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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Assimilation of Passive and Active Microwave Soil Moisture Retrievals

Root-zone soil moisture is an important control over the partition of land surface energy and moisture, and the assimilation of remotely sensed near-surface soil moisture has been shown to improve model profile soil moisture [1]. To date, efforts to assimilate remotely sensed near-surface soil moisture at large scales have focused on soil moisture derived from the passive microwave Advanced Microwave Scanning Radiometer (AMSR-E) and the active Advanced Scatterometer (ASCAT; together with its predecessor on the European Remote Sensing satellites (ERS. The assimilation of passive and active microwave soil moisture observations has not yet been directly compared, and so this study compares the impact of assimilating ASCAT and AMSR-E soil moisture data, both separately and together. Since the soil moisture retrieval skill from active and passive microwave data is thought to differ according to surface characteristics [2], the impact of each assimilation on the model soil moisture skill is assessed according to land cover type, by comparison to in situ soil moisture observations.

Draper, C. S.↗
NASA NTRS2015

X-Ray Amorphous Phases in Antarctica Dry Valley Soils: Insight into Aqueous Alteration Processes on Mars?

The Chemistry and Mineralogy (CheMin) instrument onboard the Mars Curiosity rover has detected abundant amounts (approx. 25-30 weight percentage) of X-ray amorphous materials in a windblown deposit (Rocknest) and in a sedimentary mudstone (Cumberland and John Klein) in Gale crater, Mars. On Earth, X-ray amorphous components are common in soils and sediments, but usually not as abundant as detected in Gale crater. One hypothesis for the abundant X-ray amorphous materials on Mars is limited interaction of liquid water with surface materials, kinetically inhibiting maturation to more crystalline phases. The objective of this study was to characterize the chemistry and mineralogy of soils formed in the Antarctica Dry Valleys, one of the driest locations on Earth. Two soils were characterized from different elevations, including a low elevation, coastal, subxerous soil in Taylor Valley and a high elevation, ultraxerous soil in University Valley. A variety of techniques were used to characterize materials from each soil horizon, including Rietveld analysis of X-ray diffraction data. For Taylor Valley soil, the X-ray amorphous component ranged from about 4 weight percentage in the upper horizon to as high as 15 weight percentage in the lowest horizon just above the permafrost layer. Transmission electron microscopy indicated that the presence of short-range ordered (SRO) smectite was the most likely candidate for the X-ray amorphous materials in the Taylor Valley soils. The SRO smectite is likely an aqueous alteration product of mica inherited from granitic materials during glaciation of Taylor Valley. The drier University Valley soils had lower X-ray amorphous contents of about 5 weight percentage in the lowest horizon. The X-ray amorphous materials in University Valley are attributed to nanoparticles of TiO2 and possibly amorphous SiO2. The high abundance of X-ray amorphous materials in Taylor Valley is surprising for one of the driest places on Earth. These materials may have been physically and chemical altered during soil formation, however, the limited interaction with water and low temperatures may result in the formation of "immature" X-ray amorphous or SRO materials. Perhaps, a similar process contributes to the formation of the high content of X-ray amorphous materials detected on Mars.

