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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Single Application of Compost Can Leave Lasting Impacts on Soil Microbial Community Structure and Alter Cross-Domain Interaction Networks

Our current understanding suggests that nutrient management strategies applied to agricultural soils over multiple years are required to cause major and stable shifts in soil microbial communities. However, some studies suggest that agricultural soils can benefit even from sporadic, single additions of organic matter. Here we investigate how single additions of high-quality organic matter can cause significant shifts in microbial soil communities over multiple cropping cycles. We grew radishes in a tropical Oxisol soil for six crop cycles after a single application of a high-nitrogen compost or urea. At planting and before biomass harvest, we sampled soils influenced by the radish rhizosphere and sequenced bacterial and archaeal 16S and fungal ITS rDNA marker genes. We measured microbial richness and diversity, community composition and structure, and constructed correlation networks to predict cross-domain microbial interactions. We found that a single application of compost, compared to urea or control, resulted in a persistent improved plant biomass response and led to sustained changes in the soil microbial community throughout the duration of the 227-day study. Compost altered the structure of both the fungal and prokaryotic microbial communities, introduced new microorganisms that persisted in the resident soil system, and altered soil microbial correlation network structure and hub taxa. In contrast, fertilization with urea did not significantly alter the structure of soil microbial communities compared to the control but reduced network complexity and altered hub taxa. This study highlights the significant impacts that high-quality organic matter fertilization can exert on agricultural soil microbiomes and adds to the growing body of knowledge on using organic fertilizers as a way to steer the soil microbiome toward a healthier soil.

Heisey, Steven↗
DOE OSTI2022

Sensitive Measures of Soil Health Reveal Carbon Stability Across a Management Intensity and Plant Biodiversity Gradient

Soil carbon (C) is a major driver of soil health, yet little is known regarding how sensitive measures of soil C shift temporally within a single growing season in response to short-term weather perturbations. Our study aimed to i) Examine how long-term management impacts soil C cycling and stability across a management intensity and plant biodiversity gradient and ii) Assess how sensitive soil health indicators change temporally over the course of a single growing season in response to recent weather patterns. Here we quantify a variety of sensitive soil C measures at four time points across the 2021 growing season at the W.K. Kellogg Biological Station’s Long Term Ecological Research Trial (LTER) located in southwest Michigan, USA. The eight systems sampled included four annual soybean ( Glycine max ) systems that ranged in management intensity (conventional, no-till, reduced input, and biologically-based), two perennial biofuel cropping systems (switchgrass ( Panicum virgatum) and hybrid poplars ( Populus nigra x P.maximowiczii )), and two unmanaged systems (early successional system and a mown but never tilled grassland). We found that unmanaged systems with increased perenniality enhanced mineralizable C (Min C) and permanganate oxidizable C (POXC) values. Additionally, all soil health indicators were found to be sensitive to changes in short-term weather perturbations over the course of the growing season. The implications of this study are threefold. First, this study assess indicators of labile and stable C pools over the course of the growing season and reflects the stability of soil C in different systems. Second, POXC, Min C, and ß-glucosidase (GLU) activity are sensitive soil health indicators that fluctuate temporally, which means that these soil health indicators could help elucidate the impact that weather patterns have on soil C dynamics. Lastly, for effective monitoring of soil C, sampling time and frequency should be considered for a comprehensive understanding of soil C cycling within a system.

