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At least 163 records · Page 9
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NASA NTRS2020

SCoBi Multilayer: A Signals of Opportunity Reflectometry Model for Multilayer Dielectric Reflections

A multilayer module is incorporated into the Signals of Opportunity (SoOp) Coherent Bistatic Scattering model (SCoBi) for determining the reflections and propagation of electric fields within a series of multilayer dielectric slabs. This module can be used in conjunction with other SCoBi components to simulate complex, bistatic simulation schemes that include features such as surface roughness, vegetation, antenna effects, and multilayer soil moisture interactions on reflected signals. This paper introduces the physics underlying the multilayer module and utilizes it to perform a simulation study of the response of SoOp-R measurements with respect to subsurface soil moisture parameters. For a frequency range of 100–2400 MHz, it is seen that the SoOp-R response to a single dielectric slab is mostly frequency insensitive; however, the SoOp-R response to multilayer dielectric slabs will vary between frequencies. The relationship between SoOp-R reflectivity and the contributing depth is visualized, and the results show that SoOp-R measurements can display sensitivity to soil moisture below the penetration depth. By simulation of simple soil moisture profiles with different wetting and drying gradients, the dielectric contrast between layers is shown to be the greatest contributing factor to subsurface soil moisture sensitivity. Overall, it is observed that different frequencies can sense different areas of a soil moisture profile, and this behavior can enable subsurface soil moisture data products from SoOp-R observations.

Dylan Boyd↗
NASA NTRS2023

Evaluation of the Tau-Omega Model over a Dense Corn Canopy at P- and L-band

As an emerging technique, P-band (0.3-1GHz) may improve soil moisture remote sensing compared to L-band (1.4 GHz) SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, because of its greater moisture retrieval depth resulting from its longer wavelength. Consequently, a number of tower-based experiments were undertaken in Victoria, Australia, to understand and quantify potential improvements. The study reported here has extended the evaluation of the tau-omega model to a scenario with a dense corn canopy whose vegetation water content reached ~20 kg/m 2 , and compared the soil moisture retrieval performance at P- and L-band. Based on the locally calibrated parameters, the results from both the SCA (Single Channel Algorithm) and DCA (Dual Channel Algorithm) approaches presented a clear reduction in vegetation impact at P-band compared to L-band. While the root-mean-square error (RMSE) for P-band did not achieve the 0.04-m 3 /m 3 target accuracy of SMOS and SMAP, i.e., 0.054 m 3 /m 3 for the SCA and 0.074 m 3 /m 3 for the DCA, this performance can be regarded as acceptable considering the extremely high vegetation water content. In comparison, the RMSEs at L-band were larger than 0.1 m 3 /m 3 for both the SCA and the DCA approaches. Additionally, DCA performed better in correlation coefficient and unbiased RMSE, while SCA performed better in RMSE at P-band due to the larger bias when using DCA. Moreover, the calibrated vegetation parameters at P-band were found to apply to broader conditions than those at L-band, likely due to the reduced vegetation impact.

P-band↗
NASA NTRS1986

Observations of the seasonal variability of soil moisture and vegetation cover over Africa using satellite microwave radiometry

Multispectral passive microwave data from the scanning multichannel microwave radiometer (SMMR) on the Nimbus-7 satellite were processed selectively for a 1 yr period over Africa. The data show a wide dynamic range of brightness temperature (180 to 290 K), corresponding to variations in surface features such as moisture, temperature, vegetation, roughness, and large-scale topography. It appears that soil moisture variability is detectable with the SMMR over large regions of Africa. To what extent roughness and vegetation affect this capability is not clear. The lowest SMMR frequency is C-band (6.6 GHz), thus any soil moisture sensitivity at this frequency would be much improved by a sensor at L-band (1 to 2 GHz) less affected by roughness and vegetation.

Njoku, Eni G.↗
NASA NTRS1983

A simulation study of scene confusion factors in sensing soil moisture from orbital radar

Simulated C-band radar imagery for a 124-km by 108-km test site in eastern Kansas is used to classify soil moisture. Simulated radar resolutions are 100 m by 100 m, 1 km by 1km, and 3 km by 3 km. Distributions of actual near-surface soil moisture are established daily for a 23-day accounting period using a water budget model. Within the 23-day period, three orbital radar overpasses are simulated roughly corresponding to generally moist, wet, and dry soil moisture conditions. The radar simulations are performed by a target/sensor interaction model dependent upon a terrain model, land-use classification, and near-surface soil moisture distribution. The accuracy of soil-moisture classification is evaluated for each single-date radar observation and also for multi-date detection of relative soil moisture change. In general, the results for single-date moisture detection show that 70% to 90% of cropland can be correctly classified to within +/- 20% of the true percent of field capacity. For a given radar resolution, the expected classification accuracy is shown to be dependent upon both the general soil moisture condition and also the geographical distribution of land-use and topographic relief. An analysis of cropland, urban, pasture/rangeland, and woodland subregions within the test site indicates that multi-temporal detection of relative soil moisture change is least sensitive to classification error resulting from scene complexity and topographic effects.