Ming, D. W.↗
NASA NTRS2018

Improving the SMAP Level-4 Soil Moisture Product

The NASA Soil Moisture Active Passive (SMAP) mission generates, among other data sets, the Level 4 Soil Moisture (L4 SM) product. The L4 SM algorithm is based on the assimilation of SMAP radiometer brightness temperature observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. The L4 SM data are published with a mean latency of approx. 2.5 days from the time of observation and provide global, three-hourly, 9 km resolution estimates of surface and root-zone soil moisture and related land surface states and fluxes. In 2018, the product was upgraded from Version 3 to Version 4. Underlying the new version is a revised modeling system that includes improved input parameter datasets for land cover, topography, and vegetation height that are based on recent, high quality space-borne remote sensing observations. Land cover inputs were updated to the GlobCover2009 product, which is based on satellite observations from the Medium Resolution Imaging Spectrometer. Topographic statistics now rely on observations from the Shuttle Radar Topography Mission. Finally, vegetation height inputs are derived from space-borne Lidar measurements. Additionally, SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements were used to calibrate a particular Catchment model parameter that governs the recharge of soil moisture from the models root-zone excess reservoir into the surface excess reservoir. Specifically, the replenishment of soil moisture near the surface from below under non-equilibrium conditions was substantially reduced, which brings the models surface soil moisture more in line with the SMAP Level 2 and in situ soil moisture. Finally, the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4. Considerable effort went into the version upgrade, creating an expectation that the new version is improved over the old version. Indeed, some aspects of the new version are clearly better. However, other aspects are not, and on balance, the overall improvement is modest at best. In this presentation we summarize the skill of the new and old versions vs. independent in situ measurements and in terms of data assimilation diagnostics, including, for example, the statistics of the (soil moisture) analysis increments and the observation minus forecast (brightness temperatures) residuals. We share our experience with trying to improve to the L4 SM product and the lessons learned from the effort.

Reichle, Rolf↗
NASA NTRS2018

Soil Moisture Active Passive (SMAP) Project Assessment Report for Version 4 of the L4_SM Data Product

This report provides an assessment of Version 4 of the SMAP Level 4 Surface and Root Zone Soil Moisture (L4_SM) product, released on 14 June 2018. The assessment includes comparisons of L4_SM soil moisture and temperature estimates with in situ measurements from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product, including observation-minus-forecast (O-F) brightness temperature residuals and soil moisture analysis increments.Together, the core validation site comparisons and the statistics of the assimilation diagnostics areconsidered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to upscaling errors from the point-scale to the grid-cell scale of the data product.The Version 4 L4_SM product benefits from an improved land surface modeling system and from retrospective surface meteorological forcing data that are as consistent as possible with the present-day datain terms of their climatology. Specifically, the model changes include revised parameters and parameterizations for (i) the surface energy balance, (ii) recharge from below of the model's surface excess reservoir, and (iii) the snow depletion curve. Updated ancillary inputs include improved datasets for landcover, topography, and vegetation height. The Version 4 algorithm further includes a revised approach to precipitation corrections that improves the precipitation climatology in Africa and the high-latitudes. Moreover, for system calibration the model is forced retrospectively with MERRA-2 reanalysis data, which are more consistent with the near-real time GEOS forward processing (FP) data used during the SMAP period than the retrospective GEOS data that were available for previous L4_SM versions. An analysis of the time-average surface and root zone soil moisture shows that the global pattern ofarid and humid regions is captured by the Version 4 L4_SM estimates. Owing to the changes in the landsurface modeling system, surface soil moisture is typically drier by several volumetric percent in Version 4 compared to Version 3, whereas root zone soil moisture is wetter in Version 4 in some regions and drierin others. Because of these climatological differences, the Version 3 and Version 4 products should not be combined into a single dataset for use in applications.Results from the core validation site comparisons indicate that Version 4 of the L4_SM data product meets the self-imposed L4_SM accuracy requirement, which is formulated in terms of the RMSE after removal of the long-term mean difference (ubRMSE). The overall ubRMSE of the 3-hourly L4_SM dataat the 9 km scale is 0.039 m3 m-3 for surface soil moisture and 0.029 m3 m-3 for root zone soil moisture,below the 0.04 m3 m-3 requirement. The L4_SM estimates are an improvement over estimates from a model-only Nature Run version 7.2 (NRv7.2), which demonstrates the beneficial impact of the SMAP brightness temperature data. Overall, L4_SM surface and root zone soil moisture estimates are more skillful than NRv7.2 estimates, with statistically significant improvements at the 5% level for surface soil moisture R and anomaly R values. Results from comparisons of the L4_SM product to i