Martin, Tvisha↗
DOE OSTI2023

Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts

Soil dust aerosols are a key component of the climate system, as they interact with short- and long-wave radiation, alter cloud formation processes, affect atmospheric chemistry and play a role in biogeochemical cycles by providing nutrient inputs such as iron and phosphorus. The influence of dust on these processes depends on its physicochemical properties, which, far from being homogeneous, are shaped by its regionally varying mineral composition. The relative amount of minerals in dust depends on the source region and shows a large geographical variability. However, many state-of-the-art Earth system models (ESMs), upon which climate analyses and projections rely, still consider dust mineralogy to be invariant. The explicit representation of minerals in ESMs is more hindered by our limited knowledge of the global soil composition along with the resulting size-resolved airborne mineralogy than by computational constraints. In this work we introduce an explicit mineralogy representation within the state-of-the-art Multiscale Online Nonhydrostatic AtmospheRe CHemistry (MONARCH) model. We review and compare two existing soil mineralogy datasets, which remain a source of uncertainty for dust mineralogy modeling and provide an evaluation of multiannual simulations against available mineralogy observations. Soil mineralogy datasets are based on measurements performed after wet sieving, which breaks the aggregates found in the parent soil. Our model predicts the emitted particle size distribution (PSD) in terms of its constituent minerals based on brittle fragmentation theory (BFT), which reconstructs the emitted mineral aggregates destroyed by wet sieving. Our simulations broadly reproduce the most abundant mineral fractions independently of the soil composition data used. Feldspars and calcite are highly sensitive to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semi-arid regions worldwide. For the least abundant or more difficult-to-determine minerals, such as iron oxides, uncertainties in soil mineralogy yield differences in annual mean aerosol mass fractions of up to ~ 100 %. Although BFT restores coarse aggregates including phyllosilicates that usually break during soil analysis, we still identify an overestimation of coarse quartz mass fractions (above 2 µm in diameter). In a dedicated experiment, we estimate the fraction of dust with undetermined composition as given by a soil map, which makes up ~ 10 % of the emitted dust mass at the global scale and can be regionally larger. Changes in the underlying soil mineralogy impact our estimates of climate-relevant variables, particularly affecting the regional variability of the single-scattering albedo at solar wavelengths or the total iron deposited over oceans. All in all, this assessment represents a baseline for future model experiments including new mineralogical maps constrained by high-quality spaceborne hyperspectral measurements, such as those arising from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) mission.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A hybrid data–model approach to map soil thickness in mountain hillslopes

Abstract. Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.

58 GEOSCIENCES↗
NASA NTRS1980

Effect of texture on microwave emission from soils

The intensity brightness temperature, T(B), of the microwave emission from the soil is determined primarily by its dielectric properties. The large difference between the dielectric constant of water and that of dry soil produces a strong dependence of the soil's dielectric constant on its moisture content. This dependence is effected by the texture of the soil because the water molecules close to the particle surface are tightly bound and do not contribute significantly to the dielectric properties. Since this surface area is a function of the particle size distribution (soil texture), being larger for clay soils with small particles, and smaller for sandy soils with larger particles, the dielectric properties will depend on soil texture. This dependence has been demonstrated by laboratory measurements of the dielectric constant for soils which are briefly summarized. The dependence of the microwave emission on texture is demonstrated by measurements of T(B) from an aircraft platform for a wide range of soil textures. It is concluded that the effect of soil texture differences on the observed T(B) values can be normalized by expressing the soil moisture values as a percentage of field capacity for the soil.

Schmugge, T. J.↗
NASA NTRS1981

Soil spectral characterization

The spectral characterization of soils is discussed with particular reference to the bidirectional reflectance factor as a quantitative measure of soil spectral properties, the role of soil color, soil parameters affecting soil reflectance, and field characteristics of soil reflectance. Comparisons between laboratory-measured soil spectra and Landsat MSS data have shown good agreement, especially in discriminating relative drainage conditions and organic matter levels in unvegetated soils. The capacity to measure both visible and infrared soil reflectance provides information on other soil characteristics and makes it possible to predict soil response to different management conditions. Field and laboratory soil spectral characterization helps define the extent to which intrinsic spectral information is available from soils as a consequence of their composition and field characteristics.

