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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model: Overview - Part 1

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Crosson, William L.↗
NASA NTRSDate not supplied

Olivine Dissolution and Formation of Secondary phases in Ultramafic Soils

Introduction: Olivine has been proposed as an indicator for the duration of water-rock interaction within Martian rocks and sediments [1-3]. The use of olivine as a mineralogical indicator for past aqueous alteration on Mars requires interpretation of a complex combination of factors including pH, temperature, and composition [5,6]. Here, we examine the persistence of natural olivine within terrestrial ultramafic soils (Fe/Mg-rich, Al-poor) developing under different climatic conditions and the incipient dissolution of emplaced forsterite (Fo) and fayalite (Fa) surfaces to investigate environmental effects on incipient olivine dissolution, olivine persistence in soils, and formation of secondary phases. Methods: Field Sites. We examined olivine weathering and secondary material formation in ultramafic soils at 6 sites in the Klamath Mountains (KM) of northern California with a mean annual temperature of ~12.8℃ and precipitation of ~55.7-95.4 cm/year [7], and soil pH of ~6.5-7.3; 4 sites in the Tablelands (TB) of Newfoundland, Canada with a mean annual temperature of <3.9℃ and precipitation of ~120.0 cm/year [8], and soil pH of ~7.7; and at 3 sites at Pickhandle Gulch (PG), Nevada with a mean annual temperature of ~14.4℃ and precipitation of ~14.1 cm/year [7], and soil pH of ~8.5. Sampling sites span an age range of ~12.1-50+ kya in the Klamath Mountains [9,10] and ~13-30 kya in the Tablelands [11]. Pickhandle Gulch sites are undated. Parent Material and Soil Analyses. Polished thin sections of bulk soil prepared by Wagner Petrographic, Inc were carbon-coated and analyzed on a JEOL 2100 SEM in back-scattered electron mode in the EMIL lab at UNLV and at the 13-ID-E synchrotron beamline at Argonne National Laboratory using µXRF, µXRD, and XAS. Soil and parent material samples were powdered in a Fritsch pulverisette and analyzed by XRD and soil by VNIR. Soil preparation is further described in [12]. Disk Preparation, Burial, and Collection. Fo disks were cut from a column prepared via hot-pressing and Fa disks by sintering synthetic fayalite powder, see [13] for detail. Disks were polished to a 0.25-micron level with diamond grit. Disks were buried in 3 KM soils, 4 TB soils, and 3 PG soils, collected after exactly 365 days, and washed gently with 100% reagent grade ethanol to remove potential adhered soil material. Weathered disks and soil samples were stored in a -20℃ freezer until analysis. Unaltered control disks prepared identically to the buried disks were stored at -20℃ for the duration of the experiment. Disk Analyses. One Fo and Fa disk from each climate zone was analyzed on a variable pressure Zeiss Supra 40VP SEM at Northern Arizona University. A separate Fo and Fa disk from each climate zone was analyzed by XPS using a Physical Electronics VersaProbe II at the Penn State Univ. Materials Characterization Lab after a Na-dodecyl sulfate wash and ozonation to remove carbon contamination as in [14]. VNIR measurements were conducted at Johnson Space Center using an ASD FieldSpec3 under ambient lab conditions on a separate Fo and Fa disk from each climate zone. One separate Fo and Fa control sample was analyzed for each technique for comparison with weathered samples. XPS uncertainty was determined from 5 repeat measurements on controls. Results: Bedrock and Soil Results Olivine is present in the parent material in the KM and TB. Olivine is found in ~12.1 ka KM soils but is absent from all older soils, while persisting into the oldest (>20 ka) TB soil (Figure 1). In both locations, olivine is found as cores surrounded by a serpentine rind (Figure 2). VNIR spectra from the analyzed soils possess strong OH-associated spectral features at ~2.33 µm indicating the presence of Mg-rich phyllosilicates as well as ferric-oxide features at ~0.92 µm in the KM (Figure 3). Primary crystalline silicate grains mostly incorporate Fe2+, while poorly crystalline weathering rinds are best fit by ferric oxide XAS standards (Figure 4). µXRF also shows that Fe and Ni concentrate in weathering rinds and Cr remains within interior silicate grains (Figure 4). Buried Sample Results All Fo surfaces exhibited formation of dissolution features including shallow pitting not observed on controls. Dissolution features were most visually widespread on the KM disk (Figure 5). Leaching of Mg from KM and TB Fo disks was evident from <1.6 Mg/Si ratios measured by XPS (Figure 6). Fe-rich precipitates in SEM (Figure 5) and Fe presence in XPS scans (Figure 6) indicate Fe deposition onto KM and TB Fo disk surfaces. The appearance of a spectral feature at 0.55 µm in the VNIR spectra from the TB Fo suggests this Fe is ferric (Figure 7). The PG Fo appears least altered, with minimal formation of dissolution features in SEM (Figure 5), a Mg/Si ratio inconsistent with leaching (~2) (Figure 6), and VNIR spectra almost identical to the control sample. Analysis of Fa surfaces is ongoing. The higher temperatures and more acidic pH in the KM soils likely drive the faster dissolution of the Fo disks described above. While the TB soils experience greater precipitation than in the KM, the cooler temperatures and more basic soil pH facilitate observable but more limited alteration. The dry climate and basic soil pH at PG lead to minimal dissolution of the PG disk surfaces.