Reichle, Rolf H.↗
NASA NTRS2019

Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with a mean latency of ~2.5 days. The underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially-distributed ensemble Kalman filter. Version 4 of the L4_SM modeling system includes a reduction in the upward recharge of surface soil moisture from below under non-equilibrium conditions, resulting in reduced bias and improved dynamic range of L4_SM surface soil moisture compared to earlier versions. This change and additional technical modifications to the system reduce the mean and standard deviation of the observation-minus-forecast Tb residuals and overall soil moisture analysis increments while maintaining the skill of the L4_SM soil moisture estimates versus independent in situ measurements; the average, bias-adjusted RMSE in Version 4 is 0.039 m(exp 3) m(exp -3) for surface and 0.026 m(exp 3) m(exp -3) for root-zone soil moisture. Moreover, the coverage of assimilated SMAP observations in Version 4 is near-global owing to the use of additional satellite Tb records for algorithm calibration. L4_SM soil moisture uncertainty estimates are biased low (by 0.01-0.02 m(exp 3) m(exp -3)) against actual errors (computed versus in situ measurements). L4_SM runoff estimates, an additional product of the L4_SM algorithm, are biased low (by 35 mm year (exp -1)) against streamflow measurements. Compared to Version 3, bias in Version 4 is reduced by 46% for surface soil moisture uncertainty estimates and by 33% for runoff estimates.

RMSE↗
NASA NTRS2020

A Roadmap for High-Resolution Satellite Soil Moisture Applications - Confronting Product Characteristics with User Requirements

Soil moisture observations are of broad scientific interest and practical value for a wide range of applications. The scientific community has made significant progress in estimating soil moisture from satellite-based Earth observation data, particularly in operationalizing coarse-resolution (25-50 km) soil moisture products. This review summarizes existing applications of satellite-derived soil moisture products and identifies gaps between the characteristics of currently available soil moisture products and the application requirements from various disciplines. We discuss the efforts devoted to the generation of high-resolution soil moisture products from satellite Synthetic Aperture Radar (SAR) data such as Sentinel-1 C-band backscatter observations and/or through downscaling of existing coarse-resolution microwave soil moisture products. Open issues and future opportunities of satellite-derived soil moisture are discussed, providing guidance for further development of operational soil moisture products and bridging the gap between the soil moisture user and supplier communities.

Jian Peng↗
NASA NTRS2021

Using SMAP Level-4 Soil Moisture to Constrain MOD16 Evapotranspiration over the Contiguous USA

Evapotranspiration (ET) is a key hydrologic variable linking the Earth’s water, carbon and energy cycles. At large spatial scales, remote sensing-based (RS) models are often used to quantify ET. Despite the large number of RS ET models available, few include soil moisture as a key environmental input, which can degrade model accuracy and utility. Here, we use model assimilation enhanced soil moisture estimates from the NASA SMAP (Soil Moisture Active Passive) mission as a water supply control in the MOD16 ET algorithm framework. SMAP-derived daily surface (0-5cm depth) and root zone (0-1m depth) soil moisture are used with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation observations, and 4km gridded regional surface meteorology (Gridmet) as primary inputs for estimating daily ET and underlying model soil and stomatal conductance terms. We calibrated the model environmental response parameters using tower eddy covariance ET observations representing major North American biomes. The model ET results were validated using a holdout set of tower observations spanning a large regional climate gradient. The updated ET estimates outperform the baseline MOD16 product across all tower validation sites (RMSE = 0.758 vs 1.108 mm day-1; R2 = 0.68 vs0.45, respectively). Smaller relative improvements were obtained using a recalibrated model with 4km Gridmet meteorology, but no soil moisture control (RMSE = 0.813 mm day-1; R2 = 0.66), indicating that these changes are essential for the improved model performance. The soil moisture-constrained model improvements and relative benefits from the SMAP observations are greater in arid climates, consistent with stronger soil moisture control on ET in water-limited regions. The use of SMAP soil moisture as an additional model constraint improves MOD16 regional performance and provides a new framework for investigating both soil and atmosphere controls on ET.