Stoner, E. R.↗
NASA NTRS1994

Factors and processes governing the C-14 content of carbonate in desert soils

A model is presented describing the factors and processes which determine the measured C-14 ages of soil calcium carbonate. Pedogenic carbonate forms in isotopic equilium with soil CO2. Carbon dioxide in soils is a mixture of CO2 derived from two biological sources: respiration by living plant roots and respiration of microorganisms decomposing soil humus. The relative proportion of these two CO2 sources can greatly affect the initial C-14 content of pedogenic carbonate: the greater the contribution of humus-derived CO2, the greater the initial C-14 age of the carbonate mineral. For any given mixture of CO2 sources, the steady-state (14)CO2 distribution vs. soil depth can be described by a production/diffusion model. As a soil ages, the C-14 age of soil humus increases, as does the steady-state C-14 age of soil CO2 and the initial C-14 age of any pedogenic carbonate which forms. The mean C-14 age of a complete pedogenic carbonate coating or nodule will underestimate the true age of the soil carbonate. This discrepancy increases the older a soil becomes. Partial removal of outer (and younger) carbonate coatings greatly improves the relationship between measured C-14 age and true age. Although the production/diffusion model qualitatively explains the C-14 age of pedogenic carbonate vs. soil depth in many soils, other factors, such as climate change, may contribute to the observed trends, particularily in soils older than the Holocene.

Amundson, Ronald↗
NASA NTRS2000

Constraints on Martian Soil Composition as Inferred from Viking XRFS and Pathfinder APXS and IMP Data

With the successful operation of the Alpha Proton X-Ray Spectrometer (APXS) during 1997's Mars Pathfinder (MPF) mission, geochemistry data are now available from three sites on Mars. APXS raw spectra for six soils and five rocks have been converted to compositional abundances. The Viking Lander X-Ray Fluorescence Spectrometer (XRFS) successfully measured elemental abundances of nine soils at Viking 1 and eight soils at Viking 2. Although the three landing sites are located in different parts of Mars, the soils exhibit broad similarities, with an iron-rich chemistry similar to that of palagonite. However, the Pathfinder sods show some significant differences from Viking soils, notably an enrichment in silica and depletion in sulfur. The XRFS samples consisted of near-surface and deep (up to 22 cm) soils acquired by a collector head at the cod of a retractable boom. It was possible to collect and analyze pebbles as large a 2 cm, but only sod, some in the form of consolidated clods, was sampled. In contrast, the APXS measured materials in situ. This resulted in MPF "rock" analyses that probably had a significant dust component and, as explored here, "soil" analyses that may have contained a rocky component We examine several possibilities to explain these differences and other attributes of the APXS and XRFS data sets: 1) The APXS soil measurements actually sampled a mixture of Viking-like soil and small bits of high-silica, low-sulfur rock, 2) The soils were derived from high-silica rocks mixed with a minor component of globally-homogenized dust; these soils are chemically distinct and have a separate geologic history from the Viking soils. 3) The weathering environment was different at the Pathfinder landing site compared to the Viking sites, and 4) Uncertainties in the XRFS and APXS measurements result in reported elemental abundances different than those that are actually present We show that none of the possibilities can be discounted, but that an MPF soil distinct in composition from Viking sods is best supported by the available data.

Bridges, N. T.↗
NASA NTRS2000

BOREAS TGB-12 Soil Carbon and Flux Data of NSA-MSA in Raster Format

The BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites. This data set provides: (1) estimates of soil carbon stocks by horizon based on soil survey data and analyses of data from individual soil profiles; (2) estimates of soil carbon fluxes based on stocks, fire history, drain-age, and soil carbon inputs and decomposition constants based on field work using radiocarbon analyses; (3) fire history data estimating age ranges of time since last fire; and (4) a raster image and an associated soils table file from which area-weighted maps of soil carbon and fluxes and fire history may be generated. This data set was created from raster files, soil polygon data files, and detailed lab analysis of soils data that were received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994. Also used were soils data from Susan Trumbore and Jennifer Harden (BOREAS TGB-12). The binary raster file covers a 733-km 2 area within the NSA-MSA.