A D Feldman↗
NASA NTRS2001

Ectomycorrhizal fungal associates of Pinus contorta in soils associated with a hot spring in Norris Geyser Basin, Yellowstone National Park, Wyoming

Molecular methods and comparisons of fruiting patterns (i.e., presence or absence of fungal fruiting bodies in different soil types) were used to determine ectomycorrhizal (EM) associates of Pinus contorta in soils associated with a thermal soil classified as ultra-acidic to extremely acidic (pH 2 to 4). EM were sampled by obtaining 36 soil cores from six paired plots (three cores each) of both thermal soils and forest soils directly adjacent to the thermal area. Fruiting bodies (mushrooms) were collected for molecular identification and to compare fruiting body (above-ground) diversity to below-ground diversity. Our results indicate (i) that there were significant decreases in both the level of EM infection (130 +/- 22 EM root tips/core in forest soil; 68 +/- 22 EM root tips/core in thermal soil) and EM fungal species richness (4.0 +/- 0.5 species/core in forest soil; 1.2 +/- 0.2 species/core in thermal soil) in soils associated with the thermal feature; (ii) that the EM mycota of thermal soils was comprised of a small set of dominant species and included very few rare species, while the EM mycota of forest soils contained a few dominant species and several rare EM fungal species; (iii) that Dermocybe phoenecius and a species of Inocybe, which was rare in forest soils, were the dominant EM fungal species in thermal soils; (iv) that other than the single Inocybe species, there was no overlap in the EM fungal communities of the forest and thermal soils; and (v) that the fungal species forming the majority of the above-ground fruiting structures in thermal soils (Pisolithus tinctorius, which is commonly used in remediation of acid soils) was not detected on a single EM root tip in either type of soil. Thus, P. tinctorius may have a different role in these thermal soils. Our results suggest that this species may not perform well in remediation of all acid soils and that factors such as pH, soil temperature, and soil chemistry may interact to influence EM fungal community structure. In addition, we identified at least one new species with potential for use in remediation of hot acidic soil.

Pinus/microbiology↗
NASA NTRS1993

Application of neural network to remote sensing of soil moisture using theoretical polarimetric backscattering coefficients