Ulaby, F. T.↗
NASA NTRS1980

Microwave radiometer measurements of soil moisture content

A unique set of radiometer measurements is presented, recorded during a 1000-h day and night monitoring of irrigated fields from fully saturated to completely dry. Radiometer measurements were recorded at 2.8-cm (X-band) and 21.4-cm (L-band) wavelengths for a range of incident angles from nadir to 50 deg. Soil moisture and soil temperature profile measurements were recorded to a depth of 15 cm. The test site was located in east-central Texas on a clay soil (Miller clay). Three bare-surface plots were used, each having a distinctly different surface roughness. Vegetated plots were also measured. The data quantify the sensitivity of microwave radiometer measurements to soil moisture variations, the effect of surface roughness on the measurement, and the influence of surface vegetation.

Newton, R. W.↗
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 NTRS1988

Soil Transport Implement

The design of the Soil Transport Implement (STI) for SKITTER is presented. The purpose of STI is to provide a protective layer of lunar soil for the lunar modules. The objective is to cover the lunar module with a layer of soil approximately two meters thick within a two week period. The amount of soil required to cover the module is roughly 77 dump truck loads or three million earth pounds. A spinning disk is employed to accomplish its task. STI is an autonomous, teleoperated system. The design incorporates the latest advances in composite materials and high strength, light weight alloys to achieve a high strength to weight ratio. The preliminary design should only be used to assess the feasibility of employing a spinning wheel as a soil transport implement. A mathematical model of the spinning wheel was used to evaluate the performance of this design.

Dixon, William↗
NASA NTRS2020

Toward P-Band Passive Microwave Sensing of Soil Moisture

Currently, near-surface soil moisture at a global scale is being provided using National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active Passive (SMAP) and European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity (SMOS) satellites, both of which utilize L-band (1.4 GHz; 21 cm wavelength ) passive microwave remote sensing techniques. However, a fundamental limitation of this technology is that the water content can only be measured for approximately the top 5-cm layer of soil moisture, and only over low-to-moderate vegetation covered areas in order to meet the 0.04 m3/m3 target accuracy, limiting its applicability. Consequently, a longer wavelength radiometer is being explored as a potential solution for measuring soil moisture in a deeper surface layer of soil and under denser vegetation. It is expected that P-band ( wavelength of 40 cm and frequency of 750 MHz) could potentially provide soil moisture information for the top 10-cm layer of soil, being one-tenth to one-quarter of the wavelength. In addition, P-band is expected to have higher soil moisture retrieval accuracy due to its reduced sensitivity to vegetation water content and surface roughness. To demonstrate the potential of P-band passive microwave soil moisture remote sensing, a short-term airborne field experiment was conducted over a center pivot irrigated farm at Cressy in Tasmania, Australia, in January 2017. First results showing a comparison of airborne P-band brightness temperature observations against airborne L-band brightness temperature observations and ground soil moisture measurements are presented. The P-band brightness temperature was found to have a similar but stronger response to soil moisture compared to L-band.

Nan Ye↗
NASA NTRS2005

Using IKONOS Imagery to Estimate Surface Soil Property Variability in Two Alabama Physiographies

Knowledge of surface soil properties is used to assess past erosion and predict erodibility, determine nutrient requirements, and assess surface texture for soil survey applications. This study was designed to evaluate high resolution IKONOS multispectral data as a soil- mapping tool. Imagery was acquired over conventionally tilled fields in the Coastal Plain and Tennessee Valley physiographic regions of Alabama. Acquisitions were designed to assess the impact of surface crusting, roughness and tillage on our ability to depict soil property variability. Soils consisted mostly of fine-loamy, kaolinitic, thermic Plinthic Kandiudults at the Coastal Plain site and fine, kaolinitic, thermic Rhodic Paleudults at the Tennessee Valley site. Soils were sampled in 0.20 ha grids to a depth of 15 cm and analyzed for % sand (0.05 - 2 mm), silt (0.002 -0.05 mm), clay (less than 0.002 mm), citrate dithionite extractable iron (Fe(sub d)) and soil organic carbon (SOC). Four methods of evaluating variability in soil attributes were evaluated: 1) kriging of soil attributes, 2) co-kriging with soil attributes and reflectance data, 3) multivariate regression based on the relationship between reflectance and soil properties, and 4) fuzzy c-means clustering of reflectance data. Results indicate that co-kriging with remotely sensed data improved field scale estimates of surface SOC and clay content compared to kriging and regression methods. Fuzzy c-means worked best using RS data acquired over freshly tilled fields, reducing soil property variability within soil zones compared to field scale soil property variability.

