Search NASA⌕ Search

SEARCH · Search NASA

Results for “Soil saturation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Laboratory Evaluation to Increase Effectiveness of Field-Scale Soil Flushing in the Hanford 100 Areas

This laboratory study was initiated to develop an improved technical understanding of the key controlling geochemical and physical processes of mobilizing residual Cr(VI) in Hanford sediments to provide increased effectiveness in soil flushing activities planned for Hanford 100 Areas. This was accomplished by (1) quantifying Cr mass, release rate, and surface phase changes as Cr is leached from different Hanford sediments; (2) maximizing Cr leaching during soil flushing by evaluating different leach solutions and leach strategies; and (3) minimizing leaching of remaining residual Cr after soil flushing ends. To quantify geochemical controls on Cr leaching from sediments, sequential liquid extractions were used to identify aqueous, adsorbed, and solid Cr surface phases (precipitates or Cr incorporated into other phases) before and after water-saturated and unsaturated leach experiments with pH 8 artificial groundwater and with other amendment solutions. Changes in the Cr release rate from sediments were also correlated to changes in Cr surface phases. During unsaturated infiltration, water advection occurs primarily through larger pores, with less movement through smaller grain layers leading to a slower release of Cr trapped in smaller pores next to smaller grains. To quantify these physical controls on Cr leaching from sediments, 10 ft high 1-D infiltration experiments were conducted at differing leach solution application rates, and Cr leaching during application and subsequent residual water flow was quantified. Qualitative 2-D experiments were also conducted to evaluate surfactant addition to increase flow in low-permeability zones. Cr-contaminated Hanford sediments from the 100-H, 100-K, 100-D, and 200 East Areas exhibited a wide variety of leach behavior from rapid release of a small amount of Cr to fast (i.e., minutes) and slow (i.e., hours or more) release of high Cr. Sediments with low labile Cr (< 0.2 µg/g) exhibited fast Cr release but less overall release, and most (>95%) leached from the sediments within a few pore volumes in water-saturated columns. With nearly no sorption of chromate, aqueous and sorbed Cr (as chromate) were released quickly from sediment within the first few pore volumes, resulting in high leach concentrations, fast initial Cr release rates, and changes in pre- and post-leach extractions. In contrast, sediments with high labile Cr (0.2 to 20 µg/g) exhibited a combination of fast and slow Cr release due to multiple Cr surface phases contributing to Cr release from the sediment at different rates with greater overall release. This resulted in elevated Cr effluent concentrations even after dozens of pore volumes were leached. In addition, the Cr release rate decreased with increasing time during leaching. A decrease in labile Cr (aqueous Cr, adsorbed Cr, pH 5 acetate dissolved precipitates) in post-leach extractions compared to pre-leach extractions showed initial aqueous and adsorbed Cr release, followed by dissolution of high-solubility precipitates such as CaCrO 4 (if present), then slow dissolution of calcite that may contain some chromate and possibly BaCrO 4 . The labile Cr in all sediments correlated well with the Cr release rate (r 2 = 0.81). Labile Cr also correlated well with the leached mass in water-saturated columns (r 2 = 0.98), and therefore can be used for prediction of potential removal with flushing applications.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Fe-Coated Optical Fiber for Distributed Corrosion Monitoring in Soil and Aqueous Environment

Natural gas pipeline corrosion represents a substantial cost and safety concern during normal operations. The effective and real-time monitoring of corrosion is important to detect and mitigate pipeline risks before corrosion-related catastrophic events happen. Here, we describe the use of iron (Fe) coated optical fiber sensors (OFS) for distributed corrosion monitoring where Fe acts as a corrosion proxy. By using an optical backscattering reflectometer (OBR), corrosion was monitored based on the increase in the backscattered intensity amplitude of the light being passed as Fe underwent corrosion. The Fe-coated OFSs were prepared with a film thickness between 25–225 nanometers (nm) by an electroless plating approach and corroded by a carbon dioxide (CO2)-saturated acidic electrolyte. The corrosion rate (CR) was approximately 2.5 millimeters (mm)/year for Fe with a film thickness of 30 nm and increased with film thickness. Backscattering-based CRs were supported by visible light transmission measurements, which have been previously demonstrated. Additionally, a correlation between the intermittent transmission and residual Fe film thickness during the corrosion of Fe was established. The corrosion sensor was also tested in soil at > 1 foot depth. Fe-coated OFS was loaded into a Draka cable, which provides mechanical support during fiber deployment and installation into either acidified soil, top-soil, or sandy soil (50/50).