Hall, Forrest G.↗
NASA NTRS2003

Using Remote Sensing Platforms to Estimate Near-Surface Soil Properties

Evaluation of near-surface soil properties via remote sensing (RS) could facilitate soil survey mapping, erosion prediction, fertilization regimes, and allocation of agrochemicals. The objective of this study was to evaluate the relationship between soil spectral signature and near surface soil properties in conventionally managed row crop systems. High resolution RS data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor (ATLAS) multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0-1 cm) were collected from 163 sampling points for soil water content, soil organic carbon (SOC), particle size distribution (PSD), and citrate dithionite extractable iron (Fed) content. Surface roughness, soil water content, and crusting were also measured at sampling. Results showed RS data acquired from lands with less than 4 % surface soil water content best approximated near-surface soil properties at the Coastal Plain site where loamy sand textured surfaces were predominant. Utilizing a combination of band ratios in stepwise regression, Fed (r2 = 0.61), SOC (r2 = 0.36), sand (r2 = 0.52), and clay (r2 = 0.76) were related to RS data at the Coastal Plain site. In contrast, the more clayey Ridge and Valley soils had r-squares of 0.50, 0.36, 0.17, and 0.57. for Fed, SOC, sand and clay, respectively. Use of estimated eEmissivity did not generally improve estimates of near-surface soil attributes.

Sullivan, D. G.↗
NASA NTRS1996

X-ray digital imaging petrography of lunar mare soils: modal analyses of minerals and glasses

It is essential that accurate modal (i.e., volume) percentages of the various mineral and glass phases in lunar soils be used for addressing and resolving the effects of space weathering upon reflectance spectra, as well as for their calibration such data are also required for evaluating the resource potential of lunar minerals for use at a lunar base. However, these data are largely lacking. Particle-counting information for lunar soils, originally obtained to study formational processes, does not provide these necessary data, including the percentages of minerals locked in multi-phase lithic fragments and fused-soil particles, such as agglutinates. We have developed a technique for modal analyses, sensu stricto, of lunar soils, using digital imaging of X-ray maps obtained with an energy-dispersive spectrometer mounted on an electron microprobe. A suite of nine soils (90 to 150 micrometers size fraction) from the Apollo 11, 12, 15, and 17 mare sites was used for this study. This is the first collection of such modal data on soils from all Apollo mare sites. The abundances of free-mineral fragments in the mare soils are greater for immature and submature soils than for mature soils, largely because of the formation of agglutinitic glass as maturity progresses. In considerations of resource utilization at a lunar base, the best lunar soils to use for mineral beneficiation (i.e., most free-mineral fragments) have maturities near the immature/submature boundary (Is/FeO approximately or = 30), not the mature soils with their complications due to extensive agglutination. The particle data obtained from the nine mare soils confirm the generalizations for lunar soils predicted by L.A. Taylor and D.S. McKay (1992, Lunar Planet Sci. Conf. 23rd, pp. 1411-1412 [Abstract]).

NASA Discipline Exobiology↗
NASA NTRS2008

A Vegetation Correction Methodology for Time Series Based Soil Moisture Retrieval From C-band Radar Observations

