A hydrogen flame ionization detector for Martian/lunar life detection experiments
Hydrogen flame ionization detector /HYFID/ for life-derived organic matter in Martian/Lunar soil
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Hydrogen flame ionization detector /HYFID/ for life-derived organic matter in Martian/Lunar soil
Over 20 percent of the land surface of Canada and Alaska is covered by peatlands, which may be defined as any waterlogged ecosystem with a minimum thickness of 20 cm of organic matter in the soil. Past investigations have demonstrated the value of aerial photographs in identifying the major vegetation types and analyzing the biotic and hydrogeologic processes that control the development of these peatlands. In the present study, LANDSAT TM imagery was used in conjunction with field studies to determine the utility of this satellite sensor for detecting these important processes. Although the vegetation landforms within these major peat basins are visible on aerial photographs, LANDSAT TM imagery provides essential new evidence for their analysis. Spectral data from the LANDSAT TM system provides: (1) synoptic views of the patterns across large portions of these peat basins, indicating important physiographic controls on peatland development, (2) more sensitive detection of the major vegetation types, allowing rapid quantitative estimates to be made of their distribution and aerial extent, (3) discrimination of bog areas with potentially rapid or slow rates of peat accumulation, (4) identification of discharge zones for groundwater, which apparently represents the most important source of alkalinity in these peat basins, and (5) detection of flow patterns in water tracks that appear nearly uniform on standard aerial photographs.
Description of an extraction from a Dublin clay loam soil of a colloidal organic matter complex that is urease active and, by X-ray analysis, free of clays. Urease activity in the clay-free precipitates, as in the soil, was not destroyed by the activity of an added proteolytic enzyme, pronase. This is attributed to the circumstance that native soil urease resides in organic colloidal particles with pores large enough for water, urea, ammonia, and carbon dioxide to pass freely, but nevertheless small enough to exclude pronase.
The spectral characterization of soils is discussed with particular reference to the bidirectional reflectance factor as a quantitative measure of soil spectral properties, the role of soil color, soil parameters affecting soil reflectance, and field characteristics of soil reflectance. Comparisons between laboratory-measured soil spectra and Landsat MSS data have shown good agreement, especially in discriminating relative drainage conditions and organic matter levels in unvegetated soils. The capacity to measure both visible and infrared soil reflectance provides information on other soil characteristics and makes it possible to predict soil response to different management conditions. Field and laboratory soil spectral characterization helps define the extent to which intrinsic spectral information is available from soils as a consequence of their composition and field characteristics.
Poultry litter and compost can alter the moisture holding capacity of a soil. These organic materials can also increase the nutrient status of a soil during the decomposition process by microbial actions. The objective of this study was to evaluate the effect of poultry litter and compost on the dielectric constant and moisture holding capacity of soil. The Delta-T theta-probe was used to measure volumetric soil water content and the apparent dielectric constant of the upper 6-cm of the soil profile. Soil texture, pH, and organic matter were also determined for each plot. Results of these analyses indicated that the pH of the soil ranged from 6.4 to 7.7 and the volumetric soil moisture content ranged from 0.06 to 0.18 cu m/cu m for the upper 6-cm of the soil profile. The effect of poultry litter and compost on soil properties resulted in an increase in the volumetric moisture content and dielectric constant of the soil due to the improvement of the soil structure.
The carbon-rich soils and peatlands of high-latitude ecosystems could substantially influence atmospheric concentrations of CO2 and CH4 in a changing climate. Currently, cold, often waterlogged conditions retard decomposition, and release of carbon back to the atmosphere may be further slowed by physical protection of organic matter in permafrost. As a result, many northern ecosystems accumulate carbon over time (Billings et al., 1982; Poole and Miller, 1982), and although such rates of accumulation are low, thousands of years of development have left Arctic ecosystems with an extremely high soil carbon content; Schlesinger's (1984) average value of 20.4 kg C/m(sup 2) leads to a global estimate of 163 x 10(exp 15) g C. All GCM simulations of a doubled CO2 climate predict the greatest warming to occur in the polar regions (Dickinson, 1986; Mitchell, 1989). Given the extensive northern carbon pools and the strong sensitivity of decomposition processes to temperature, even a slight warming of the soil could dramatically alter the carbon balance of Arctic ecosystems. If warming accelerates rates of decomposition more than rates of primary production, a sizeable additional accumulation of CO2 in the atmosphere could occur. Furthermore, CH4 produced in anaerobic soils and peatlands of the Arctic already composes a good percentage of the global efflux (Cicerone and Oremlund, 1988); if northern soils become warmer and wetter as a whole, CH4 emissions could dramatically rise. A robust understanding of the primary controls of carbon fluxes in Arctic ecosystems is critical. As a framework for a systematic examination of these controls, we discussed a conceptual model of regional-scale Arctic carbon turnover, including CH4 production, and based upon the Century soil organic matter model.