SMAP↗
NASA NTRS2019

Assimilation of Sentinel 1 and SMAP–based satellite soil moisture retrievals into SWAT hydrological model: the impact of satellite revisit time and product spatial resolution on flood simulations in small basins

In runoff generation process, soil moisture plays an important role as it controls the magnitude of the flood events in response to the rainfall inputs. In this study, we investigated the ability of a new era of satellite soil moisture retrievals to improve the Soil & Water Assessment Tool (SWAT) daily discharge simulations via soil moisture data assimilation for two small (< 500 km2) and hydrologically different catchments located in Central Italy. We ingested 1) the Soil Moisture Active and Passive (SMAP) Enhanced L3 Radiometer Global Daily 9 km EASE-Grid soil moisture, 2) the Advanced SCATterometer (ASCAT) H113 soil moisture product released within the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (HSAF) which has a nearly daily temporal resolution and sampling of 12.5 km, and 3) a fused ASCAT/Sentinel-1 (S1) satellite soil moisture product named SCATSAR-SWI with temporal and spatial sampling of 1 day and 1 km, respectively into SWAT hydrological model via the Ensemble Kalman Filter (EnKF). Different configurations were tested with the aim of exploring the effect of the hydrological regime, the land use conditions, the spatial sampling and the revisit time of the products (which controls the amount of available data to be potentially ingested). Results show a general improvement of SWAT discharge simulations for all products in terms of error and Nash Sutcliffe efficiency index. In particular, we found a relatively good behavior of both the active and the passive products in terms of low flows improvement especially for the catchment characterized by a higher baseflow component. The benefit of the higher spatial resolution of SCATSAR-SWI obtained via S1 over ASCAT was small, likely due to very challenging areas for the S1 retrieval. Eventually, better performances were obtained for the passive product in the more forested catchment. With the aim of exploring the benefit of having more frequent satellite soil moisture observations to be ingested, we tested the performance of the ASCAT product with a reduced temporal sampling obtained by temporally matching ASCAT observations to that of SMAP. The results show a significant reduction of the performance of ASCAT, suggesting that the correction frequency (due to the higher number of observations available) for small catchments is an important aspect for improving flood forecasting as it helps to adjust more frequently the pre-storm soil moisture conditions.

Shima Azimi↗
NASA NTRS1994

Hycrest crested wheatgrass accelerates the degradation of pentachlorophenol in soil

We investigated the effects of vegetation on the fate of pentachlorophenol (PCP) in soil using a novel high-flow sealed test system. Pentachlorophenol has been widely used as a wood preservative, and this highly toxic biocide contaminates soil and ground water at many sites. Although plants are known to accelerate the rates of degradation of certain soil contaminants, this approach has not been thoroughly investigated for PCP. The fate of [14C]PCP, added to soil at a concentration of 100 mg/kg, was compared in three unplanted and three planted systems. The plant used was Hycrest, a perennial, drought-tolerant cultivar of crested wheatgrass [Agropyron desertorum (Fischer ex Link) Schultes]. The flow-through test system allowed us to maintain a budget for 14C-label as well as monitor mineralization (breakdown to 14CO2) and volatilization of the test compound in a 155-d trial. In the unplanted systems, an average of 88% of the total radiolabel remained in the soil and leachate and only 6% was mineralized. In the planted system, 33% of the radiolabel remained in the soil plus leachate, 22% was mineralized, and 36% was associated with plant tissue (21% with the root fraction and 15% with shoots). Mineralization rates were 23.1 mg PCP mineralized kg-1 soil in 20 wk in the planted system, and for the unplanted system 6.6 mg PCP kg-1 soil for the same time period. Similar amounts of volatile organic material were generated in the two systems (1.5%). Results indicated that establishing crested wheatgrass on PCP-contaminated surface soils may accelerate the removal of the contaminant.