Sullivan, Dana↗
NASA NTRS1986

Estimation of soil moisture from diurnal surface temperature observations

A coupled heat and moisture balance model was used to determine the thermal inertia of a grass covered top soil under different meteorological conditions. Relations between thermal inertia and soil moisture were established using the De Vries models for thermal conductivity and heat capacity to relate soil moisture and thermal inertia as a function of soil type. A sensitivity study of the surface roughness length and thermal inertia on diurnal surface temperature shows the necessity of focusing on the night time surface temperature rather than on the day time surface temperature, in order to estimate the soil moisture content of the top soil.

Vandegriend, A. A.↗
NASA NTRS2001

Surface Analysis Evaluation of Handwipe Cleaning for the Space Shuttle RSRM

In this paper we discuss the role of surface-sensitive spectroscopy (electron spectroscopy for chemical analysis, or ESCA) in the selection of solvents to replace 1,1,1-trichloroethane in handwipe cleaning of bonding surfaces on NASA's Space Shuttle Reusable Solid Rocket Motor (RSRM). Removal of common process soils from a wide variety of metallic and polymeric substrates was characterized. The cleaning efficiency was usually more dependent on the type of substrate being cleaned and the specific process soil than on the solvent used. A few substrates that are microscopically rough or porous proved to be difficult to clean with any cleaner, and some soils were very tenacious and difficult to remove from any substrate below detection limits. Overall, the work showed that a wide variety of solvents will perform at least as well as 1,1,1-trichloroethane.

Lesley, Michael W.↗
NASA NTRSDate not supplied

Estimating Everglades Peat Reserves and Losses on a Landscape-Scale

The Florida Everglades formed from sawgrass and other aquatic plant remains that have accumulated over thousands of years and is a patterned peatland. The region was initially drained for agricultural and urban development in the late 1800s by the lowering of Lake Okeechobee water levels through canal drainage. Over the course of the past century, the Everglades has further been altered by the construction of canals and levees to provide control of Lake Okeechobee and Everglades water levels. The drainage has caused the loss of much of the peat of the Everglades by peat fires and microbial oxidation, much of it occurring between the 1880s and the 1950s. Plans for Everglades restoration are designed to provide additional water to flow through the remaining Everglades. Additional water into the Everglades would prevent further peat loss by reducing oxidation and encouraging the accretion of peat that has been lost. This potentially could result in the sequestration of significant quantities of atmospheric carbon. We used historic records to determine how much peat was there originally and how much has been lost. Over the past century and a half, approximately 50% of the original Everglades has been lost to agricultural and urban development. In addition, much of the peat in the remaining Everglades Protection Area has been lost. We used Geographic Information Systems technologies to determine the volume and mass of peat, the mass of carbon for each of the predrainage landscapes as well as the current Everglades regions. To do this, historical and current surveys within the Everglades were used to create elevation maps. With these maps, along with a bedrock contour map of south Florida, and soils data from the USEPA, we estimated Everglades peat characteristics. These calculations provide rough, quantitative estimates of the historical Everglades, its current condition, and the changes in the peat soil that have taken place since the 19th century. Given the uncertainties in the hindcasting of some of these data sets, this analysis provides rough estimates for the peat volumes and related constituents. Our calculations indicate that the current Everglades roughly contains less than 24% of theoriginal peat volume, 17% of its mass and 19% of its carbon. The work described was fully supported by the South Florida Water Management District, West Palm Beach, FL

Thomas Walter Dreschel↗
NASA NTRS1981

A backscatter model for a randomly perturbed periodic surface

A backscatter model for a randomly perturbed periodic surface has been developed to explain backscatter measurements from a bare soil surface with row structure. It is assumed that the row-direction effect can be included by integrating the scattering coefficient due to the random roughness component over the underlying periodic component of the soil surface. It is found that the row-direction effect is strongly dependent upon the rate of change of this scattering coefficient with the local angle, and that the maximum difference between looking parallel and perpendicular to the row direction occurs around an incidence angle whose tangent is equal to the slope of the periodic surface at the inflection point.

