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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Data for “Fast-decaying plant litter enhances soil carbon in temperate forests, but not through microbial physiological traits”

This data package contains data and code used in the paper “Fast-decaying plant litter enhances soil carbon in temperate forests, but not through microbial physiological traits”. This paper details results from two studies: 1) a laboratory leaf litter incubation experiment (lab experiment) and 2) a multi-site observational field study (field study). Both studies were designed to test the relationships among litter quality, microbial physiological traits, and mineral-associated soil carbon (C). In the lab experiment, we incubated 16 temperate tree litters of differing chemical quality with isotopically distinct soil, measuring microbial physiological traits and the flow of litter-derived C into the mineral-associated soil C pool. In the field study, we sampled soils (0-5 cm) across six eastern US temperate forests, measuring microbial physiological traits, soil abiotic properties, and leaf litter chemistry. The analysis data are provided in two .csv files corresponding to either the lab experiment or field study. Both files contain data on litter chemistry, microbial physiological traits (growth and turnover rates, and carbon use efficiency [CUE]), and the mineral-associated soil C pool. The lab experiment dataset additionally contains litter-derived versus soil-derived soil C, respiration, and litter decomposition parameters. The field study dataset additionally contains site-level climatic information, ectomycorrhizal dominance of plots, and plot-level soil properties. Also provided is the R code and output reproducing the results in the related publication. Analyses were originally performed using R version 3.6.1 using the package "lavaan" and the packages listed on lines 5-7 in the file "data.R".

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Root size and soil physicochemical properties drive microscale spatial patterns of Fe and As retention in the rice rhizosphere

Background and Aims: Radial oxygen loss from rice roots in flooded soils oxidizes and precipitates dissolved Fe(II), Mn(II), and As(III) into mixed Fe(III), Mn(III/IV), and As(V) as root plaque and in the rhizosphere soil. It is unknown how different soils and root sizes impact the spatial extent of Fe and As retention outside the root. Methods: We imaged cross-sections of 90 roots from 6 different soils using synchrotron μXRF imaging followed by k-means clustering and elliptical averaging to distinguish bulk soil, rhizosphere, plaque, and roots based on As and Fe patterns. Results: We found preferential As retention in the plaque and rhizospheres of most roots except small (< 0.45 mm) roots in silty soils with low P or high As. In contrast, clayey soils had similar As-Fe correlations across plaque, rhizosphere, and bulk soil. Large (> 0.45 mm) roots often had no oxidized rhizosphere region. We obtained an extensive dataset of 256 As and 155 Mn synchrotron μXANES measurements, which revealed that rhizosphere and plaque As was mainly inorganic As(V) and As(III), and Mn oxidation state varied between soils but not between belowground locations. Conclusion: Small roots in coarse-textured soils were less likely to have As retention in the plaque or rhizosphere compared to large roots and fine-textured soils. Furthermore, the unique and extensive data in this study provides new insight into soil and root size impacts on As retention in the rhizosphere. It is essential to investigate a representative number of samples to draw conclusions from XRF imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2022

C fluxes from incubations of California upland soil with and without synthetic iron (hydr)oxide associations