Non-NASA Center↗
NASA NTRS2013

Impacts of Soil-aquifer Heat and Water Fluxes on Simulated Global Climate

Climate models have traditionally only represented heat and water fluxes within relatively shallow soil layers, but there is increasing interest in the possible role of heat and water exchanges with the deeper subsurface. Here, we integrate an idealized 50m deep aquifer into the land surface module of the GISS ModelE general circulation model to test the influence of aquifer-soil moisture and heat exchanges on climate variables. We evaluate the impact on the modeled climate of aquifer-soil heat and water fluxes separately, as well as in combination. The addition of the aquifer to ModelE has limited impact on annual-mean climate, with little change in global mean land temperature, precipitation, or evaporation. The seasonal amplitude of deep soil temperature is strongly damped by the soil-aquifer heat flux. This not only improves the model representation of permafrost area but propagates to the surface, resulting in an increase in the seasonal amplitude of surface air temperature of >1K in the Arctic. The soil-aquifer water and heat fluxes both slightly decrease interannual variability in soil moisture and in landsurface temperature, and decrease the soil moisture memory of the land surface on seasonal to annual timescales. The results of this experiment suggest that deepening the modeled land surface, compared to modeling only a shallower soil column with a no-flux bottom boundary condition, has limited impact on mean climate but does affect seasonality and interannual persistence.

soil↗
NASA NTRS2012

Soil Carbon and Nutrient Changes Associated with Deforestation for Pasture in Southern Costa Rica

We assessed the effects of deforestation on soil carbon (C) and nutrient stocks in the premontane landscape near Las Cruces Biological Station in southern Costa Rica, where forests were cleared for pasture in the mid-1960s. We excavated six soil pits to a depth of 1 m in both pasture and primary forest, and found that C stocks were ~20 kg C per square meters in both settings. Nevertheless, soil delta C-13 suggests ~50 percent of the forest-derived soil C above 40 cm depth has turned over since deforestation. Soil nitrogen (N) and phosphorus (P) stocks derived from the soil pits were not significantly different between land uses (P = 0.43 and 0.61, respectively). At a larger spatial scale, however, the ubiquity of ruts produced by cattle-induced erosion indicates that there are substantial soil effects of grazing in this steep landscape. Ruts averaged 13 cm deep and covered ~45 percent of the landscape, and thus are evidence of the removal of 0.7 Mg C/ ha/yr, and 70, 9 and 40 kg/ha/yr of N, P and potassium (K), respectively. Subsoils in this region are ~10 times less C- and N-rich, and ~2 times less P- and K-rich than the topsoil. Thus, rapid topsoil loss may lead to a decline in pasture productivity in the coming decades. These data also suggest that the soil C footprint of deforestation in this landscape may be determined by the fate of soil C as it is transported downstream, rather than C turnover in situ.

soil carbon↗
NASA NTRSDate not supplied

The Chemistry and Mineralogy of Mars Soils: A Tour of Landed Mission Results from the Last 45 years