A methodology is presented to correct backscatter (sigma(sup 0)) observations for the effect of vegetation. The proposed methodology is based on the concept that the ratio of the surface scattering over the total amount of scattering (sigma(sup 0)(sub soil)/sigma(sup 0)) is only affected by the vegetation and can be described as a function of the vegetation water content. Backscatter observations sigma(sup 0) from the soil are not influenced by vegetation. Under bare soil conditions (sigma(sup 0)(sub soil)/sigma(sup 0)) equals 1. Under low to moderate biomass and soil moisture conditions, vegetation affects the observed sigma(sup 0) through absorption of the surface scattering and contribution of direct scattering by the vegetation itself. Therefore, the contribution of the surface scattering is smaller than the observed total amount of scattering and decreases as the biomass increases. For dense canopies scattering interactions between the soil surface and vegetation elements (e.g. leaves and stems) also become significant. Because these higher order scattering mechanisms are influenced by the soil surface, an increase in (sigma(sup 0)(sub soil)/sigma(sup 0)) may be observed as the biomass increases under densely vegetated conditions. This methodology is applied within the framework of time series based approach for the retrieval of soil moisture. The data set used for this investigation has been collected during a campaign conducted at USDA's Optimizing Production Inputs for Economic and Environmental Enhancement OPE-3) experimental site in Beltsville, Maryland (USA). This campaign took place during the corn growth cycle from May 10th to 0ctober 2nd, 2002. In this period the corn crops reached a vegetation water content of 5.1 kg m(exp -2) at peak biomass and a soil moisture range varying between 0.00 to 0.26 cubic cm/cubic cm. One of the deployed microwave instruments operated was a multi-frequency (C-band (4.75 GHz) and L-band (1.6 GHz)) quad-polarized (HH, HV, VV, VH) radar which was mounted on a 20 meter long boom. In the OPE-3 field campaign, radar observations were collected once a week at nominal times of 8 am, 10 am, 12 noon and 2 pm. During each data run the radar acquired sixty independent measurements within an azimuth of 120 degrees from a boom height of 12.2 m and at three different incidence angles (15,35, and 55 degrees). The sixty observations were averaged to provide one backscatter value for the study area and its accuracy is estimated to be 51.0 dB. For this investigation the C-band observations have been used. Application of the proposed methodology to the selected data set showed a well-defined relationship between (sigma(sup 0)(sub soil)/sigma(sup 0)) and the vegetation water content. It is found that this relationship can be described with two experimentally determined parameters, which depend on the sensing configuration (e.g. incidence angle and polarization). Through application of the proposed vegetation correction methodology and the obtained parameterizations, the soil moisture retrieval accuracy within the framework of a time series based approach is improved from 0.033 to 0.032 cubic cm/cubic cm, from 0.049 to 0.033 cubic cm/cubic cm and from 0.079 to 0.047 cubic cm/cubic cm for incidence angles of 15,35 and 55 degrees, respectively. Improvement in soil moisture retrieval due to vegetation correction is greater at larger incidence angles (due to the increased path length and larger vegetation effects on the surface signal at the larger angles).

Joseph, Alicia T.↗
NASA NTRS2014

Soil Moisture Data Assimilation in the NASA Land Information System for Local Modeling Applications and Improved Situational Awareness

As part of the NASA Soil Moisture Active Passive (SMAP) Early Adopter (EA) program, the NASA Shortterm Prediction Research and Transition (SPoRT) Center has implemented a data assimilation (DA) routine into the NASA Land Information System (LIS) for soil moisture retrievals from the European Space Agency's Soil Moisture Ocean Salinity (SMOS) satellite. The SMAP EA program promotes application‐driven research to provide a fundamental understanding of how SMAP data products will be used to improve decision‐making at operational agencies. SPoRT has partnered with select NOAA/NWS Weather Forecast Offices (WFOs) that use output from a real‐time regional configuration of LIS, without soil moisture DA, to initialize local numerical weather prediction (NWP) models and enhance situational awareness. Improvements to local NWP with the current LIS have been demonstrated; however, a better representation of the land surface through assimilation of SMOS (and eventually SMAP) retrievals is expected to lead to further model improvement, particularly during warm‐season months. SPoRT will collaborate with select WFOs to assess the impact of soil moisture DA on operational forecast situations. Assimilation of the legacy SMOS instrument data provides an opportunity to develop expertise in preparation for using SMAP data products shortly after the scheduled launch on 5 November 2014. SMOS contains a passive L‐band radiometer that is used to retrieve surface soil moisture at 35‐km resolution with an accuracy of 0.04 cu cm cm (exp -3). SMAP will feature a comparable passive L‐band instrument in conjunction with a 3‐km resolution active radar component of slightly degraded accuracy. A combined radar‐radiometer product will offer unprecedented global coverage of soil moisture at high spatial resolution (9 km) for hydrometeorological applications, balancing the resolution and accuracy of the active and passive instruments, respectively. The LIS software framework manages land surface model (LSM) simulations and includes an Ensemble Kalman Filter for conducting land surface DA. SPoRT has added a module to read, quality‐control and bias‐correct swaths of Level II SMOS soil moisture retrievals prior to assimilation within LIS. The impact of SMOS DA is being tested using the Noah LSM. Experiments are being conducted to examine the impacts of SMOS soil moisture DA on the resulting LISNoah fields and subsequent NWP simulations using the Weather Research and Forecasting (WRF) model initialized with LIS‐Noah output. LIS‐Noah soil moisture will be validated against in situ observations from Texas A&M's North American Soil Moisture Database to reveal the impact and possible improvement in soil moisture trends through DA. WRF model NWP case studies will test the impacts of DA on the simulated near‐surface and boundary‐layer environments, and precipitation during both quiescent and disturbed weather scenarios. Emphasis will be placed on cases with large analysis increments, especially due to contributions from regional irrigation patterns that are not represented by precipitation input in the baseline LIS‐Noah run. This poster presentation will describe the soil moisture DA methodology and highlight LIS‐Noah and WRF simulation results with and without assimilation.

