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At least 109 records · Page 6

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NASA NTRS2011

Canadian Experiment for Soil Moisture in 2010 (CanEX-SM10): Overview and Preliminary Results

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean salinity (SMOS) mission validation and the pre-launch assessment of Soil Moisture and Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (Leaf Area Index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Besides, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km x 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data. The Radio frequency interference (RFI) observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of SMOS soil moisture product matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates are more closely correlated with measured soil moisture.

Magagi, Ramata↗

NASA NTRS1996

A Numerical Simulation of Scattering from One-Dimensional Inhomogeneous Dielectric Random Surfaces

In this paper, an efficient numerical solution for the scattering problem of inhomogeneous dielectric rough surfaces is presented. The inhomogeneous dielectric random surface represents a bare soil surface and is considered to be comprised of a large number of randomly positioned dielectric humps of different sizes, shapes, and dielectric constants above an impedance surface. Clods with nonuniform moisture content and rocks are modeled by inhomogeneous dielectric humps and the underlying smooth wet soil surface is modeled by an impedance surface. In this technique, an efficient numerical solution for the constituent dielectric humps over an impedance surface is obtained using Green's function derived by the exact image theory in conjunction with the method of moments. The scattered field from a sample of the rough surface is obtained by summing the scattered fields from all the individual humps of the surface coherently ignoring the effect of multiple scattering between the humps. The statistical behavior of the scattering coefficient sigma(sup 0) is obtained from the calculation of scattered fields of many different realizations of the surface. Numerical results are presented for several different roughnesses and dielectric constants of the random surfaces. The numerical technique is verified by comparing the numerical solution with the solution based on the small perturbation method and the physical optics model for homogeneous rough surfaces. This technique can be used to study the behavior of scattering coefficient and phase difference statistics of rough soil surfaces for which no analytical solution exists.

Sarabandi, Kamal↗

DOE OSTI2020

Depth-resolved soil data from DOE-LM Riverton Wyoming site, April-September 2017

Averaged (from 3 to 5 replicates) and QA/QC:d data for soil cores taken roughly every month in the period April 5 to September 13 in 2017 at a location close to DOE Legacy Management well 855 at the Riverton, Wyoming floodplain site. The groundwater at this site exhibits persistent U, Mo, and sulfate plumes and is one of the field sites in focus for the SLAC Groundwater Quality SFA program. Cores were separated into 5-20 cm segments based on soil horizonation and each segment was subsampled for microbial analyses (data available at the NCBI Single Read Archive (SRA) Database, https://www.ncbi.nlm.nih.gov/sra/, BioProject ID# PRJNA626616) in addition to the soil analyses included in this dataset. The soil analyses dataset includes aerobic and anaerobic respiration rate estimates (MicroResp method), water content estimates (on collected samples, not in situ), total C, N, S, Ca, K, Mg, Na, Fe, As, Mo, U, and organic-C concentrations, water extractable concentrations of the same elements, and HCl-extractable Fe(II) concentrations.There are four files included in this data package: metadataFile.csv, avgDataFile.csv, methodFile.csv, and RVT2017soilData.xlsx. The excel file contains the same information as the individual csv-files (in separate tabs).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1993

Classification of simple vegetation types using POLSAR image data

Mapping basic vegetation or land cover types is a fairly common problem in remote sensing. Knowledge of the land cover type is a key input to algorithms which estimate geophysical parameters, such as soil moisture, surface roughness, leaf area index or biomass from remotely sensed data. In an earlier paper, an algorithm for fitting a simple three-component scattering model to POLSAR data was presented. The algorithm yielded estimates for surface scatter, double-bounce scatter and volume scatter for each pixel in a POLSAR image data set. In this paper, we show how the relative levels of each of the three components can be used as inputs to simple classifier for vegetation type. Vegetation classes include no vegetation cover (e.g. bare soil or desert), low vegetation cover (e.g. grassland), moderate vegetation cover (e.g. fully developed crops), forest and urban areas. Implementation of the approach requires estimates for the three components from all three frequencies available using the NASA/JPL AIRSAR, i.e. C-, L- and P-bands. The research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration.