There is a growing understanding that interactions with soil mineral phases contribute to the accumulation and retention of otherwise degradable organic matter in soils and sediments. However, the bioavailability of organic compounds in mineral-organic-associations, especially under varying environmental conditions is not well known. To assess the impact of mineral association and warming on the decomposition of an easily respirable organic substrate (glucose), we conducted a series of laboratory incubations at different temperatures with field-collected soils from 10 to 20 cm, 50–60 cm, and 80–90 cm depth at the Blodgett Forest Research Station. We added 13C-labeled glucose either directly to native soil or sorbed to one of two synthetic iron (hydr)oxide phases (goethite and ferrihydrite) that differ in crystallinity and affinity for sorbing glucose. We incubated the soils at 25 deg C. This dataset comprises the added glucose C and the cumulative fluxes over 72 and 1896 hours. We found that: (1) association with the Fe (hydr)oxide minerals reduced the decomposition rate of glucose by > 99.5% relative to rate of decomposition for free glucose in soil; (2) the respiration rate per gram carbon did not differ appreciably with depth, suggesting a similar degree of decomposability for native C across depths and that under the incubation conditions total carbon availability represents the principal limitation on respiration under these conditions as opposed to reduced abundance of decomposers or moisture and oxygen limitations; (3) addition of free glucose enhanced native carbon respiration at all soil depths with the largest effect at 50–60 cm; (4) in general respiration of the organo-mineral complex (glucose and iron-(hydr)oxide) was less temperature sensitive than was respiration of native carbon; (5) the addition of organic free mineral decreased the rate of soil respiration in the intermediate 50–60 cm depth soil. The results emphasize the key role of MOAs in regulating the fluxes of carbon from soils to the atmosphere and in turn the stocks of soil carbon.This dataset lists the glucose additions made to soils with and without added geothite or ferrihidrite and the C respired over the timespan of the incubations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Within‐field soil moisture variability and time‐invariant spatial structures of agricultural fields in the US Midwest

Abstract Understanding soil moisture variability and estimating high‐resolution soil moisture at subfield to field scales is critical for agricultural research and applications. However, systematic investigation of subfield scale soil moisture variability over cropland is still lacking from both measurement and satellite remote sensing. In this study, we aim to investigate (1) the characteristics of within‐field soil moisture distribution over typical cropland in the US Midwest and (2) the capabilities of satellite remote sensing in capturing the spatiotemporal variabilities of soil moisture at subfield scale. Specifically, we conducted soil moisture field experiments in three typical commercial agricultural fields (∼85 acres per field) in central Illinois, representing typical commercial farmlands in the US Midwest, and compared the soil moisture measurements with satellite remote sensing data from optical and active microwave sensors. In each field, dense soil moisture samples (spaced at 50–60 m) were obtained for two dry down events in May and July 2021, and multiple long‐term soil moisture stations were installed. We found prominent time‐invariant spatial structures of soil moisture at within‐field scales both during the dry down period and over longer time scales, and the stability is minimally affected by plant water use during the growing season. Comparing the field campaign measurements with satellite remote sensing data, we found that surface reflectance of shortwave infrared bands, such as SWIR1 (1610 nm) from Sentinel‐2, can capture relative surface soil moisture patterns at within‐field scales, but their relationships with soil moisture are field specific. These findings and the improved understanding of within‐field soil moisture dynamics could potentially help future research on high‐resolution soil moisture estimation with multi‐source remote sensing data.

Yang, Yi↗
DOE OSTI2021

Low soil phosphorus availability triggers maize growth stage specific rhizosphere processes leading to mineralization of organic P

Phosphorus (P) is one of the essential nutrients for all living organisms. High-quality mineral P fertilizer is a finite resource with only an estimated 80-100 years of reserves remaining globally. Both plants and microbes have developed several mechanisms such as secretion of organic acids, acidification of the rhizosphere, and production of extracellular enzymes to enhance the acquisition of P from soils. Increased activity of phosphatase occurs in response to P deficiency as part of P starvation responses which catalyze the hydrolysis of P o from soil organic matter (SOM) and release inorganic P (P i ). However, the specific interactions between plants and microbial communities in the rhizosphere soil triggered by P deficiency are largely unknown. In a greenhouse study we grew maize plants in low and high P containing soils and examined the role of rhizosphere processes on mineralization of organic P (P o ) in soils with low and high P availability and different P speciation 23 (Po dominated versus P i dominated) and determine its effect on plant growth. Plant biomass was determined and rhizosphere soils, and soil samples from pots with and without plants were collected at key vegetative growth stages (VGS). A variety of biogeochemical parameters (e.g. microbial biomass C and P, potential phosphatase activities, plant biomass P, soil P species etc.) were determined using both conventional (sequential chemical extraction) and advanced (e.g. high resolution mass spectrometry (FTICR-MS) and 3129 P solution NMR) techniques. Here, we used phospholipid fatty acid (PLFA) and DNA based microbial community analysis to track changes in microbial community structure and diversity. Low P availability induced changes in biogeochemical processes and microbial community composition in the rhizosphere soils of plants grown in low P containing soil. At early VGS, the plants in low P soil were visually struggling which correlated with greater rhizosphere potential acid phosphatase activity and a reduction in P o fractions. At late VGS, the plants appeared to recover which correlated with a decrease in Meh (III) extractable P, an increase in microbial biomass C and P and greater total P in the plant biomass (roots, shoots and leaves). In high P containing soil, on the other hand, greater P availability masked these rhizosphere processes. Our results confirmed the degradation of SOM and Po mineralization in rhizosphere soils driven by microbe and plant need for P. P deficiency may have favored the abundance of fungi which utilized easily degradable root exudates for the production of extracellular enzymes responsible for the mineralize of SOM P o . This study highlights the intertwined mechanisms involved, and emphasizes the importance of, P o sources for plant nutrition and survival in marginal soils.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Bioavailable soil Pb minimized by in situ transformation to plumbojarosite