Ulaby, F. T.↗
NASA NTRS1984

Soil temperature extrema recovery rates after precipitation cooling

From a one dimensional view of temperature alone variations at the Earth's surface manifest themselves in two cyclic patterns of diurnal and annual periods, due principally to the effects of diurnal and seasonal changes in solar heating as well as gains and losses of available moisture. Beside these two well known cyclic patterns, a third cycle has been identified which occurs in values of diurnal maxima and minima soil temperature extrema at 10 cm depth usually over a mesoscale period of roughly 3 to 14 days. This mesoscale period cycle starts with precipitation cooling of soil and is followed by a power curve temperature recovery. The temperature recovery clearly depends on solar heating of the soil with an increased soil moisture content from precipitation combined with evaporation cooling at soil temperatures lowered by precipitation cooling, but is quite regular and universal for vastly different geographical locations, and soil types and structures. The regularity of the power curve recovery allows a predictive model approach over the recovery period. Multivariable linear regression models alloy predictions of both the power of the temperature recovery curve as well as the total temperature recovery amplitude of the mesoscale temperature recovery, from data available one day after the temperature recovery begins.

Welker, J. E.↗
NASA NTRS1978

A scheme for computing surface layer turbulent fluxes from mean flow surface observations

A physical model and computational scheme are developed for generating turbulent surface stress, sensible heat flux and humidity flux from mean velocity, temperature and humidity at some fixed height in the atmospheric surface layer, where conditions at this reference level are presumed known from observations or the evolving state of a numerical atmospheric circulation model. The method is based on coupling the Monin-Obukov surface layer similarity profiles which include buoyant stability effects on mean velocity, temperature and humidity to a force-restore formulation for the evolution of surface soil temperature to yield the local values of shear stress, heat flux and surface temperature. A self-contained formulation is presented including parameterizations for solar and infrared radiant fluxes at the surface. Additional parameters needed to implement the scheme are the thermal heat capacity of the soil per unit surface area, surface aerodynamic roughness, latitude, solar declination, surface albedo, surface emissivity and atmospheric transmissivity to solar radiation.

Hoffert, M. I.↗
NASA NTRS1983

Radiometer requirements for Earth-observation systems using large space antennas

Requirements are defined for Earth observation microwave radiometry for the decade of the 1990's by using large space antenna (LSA) systems with apertures in the range from 50 to 200 m. General Earth observation needs, specific measurement requirements, orbit mission guidelines and constraints, and general radiometer requirements are defined. General Earth observation needs are derived from NASA's basic space science program. Specific measurands include soil moisture, sea surface temperature, salinity, water roughness, ice boundaries, and water pollutants. Measurements are required with spatial resolution from 10 to 1 km and with temporal resolution from 3 days to 1 day. The primary orbit altitude and inclination ranges are 450 to 2200 km and 60 to 98 deg, respectively. Contiguous large scale coverage of several land and ocean areas over the globe dictates large (several hundred kilometers) swaths. Radiometer measurements are made in the bandwidth range from 1 to 37 GHz, preferably with dual polarization radiometers with a minimum of 90 percent beam efficiency. Reflector surface, root mean square deviation tolerances are in the wavelength range from 1/30 to 1/100.

Keafer, L. S., Jr.↗
NASA NTRS1983

Monte Carlo simulation of the effect of soil moisture variation on the microwave emission from soils

In this paper, results of a Monte Carlo simulation of the effect of noise on the relationship between the microwave emissivity of soil and its moisture content are presented. It is found that whenever the magnitude of the noise for the independent variable, in this case the soil moisture, is increased, both the slope of the regression and the correlation coefficient decrease. In particular, when the noise has magnitude equivalent to a coefficient of variation of 0.25, the slope and correlation coefficient are in good agreement with those obtained from the data of a 21-cm airborne microwave radiometer which was flown over a test site in hand county, South Dakota. The comparison was made using a linear relationship to determine the estimated emissivity from the ground measurements of soil moisture. The linear relationship was derived from a radiative transfer model calculation of the microwave emissivities using realistic soil-moisture profiles. The effect of surface roughness was included in the relationship, and the variability of the surface roughness was also simulated by a Monte Carlo technique.

Mo, T.↗
NASA NTRS1991

Soil moisture measurements from airborne SAR

The preliminary results of algorithm development and testing for soil moisture retrieval at high incidence angles are reported. Based on first-order surface backscattering models, a physically based algorithm for retrieval of soil moisture was developed and evaluated using NASA/JPL aircraft synthetic aperture radar (SAR) data. It shows that the copolarization ratio is sensitive to soil moisture change but not to surface roughness at high incidence angles. This algorithm performed well at L-band and should be useful. This study suggests that incidence angles greater than 40 degrees are optimal for such monitoring.

Shi, Jiancheng↗