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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

The Sensitivity of Ground-Reflected GNSS Signals to Near-Surface Soil Moisture, as Recorded by Spaceborne Receivers

Spatial and temporal variations in near-surface soil moisture are important to measure for climate studies, numerical weather forecasts, and drought monitoring. Several previous studies have shown success in using ground-reflected Global Navigation Satellite System (GNSS) signals as a form of bistatic radar to sense soil moisture. However, the ability of this type of data to sense soil moisture variations from space is still a nascent field of study. In the past two years, three satellites have been launched that were either designed to capture ground-reflected GNSS signals or have been modified to record these signals. The data provided by these satellites are giving scientists an unprecedented opportunity to investigate their ability to detect changes in Earth’s land surface, including but certainly not limited to near-surface soil moisture. This paper will present spaceborne observations of ground-reflected GNSS signals and evaluate their sensitivity to near-surface soil moisture. This sensitivity will be compared to empirical and theoretical sensitivities of monostatic L-band radar measurements to soil moisture. We will also comment on possibilities for retrieval algorithm development, using techniques employed for monostatic radar as a guide.

Chew, C.↗
NASA NTRS2019

Soil Moisture Data Assimilation to Estimate Irrigation Water Use

Knowledge of irrigation is essential to support food security, manage depleting water resources, and comprehensively understand the global water and energy cycles. Despite the importance of understanding irrigation, little consistent information exists on the amount of water that is applied for irrigation. In this study, we develop and evaluate a new method to predict daily to seasonal irrigation magnitude using a particle batch smoother data assimilation approach, where land surface model soil moisture is applied in different configurations to understand how characteristics of remotely sensed soil moisture may impact the performance of the method. The study employs a suite of synthetic data assimilation experiments, allowing for systematic diagnosis of known error sources. Assimilation of daily synthetic soil moisture observations with zero noise produces irrigation estimates with a seasonal bias of 0.66% and a correlation of 0.95 relative to a known truth irrigation. When synthetic observations were subjected to an irregular overpass interval and random noise similar to the Soil Moisture Active Passive satellite (0.04 cm(exp 3) cm(exp -3)), irrigation estimates produced a median seasonal bias of <1% and a correlation of 0.69. When systematic biases commensurate with those between NLDAS‐2 land surface models and Soil Moisture Active Passive are imposed, irrigation estimates show larger biases. In this application, the particle batch smoother outperformed the particle filter. The presented framework has the potential to provide new information into irrigation magnitude over spatially continuous domains, yet its broad applicability is contingent upon identifying new method(s) of determining irrigation schedule and correcting biases between observed and simulated soil moisture, as these errors markedly degraded performance.

R Abolafia-Rosenzweig↗
NASA NTRS2020

Soil Moisture as a Harbinger of Increased Forecast Reliability at Subseasonal Time Scales

The shape of the nonlinear relationship between evapotranspiration and soil moisture (the "ET-W relationship") helps control the evolution of soil moisture with time. Together, the shape of the relationship and the magnitude of the soil moisture anomaly at the beginning of a subseasonal forecast help determine whether a given anomaly will still be present at subseasonal leads, allowing it to contribute to skill in subseasonal temperature and precipitation prediction at those leads. In this study we examine subseasonal prediction in the context of soil moisture initialization using a suite of forecasts performed with the NASA GEOS seasonal forecast system. Large soil moisture anomalies are in fact found to be harbingers of increased skill in the subseasonal forecasts. Furthermore, accounting explicitly for the nonlinear shape of the ET-W relationship improves our ability to quantity the increase in forecast reliability associated with soil moisture initialization.

Koster, Randal↗
NASA NTRS2020

Phyllosilicate Transitions in Ferromagnesian Soils: Short-Range Order Materials and Smectites Dominate Secondary Phases

Analyses of X-ray diffraction (XRD) patterns taken by the CheMin instrument on the Curiosity Rover in Gale crater have documented the presence of clay minerals interpreted as smectites and a suite of amorphous to short-range order materials termed X-ray amorphous materials. These X-ray amorphous materials are commonly ironrich and aluminum poor and likely some of them are weathering products rather than primary glasses due to the presence of volatiles. Outstanding questions remain regarding the chemical composition and mineral structure of these X-ray amorphous materials and the smectites present at Gale crater and what they indicate about environmental conditions during their formation. To gain a better understanding of the mineral transitions that occur within ferromagnesian parent materials, we have investigated the development of secondary clay minerals and shortrange order materials in two soil chronosequences with varying climates developing on ultramafic bedrock. Field Sites: We investigated soil weathering within two field locations, the Klamath Mountains of Northern California, and the Tablelands of Newfoundland, Canada. Both sites possess age dated or correlated recently deglaciated soils and undated but substantially older soils. In the Klamath mountains the Trinity Ultramafic Body was deglaciated roughly 15,000 years bp while in the Tablelands a moraine was dated to about 17,600 years bp. The Klamath Mountains feature a seasonally wet and dry climate while the Tablelands are wet year-round with saturated soil conditions observed during sampling and standing water observed within 3 of 4 soil pit sampling locations.