corrosion↗
DOE OSTI2026

A ModEx Framework for Watershed Subsurface Investigation With Limited Geophysical Data Using Machine Learning and Hydrologic Modeling

Abstract Subsurface heterogeneity influences watershed hydrology strongly but remains difficult to characterize at catchment scales with sparse and costly field data. Geophysical surveys such as electromagnetic induction (EMI) provide local spatial subsurface images yet scaling them to watershed scales and converting EMI‐derived resistivity into hydraulic properties remains a challenge. We present a Model–Experiment (ModEx) framework that integrates limited EMI data with machine learning (ML) and hydrologic modeling to improve process representation and guide field investigations. Sparse EMI surveys were scaled to the catchment scale using a Random Forest model, and the resulting resistivity fields were combined with nearby borehole constraints to parameterize a hydrologic model. The EMI‐informed hydrological simulations improved predictions of streamflow sustained by subsurface flow and shallow saturation patterns. By combining EMI data and ML with hydrologic modeling, the ModEx framework guides future subsurface surveys, providing a transferable and efficient strategy for data–model integration across diverse watersheds. Plain Language Summary Mapping the underground network of soil and rock that controls water is essential for predicting floods and droughts, but seeing underground is difficult and expensive. We cannot drill everywhere, so scientists use geophysical tools to scan broad areas. There are two key challenges: these geophysical scans are often sparse across the whole watershed, and the geophysical data is hard to translate into water‐related properties. We used artificial intelligence to solve these problems. We taught a computer to find patterns linking the limited geophysical data to the land surface properties. This allowed it to fill in the gaps and create a complete, useful subsurface map for the entire watershed. This new map improves hydrologic simulations, leading to more accurate predictions of water movement in the watershed. It also helps scientists build better models with less data and generates a priority map showing where to measure next, making future investigations more efficient. Key Points Limited EMI scaled with ML improves catchment‐scale subsurface parameterization for hydrologic models The framework integrates hydrologic modeling with limited geophysical data to support subsurface investigation design ModEx framework offers a transferable data–model integration strategy that quantifies and reduces uncertainty guiding watershed studies

Chen, Hang↗
DOE OSTI2021

Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling

Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO 2 -saturated photosynthesis rate, V max,27 ), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, V max,27 , Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R 2 = 0.73) and LAI (R 2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R 2 = 0.79), nitrogen concentration (R 2 = 0.83), nitrogen content (R 2 = 0.77), and V max,27 (R 2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R 2 = 0.80), nitrogen (R 2 = 0.85) and V max,27 (R 2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy V max,27 , have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fe Coated Optical Fiber for Distributed Corrosion Monitoring in Soil and Aqueous Environment

Natural gas pipeline corrosion represents a substantial cost and safety concern during normal operations. Effective monitoring of corrosion is important to detect and mitigate pipeline risks before corrosion-related catastrophic events happen. Here, we describe the use of Fe-coated optical fiber sensors (OFS) for distributed corrosion monitoring where Fe acts as a corrosion proxy. By using an optical backscatterring reflectometer (OBR), corrosion was monitored based on the increase in backscattered intensity amplitude of the light being passed as Fe undergoes corrosion. The Fe-coated OFSs were prepared with a film thickness between 25-225 nm by an electroless plating approach and corroded by a CO2-saturated acidic electrolyte. The corrosion rate was approximately 2.5 mm/yr for Fe with a film thickness of 30 nm and increased with film thickness. Backscattering-based corrosion rates were supported by visible light transmission measurements, which have been previously demonstrated. Additionally, a correlation between the intermittent transmission and residual Fe film thickness during corrosion of Fe was established. Corrosion was measurable out to > 100 m which is a significant improvement over our previous work showing corrosion sensing at < 10 m. The corrosion sensor was also tested in soil at >1 ft depth. Fe-coated OFS was loaded into a Draka cable which provides mechanical support during fiber deployment and installation into either acidified soil, top-soil, or sandy soil.