Exposure to lead (Pb) during early life has persistent adverse health effects. During childhood, ingestion of bioavailable Pb in contaminated soils can be a major route of Pb absorption. Remediation to alter physiochemical properties of soil-borne Pb can reduce Pb bioavailability. Our laboratory-based approach for soil Pb remediation uses addition of iron (Fe) sulfate and application of heat to promote formation of plumbojarosite (PLJ), a sparingly soluble Pb-Fe hydroxysulfate mineral. We treated two soils with anthropogenic Pb contamination and samples of clean topsoil spiked with various Pb compounds (i.e., carbonate, chloride, phosphate [P], or sulfate) to convert native Pb species to PLJ and used a mouse assay to assess relative bioavailability (RBA) of Pb in untreated (U) and remediated soils. Bone and blood Pb levels were significantly lower (P < 0.001, Student's t test) in mice that consumed diets amended with remediated soils than with U soils. Estimated RBA for Pb in both remediated natural soils and Pb-mineral spiked soils were reduced by >90% relative to Pb RBA for U soils, which is substantially more effective than other soil amendments, including P. X-ray absorption spectroscopy showed that >90% of all Pb species in remediated soils were converted to PLJ, and ingested PLJ was not chemically transformed during gastrointestinal tract transit. Post treatment neutralization of soil pH did not affect PLJ stability, indicating the feasibility in field conditions. These results suggest that formation of PLJ in contaminated soils can reduce the RBA of Pb and minimize this medium’s role as a source of Pb exposure for young children.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The direct and indirect drivers shaping RNA viral communities in grassland soil

Recent studies have revealed diverse RNA viral communities in soils. Yet, how environmental factors influence soil RNA viruses remains largely unknown. Here, we recovered RNA viral communities from 24 metatranscriptomes sequenced from grassland soils managed under a range of environmental conditions including 1) water content: 100% and 25% water holding capacity, 2) plant presence: planted with tall wheatgrass (Thinopyrum ponticum) and bare soil, 3) cultivar type: Alkar and Jose, and 4) soil depth: 0-5 cm and 15-25 cm. The recovered RNA viral communities were novel with nearly one-third of the RNA viral contigs uniquely detected in the studied grassland. The classified RNA viral contigs are mostly known as eukaryotic RNA viruses (74.7%) belonging to Phyla Duplornaviricota, Kitrinoviricota, Lenarviricota, and Pisuviricota. Eukaryotic RNA viruses of Family Mitoviridae as well as their natural hosts, Fungi, are one of the most dominant taxa. Consistent with the results of nonmetric multidimensional scaling analysis, the four environmental conditions (water content, plant presence, cultivar, and soil depth) significantly influence the assemblages of soil RNA viral communities as suggested by the correlation analysis and the random forest algorithm. The modularity analysis of the factor network and the structural equation modeling further support the hierarchical associations among the four environmental factors and the community factors representing the co-existing eukaryotic, prokaryotic, and RNA viral communities. The soil water content, plant presence, and type of cultivar demonstrate a significant positive impact on eukaryotic RNA viral richness directly as well as indirectly on eukaryotic RNA viral abundance via influencing the co-existing eukaryotic members in this soil. Our data also provide statistical support for the negative influence of soil depth on soil eukaryotic richness and abundances resulting in its indirect impact on soil eukaryotic RNA viral communities. This study provides field-relevant information on how environmental and community factors collectively shape soil RNA communities and contribute to ecological understanding of RNA viral survival under various environmental conditions and virus-host interactions in soil.