Eight landed missions demonstrated that martian soils (defined as loose, unconsolidated surface material) consist of basaltic mineralogy, iron (hydr)oxides, amorphous material, sulfate, chloride, (per)chlorate, nitrate, carbonate, and possible organic C. The Viking Lander 1 and 2 (1976), Mars Pathfinder (1997), and Mars Exploration Rover (MER-A,B) (2004) missions determined that martian soil was similar at all landing sites by having mafic chemistry, high S (~ 7 wt.% SO3) and Cl (0.7 wt.%), and spectral detections of poorly crystalline and crystalline Fe-(hydr)oxide phases. The MER Mössbauer spectrometers detected Fe-bearing olivine and pyroxene along with magnetite, nano-phase Fe-oxides (npOx), hematite, and ferric sulfate. The 2008 Phoenix Lander instrumentation measured a soil pH of 7.7 and detected ~0.6 wt.% perchlorate, 3-5 wt.% Ca-rich carbonate and carbon (500 gC/g) consistent with oxidized organics and Fe-rich carbonate. The 2012 Mars Science Laboratory (MSL) rover (Gale Crater) through X-ray diffraction detected plagioclase feldspar, pyroxene, magnetite, hematite, anhydrite, and quartz, with the balance being composed of Si/Fe-containing amorphous (30 to 40 wt.%) material. MSL evolved gas analysis detected (per)chlorate (0.4 wt.% ClO4), nitrate (0.23 wt.%), along with minor Fe/Mg sulfate and oxidized organic C (~2000 gC/g). Limited pedogenesis may have occurred at the 2018 InSight landing site and in one Gusev Crater soil (MER-B) which have 3 to 10 cm-thick duricrust horizons consistent with atmospheric water vapor interactions with soil salts. Martian soil primary mineralogy was derived mostly from local rock (volcanic, sedimentary) that is largely basaltic planet wide. Secondary minerals [e.g., sulfate, chloride, perchlorate, nitrate, carbonate, Fe-(hydr)oxides] in martian soils formed from oxidative aqueous alteration processes and were likely derived from a combination of local sedimentary rock sources and the global bright dust. The Mars 2020 mission will collect soil for Earth return to enable a thorough assessment of the nature and origin of martian soil.

Mars↗
NASA NTRS2021

The Analysis of Using Satellite Soil Moisture Observations for Flood Detection, Evaluating Over the Thailand’s Great Flood of 2011

A flood monitoring and warning system provides critical information that can protect property and save lives. A basin-scale flood monitoring system requires an effective observation platform that offers extensive ground coverage of flood conditions, low latency, and high spatiotemporal resolution. While satellite imagery offers substantial spatial flood extent in detail due to its high spatial resolution, the coarse temporal resolution and cloud obstruction limit its near real-time application. Daily soil moisture data derived from satellite sensors at a scale of a few km can be used to monitor extreme wet surface conditions arising in flood occurrences. This study analyses the flood detection capabilities of several sources of soil moisture information, including the Soil Moisture and Ocean Salinity mission (SMOS), the Advanced Microwave Scanning Radiometer on EOS (AMSR-E), the Advanced SCATterometer (ASCAT) on MetOp, the Global Land Data Assimilation System (GLDAS), and the WaterGAP Global Hydrology Model (WGHM). In addition to soil moisture, the analysis includes measurements of surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation measurements from the Tropical Rainfall Measuring Mission (TRMM), and terrestrial water storage estimates from the Gravity Recovery And Climate Experiment (GRACE) as proxies for flood inundations. The analysis was conducted over the Chao Phraya River Basin (CPB) in Thailand, where the Great Flood of 2011 led to one of the most significant economic losses in the country's history. Satellite-derived soil moisture exhibits a stronger correlation with the flood inundations than the precipitation, model-derived soil moisture, and terrestrial water storage data. SMOS soil moisture observation agrees best with the MODIS-derived flood extent/occurrence, both in terms of spatial distribution and timing, and providing approximated flood lead-time of one week or longer. A neural network constructed from SMOS and MODIS data is used to predict flood intensity/occurrence (given soil moisture input) with a predicted time window from eight days to thirty-two days. The short-term prediction (e.g., eight days) achieves the highest accuracy with an averaged recovery rate of approximately 60% (correlation coefficient). This study's results suggest a potential application of satellite soil moisture data in assisting flood monitoring and warning systems.