This paper investigates the potential application of neural network to inversion of soil moisture using polarimetric remote sensing data. The neural network used for the inversion of soil parameters is multi-layer perceptron trained with the back-propagation algorithm. The training data include the polarimetric backscattering coefficients obtained from theoretical surface scattering models together with an assumed nominal range of soil parameters which are comprised of the soil permittivity and surface roughness parameters. Soil permittivity is calculated from the soil moisture and the assumed soil texture based on an empirical formula at C-, L-, and P-bands. The rough surface parameters for the soil surface, which is described by the Gaussian random process, are the root-mean-square (rms) height and correlation length. For the rough surface scattering, small perturbation method is used for the L-band frequency, and Kirchhoff approximation is used for the C-band frequency to obtain the corresponding backscattering coefficients. During the training, the backscattering coefficients are the inputs to the neural net and the output from the net are compared with the desired soil parameters to adjust the interconnecting weights. The process is repeated for each input-output data entry and then for the entire training data until convergence is reached. After training, the backscattering coefficients are applied to the trained neural net to retrieve the soil parameters which are compared with the desired soil parameters to verify the effectiveness of this technique. Several cases are examined. First, for simplicity, the correlation length and rms height of the soil surface are fixed while soil moisture is varied. Soil moisture obtained using the neural networks with either L-band or C-band backscattering coefficients for the HH and VV polarizations as inputs is in good agreement with the desired soil moisture. The neural net output matches the desired output for the soil moisture range of 16 to 60 percent for the C-band case. The next case investigated is to vary both soil moisture and rms height while keeping the correlation length fixed. For this case, C-band backscattering coefficients are not sufficient for retrieving two parameters because the Kirchhoff approximation gives the same HH and VV backscattering coefficients. Therefore, the backscattering coefficients at two different frequency bands are necessary to find both the soil moisture and rms height. Finally, the neural nets are also applied to simultaneously invert soil moisture, rms height, and correlation length. Overall, the soil moisture retrieved from the neural network agrees very well with the desired soil moisture. This suggests that the neural network shows potential for retrieval of soil parameters from remote sensing data.

Wang, L.↗
NASA NTRS2017

Validation and Scaling of Soil Moisture in a Semi-Arid Environment: SMAP Validation Experiment 2015 (SMAPVEX15)

The NASA SMAP (Soil Moisture Active Passive) mission conducted the SMAP Validation Experiment 2015 (SMAPVEX15) in order to support the calibration and validation activities of SMAP soil moisture data products. The main goals of the experiment were to address issues regarding the spatial disaggregation methodologies for improvement of soil moisture products and validation of the in situ measurement upscaling techniques. To support these objectives high-resolution soil moisture maps were acquired with the airborne PALS (Passive Active L-band Sensor) instrument over an area in southeast Arizona that includes the Walnut Gulch Experimental Watershed (WGEW), and intensive ground sampling was carried out to augment the permanent in situ instrumentation. The objective of the paper was to establish the correspondence and relationship between the highly heterogeneous spatial distribution of soil moisture on the ground and the coarse resolution radiometer-based soil moisture retrievals of SMAP. The high-resolution mapping conducted with PALS provided the required connection between the in situ measurements and SMAP retrievals. The in situ measurements were used to validate the PALS soil moisture acquired at 1-km resolution. Based on the information from a dense network of rain gauges in the study area, the in situ soil moisture measurements did not capture all the precipitation events accurately. That is, the PALS and SMAP soil moisture estimates responded to precipitation events detected by rain gauges, which were in some cases not detected by the in situ soil moisture sensors. It was also concluded that the spatial distribution of the soil moisture resulted from the relatively small spatial extents of the typical convective storms in this region was not completely captured with the in situ stations. After removing those cases (approximately10 of the observations) the following metrics were obtained: RMSD (root mean square difference) of0.016m3m3 and correlation of 0.83. The PALS soil moisture was also compared to SMAP and in situ soil moisture at the 36-km scale, which is the SMAP grid size for the standard product. PALS and SMAP soil moistures were found to be very similar owing to the close match of the brightness temperature measurements and the use of a common soil moisture retrieval algorithm. Spatial heterogeneity, which was identified using the high-resolution PALS soil moisture and the intensive ground sampling, also contributed to differences between the soil moisture estimates. In general, discrepancies found between the L-band soil moisture estimates and the 5-cm depth in situ measurements require methodologies to mitigate the impact on their interpretations in soil moisture validation and algorithm development. Specifically, the metrics computed for the SMAP radiometer-based soil moisture product over WGEW will include errors resulting from rainfall, particularly during the monsoon season when the spatial distribution of soil moisture is especially heterogeneous.

SMAPVEX15↗
NASA NTRSDate not supplied

Examination of L-Band Brightness Temperature Forecasts in the SMAP Level-4 Soil Moisture Analysis