Feldman, A. D.↗
NASA NTRS2020

Increased Likelihood of Appreciable Afternoon Rainfall Over Wetter or Drier Soils Dependent Upon atmospheric Dynamic Influence

The relationship between morning soil moisture and afternoon rainfall persists as an important yet unresolved challenge in land‐atmosphere interaction study, complicated in part by atmospheric influence. Here, we address this relationship by utilizing NASA's satellite soil moisture and precipitation data for the warm season (June–September) of 2015–2019 over Northern Hemisphere land (0–60°N). Raining days are partitioned into low, medium, and high regimes of atmospheric water vapor convergence. Under the low convergence regime, afternoon rainfall is more likely to occur over wetter soils or higher relative humidity; for days with high moisture convergence, occurrence favors drier soils or lower relative humidity. For each regime, afternoon rainfall occurrence favors warmer morning soil or air temperature. These conclusions are not affected by the threshold magnitude utilized to identify afternoon rainfall events by accumulation, but the threshold value does affect the soil moisture (or relative humidity)‐precipitation relationship when convergence regimes are not considered.

J. Welty↗
NASA NTRS2021

Evaluation of 18 Satellite- and Model-Based Soil Moisture Products Using in Situ Measurements From 826 Sensors

Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E SWI , SMOS SWI , AMSR2 SWI , and ASCAT SWI , with the L-band-based SMAPL3E SWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCI SWI ), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3E SWI . The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

Hylke E. Beck↗
NASA NTRS2020

Exploring Spatiotemporal Relations Between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

An understanding of streamflow variability and its response to changes in climate conditions is essential for water resource planning and management practices that will help to mitigate the impacts of extreme events such as floods and droughts on agriculture and other human activities. This study investigated the relationship between precipitation, soil moisture, and streamflow over a wide range of watersheds across the United States using Google Earth Engine (GEE). The correlation analyses disclosed a strong association between precipitation, soil moisture, and streamflow, however, soil moisture was found to have a higher correlation with the streamflow relative to precipitation. Results indicated different strength of the association depends on the watershed classes and lag times assessments. The perennial watersheds showed higher coherence compared to intermittent watersheds. Previous month precipitation and soil moisture have a stronger influence on the current month streamflow, particularly in the snow-dominated watersheds. Monthly streamflow forecasting models were developed using an autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The results showed that the SVM model generally performed better than the ARIMA model. Overall streamflow forecasting model performance varied considerably among watershed classes, and perennial watersheds tend to exhibit better predictably compared to intermittent watersheds due to lower streamflow variability. The SVM models with precipitation and streamflow inputs performed better than those with streamflow input only. Results indicated that the inclusion of antecedent root-zone soil moisture improved the streamflow forecasting in most of the watersheds, and the largest improvements occurred in the intermittent watersheds. In conclusion, this work demonstrated that knowing the relationship between precipitation, soil moisture, and streamflow in different watershed classes will enhance the understanding of the hydrologic process and can be effectively utilized in improving streamflow forecasting for better satellite-based water resource management strategies.

Nazmus Sazib↗
NASA NTRS2021

Land Surface Modeling Over the Dry Chaco: The Impact of Model Structures, and Soil, Vegetation and Land Cover Parameters

In this study, we tested the impact of a revised set of soil, vegetation and land cover parameters on the performance of three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS). The impact of this revision was tested over the South American Dry Chaco, an ecoregion characterized by deforestation and forest degradation since the 1980s. Most large-scale LSMs may lack the ability to correctly represent the ongoing deforestation processes in this region, because most LSMs use climatological vegetation indices and static land cover information. The default LIS parameters were revised with improved soil parameters, satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction) instead of climatological vegetation indices, and yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and “efficiency space” for various baseline and revised experiments showed that large regional and long-term differences in the simulated water budget partitioning relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, the different LSM structures redistributed water differently in response to these parameter updates. A time-series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature (Tb) showed that no LSM setup significantly outperformed another for the entire region and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the bias between simulated surface soil moisture and pixel-scale in situ observations and the bias between simulated Tb and regional Soil Moisture Ocean Salinity (SMOS) observations. Our results suggest that the different hydrological responses of various LSMs to vegetation changes may need further attention to gain benefits from vegetation data assimilation.