aqueous environment↗
NASA NTRS1990

Solid-support substrates for plant growth at a lunar base

Zeoponics is only in its developmental stages at the Johnson Space Center and is defined as the cultivation of plants in zeolite substrates that contain several essential plant growth cations on their exchange sites, and have minor amounts of mineral phases and/or anion-exchange resins that supply essential plant growth anions. Zeolites are hydrated aluminosilicates of alkali and alkaline earth cations with the ability to exchange most of their constituent exchange cations as well as hydrate/dehydrate without change to their structural framework. Because zeolites have extremely high cation exchange capabilities, they are very attractive media for plant growth. It is possible to partially or fully saturate plant-essential cations on zeolites. Zeoponic systems will probably have their greatest applications at planetary bases (e.g., lunar bases). Lunar raw materials will have to be located that are suited for the synthesis of zeolites and other exchange resings. Lunar 'soil' simulants have been or are being prepared for zeolite/smectite synthesis and 'soil' dissolution studies.

Ming, D. W.↗
NASA NTRS1999

Population of Nitrifying Bacteria and Nitrification in Ammonium Saturated Clinoptilolite

As humans begin to spend longer periods of time in space, plants will be incorporated into life support systems. Ammonium saturated clinoptilolite is one plant growth substrate but a balance between ammonium and nitrate is needed. A laboratory study was conducted to determine effects of nitrifying bacteria on ammonium concentrations and kinetics of nitrification. Columns containing clinoptilolite substrate amended with nitrifying bacteria obtained from soil enrichment were analyzed weekly for a 90 day period. The enrichment culture initially contained 1 x 10(exp 5) ammonium oxidizing bacteria and 1 x 10(exp 2) nitrite oxidizing bacteria per gram of substrate. Populations of ammonium oxidizing bacteria increased to 1 x 10(exp 6) and nitrite oxidizing bacteria increased to 1 x 10(exp 3) per gram of substrate. The nitrification rate was approximately 0.25mg NO3(-)-N/kg.hr. Experiments were also conducted to enumerate nitrifying bacteria in a clinoptilolite substrate used to grow wheat (Triticum aestivum L.). Seventy days following the initial inoculation with an unknown number of commercial nitrifying bacteria, 1 x 10(exp 5) ammonium oxidizing bacteria per gram of substrate were present. The number of nitrite oxidizing bacteria was between 1 x 10(exp 3) to 10(exp 4) per gram of substrate as measured by the most probable number method. Nitrification rates were approximately 0.20mg NO3(-)-N/kg.hr. Clinoptilolite readily exchanged sufficient concentrations of ammonium to support nitrifying bacteria and they survived well in this medium.

McGilloway, R. L.↗
DOE OSTI2025

Fe-Coated Optical Fiber for Distributed Corrosion Monitoring in Soil and Aqueous Environments

Natural gas pipeline corrosion represents a substantial cost and safety concern during normal operations. The effective and real-time monitoring of corrosion is important to detect and mitigate pipeline risks before corrosion-related catastrophic events happen. Here, we describe the use of iron (Fe) coated optical fiber sensors (OFS) for distributed corrosion monitoring where Fe acts as a corrosion proxy. By using an optical backscattering reflectometer (OBR), corrosion was monitored based on the increase in the backscattered intensity amplitude of the light being passed as Fe underwent corrosion. The Fe-coated OFSs were prepared with a film thickness between 25–225 nanometers (nm) by an electroless plating approach and corroded by a carbon dioxide (CO2)-saturated acidic electrolyte. The corrosion rate (CR) was approximately 2.5 millimeters (mm)/year for Fe with a film thickness of 30 nm and increased with film thickness. Backscattering-based CRs were supported by visible light transmission measurements, which have been previously demonstrated. Additionally, a correlation between the intermittent transmission and residual Fe film thickness during the corrosion of Fe was established. The corrosion was measurable out to > 100 meters (m), which is a significant improvement over our previous work, which showed corrosion sensing at < 10 m. The corrosion sensor was also tested in soil at > 1 foot depth.