Case, Jonathan L.↗
NASA NTRSDate not supplied

Modeling the Worst-Case-Scenario of Soil Drydown for Agricultural Drought Monitoring in Kansas Using NASA Earth Observations and In-situ Parameters

Climatologists in Kansas have observed rapid shifts from wet to dry conditions and rapid intensification of drought conditions for the state in recent years. As a leading state in agricultural production, drought events can lead to decreased crop yields and impact the state’s economy. The exponential decay of soil moisture content is a major consequence of drought and can exacerbate these impacts on agricultural production. This study examined the rate of soil moisture drydown in Kansas to understand and monitor the worst-case-scenario of short-term soil water shortage. Soil Moisture Active Passive (SMAP) L-band Radiometer rootzone soil moisture imagery from March 2015 to June 2019 was used to estimate the minimum and maximum soil moisture capacity throughout the state. In situ soil moisture parameters, such as the exponential decay constant, were approximated from the Kansas Mesonet system. These variables were inputs into an exponential decay model that forecasted gridded rootzone soil moisture in a scenario that assumed no precipitation for a user-identified period of time. The forecasts were output on the order of weeks to capture the rapid shifts in soil moisture content. Another model was used to identify areas in Kansas currently below a given percentage of the relative soil moisture saturation, measured as a function of minimum, maximum, and current soil moisture. This model forecasted the number of days until each cell in the soil moisture grid reached the input percentage of relative soil moisture saturation. The gridded model outputs and workflow were developed in partnership with the Kansas Water Office and Kansas Office of the State Climatologist at Kansas State University, as a part of their drought monitoring efforts.

NASA DEVELOP↗
NASA NTRS2021

First Attempt of Global-scale Assimilation of Subdaily Scale Soil Moisture Estimates from CYGNSS and SMAP into a Land Surface Model

Soil moisture performs a key function in the hydrologic process and understanding the global-scale water cycle. However, estimations of soil moisture taken from current sun-synchronous orbit (SSO) satellites are limited in that they are neither spatially nor temporally continuous. This limitation creates discontinuous soil moisture observation from space and hampers our understanding of the fundamental processes that control the surface hydrologic cycle across both time and space domains. Here, we propose to use frequent soil moisture observations from NASA’s constellation of eight micro-satellites called the Cyclone Global Navigation Satellite System(CYGNSS) together with the Soil Moisture Active Passive (SMAP) to assimilate subdaily-scale soil moisture intoa land surface model(LSM). Our results, which are based on triple collocation analysis(TCA), show how current scientific advances in satellite systems can fill previous gaps in soil moisture observations in subdaily scale bypast observations, and eventually adds value to improvements in global scale soil moisture estimates in LSMs. Overall, TCA-based fractional mean square errors (fMSE) of LSM soil moisture are improved by 61% with the synergetic assimilation of CYGNSS data with SMAP soil moisture observations. However, assimilating satellite-based soil moisture over dense vegetation areas can degrade the performance of LSMs as these areas propagate erroneous soil moisture information to LSMs. To our knowledge, this study isthe first global assimilation of GNSS-based soil moisture observations in land surface models.