Wu, Ruonan↗
DOE OSTI2025

GLBRC Soil Yearlong Incubation 13C-SIP-Lipidomics

Data package for Lipids represent a dynamic, yet stable pool of microbially-derived soil carbon This data is published under a CC0 license. The authors encourage data reuse and request attribution by referencing the below citations for the data packages and associated manuscript. Please cite as: Rempfert KR, Bell SL, Kasanke CP, Kyle JE, Hofmockel KS. 2025. GLBRC Soil Yearlong Incubation 13C-SIP-Lipidomics. [Data Set] PNNL DataHub. doi: Rempfert KR, Bell SL, Kasanke CP, Kyle JE, Hofmockel KS. 2025. MSV000097435: GLBRC soil yearlong incubation 13C-SIP-Lipidomics [Data Set] MassIVE. doi:10.25345/C57659T3K Rempfert KR, Bell SL, Kasanke CP, Kyle JE, Hofmockel KS. 2025. Lipids represent a dynamic, yet stable pool of microbially-derived soil carbon. In Prep This data package consists of compound-specific 13C SIP-lipidomics data from a yearlong tracer incubation experiment designed to investigate microbial lipid persistence in switchgrass bioenergy crop soils. In order to explore how lipid structure may modulate the persistence of C in soil lipids, we leveraged soils from two sites (Michigan - sandy texture, Wisconsin - silty texture) operated by the U.S. Department of Energy-funded Great Lakes Bioenergy Research Center (GLBRC). These sites had comparable climates, identical management practices, but contrasting soil textures, allowing us to assess the variability of lipid accrual or degradation in soils as well as provide insight regarding the degree to which edaphic properties may regulate the retention of soil lipids. Untargeted lipidomics analyses were performed to identify 13C-labeled lipids in the soil microbiome after long-term incubation. Soils were supplemented with 100 micrograms glucose per gram dry soil (99 atom % 13C or natural abundance for paired control) and incubated; samples were collected two months and one year after glucose addition. Lipid extracts (MPLEx) were analyzed by LC-MS/MS and identified using LIQUID. Calculation of isotopic enrichment of lipids was performed by targeted approach using TarMet to quantify lipid isotopologues and IsoCorrectoR to correct for natural abundance isotopes. Contents: Data package contents reported here are the first version and contain downstream analysis files for the raw LC-MS mass spectrometry files (.mzXML) deposited at the MassIVE database repository under accession MSV000097435 (80 experimental runs; 5.85 GB) | MassIVE DOI: 10.25345/C57659T3K. Support files include the additional data download 'Read Me' file containing data descriptor information. Reported data download contents are structured for compliance with project data sharing guidelines, community standards initiatives, and sponsor stakeholder policies supporting FAIR data principles. Data processing software, analysis tools, and data workflows are listed below corresponding to the host repository long-term location. Available Data Downloads (0.3 GB): "GLBRC soil yearlong incubation 13C-SIP-Lipidomics_readme.txt" - 'Read Me' data package content file (txt) "GLBRC_DataPackage_analysis files" - Data processing files (Rmd) and saved intermediate data processing outputs (rds, csv, xlsx) "GLBRC_13C_lipidomics_dataset.xlsx" - processed data in tabular format (xlsx) Linked Software: LIQUID LC-MS Analysis Software | 10.5281/zenodo.6459462 Lipid Mini-On Software Tools | 10.5281/zenodo.1492803 pmartR Omics Statistical Software | 10.5281/zenodo.6108667 xcms (v4.3.3) TarMet (v1.1.1) IsoCorrectoR (1.24.0) Funding Acknowledgments: This research was supported by an Early Career Research Program award funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (OBER) Genomic Science program under FWP 68292, FWP 07880 and EMSL Exploratory Research Project 51095. A portion of this work was performed in the William R. Wiley Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by OBER and located at Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated by Battelle for the DOE under Contract DE-AC05-76RLO1830.