Michiel Maertens↗
NASA NTRS2020

Joint retrieval of soil moisture and permafrost active layer thickness using L-band InSAR and P-band PolSAR

Seasonal subsidence measured by repeat-pass interferometric synthetic aperture radar (InSAR) can be used to infer the active layer thickness (ALT) in permafrost regions. The differential volume of soil water undergoing the phase change over the thaw season is one of the factors impacting the seasonal subsidence and is a function of both soil moisture profile and thaw depth. Without the information about soil moisture, this InSAR approach can have large biases in the ALT estimates when soil moisture profile is below saturation. Soil moisture and ALT can also be estimated from polarimetric synthetic aperture radar (PolSAR) backscatter observations but the sensing depth of the PolSAR approach is limited when deep ALT is present. In this paper, we integrated these two approaches and applied a joint retrieval method to estimate the soil moisture profiles and ALT from the L-band InSAR and P-band PolSAR data acquired over the Arctic-boreal region during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign.

Schaefer, Kevin M.↗
NASA NTRSDate not supplied

The Plant Water Management Experiments: Soil

A simple means of watering plants in the low-g environment aboard orbiting spacecraft is not obvious. Since the beginning of spaceflight, numerous approaches have been pursued to water plants that seek to maximize plant viability and system reliability, while minimizing crew time and system complexity. We are not there yet. The Plant Water Management (PWM) Soil experiments seek to apply recent advances in low-g capillary fluidics phenomena to the challenges faced by plant growth operations aboard spacecraft. The primary challenge is to establish earth-like flows minimizing low-g specific adaptations required of the plants. This is difficult due to the ever-present fluid physics challenges of poorly-wetting multiphase inertial-visco-capillary flows in geometrically complex conduits and containers. In this paper, we present recent flight results for the PWM Soil experiments where arcillite ‘soil reservoirs’ are arranged in a non-wetting host soil that serves as an O2-breathing wetting barrier. In this way, a largely terrestrial water-soil environment is mimicked where, as liquid is evapo-transpired through the growing plant foliage, the effective water table passively ‘falls’ reducing viscous lengths and increasing water uptake for the plant. We present data from 6 days of 24-7 experiments on the ISS testing 3 different plant root models. We also present and correlate a capillary flow model which captures the primary features of the flow. Our summary is valued for the assessment of current and future low-g plant watering systems employing soil media.

microgravity↗
NASA NTRSDate not supplied

Assessing the Impact of SMAP Soil Moisture Data Assimilation on the Simulation and Prediction of Tropical Cyclone Idai

The role of soil moisture in the evolution of tropical cyclones (TCs) approaching land has long been recognized. Dry land surface conditions can lead to faster dissipation of a TC over land (often associated with changes in precipitation structure), whereas very wet conditions can help sustain or even re-intensify a TC. The ability to forecast post-landfall TC evolution and mitigate the associated socio-economic impact thus hinges on accurate knowledge of land surface conditions prior to landfall. The NASA Soil Moisture Active Passive (SMAP) mission provides accurate observations of soil moisture globally and at high revisit times of 2-3 days. It has been shown that the assimilation of SMAPbrightness temperatures (Tb) significantly improves modeled land surface states and thus has the potential to constrain land surface initial conditions in TC forecasts. In this presentation, we investigate this potential through an extensive set of Observing System Experiments that systematically assess the impact of assimilating SMAP Tbs on TC forecast skill in the Goddard Earth Observing System. Focusing on the case of TC Idai, we show that the assimilation of SMAP generally leads to a drier soil moisture analysis for the land surface underneath much of the storm’s circulation. An exception is the direct TC track, where SMAP assimilation increases soil moisture. These changes are reflected in the surface fluxes, which propagate the land surface state changes to the atmosphere. This results a TC that is overall weaker with lower winds, but with a better-defined eye and a slightly smaller track error than a simulation without SMAP DA. The changes in soil moisture resulting from the assimilation of SMAP also impact the total precipitation amounts as well as the precipitation structure associated with TC Idai.