The NASA Soil Moisture Active Passive (SMAP) mission [4] has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model [5] using a spatially distributed ensemble Kalman filter, the NASA Global Modeling and Assimilation Office generates the SMAP Level-4 Soil Moisture (L4_SM) product, which provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and applications [6]. The L4_SM product also includes estimates of soil temperature, land surface fluxes, and assimilation diagnostics such as the model forecast and observed Tb values [7]. The output from the L4_SM system is routinely monitored by the L4_SM team. Such monitoring provides valuable information; instances of unusually large Tb observation-minus-forecast (O-F) residuals can indicate events during which soil moisture conditions are poorly described in the land modeling system [7,8]. For example, repeated occurrences of very large Tb O-F values in central Australia were traced back to deficiencies in the gauge-based precipitation product used in the land modeling system through L4_SM Version 5; this discovery prompted the use, beginning in Version 6, of satellite- and gauge-based precipitation observations outside of North America [9]. Recently, a statistical analysis of the Tb O-F residuals revealed systematic errors in the “tau-omega” L-band radiative transfer model that converts the land-model simulated soil moisture and temperature into the forecast Tb prior to the L4_SM analysis. The L4_SM analysis is built on the ensemble Kalman filter and assumes unbiased forecast errors. The presence of systematic Tb forecast errors could thus adversely impact the quality of the analyzed soil moisture. In this paper, we examine the Tb O-F residuals of the latest Version 7 L4_SM data (Science Version ID Vv7030 and Vv7032) [10,11,12]. In the L4_SM Version 7 algorithm, key parameters of the L-band radiative transfer model, including soil roughness, scattering albedo, and a (seasonally varying) climatology of vegetation opacity, are obtained from the SMAP Level-2 Radiometer retrieval product (Version 5) [1]. Additionally, we also use ground measurements of surface soil moisture and soil temperature from the SMAP core validation sites as in situ reference of soil conditions [2,3]. As part of the L4_SM system calibration, the seasonally-varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations [7]. That is, the L4_SM system is designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (potentially erroneous) climatology. The Tb O-F residuals examined here are thus computed using SMAP Tb observations after they are rescaled to the mean seasonal cycle of Tb from the modeling system without SMAP data assimilation. Consequently, the long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. Despite the small time-average values of the Tb O-F residuals, the model forecast Tb was nevertheless found to exhibit undesirable systematic errors. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m3 m-3) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m3 m-3), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature. In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to further improve the L4_SM algorithm.

Rolf Reichle↗
NASA NTRSDate not supplied

Validation of Remotely Sensed and Modeled Soil Moisture at Forested and Unforested Sites