Rempfert, Kaitlin R [Pacific Northwest National La↗
DOE OSTI2022

The quality of organic amendments affects soil microbiome and nitrogen-cycling bacteria in an organic farming system

Organic amendments are applied in organic farming systems to provide nutrients for crop uptake and to improve soil health. Compost is often favored over fresh manure for food safety reasons, while fresh manure can be a valuable source of readily available nitrogen (N). However, the potential for fresh versus composted manure to differentially affect soil microbial and N-cycling functional communities over multiple seasons remains unknown. We compared the effect of composted vs. fresh cattle manure on soil microbial communities using taxonomic and functional approaches. Soils were collected from field plots with three organic N treatments: control (no amendment), composted manure (compost, 224 kg/ha total N), and fresh manure (manure, 224 kg/ha total N) in an organic production system. Illumina amplicon sequencing was used to comprehensively assess the bacterial community (16S rRNA genes), fungal community (ITS), ureolytic community ( ureC ), chitinolytic community ( chiA ), bacterial ammonia oxidizers (AOB amoA ), and nitrite oxidizers ( Nitrospira nxrB ). The results showed that both compost and manure treatment significantly changed the soil microbial communities. Manure had a stronger effect than compost on soil bacterial and fungal community composition, as well as on the ureolytic and chitinolytic communities, while compost treated soils had higher microbial richness than manure treated soils. Both taxonomic and functional approaches showed that the microbial community was more responsive to fresh manure than to compost. Manure treated soil also had more complex microbial interactions than compost treated soil. The abundance and community composition of N-cycling functional groups often played more limited roles than soil chemical properties (soil organic carbon, extractable organic carbon, and pH) in driving N-cycling processes. Results from our study may guide strategies for the management of organic amendments in organic farming systems and provide insights into the linkages between soil microbial communities and soil function.

Ouyang, Yang↗
DOE OSTI2022

Soil-related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model

Terrestrial biogeochemical models are essential tools to quantify climate–carbon cycle feedback and plant–soil relations from local to global scale. In this study, a theoretical basis is provided for the latest version of the Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a branch of the original Biome-BGC model with a large number of developments and structural changes. Earlier model versions performed poorly in terms of soil water content (SWC) dynamics in different environments. Moreover, lack of detailed nitrogen cycle representation was a major limitation of the model. Since problems associated with these internal drivers might influence the final results and parameter estimation, additional structural improvements were necessary. In this paper the improved soil hydrology as well as the soil carbon and nitrogen cycle calculation methods are described in detail. Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case studies focusing on soil hydrology, soil nitrogen cycle, and soil organic carbon content estimation. Soil-hydrology-related results are compared to observation data from an experimental lysimeter station. The results indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0 (explained variance increased from 0.121 to 0.8 for SWC and from 0.084 to 0.46 for soil evaporation; bias changed from -0.047 to -0.007 m 3 m -3 for SWC and from -0.68 to -0.2 mm d -1 for soil evaporation). Simulations related to nitrogen balance and soil CO 2 efflux were evaluated based on observations made in a long-term field experiment under crop rotation. The results indicated that the model is able to provide realistic nitrate content estimation for the topsoil. Soil nitrous oxide (N 2 O) efflux and soil respiration simulations were also realistic, with overall correspondence with the observations (for the N 2 O efflux simulation bias was between -0.13 and -0.1 mgNm -2 d -1 , and normalized root mean squared error (NRMSE) was 32.4 %–37.6 %; for CO 2 efflux simulations bias was 0.04–0.17 gCm -2 d -1 , while NRMSE was 34.1 %–40.1 %). Sensitivity analysis and optimization of the decomposition scheme are presented to support practical application of the model. The improved version of Biome-BGCMuSo has the ability to provide more realistic soil hydrology representation as well as nitrification and denitrification process estimation, which represents a major milestone.