corrosion↗
DOE OSTI2025

Fe-Coated Optical Fiber for Distributed Corrosion Monitoring in Soil and Aqueous Environments

Natural gas pipeline corrosion represents a substantial cost and safety concern during normal operations. The effective and real-time monitoring of corrosion is important to detect and mitigate pipeline risks before corrosion-related catastrophic events happen. Here, we describe the use of iron (Fe) coated optical fiber sensors (OFS) for distributed corrosion monitoring where Fe acts as a corrosion proxy. By using an optical backscattering reflectometer (OBR), corrosion was monitored based on the increase in the backscattered intensity amplitude of the light being passed as Fe underwent corrosion. The Fe-coated OFSs were prepared with a film thickness between 25–225 nanometers (nm) by an electroless plating approach and corroded by a carbon dioxide (CO2)-saturated acidic electrolyte. The corrosion rate (CR) was approximately 2.5 millimeters (mm)/year for Fe with a film thickness of 30 nm and increased with film thickness. Backscattering-based CRs were supported by visible light transmission measurements, which have been previously demonstrated. Additionally, a correlation between the intermittent transmission and residual Fe film thickness during the corrosion of Fe was established. The corrosion was measurable out to > 100 meters (m), which is a significant improvement over our previous work, which showed corrosion sensing at < 10 m. The corrosion sensor was also tested in soil at > 1 foot depth.

aqueous environment↗
DOE OSTI2020

Distribution of Infiltration in the 216-U-10 and 216-B-3 Pond Systems 1944-1997

A modeling system to simulate fate and transport of contaminant plumes in groundwater beneath the Central Plateau to support the Cumulative Impact Evaluation (CIE) and Composite Analysis (CA) is being developed. The CIE will evaluate the cumulative effect of multiple Central Plateau radiological and chemical sources on groundwater quality (DOE/RL-2018-69, Cumulative Impact Evaluation Technical Approach Document ). This will enable cleanup decisions to be evaluated in the context of all Central Plateau waste sites and existing groundwater conditions. The CA evaluates the cumulative effect of all sources of radioactive materials on radiological dose to future members of the public (DOE/RL-2018-60, Annual Status Report (FY 2018): Composite Analysis of Low Level Waste Disposal in the Central Plateau of the Hanford Site ). Fate and transport modeling will be used to forecast future groundwater concentrations for both the CIE and CA. The CIE/CA subsurface modeling system is divided into source (inventory), vadose zone, and saturated zone facets. Source inventory is a necessary input to the vadose zone facet, and releases from the vadose zone to groundwater are inputs to the saturated zone facet. The vadose zone facet simulates historical and future releases from the vadose zone to groundwater for both liquid waste and solid waste disposal sites. Effluent release volumes and radionuclide inventory estimates for liquid waste sites are contained in ECFHANFORD- 17-0079, Hanford Soil Inventory Model (SIM-v2) Calculated Radionuclide Inventory of Direct Liquid Discharges to Soil in the Hanford Site’s 200 Areas , herein referred to as SIM-v2. The liquid waste sites in SIM-v2 include the 216-B-3 Pond near 200 East Area and the 216-U-10 Pond in 200 West Area. The 216-B-3 Pond system consisted of a main pond and 3 expansion lobes that were operational during overlapping time periods. It also included influent ditches, but these are not listed as liquid waste sources in SIM-v2 and are not included in the CIE/CA modeling. The total effluent volume released to the pond system was measured, but the partitioning of this volume to the various expansion lobes was not determined and is not accounted for in SIM-v2. Regarding the 216-U-10 Pond, it received water from several influent ditches. The pond and the influent ditches are listed as liquid waste sites in SIM-v2 and are included as sources in the CIE/CA modeling. Because water in the ditches flowed into the pond, only a portion of the effluent volumes and contaminant inventories assigned to the ditches in SIM-v2 infiltrated from the ditches. Thus, partitioning of infiltration and contaminant inventories between the ditches and the pond is not fully accounted for in SIM-v2. Further, some of the water in the 216-U-10 Pond entered overflow ditches and the amount of this overflow was not recorded and is not accounted for in SIM-v2. Thus, the purpose of this Environmental Calculation File (ECF) is to partition the infiltration of wastewater and contaminant inventory between the main pond and each expansion lobe of the 216-B-3 Pond system, and to partition the infiltration of wastewater and contaminant inventory between the ditches and pond of the 216-U-10 Pond system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2007