Satellite soil moisture↗
NASA NTRS1975

Microwave remote sensing of soil water content

Microwave remote sensing of soils to determine water content was considered. A layered water balance model was developed for determining soil water content in the upper zone (top 30 cm), while soil moisture at greater depths and near the surface during the diurnal cycle was studied using experimental measurements. Soil temperature was investigated by means of a simulation model. Based on both models, moisture and temperature profiles of a hypothetical soil were generated and used to compute microwave soil parameters for a clear summer day. The results suggest that, (1) soil moisture in the upper zone can be predicted on a daily basis for 1 cm depth increments, (2) soil temperature presents no problem if surface temperature can be measured with infrared radiometers, and (3) the microwave response of a bare soil is determined primarily by the moisture at and near the surface. An algorithm is proposed for monitoring large areas which combines the water balance and microwave methods.

Cihlar, J.↗
NASA NTRS1977

Noble gas evidence for the depositional and irradiational history of 60010-60009 core soils

Isotopic abundances of the noble gases have been determined in grain size separates of eleven soils from different depths in the 60010-60009 double drive tube and in magnetic and plagioclase separates from a few of these soils. Data for the 60010 core are presented here. The entire core was deposited a maximum of approximately 125 m.y. ago as deduced from the Ar-38 cosmic ray exposure age of soil 60009,457. Soils in the topmost 12 cm of the core show loss of cosmogenic He-3 and Ne-21 and gain of trapped solar gases in proportion to the degree of surface reworking by micrometeorites as deduced from FMR data. A variety of compositional and irradiational evidence suggests that soils in the core were formed by mixing of three or more components during or immediately prior to core deposition less than about 125 m.y. ago. Based on cosmogenic noble gases and a variety of other data soils 60009,457 and 60010,3107 are similar (and possibly identical) to two of the end member soils which formed the mixture. More mature soils in the core, however, could not have matured in situ from these two soils because of significant differences in noble gas abundances and chemical composition.

Bogard, D. D.↗
NASA NTRS1979

Atlas of soil reflectance properties

A compendium of soil spectral reflectance curves together with soil test results and site information is presented in an abbreviated manner listing those soil properties most important in influencing soil reflectance. Results are presented for 251 soils from 39 states and Brazil. A narrative key describes relationships between soil parameters and reflectance curves. All soils are classified according to the U.S. soil taxonomy and soil series name for ease of identification.

Stoner, E. R.↗
NASA NTRS1980

Thermography for estimating near-surface soil moisture under developing crop canopies

Previous investigations of thermal infrared techniques using remote sensors (thermography) for estimating soil water content have been limited primarily to bare soil. Ground-based and aircraft investigations were conducted to evaluate the potential for extending the thermography approach to developing crop canopies. A significant exponential relationship was found between the volumetric soil water content in the 0-4 cm soil layer and the diurnal difference between surface soil temperature measured at 0230 and 1330 LST (satellite overpass times of NASA's Heat Capacity Mapping Mission - HCMM). Surface soil temperatures were estimated using minimum air temperature, percent cover of the canopy and remote measurements of canopy temperature. Results of the investigation demonstrated that thermography can potentially be used to estimate soil temperature and soil moisture throughout a complete growing season for a number of different crops and soils.

Heilman, J. L.↗
NASA NTRS1981

Evaluation of HCMM data for assessing soil moisture and water table depth

Soil moisture in the 0-cm to 4-cm layer could be estimated with 1-mm soil temperatures throughout the growing season of a rainfed barley crop in eastern South Dakota. Empirical equations were developed to reduce the effect of canopy cover when radiometrically estimating the soil temperature. Corrective equations were applied to an aircraft simulation of HCMM data for a diversity of crop types and land cover conditions to estimate the soil moisture. The average difference between observed and measured soil moisture was 1.6% of field capacity. Shallow alluvial aquifers were located with HCMM predawn data. After correcting the data for vegetation differences, equations were developed for predicting water table depths within the aquifer. A finite difference code simulating soil moisture and soil temperature shows that soils with different moisture profiles differed in soil temperatures in a well defined functional manner. A significant surface thermal anomaly was found to be associated with shallow water tables.

Moore, D. G.↗