58 GEOSCIENCES↗
NASA NTRS1997

Retrieval of Soil Moisture and Roughness from the Polarimetric Radar Response

The main objective of this investigation was the characterization of soil moisture using imaging radars. In order to accomplish this task, a number of intermediate steps had to be undertaken. In this proposal, the theoretical, numerical, and experimental aspects of electromagnetic scattering from natural surfaces was considered with emphasis on remote sensing of soil moisture. In the general case, the microwave backscatter from natural surfaces is mainly influenced by three major factors: (1) the roughness statistics of the soil surface, (2) soil moisture content, and (3) soil surface cover. First the scattering problem from bare-soil surfaces was considered and a hybrid model that relates the radar backscattering coefficient to soil moisture and surface roughness was developed. This model is based on extensive experimental measurements of the radar polarimetric backscatter response of bare soil surfaces at microwave frequencies over a wide range of moisture conditions and roughness scales in conjunction with existing theoretical surface scattering models in limiting cases (small perturbation, physical optics, and geometrical optics models). Also a simple inversion algorithm capable of providing accurate estimates of soil moisture content and surface rms height from single-frequency multi-polarization radar observations was developed. The accuracy of the model and its inversion algorithm is demonstrated using independent data sets. Next the hybrid model for bare-soil surfaces is made fully polarimetric by incorporating the parameters of the co- and cross-polarized phase difference into the model. Experimental data in conjunction with numerical simulations are used to relate the soil moisture content and surface roughness to the phase difference statistics. For this purpose, a novel numerical scattering simulation for inhomogeneous dielectric random surfaces was developed. Finally the scattering problem of short vegetation cover above a rough soil surface was considered. A general scattering model for grass-blades of arbitrary cross section was developed and incorporated in a first order random media model. The vegetation model and the bare-soil model are combined and the accuracy of the combined model is evaluated against experimental observations from a wheat field over the entire growing season. A complete set of ground-truth data and polarimetric backscatter data were collected. Also an inversion algorithm for estimating soil moisture and surface roughness from multi-polarized multi-frequency observations of vegetation-covered ground is developed.

Sarabandi, Kamal↗
NASA NTRS2003

Soil Sampling Techniques For Alabama Grain Fields

Characterizing the spatial variability of nutrients facilitates precision soil sampling. Questions exist regarding the best technique for directed soil sampling based on a priori knowledge of soil and crop patterns. The objective of this study was to evaluate zone delineation techniques for Alabama grain fields to determine which method best minimized the soil test variability. Site one (25.8 ha) and site three (20.0 ha) were located in the Tennessee Valley region, and site two (24.2 ha) was located in the Coastal Plain region of Alabama. Tennessee Valley soils ranged from well drained Rhodic and Typic Paleudults to somewhat poorly drained Aquic Paleudults and Fluventic Dystrudepts. Coastal Plain s o i l s ranged from coarse-loamy Rhodic Kandiudults to loamy Arenic Kandiudults. Soils were sampled by grid soil sampling methods (grid sizes of 0.40 ha and 1 ha) consisting of: 1) twenty composited cores collected randomly throughout each grid (grid-cell sampling) and, 2) six composited cores collected randomly from a -3x3 m area at the center of each grid (grid-point sampling). Zones were established from 1) an Order 1 Soil Survey, 2) corn (Zea mays L.) yield maps, and 3) airborne remote sensing images. All soil properties were moderately to strongly spatially dependent as per semivariogram analyses. Differences in grid-point and grid-cell soil test values suggested grid-point sampling does not accurately represent grid values. Zones created by soil survey, yield data, and remote sensing images displayed lower coefficient of variations (8CV) for soil test values than overall field values, suggesting these techniques group soil test variability. However, few differences were observed between the three zone delineation techniques. Results suggest directed sampling using zone delineation techniques outlined in this paper would result in more efficient soil sampling for these Alabama grain fields.