A Comparison of Land Surface Model Soil Hydraulic Properties Estimated by Inverse Modeling and Pedotransfer Functions

Soil hydraulic properties (SHPs) regulate the movement of water in the soil. This in turn plays an important role in the water and energy cycles at the land surface. At present, SHPS are commonly defined by a simple pedotransfer function from soil texture class, but SHPs vary more within a texture class than between classes. To examine the impact of using soil texture class to predict SHPS, we run the Noah land surface model for a wide variety of measured SHPs. We find that across a range of vegetation cover (5 - 80% cover) and climates (250 - 900 mm mean annual precipitation), soil texture class only explains 5% of the variance expected from the real distribution of SHPs. We then show that modifying SHPs can drastically improve model performance. We compare two methods of estimating SHPs: (1) inverse method, and (2) soil texture class. Compared to texture class, inverse modeling reduces errors between measured and modeled latent heat flux from 88 to 28 w/m(exp 2). Additionally we find that with increasing vegetation cover the importance of SHPs decreases and that the van Genuchten m parameter becomes less important, while the saturated conductivity becomes more important.

Gutmann, Ethan D.↗
NASA NTRSDate not supplied

Plant Water Management in Microgravity

The NASA Plant Water Management (PWM) technology demonstrations aboard ISS apply recent advances in microgravity capillary fluidics research towards the mundane yet problematic challenges of simply watering plants in space. Plant growth in a low-g environment is often hampered by inadequate aeration and over-saturation of the root zone. The present effort aims to exploit the passive capillary forces of poorly wetting liquids (i.e., contaminated water) within unique system geometries that effectively replace the role of gravity in providing sufficient aeration and hydration for simulated plants. Several flight demonstrations have been completed on ISS, including soil and hydroponic models in single and parallel channel networks. Two future demonstrations are still in work and plan to further develop the system to handle sustained plant growth with additional sensors to monitor the growth environment. The results to date demonstrate proof-of-concept, system stability, limits of operation, and more, for simulated plant models. Eventually, real plants will be incorporated into these systems and tested on orbit. The implications are discussed in relation to plant growth facilities for further near-term microgravity plant science research as well as for automated food production for long duration human exploration missions.

microgravity↗
NASA NTRS1983

Geologic exploration: The contribution of LANDSAT-4 thematic mapper data

The major advantages of the TM data over that of MSS systems are increased spatial resolution and a greater number of narrow, strategically placed spectral bands. The 30 meter pixel size permits finer definition of ground features and improves reliability of the photointerpretation of geologic structure. The value of the spatial data increases relative to the value of the spectral data as soil and vegetation cover increase. In arid areas with good exposure, it is possible with careful digital processing and some inventive color compositing to produce enough spectral differentiation of rock types and thereby produce facsimiles of standard geologic maps with a minimum of field work or reference to existing maps. Hue-saturation value images are compared with geological maps of Death Valley, California, the Big Horn/Wind River Basin of Wyoming, the area around Cement, Oklahoma, and Detroit. False color composites of the Ontario region are also examined.

Everett, J. R.↗
NASA NTRS2019

Plant Water Management (PWM)

The NASA Plant Water Management technology demonstration exploits recent advances in microgravity capillary fluidics to develop techniques for plant growth in a space environment. Plant growth in the low-g environment is often hampered by inadequate aeration and over-saturation in the root zone. The present effort aims to make passive use of poorly wetting liquids (i.e., contaminated water) within unique system geometries that effectively replace the role of gravity in providing sufficient aeration and hydration for simulated plants. Several ISS demonstrations are in preparation for launch in 2019, including hydroponic and soil approaches. Supportive terrestrial and low-g drop tower tests are conducted to aid in experiment design via proof-of-concepts, limits of operation, system stability, and others. The implications to short and long term applications are discussed in relation to plant growth facilities for both near-term microgravity plant science research and long duration human exploration missions.