Soil moisture is an important driver for forest ecosystems, influencing fire occurrence and extent, insect and pathogen impacts, and tree growth, which creates a need for regular, globally extensive soil moisture information that only satellite-based sensors or models can achieve. However, the reliability of soil moisture measurements in forests is not well understood due to a lack of suitable validation sites (especially relative to unforested ecosystems) and interference caused by high vegetation water content on remotely sensed measurements; although recent studies have started to address this gap [1], [2], [3], [4]. Here we validate the performance of multiyear remotely sensed (SMAP/Sentinel), remotely sensed data assimilation modelled (SMAP-L4), and modelled (NLDAS) surface and root zone (0-1 m) soil moisture datasets with data from in-situ sensors at 39 National Ecological Observatory Network (NEON) sites throughout the contiguous US. Due to differences in spatial resolution, NEON soil moisture (~0.2 km measurement zone) correlations were expected to be stronger with the SMAP/Sentinel product (3 km resolution) than with coarser resolution SMAP-L4 (9 km resolution) or NLDAS products (13 km resolution). However, given the sensitivity of satellite measurements to vegetation water content we expected a deterioration in the correlations based on remotely sensed measurements (SMAP/Sentinel and SMAP-L4) as aboveground biomass increased, whereas the model-based data (NLDAS) was expected to be largely insensitive to vegetation type. We recognize that the SMAP/Sentinel product was developed for unforested regions, therefore our application is outside its primary use case. Soil moisture is measured at up to 8 depths in five soil plots spaced up to 40 m apart at each NEON terrestrial site. Correlation parameters were calculated for the three remotely sensed and modelled data products relative to in-situ measurement following Entekhabi et al. [5]. The datasets comprised 94 (SMAP-L4), 28 (SMAP/Sentinel), and 106 (NLDAS) sites-years for surface soils and 13 (SMAP-L4) and 14 (NLDAS) site-years for the root zone. At unforested sites, the performance of the three remotely sensed and modelled data products was similar for surface soils (Table 1). For example, unbiased RMSD (ubRMSD), which SMAP uses as its primary performance metric [6], ranged from 0.05 to 0.06 m3 m-3 (Table 1), indicating the ability of all three products to track changes in soil moisture over time. The performance of the three products deteriorated at forested sites, however, while the difference in performance was modest for SMAP-L4 and NLDAS, the deterioration in SMAP/Sentinel performance was substantial. For instance, SMAP/Sentinel ubRMSD increased from 0.06 to 0.11 m3 m-3 and absolute mean difference (Abs MD; which includes measurement bias and spatial representativeness errors) increased from 0.06 to 0.16 m3 m-3, indicating both a reduction in ability to track temporal changes and absolute amounts of soil moisture in forest ecosystems. SMAP-L4 and NLDAS had lower unbiased RMSD for root zone (0-1 m) than surface soils at both forested and unforested sites (Tables 1 and 2; SMAP/Sentinel does not produce a root zone measurement). However, in most cases the correlation coefficient (r) was lower for the root zone than surface soils, suggesting the lower unbiased RMSD may be attributed to greater temporal stability of soil moisture in the root zone rather than improved data product performance. Mean difference and absolute mean difference, which encompass measurement bias and spatial representativeness errors, were greater for root zone than surface soils at unforested sites for both data products, but the opposite was generally true at forested sites. As with surface soils, there was relatively little change in the performance of SMAP-L4 and NLDAS between the unforested and forested sites. In summary, all three data products were able to adequately represent soil moisture at unforested sites, at least when aggregating across sites. However, while the performance of all three products deteriorated at forested sites, SMAP-L4 and NLDAS maintained sufficient performance to remain suitable for some use cases (ubRMSD <0.06 m3 m-3 and RMSD <0.13 m3 m-3). In contrast, the relatively poorer performance of the SMAP/Sentinel product at forested sites seems insufficient for most use cases (ubRMSD >0.1 m3 m-3 and RMSD >0.2 m3 m-3). We attribute the large reduction in the performance of the SMAP/Sentinel product in forests to its use of C-band wavelengths, which are particularly sensitive to vegetation interference, and apparently outweighed any gains provided by its higher spatial resolution. A combined SMAP/NISAR soil moisture product may provide improved performance relative to SMAP/Sentinel due to NISAR’s use of L-band wavelengths, which are less sensitive to vegetation (NISAR is scheduled for launch in early 2024).