SMAP↗
NASA NTRS2021

Soil Moisture–Atmosphere Feedbacks Dominate Land Carbon Uptake Variability

Year-to-year changes in carbon uptake by terrestrial ecosystems play an essential role in determining atmospheric carbon dioxide concentrations. It remains uncertain to what extent temperature and water availability can explain these variations at the global scale. Here we use factorial climate model simulations and show that variability in soil moisture drives 90% of the inter-annual variability in global land carbon uptake, mainly through its impact on photosynthesis. We find that most of this ecosystem response occurs indirectly as soil moisture–atmosphere feedbacks amplify temperature and humidity anomalies, and enhance the direct effects of soil water stress. The strength of this feedback mechanism explains why coupled climate models indicate a dominant role of soil moisture which is not readily apparent in land surface model simulations and observational analyses. These findings highlight the need to account for feedbacks between soil and atmospheric dryness when estimating the carbon cycle’s response to climatic change globally, as well as when conducting field-scale investigations of the ecosystem response to droughts. Our results show that most of the global variability in modelled land carbon uptake is driven by temperature and vapour pressure deficit effects which are controlled by soil moisture.

Atmospheric dynamics↗
NASA NTRS2024

Deducing Land-Atmosphere Coupling Regimes from SMAP Soil Moisture

In recent years, there has been a growing recognition of the significance of Land-Atmosphere (L-A) interactions and feedback mechanisms in understanding and predicting Earth’s water and energy cycles. Soil moisture plays a critical role in mediating the strength of L-A interactions and is important for understanding the complex and governing processes across this interface. This study aims to identify the significance of soil moisture in identifying L-A coupling strength within the Convective Triggering Potential (CTP) and Humidity Index (HI) framework. To address this, a consistent and reliable dataset of atmospheric profiles is created by merging CTP and HI using Triple Collocation (TC) with three reanalysis datasets. The merged CTP and HI product demonstrates enhanced performance globally as compared to the individual datasets when validated with radiosonde and satellite observations. This merged product of CTP and HI is then used to compare the L-A coupling strength based on Soil Moisture Active Passive Level 3 (SMAPL3) and SMAP Level 4 (SMAPL4) over two decades (2003-2022) where L-A coupling strength is defined as the persistence probability within the dry and wet coupling regimes. Results indicate that the persistency-based coupling strength is related to the ability of soil moisture to predict future atmospheric humidity and dry vs. wet coupling state. The coupling strength in SMAPL4 is consistently stronger than in SMAPL3 and is likely due to its reliance on a land surface model and reduced susceptibility to random noise. The difference in coupling strength based on the same CTP-HI underscores the importance of soil moisture data in estimating coupling strength within the CTP-HI framework. These findings lay the groundwork for understanding the role of L-A interactions and drought evolution due to soil moisture variations, by providing insight into the quantification of coupling strength and its role in drought monitoring and forecast efforts.

Land-atmosphere coupling↗
DOE OSTI2020

Soil chemistry data across a catena in the Luquillo Experimental Forest, Puerto Rico

Landscape topography can influence biogeochemical processes across the wet tropical forest soils. This dataset contains soil chemistry data such as pH and dissolved organic carbon (DOC) from April 1, 2017 to May 1, 2018 across a catena in the Luquillo Experimental Forest, Puerto Rico. The attached CSV file contains information on sampling date, location (El Verde field station) in LEF, topographic positions (ridge, slope, and valley, respectively), field replicate number, laboratory replicate number, water extractable pH, CaCl2 extractable pH, and DOC (unit: mg/g). Overall, soil pH values were highest in the valley, followed by slope, and lowest in the ridge topographic positions. However, DOC values followed the opposite trend: ridge>slope>valley. This dataset can facilitate our understanding of the biogeochemical cycling of carbon and nutrient associated with complex topography in wet tropical forest soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Interrelationships among methods of estimating microbial biomass across multiple soil orders and biomes: Supporting data

This dataset contains environmental and soil measurements from 18 different locations across the globe including the SPRUCE experiment site and multiple sampling depths, with 17 of these locations having samples processed between 2012-2013 and one location (SPRUCE) collected in 2021 and processed in 2022. Environmental measurements include: mean annual temperature, mean annual precipitation, and 30-day presampling temperature. Soil physicochemical measurements include: particle size analysis (PSA), pH, gravimetric moisture content (GMC), bulk soil carbon (C) and nitrogen (N), total organic C and N, C:N ratio, and dissolved organic carbon (DOC). Soil biological measurements include: microbial biomass carbon (MBC) measured through chloroform fumigation extraction (CFE), gene copy numbers (GCN) of bacteria, fungi, and archaea measured through quantitative polymerase chain reaction (qPCR), DNA yield measured through Nanodrop spectrophotometry, and phospholipid fatty acids (PLFA) of bacteria and fungi measured through PLFA analysis. This data set contains one file in comma separate (*.csv) format.