Hatch, Tyler↗
NASA NTRS1983

Water and acid soluble trace metals in atmospheric particles

Continental aerosols are collected above a deciduous forest in eastern Tennessee and subjected to selective extractions to determine the water-soluble and acid-leachable concentrations of Cd, Mn, Pb, and Zn. The combined contributions of these metals to the total aerosol mass is 0.5 percent, with approximately 70 percent of this attributable to Pb alone. A substantial fraction (approximately 50 percent or more) of the acid-leachable metals is soluble in distilled water. In general, this water-soluble fraction increases with decreasing particle size and with increasing frequency of atmospheric water vapor saturation during the sampling period. The pattern of relative solubilities (Zn being greater than Mn, which is approximately equal to Cd, which is greater than Pb) is found to be similar to the general order of the thermodynamic solubilities of the most probable salts of these elements in continental aerosols with mixed fossil fuel and soil sources.

Lindberg, S. E.↗
NASA NTRS1992

Emissions of biogenic sulfur gases from Alaskan tundra

Results of sulfur emission measurements made in freshwater and marine wetlands in Alaskan tundra during the Arctic Boundary Layer Expedition 2A (ABLE 3A) in July 1988 are presented. The data indicate that this type of tundra emits very small amounts of gaseous sulfur and, when extrapolated globally, accounts for a very small percentage of the global flux of biogenic sulfur to the atmosphere. Sulfur emissions from marine sites are up to 20-fold greater than fluxes from freshwater habitats and are dominated by dimethyl sulfide (DMS). Highest emissions, with a mean of 6.0 nmol/sq m/h, occurred in water-saturated wet meadow areas. In drier upland tundra sites, highest fluxes occurred in areas inhabited by mixed vegetation and labrador tea at 3.0 nmol/sq m/h and lowest fluxes were from lichen-dominated areas at 0.9 nmol/sq m/h. DMS was the dominant gas emitted from all these sites. Emissions of DMS were highest from intertidal soils inhabited by Carex subspathacea.

Hines, Mark E.↗
DOE OSTI2020

Low–severity fire as a mechanism of organic matter protection in global peatlands: Thermal alteration slows decomposition

Worldwide, regularly-recurring wildfires shape many peatland ecosystems to the extent that fire-adapted species often dominate plant communities, suggesting that wildfire is an integral part of peatland ecology rather than an anomaly. The most destructive blazes are smoldering fires that are usually initiated in periods of drought and can combust entire peatland carbon stores. However, peatland wildfires more typically occur as low-severity surface-burns that arise in the dormant season when vegetation is desiccated, and soil moisture is high. In such low-severity fires, surface layers experience flash heating, but there is little loss of underlying peat to combustion. This study examines the potential importance of such processes in several peatlands that span a gradient from hemiboreal to tropical ecozones and experience a wide range of fire return intervals. We show that low-severity fires can increase the pool of stable soil carbon by thermally altering the chemistry of soil organic matter (SOM), thereby reducing rates of microbial respiration. Using XPS and FTIR, we demonstrate that low-severity fires significantly increase the degree of carbon-condensation and aromatization of SOM functional groups, particularly on the surface of peat aggregates. Laboratory incubations show lower CO 2 emissions from peat subjected to low-severity fire, and predict lower cumulative CO 2 emissions from burned peat after one to three years. Also, low-severity fires reduce the temperature sensitivity (Q 10 ) of peat, indicating that these fires can inhibit microbial access to SOM. The increased stability of thermally-altered SOM may allow a greater proportion of organic matter to survive vertical migration into saturated and anaerobic zones of peatlands where environmental conditions physiochemically protect carbon stores from decomposition for thousands of years. Furthermore, across latitudes, low-severity fire is an overlooked factor influencing carbon cycling in peatlands, which is relevant to global carbon budgets as climate-change alters fire regimes worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Forest aboveground biomass estimation through integration of sentinel-2 and PALSAR-2 time series: assessing models trained on GEDI and field inventory benchmarks

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico’s National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI’s reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (≥200 Mg/ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESA-CCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCI-BIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

54 ENVIRONMENTAL SCIENCES↗