Edward Ayres↗
NASA NTRS1999

Apollo 17 Soil Characterization for Reflectance Spectroscopy

It is the fine fractions that dominate the observed spectral signatures of bulk lunar soil, and the next to the smallest size fractions are the most similar to the overall properties of the bulk soil. Thus, our Lunar Soil Characterization Consortium has concentrated on understanding the inter-relations of compositional, mineralogical, and optical properties of the <45-micron size fraction and its component sizes (20-44 micron, 10-20 micron, and <10 micron size fractions). To be able to generalize our results beyond the particular sample set studied, it is necessary to quantitatively identify the observed effects of space weathering and evaluate the processes involved. For this, it is necessary to know the chemistry of each size fraction, modal abundances of each phase, average compositions of the minerals and glasses, I(sub s)/FeO values, reflectance spectra, and the physical makeup of the individual particles and their patinas. This characterization includes the important dissection of the pyroxene minerals into four separate populations, with data on both modes and average chemical compositions. Armed with such data, it should be possible to effectively isolate spectral effects of space weathering from spectral properties related to mineral and glass chemistry. Four mare soils from the Apollo 17 site were selected for characterization based upon similarities in bulk composition and their contrasting maturities, ranging from immature to submature to mature. The methodology of our characterization has been discussed previously. Results of the Apollo 17 mare soils, outlined herein, are being prepared for publication in MAPS. As shown, with decreasing grain size, the agglutinitic (impact) glass content profoundly increases. This is the most impressive change for the mare soils. In several soils we have examined, there is an over two-fold increase in the agglutinitic glass contents between the 90-150- micron and the 10-20-micron size fractions. Accompanying this increase in agglutinitic glass is a definite decrease in pyroxenes and to lesser extents, the oxides (ilmenite), volcanic glass, and olivine. Unexpectedly, however, the absolute plagioclase abundances stay relatively constant throughout the different grain sizes, although the abundance of plagioclase relative to the mafic minerals increases with decreasing particle size. These soils were chosen for study based upon their similarities in FeO and Ti02 content, allowing for direct comparisons between evolutions of chemistry between size fractions and among different maturities of soils. The bulk chemistry of these fractions was determined by EMP analyses of fused glass beads. In contrast to the systematic variations in bulk chemistry discussed below, the relatively uniform composition of agglutinitic glass with grain size and soil maturity is illustrated. The composition of the bulk fraction of each size fraction becomes more feldspathic with increasing maturity, with the effect being most pronounced for the finest fractions. The composition of the agglutinitic glass, however, is relatively invariant and more feldspathic (i.e., rich in Al2O3) than even the <10-micron fraction. This relation not only strengthens the "fusion of the finest fraction" (F(sup 3)) hypothesis, but also highlights the important role of plagioclase in the formation of agglutinitic glass. With decreasing grain size, FeO, MgO, and TiO2 contents decrease, whereas CaO, Na2O, and Al2O3 (plag components) increase for all soils. These chemical variations would appear to be coupled with the significant increase in agglutinitic glass and decrease in oxide (ilmenite),pyroxene, and volcanic glass. These changes in chemistry do not appear to be due to distinct changes in the compositions of individual phases but to their abundances. Values of I(sub s)/FeO increase with decreasing grain size, even though the bulk FeO contents decrease. That is, the percentage of the total Fe that is present as nanophase Fe(sup O) has increased substantially in the smaller size fraction. Note that the increase in nanophase FeO in smaller size fractions is significantly greater than the increase in agglutinitic glass content, with its single-domain FeO component. This would seem to indicate that at least some of the FeO is surface correlated. To illustrate this effect, if it is assumed that the nanophase FeO is entirely surface correlated, then equal masses of 15-micron and 6-micron spheres should have about 3x as much FeO in the finer fraction. The recent findings of Kelleret al. of the major role of vapor-deposited, nanophase FeO-containing patinas on most soil particles is a major breakthrough in our understanding of the distribution of FeO within agglutinitic glass and upon grain surfaces. Bidirectional reflectance spectra for a representative Apollo 17 soil (70181) are shown. The size separates all have similar albedo in the blue and follow a regular sequence in which the continuum slope increases, ferrous bands weaken, and albedo, increases with decreasing particle size. The bulk <45-micron soil is typically close to the 10-20 micron spectrum. It is important to note that although the finest fraction (<10 micron) is close in composition to the abundant agglutinitic glass in each size fraction, this size fraction is relatively featureless and does not dominate the spectrum of the bulk <45-micron soil. It has long been suspected that agglutinitic glass, to a large extent, is the product of melting of the finest fraction of the soils, with a dominance of plagioclase. Given the low abundance of pyroxene in the finest fractions of each soil the source of the FeO in these Apollo 17 agglutinitic glasses is not fully identified. We suspect the abundant volcanic glass in these samples may be a significant contributor and this hypothesis will be tested with the suite under study from other Apollo sites.