Thompson, A. N.↗
NASA NTRS2005

Using IKONOS Imagery to Estimate Surface Soil Property Variability in Two Alabama Physiographies

Knowledge of surface soil properties is used to assess past erosion and predict erodibility, determine nutrient requirements, and assess surface texture for soil survey applications. This study was designed to evaluate high resolution IKONOS multispectral data as a soil- mapping tool. Imagery was acquired over conventionally tilled fields in the Coastal Plain and Tennessee Valley physiographic regions of Alabama. Acquisitions were designed to assess the impact of surface crusting, roughness and tillage on our ability to depict soil property variability. Soils consisted mostly of fine-loamy, kaolinitic, thermic Plinthic Kandiudults at the Coastal Plain site and fine, kaolinitic, thermic Rhodic Paleudults at the Tennessee Valley site. Soils were sampled in 0.20 ha grids to a depth of 15 cm and analyzed for % sand (0.05 - 2 mm), silt (0.002 -0.05 mm), clay (less than 0.002 mm), citrate dithionite extractable iron (Fe(sub d)) and soil organic carbon (SOC). Four methods of evaluating variability in soil attributes were evaluated: 1) kriging of soil attributes, 2) co-kriging with soil attributes and reflectance data, 3) multivariate regression based on the relationship between reflectance and soil properties, and 4) fuzzy c-means clustering of reflectance data. Results indicate that co-kriging with remotely sensed data improved field scale estimates of surface SOC and clay content compared to kriging and regression methods. Fuzzy c-means worked best using RS data acquired over freshly tilled fields, reducing soil property variability within soil zones compared to field scale soil property variability.

Sullivan, Dana↗
NASA NTRS2012

Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission.

Reichle, Rolf H.↗
NASA NTRS2024

The Spectral Characteristics of Lunar Agglutinates: Visible-Near-Infrared Spectroscopy of Apollo Soil Separates

The lunar surface evolves over time due to space weathering, and the visible–near-infrared spectra of more mature (i.e., heavily weathered) soils are lower in reflectance and steeper in spectral slope (i.e., darker and redder) than their immature counterparts. These spectral changes have traditionally been attributed to the space-weathered rims of soil grains (and particularly nanophase iron therein). However, understudied thus far is the spectral role of agglutinates—the agglomerates of mineral and lithic fragments, nanophase iron, and glass that are formed by micrometeoroid impacts and are ubiquitous in mature lunar soils. We separated agglutinates and non-agglutinates from six lunar soils of varying maturity and composition, primarily from the 125–250 μm size fraction, and measured their visible–near-infrared reflectance spectra. For each soil, the agglutinate spectra are darker, redder, and have weaker absorption bands than the corresponding non-agglutinate and unsorted soil spectra. Moreover, greater soil maturity corresponds to darker agglutinate spectra with weaker absorption bands. These findings suggest that agglutinates (rather than solely the space-weathered rims) play an important role in both the darkening and reddening of mature soils—at least for the size fractions examined here. Comparisons with analog soils suggest that high nanophase iron abundance in agglutinates is likely responsible for their low reflectance and spectrally red slope. Additional studies of agglutinates are needed both to more comprehensively characterize their spectral properties (across size fractions and in mixing with non-agglutinates) and to assess the relative roles of agglutinates and rims in weathering-associated spectral changes. Plain Language Summary - In scientific study of the Moon, one key focus is surface processes: how do physical and chemical properties of the Moon’s surface change over time due to weathering (e.g., bombardment by micrometeoroids and by particles from the Sun)? Such investigations provide valuable insights into the Moon’s history (such as the ages of impact craters) that are often deduced from measurements of reflected light; as a soil is weathered it reflects light differently, which manifests visually as a progressive darkening of the soil. This phenomenon had primarily been attributed to weathering-associated development of rims on individual soil grains, but in this work we explored an alternative cause: soil particles known as agglutinates (misshapen, vesicular agglomerates of mineral fragments, iron, and glass that form due to weathering processes). We isolated agglutinates of six soil samples from the Moon and measured how they reflect light. We find that they reflect light in patterns reminiscent of how the Moon’s surface does when weathered. These findings suggest that agglutinates play a more important role than previously thought in determining the light-reflecting properties of the Moon’s surface, thus warranting greater and more nuanced consideration in future studies of how the Moon’s surface changes over time.

spectroscopy↗
NASA NTRS2023

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.