Indexing techniques that can be used to detect soil erosion utilizing the known band widths of the Landsat MSS and TM sensors are identified. The indexing techniques focus on iron oxides, clays, and organic matter as properties revealing soil erosional status. For data aquisition, a Collins visible and infrared intelligent spectrometer was used to collect data from 0.4-24 microns. Pressed polytetrafluorethylene was used as the reflectance standard and was aquired at the same time that the sample data were aquired.
Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.
The search for organic compounds on Mars, including molecules of either abiotic or biological origin is one of the key goals of the Mars Science Laboratory (MSL) mission. Previously the Viking and Phoenix Lander missions searched for organic compounds, but did not find any definitive evidence of martian organic material in the soils. The Viking pyrolysis gas chromatography mass spectrometry (GCMS) instruments did not detect any organic compounds of martian or exogenous origin above a level of a few parts-per-billion (ppb) in the near surface regolith at either landing site [1]. Viking did detect chloromethane and dichloromethane at pmol levels (up to 40 ppb) after heating the soil samples up to 500 C (Table 1), although it was originally argued that the chlorohydrocarbons were derived from cleaning solvents used on the instrument hardware, and not from the soil samples themselves [1]. More recently, it was suggested that the chlorohydrocarbons detected by Viking may have been formed by oxidation of indigenous organic matter during pyrolysis of the soil in the presence of perchlorates [2]. Although it is unknown if the Viking soils contained perchlorates, Phoenix did reveal relatively high concentrations (~0.6 wt%) of perchlorate salt in the icy regolith [3], therefore, it is possible that the chlorohydrocarbons detected by Viking were produced, at least partially, during the experiments [2,4]. The Sample Analysis at Mars (SAM) instrument suite on MSL analyzed the organic composition of the soil at Rocknest in Gale Crater using a combination of pyrolysis evolved gas analysis (EGA) and GCMS. One empty cup procedural blank followed by multiple EGA-GCMS analyses of the Rocknest soil were carried out. Here we will discuss the results from these SAM measurements at Rocknest and the steps taken to determine the source of the chlorohydrocarbons.
During 1971 and 1972 research was conducted on two fallow fields in the proposed Oahe Irrigation Project to investigate the relationship between the tonal variations observed on aerial photographs and the principal soil limitations of the area. A grid sampling procedure was used to collected detailed field data during the 1972 growing season. The field data was compared to imagery collected on May 14, 1971 at 3050 meters altitude. The imagery and field data were initially evaluated by a visual analysis. Correlation and regression analysis revealed a highly significant correlation and regression analysis revealed a highly significant correlation between the digitized color infrared film data and soil properties such as organic matter content, color, depth to carbonates, bulk density and reflectivity. Computer classification of the multiemulsion film data resulted in maps delineating the areas containing claypan and erosion limitations. Reflectance data from the red spectral band provided the best results.
In being able to identify quickly gross variations in soil features, the computer-aided classification of multispectral scanner data can be an effective aid to soil surveying. Variations in soil tone are easily seen as well as variations in features related to soil tone, e.g., drainage patterns and organic matter content. Changes in surface texture also affect the reflectance properties of soils. Inasmuch as conventional soil classes are based on both surface and subsurface soil characteristics, the technique described here can be expected only to augment and not replace traditional soil mapping.
Carbon is sequestered in soils by accumulation of recalcitrant organic matter and by bicarbontate weathering of silicate minerals.
Soils of the Palouse region of eastern Washington State were investigated using Landsat Thematic Mapper (TM) band ratios to discriminate areas where erosion has caused paleosols to be exposed. Ratioed data were clustered and plotted to show soil lines which could be subdivided into various levels of organic matter and iron oxides. Successfully classified scenes of a summer fallow (bare soil) field were obtained with band ratios 1/4, 3/4, and 5/4 to map organic carbon and 3/4, 5/4, and 5/3 for the iron/carbon ratio indicator of erosion. Regression models were made with 5/4 data and organic carbon and 5/3 data and the iron/carbon ratio. Based on this analysis, 21 percent of the test field soils are exposed or nearly exposed paleosols.
Eighty-one benchmark soils from Alabama, Georgia, Florida, Tennessee, and Mississippi were evaluated to determine the feasibility of spectrally differentiating among soil categories. Relationships among spectral properties that occur between soils and within soils were examined, using discriminant analysis. Soil spectral data were obtained from air-dried samples using an Exotech Model 20C field spectroradiometer (0.37 to 2.36 microns). Differentiating among the orders, suborders, great groups, and subgroups using reflectance spectra achieved varying percentages of accuracy. Six distinct reflectance curve forms were developed from the air-dried samples based on the shape and presence or absence of adsorption bands. Iron oxide and organic matter content were the dominant soil parameters affecting the spectral characteristics for differentiating among and between these soils.