archaea gene copy number↗
DOE OSTI2021

Manganese uptake by red maples in response to mineral dissolution rates in soil

Manganese (Mn) is an essential plant micronutrient that influences photosynthesis, ecosystem productivity, and soil carbon storage. Our objective was to quantify how Mn uptake by forest vegetation relates to Mn release into soil solution through mineral dissolution. In this work, a greenhouse pot experiment was conducted to quantify Mn uptake by red maple saplings as a function of mineral solubility to test whether Mn uptake was limited by the supply of Mn to soil solution. Differences in soil microbial community composition between treatments, particularly amongst Mn cycling bacteria and fungi, were also evaluated to assess potential microbial impacts on observed Mn fluxes. Plant Mn uptake was highest in systems supplied with dissolved Mn(II) because it was not kinetically limited by mineral weathering. Mn uptake was also higher in systems supplied with a fast-weathering substrate (shale containing Mn(II)-bearing pyrite) than a slow-weathering substrate (Mn(IV)-oxide). However, vegetation accumulated a decreasing proportion of available Mn with increasing solubility, indicating that uptake was tempered relative to solubility. The presence of bacterial phyla containing known Mn-oxidizing bacteria indicates the potential for Mn-oxidizing bacteria to influence Mn solubility within Mn-oxide and dissolved Mn treatments. A relatively low abundance of these bacteria points to their limited capacity to oxidize and retain Mn in the shale treatments, consistent with substantial Mn leaching. We conclude that Mn uptake was primarily controlled by dissolution rates of Mn-bearing minerals but modified by biological processes. The quantitative framework presented here can guide understanding of how biogeochemical processes control element cycling between plants and soils.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The role of Ca-bridged organic matter in an alkaline soil, as revealed by multimodal chemical imaging

Mineral–organic matter (OM) studies have predominantly focused on acidic soils that are abundant in iron (Fe) oxides and aluminum (Al) oxides. We have probed mineral–OM interactions in an alkaline or calcareous soil of the Aridisols class. Unlike the role of Fe and Al, the role of Ca-minerals (particularly calcite), which are ubiquitous in alkaline soils, in OM sequestration is not well understood. Multiple recent model studies with aqueous Ca2+ or synthetic calcite and a suite of OM compounds have shown Ca-OM assemblages to be spatially correlated with calcite at the microscale. To study the chemical state of both Ca and Fe and their competing role in soil organic matter (SOM) stabilization, we performed laboratory characterization using x-ray diffraction, Mössbauer spectroscopy, x-ray photoelectron spectroscopy, scanning electron microscopy, and scanning transmission electron microscopy, alongside synchrotron-based microscale chemical imaging using scanning transmission x-ray microscopy combined with near-edge x-ray absorption fine structure. Ca mineral–organic associations were found to be ubiquitous in this system and are likely critical for understanding SOM stabilization/degradation in alkaline soils. From our findings on mineralogy, speciation, and the nature of Ca-OM bridging, we identified differences in C and Ca chemistry based on the relative location of OM to Ca minerals. The OM near the calcite crystal was enriched in lipid and protein moieties, Ca-OM next to Fe minerals displayed a strong contribution from aromatic compounds, while on the surface of microbes, the carbonate was believed to be of microbial in origin, as also suggested by preliminary works reporting on the formation of amorphous calcite or nano-calcite. In Ca-OM admixed with carbonate, it was difficult to distinguish Ca-associated OM from amorphous calcite or nano-calcite.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Soil Carbon Stocks Not Linked to Aboveground Litter Input and Chemistry of Old-Growth Forest and Adjacent Prairie

The long-standing assumption that aboveground plant litter inputs have a substantial influence on soil organic carbon storage (SOC) and dynamics has been challenged by a new paradigm for SOC formation and persistence. We tested the importance of plant litter chemistry on SOC storage, distribution, composition, and age by comparing two highly contrasting ecosystems: an old-growth coast redwood (Sequoia sempervirens) forest, with highly aromatic litter, and an adjacent coastal prairie, with more easily decomposed litter. We hypothesized that if plant litter chemistry was the primary driver, redwood would store more and older SOC that was less microbially processed than prairie. Total soil carbon stocks to 110 cm depth were higher in prairie (35 kg C m —2 ) than redwood (28 kg C m —2 ). Radiocarbon values indicated shorter SOC residence times in redwood than prairie throughout the profile. Higher amounts of pyrogenic carbon and a higher degree of microbial processing of SOC appear to be instrumental for soil carbon storage and persistence in prairie, while differences in fine-root carbon inputs likely contribute to younger SOC in redwood. We conclude that at these sites fire residues, root inputs, and soil properties influence soil carbon dynamics to a greater degree than the properties of aboveground litter.

13C-NMR spectroscopy↗