Taylor, L. A.↗
NASA NTRS2005

Formation of Nanophase Iron in Lunar Soil Simulant for Use in ISRU Studies

For the prospective return of humans to the Moon and the extensive amount of premonitory studies necessary, large quantities of lunar soil simulants are required, for a myriad of purposes from construction/engineering purposes all the way to medical testing of its effects from ingestion by humans. And there is only a limited and precious quantity of lunar soil available on Earth (i.e., Apollo soils) - therefore, the immediate need for lunar soil simulants. Since the Apollo era, there have been several simulants; of these JSC-1 (Johnson Space Center) and MLS-1 (Minnesota Lunar Simulant) have been the most widely used. JSC-1 was produced from glassy volcanic tuff in order to approximate lunar soil geotechnical properties; whereas, MLS-1 approximates the chemistry of Apollo 11 high-Ti soil, 10084. Stocks of both simulants are depleted, but JSC-1 has recently gone back into production. The lunar soil simulant workshop, held at Marshall Space Flight Center in January 2005, identified the need to make new simulants for the special properties of lunar soil, such as nanophase iron (np-Fe(sup 0). Hill et al. (2005, this volume) showed the important role of microscale Fe(sup 0) in microwave processing of the lunar soil simulants JSC-1 and MLS-1. Lunar soil is formed by space weathering of lunar rocks (e.g., micrometeorite impact, cosmic particle bombardment). Glass generated during micrometeorite impact cements rock and mineral fragments together to form aggregates called agglutinates, and also produces vapor that is deposited and coats soil grains. Taylor et al. (2001) showed that the relative amount of impact glass in lunar soil increases with decreasing grain size and is the most abundant component in lunar dust (less than 20 micrometer fraction). Notably, the magnetic susceptibility of lunar soil also increases with the decreasing grain size, as a function of the amount of nanophase-sized Fe(sup 0) in impact-melt generated glass. Keller et al. (1997, 1999) also discovered the presence of abundant np-Fe(sup 0) particles in the glass patinas coating most soil particles. Therefore, the correlation of glass content and magnetic susceptibility can be explained by the presence of the np-Feo particles in glass: small particles contain relatively more np-Fe(sup 0) as glass coatings because the surface area versus mass ratio of the grain size is so increased. The magnetic properties of lunar soil are important in dust mitigation on the Moon (Taylor et al. 2005). Thus material simulating this property is important for testing mitigation methods using electromagnetic field. This np- Fe(sup 0) also produces a unique energy coupling to normal microwaves, such as present in kitchen microwave ovens. Effectively, a portion of lunar soil placed in a normal 2.45 GHz oven will melt at greater than 1200 C before your tea will boil at 100 C, a startling and new discovery reported by Taylor and Meek (2004, 2005). Several methods have been investigated in attempts to make nanophase-sized Feo dispersed within silicate glass; like in the lunar glass. We have been successful in synthesizing such a product and continue to improve on our recipe. We have performed extensive experimentation on this subject to date. Ultimately it will probably be necessary to add this np-Fe(sup 0) bearing silicate glass to lunar soil stimulant, like JSC-1, to actually produce the desired magnetic and microwave coupling properties for use in appropriate ISRU experimentation.

Liu, Yang↗
NASA NTRS2011

SMOS/SMAP Synergy for SMAP Level 2 Soil Moisture Algorithm Evaluation

Soil Moisture Active Passive (SMAP) satellite has been proposed to provide global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only soil moisture product at 40-km spatial resolution. This product and the supporting brightness temperature observations are common to both SMAP and European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are opportunities for synergies between the two missions. These include exploiting the data for calibration and validation and establishing longer term L-band brightness temperature and derived soil moisture products. In this investigation we will be using SMOS brightness temperature, ancillary data, and soil moisture products to develop and evaluate a candidate SMAP L2 passive soil moisture retrieval algorithm. This work will begin with evaluations based on the SMOS product grids and ancillary data sets and transition to those that will be used by SMAP. An important step in this analysis is reprocessing the multiple incidence angle observations provided by SMOS to a global brightness temperature product that simulates the constant 40 degree incidence angle observations that SMAP will provide. The reprocessed brightness temperature data provide a basis for evaluating different SMAP algorithm alternatives. Several algorithms are being considered for the SMAP radiometer-only soil moisture retrieval. In this first phase, we utilized only the Single Channel Algorithm (SCA), which is based on the radiative transfer equation and uses the channel that is most sensitive to soil moisture (H-pol). Brightness temperature is corrected sequentially for the effects of temperature, vegetation, roughness (dynamic ancillary data sets) and soil texture (static ancillary data set). European Centre for Medium-Range Weather Forecasts (ECMWF) estimates of soil temperature for the top layer (as provided as part of the SMOS ancillary data) were used to correct for surface temperature effects and to derive microwave emissivity. ECMWF data were also used for precipitation forecasts, presence of snow, and frozen ground. Vegetation options are described below. One year of soil moisture observations from a set of four watersheds in the U.S. were used to evaluate four different retrieval methodologies: (1) SMOS soil moisture estimates (version 400), (2) SeA soil moisture estimates using the SMOS/SMAP data with SMOS estimated vegetation optical depth, which is part of the SMOS level 2 product, (3) SeA soil moisture estimates using the SMOS/SMAP data and the MODIS-based vegetation climatology data, and (4) SeA soil moisture estimates using the SMOS/SMAP data and actual MODIS observations. The use of SMOS real-world global microwave observations and the analyses described here will help in the development and selection of different land surface parameters and ancillary observations needed for the SMAP soil moisture algorithms. These investigations will greatly improve the quality and reliability of this SMAP product at launch.

Bindlish, Rajat↗