Hyunglok Kim↗
NASA NTRS2023

Towards Soil Moisture Profile Estimation in the Root Zone Using L- and P-Band Radiometer Observations: A Coherent Modelling Approach

Precision irrigation management and crop water stress assessment rely on accurate estimation of root zone soil moisture. However, only the top 5cm soil moisture can be estimated using the two current passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), which operate at L-band (wavelength of ~21cm). Since the contributing depth of the soil to brightness temperature increases with observation wavelength, it is expected that a P-band (wavelength of ~40cm) radiometer could potentially provide soil moisture information from deeper layers of the soil profile. Moreover, by combining both L- and P- bands, it is hypothesized that the soil moisture profile can be estimated even beyond their individual observation depths. The aim of this study was to demonstrate the potential of combined L-band and P-band radiometer observations to estimate the soil moisture profile under flat bare soil using a stratified coherent forward model. Brightness temperature observations at L-band and P-band from a tower based experimental site across a dry (April 2019) and a wet (March 2020) period, covering different soil moisture profile shapes, were used in this study. Results from an initial synthetic study showed that the performance of a combined L-band and P-band approach was better than the performance of using either band individually, with an average depth over which reliable soil moisture profile information could be estimated (i.e. with a target root mean square error (RMSE) of less than 0.04 m3/m3) being 20cm for linear and 15cm for second-order polynomial functions. Other functions were also tested but found to have a poorer performance. Applying the method to the tower-based brightness temperature achieved an average estimation depth of 28cm (20cm) and 5cm (5cm) during the dry and wet periods respectively when using a second-order polynomial (linear) function. These findings highlight the opportunity of a satellite mission with L-band and P-band observations to accurately estimate the soil moisture profile to as deep as 30cm globally.

Foad Brakhasi↗
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↗
DOE OSTI2021

Linking soil biodiversity and ecosystem function in a Neotropical savanna

Conserving the remaining savanna ecosystems in the Brazilian savanna (Cerrado) — a global biodiversity hotspot that stores carbon and provides water to a large portion of South America — requires understanding the ecological processes maintaining their function. Nutrient cycling supports savanna function via plant litter production and decomposition by soil fauna, releasing nutrients for plant and soil organism uptake. Soil biodiversity and biogeochemistry linkages with litter dynamics in Neotropical savannas under a changing climate are poorly understood. Here, in this study, we combined two years of rainfall seasonality, leaf and wood litter production and decomposition with soil epigeic fauna abundance — the number of ground-surface dwelling invertebrates collected through pitfall traps — taxa richness, Shannon's diversity and Pielou's evenness, and 16 soil biogeochemical variables measured in 12 plots of preserved savanna. Rainfall seasonality modulated the mean soil epigeic fauna diversity and evenness across all plots, which were highest in the rainy season, in contrast to litterfall rates, which peaked in the dry season. In the dry season (April to September), the Formicidae family was the most abundant with 50% of all individuals, while in the rainy season (October to March), the Isoptera order was the most abundant with approximately 39% of individuals. Wood litter decomposition grouped with annual Hemiptera abundance, co-varying with soil epigeic fauna diversity and evenness per plot and against soil fertility variables. Leaf litter decomposition co-varied with the total epigeic fauna abundance and soil pH. We speculate that the specific need to decompose wood litter may be associated with a greater need for diversity than an abundance of soil epigeic fauna. Our work highlights the role of rainfall seasonality on soil biodiversity and physicochemistry, which is also tightly linked with litter production and decomposition. This study advances our understanding of the mechanisms governing nutrient cycling in savanna ecosystems on nutrient-impoverished soils, with implications for achieving sustainable conservation and restoration goals.

59 BASIC BIOLOGICAL SCIENCES↗