses an ongoing threat, and causes habitat destruction for many of the park’s native ecosystems. Buffelgrass amplifies fire risk in the park, aids in the destruction of historic structures, and alters stream channels. To address the rising concern of Buffelgrass presence, unique advanced spatial technique applications were necessary to construct a habitat suitability model and perform a comprehensive fire risk assessment. A habitat suitability model was developed considering climate, vegetation, and phenological variables, in addition to physical and topographical variables. Subsequently, a fire risk model was developed, taking into account fire history data, accessibility factors, climate trends and predictions, along with developed areas. Multi-Source Land Surface Phenology (LSP) Sentinel-2 and Landsat 8 Operational Land Imager (OLI) imagery were used to predict Buffelgrass hotspot locations throughout the park. These analyses allowed the identification of optimal Buffelgrass habitat and hotspot locations, as well as park zones that reflect the greatest risk for future Buffelgrass invasion and fire risk. The collective results of the habitat suitability model, fire risk assessment, and Buffelgrass hot spot identification will allow the NPS to facilitate efficient mitigation measures and management strategies, and improved resource allocation where it’s most needed.

remote sensing↗
NASA NTRS2025

Jemez Pueblo Agriculture: Monitoring Rangeland Conditions to Inform Drought and Land Management in New Mexico

The Jemez Pueblo, a community located in the semi-arid high desert region of northcentral New Mexico, faces challenges with drought mitigation and livestock management. NASA DEVELOP partnered with the Pueblo of Jemez Natural Resources Department and The Nature Conservancy to provide recent and historical rangeland conditions to aid present decision-making, which relies on cultural practices, site familiarity, available resources, and physical data. This partnership offers the opportunity to support management practices by leveraging remote sensing and virtual fencing technologies. Our project explored Rangeland Analysis Platform (RAP) products incorporating NASA Earth observation Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2 remote-sensed imagery as an agricultural land management tool. We performed linear regression analysis to evaluate RAP data to field data and explored RAP annual herbaceous biomass, ground cover trends, and climatic factors at the pasture level. We then computed study area summary statistics, performed pixel-by-pixel trend analysis across the study period, and evaluated ground cover trends near water sources. Based on available data, RAP was poorly correlated to bare ground and herbaceous biomass, generally underestimating the former and overestimating the latter. To improve the evaluation of RAP products, we suggest increasing the spatiotemporal distribution of the field dataset. According to RAP, from 1986 to 2023, the site became hotter and drier, bare ground increased, and annual forbs and grasses increased while perennials decreased. RAP products did not agree with field-collected data; however, the trends over time may be useful for informing rangeland management actions.

cattle grazing management↗
NASA NTRSDate not supplied

A NASA Airborne Lens Into Air Quality Field Studies of the Last Decade

Field studies add an enhanced perspective to our everyday observing system for air quality with the goals of better understanding the air we breathe and identifying solutions toward a healthier future. Over the last decade, over 10 air quality field studies were conducted around the US with other ranging internationally with support through large agency-led efforts down to the grass-roots collaborative style. This presentation will highlight how NASA airborne observations have fit as one piece of the integrated observing system for air quality during these field studies. Research topics to be discussed are centered around the idea of the transition to geostationary air quality satellite observations. Specific topics include how temporally and spatially resolved measurements help us learn about knowledge gaps in NOx emissions, satellite-proxies for surface air quality, as well as evaluating state-of-the-art chemical transport models and using the enhanced observations as tools for understand what models/satellites can and cannot resolve. Each research topic will aim to discuss how the field measurement strategies that made this work possible as well as specific challenges that still exist to take these results further. Lastly, this presentation will discuss strategies to be carried forward as well as new ones as a peek into future air quality airborne field work.

Laura Judd↗