Soil dust↗
DOE OSTI2024

Soil Carbon Dynamics Following Land Use Changes and Conversion to Oil Palm Plantations in Tropical Lowlands Inferred From Radiocarbon

We measured the 14C and 13C isotopic values of soil organic carbon in mineral soil from lowland tropical forests to provide insight into how quickly carbon is turning over in the soil following conversion of primary forests to oil palm plantations. In addition to areas converted to oil palm plantations in Peru, Indonesia, and Cameroon, we examine pastures and secondary forests in Peru as a comparison to the carbon cycling processes operating in the oil palm plantations.This dataset includes radiocarbon (Δ14C) and stable carbon (δ13C) isotopes of soil organic carbon in mineral soils from natural lowland forests and oil palm plantations in Peru, Indonesia, and Cameroon. We additionally examine plots of secondary forests following agricultural use and pastures on cleared natural forest in Peru. In addition to isotopic data, this dataset includes soil carbon and nitrogen concentrations and stock, soil texture (percent sand, silt, and clay), pH, ECEC (effective cation exchange capacity), base saturation, and bulk density. Soils were sampled in 4 depth increments to 100 cm depth.This dataset supports the publication Finstad et al., 2020. Finstad, K., van Straaten, O., Veldkamp, E., & McFarlane, K. (2020). Soil carbon dynamics following land use changes and conversion to oil palm plantations in tropical lowlands inferred from radiocarbon. Global Biogeochemical Cycles, 34, e2019GB006461. https://doi.org/10.1029/2019GB006461

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model

Digital soil maps are commonly data-driven as the development of physically-based models for soil mapping is difficult due to the complexity of soils. However, physically-based hydrologic models have been successful in simulating water dynamics. Since water movement is a major driver of pedogenesis, the physical rules that govern water movement might help explain and predict the spatial variation of soil properties. Here, we demonstrate the novel use of a physically-based, distributed hydrologic model to inform the spatial distribution of soil properties. The Distributed Hydrology Soil Vegetation Model (DHSVM) was utilized to simulate soil moisture content (SM) and water table depth (WTD) in two hillslope catchments under pasture and forest management wherein hydrologic model outputs were then compared with soil properties measured in situ. SM sensors and wells were installed in both catchments to validate simulations of soil water movement via Nash-Sutcliffe Efficiency (E). In-situ observations were made at 87 sites within both catchments to study the connection between simulated water movement (SM and WTD) and observed soil properties, namely the depth and thickness of the argillic (Bt), fragic (Btx), and C horizons, and the depth of redoximorphic features. The simulated time series of SM and WTD were also clustered per season using Dynamic Time Warping (DTW), which identified similarity among time series at varying timescales. Model validation suggested that simulations of surficial SM (0–20 cm) were reasonable (E = 0.45), however, simulated subsurface SM (45–60 cm) and WTD were not sufficiently accurate. The thickness of Btx horizons were spatially grouped into different populations by SM clusters from every season except spring. For the other properties, only SM dynamics of specific seasons grouped into significantly different populations, suggesting that the explanatory power of simulated water movement varies seasonally and was greater during winter. Here, we show clusters of simulated SM separated soil properties into statistically different populations, showing that hydrologic models could inform areas that followed different water dynamics related to pedogenic trajectories and related biogeochemical processes not necessarily simulated by the model. As such, physically-based modeling of water dynamics can, therefore, inform and advance digital soil mapping by linking water movement patterns stemming from hydrologic model outputs to spatial patterns of soil properties and pedogenesis.

54 ENVIRONMENTAL SCIENCES↗