We examined soil changes in the O-5 and 5-15 cm layers for one year after a fire in burned Juncus roemerianus and Spartina bakeri marshes and an unburned Juncus marsh. Each marsh was sampled (N = 25) preburn, immediately postburn, and 1, 3, 6, 9, and 12 months postburn. All marshes were flooded at the time of the fire; water levels declined below the surface by 6 months but reflooded at 12 months after the fire. Soil samples were analyzed for pH, conductivity, organic matter, exchangeable Ca, Mg, and K, available PO4-P, total Kjeldahl nitrogen (TKN), exchangeable NO3-N, NO2-N, and NH4-N. Changes due to burning were most pronounced in the surface (0-5 cm) layer. Soil pH increased 0.16-0.28 units immediately postburn but returned to preburn levels in 1 month. Organic matter increased by 1 month and remained elevated through 9 months after the fire. Calcium, Mg, K, and PO4-P all increased by 1 month after burning, and the increases persisted for 6 to 12 months. Conductivity increased in association with these cations. Burning released ions from organic matter as indicated by the increase in pH, conductivity, Ca, Mg, K, and PO4-P. NH4-N in burned marshes was elevated 6 months and NO3-N 12 months after burning. TKN showed seasonal variations but no clear fire-related changes. Nitrogen species were affected by the seasonally varying water levels as well as fire; these changes differed from those observed in many upland systems.
Annual gross productivity of the lichen-dominated cryptoendolithic community was calculated from a computer analysis of photosynthetic response based on laboratory measurements of CO2 exchange and three years (1985-1988) of field nanoclimate data. Photosynthetic optimum increased from -3 to 2 degrees C between irradiance levels of 100 and 1500 micromoles photons m-2 s-1, while the upper compensation point rose from 1 to 17 degrees C. The mean yearly total time available for metabolic activity (temperature above -10 degrees C and moisture present) was 771.3 h for horizontal rock, 421.5 h for northeast-oriented sloped rock, and 1042.2 h for a small depression in horizontal rock (the characteristic site of occasional lichen apothecia). The calculated mean gross productivity value for a horizontal rock was 1215 mg C m-2 y-1, and net photosynthetic gain was 606 mg C m-2 y-1. Net ecosystem productivity (annual accretion of cellular biomass) estimated from long-term events amounted to only about 3 mg C m-2 y-1. The difference between these two values may represent the long-term metabolic costs of the frequent dehydration-rehydration and freezing-thawing cycles or of overwintering, and may account for the leaching of organic substances to the rock. The yearly gross productivity of the cryptoendolithic microbial community of the entire Ross Desert area was estimated at approximately 120,000-180,000 kg C. Of this, 600-900 kg C is in microbial biomass, and much of the rest is soluble compounds that leach into the rocks and possibly percolate to the valleys, providing a source of organic matter for lakes, rivers, and soils.
Global and regional dust storms on Mars have been observed from Earth-based telescopes, Mars orbiters, and surface rovers and landers. Dust storms can be global and regional. Dust is material that is suspended into the atmosphere by winds and has a particle size of 1-3 micrometer. Planetary scientist refer to loose unconsolidated materials at the surface as "soil." The term ''soil'' is used here to denote any loose, unconsolidated material that can be distinguished from rocks, bedrock, or strongly cohesive sediments. No implication for the presence or absence of organic materials or living matter is intended. Soil contains local and regional materials mixed with the globally distributed dust by aeolian processes. Loose, unconsolidated surface materials (dust and soil) may pose challenges for human exploration on Mars. Dust will no doubt adhere to spacesuits, vehicles, habitats, and other surface systems. What will be the impacts on human activity? The objective of this paper is to review the chemical, mineralogical, and physical properties of the martian dust and soil.
The relationship of the physical and chemical properties of soils to their spectral reflectance as measured at six wavebands of Thematic Mapper (TM) aboard NASA's Landsat-4 satellite was examined. The results of performing regressions of over 20 soil properties on the six TM bands indicated that organic matter, water, clay, cation exchange capacity, and calcium were the properties most readily predicted from TM data. The middle infrared bands, bands 5 and 7, were the best bands for predicting soil properties, and the near infrared band, band 4, was nearly as good. Clustering 234 soil samples on the TM bands and characterizing the clusters on the basis of soil properties revealed several clear relationships between properties and reflectance. Discriminant analysis found organic matter, fine sand, base saturation, sand, extractable acidity, and water to be significant in discriminating among clusters.