Freeman, A.↗

NASA NTRS2023

Soil Moisture and Sea Surface Salinity Derived from Satellite-Borne Sensors

The monitoring of soil moisture and sea surface salinity over the Earth has been profoundly enhanced during the last thirteen years owing to new generation of satellite sensors. L-band radiometry is currently the only technology providing direct measurements of soil moisture, insensitive to surface roughness and distribution of elements in the soil, and the only technology that allows to measure sea surface salinity from space. The SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) satellite missions resolve global and local variability with a spatial resolution of approximately 43 km, a swath width close to 1000 km, and a sampling time, for each mission, of at least twice every 3 days. These resolutions and samplings can be increased by either merging data from the two sensors, and with complementary informations gathered with other passive or active sensors, or with in situ information at higher spatial resolution. Numerous scientific studies based on the use of this new type of measurement have led to a better understanding and constraint of the processes governing the variability of the water cycle, ocean circulation and the Earth's climate. The continuity of measurements, and the increased spatial and radiometric resolution is critical for fulfilling scientific needs. Future L-band radiometry missions currently being planned in Europe (the Copernicus Imaging Microwave Radiometer), and in China (the Ocean Salinity Mission) should provide better constraint on auxiliary parameters by combining multiple frequencies, but they will not have improved spatial resolution beyond SMOS and SMAP. The temporal continuity with SMOS and SMAP will most likely not be ensured. In parallel, new concepts are being developed to increase spatial resolution of both land and ocean parameters.

J. Boutin↗

NASA NTRS2021

PRELIMINARY MODEL FOR SOIL MOISTURE RETRIEVAL USING P-BAND RADIOMETER OBSERVATIONS

Soil Moisture is an important geophysical variable that needs reliable quantification for applications in hydrology, meteorology and agriculture. L-band radiometry has proved to be one of the best methods in soil moisture estimation using microwave signals. However, they provide measurements that correspond to a shallow depth of 5 cm and are also affected by the presence of overlaying vegetation and roughness. In contrast, P-band radiometry is expected to provide moisture information on a deeper layer of soil. Moreover, these lower frequency measurements are expected to be less affected by soil roughness and vegetation contributions. Consequently, this pilot study uses the Polarimetric P-band Multi beam Radiometer(PPMR) at 740 MHz to evaluate the response of theP-band radiometer over a realistic range of surface condition sat the field scale. A preliminary framework of P-band MicrowaveEmission of theBiosphere (P-MEB) has been developed as a forward model that simulates brightness temperature from soil moisture and other ancillary data collected from the field. This paper presents the model for the bare soil condition observed during June 2018 to August 2018. The results show that H-polarized PPMR data has better correlation to the soil moisture over a depth of 10 cm than the V-polarized PPMR data. A model is under improvement by incorporating a more suitable effective temperature formulation.

Edward J Kim↗

NASA NTRS1989

Microwave remote sensing and radar polarization signatures of natural fields

Theoretical models developed for simulation of microwave remote sensing of the Earth surface from airborne/spaceborne sensors are described. Theoretical model calculations were performed and the results were compared with data of field measurements. Data studied included polarimetric images at the frequencies of P band, L band, and C band, acquired with airborne polarimeters over a agricultural field test site. Radar polarization signatures from bare soil surfaces and from tree covered fields were obtained from the data. The models developed in this report include: (1) Small perturbation model of wave scatterings from randomly rough surfaces, (2) Physical optics model, (3) Geometrical optics model, and (4) Electromagnetic wave scattering from dielectric cylinders of finite lengths, which replace the trees and branches in the modeling of tree covered field. Additionally, a three-layer emissivity model for passive sensing of a vegetation covered soil surface is also developed. The effects of surface roughness, soil moisture contents, and tree parameters on the polarization signatures were investigated.

Mo, Tsan↗

NASA NTRS1985

Microwave Remote Sensing of Soil Moisture

Because of the large contrast between the dielectric constant of liquid water and that of dry soil at microwave wavelength, there is a strong dependence of the thermal emission and radar backscatter from the soil on its moisture content. This dependence provides a means for the remote sensing of the moisture content in a surface layer approximately 5 cm thick. The feasibility of these techniques is demonstrated from field, aircraft and spacecraft platforms. The soil texture, surface roughness, and vegetative cover affect the sensitivity of the microwave response to moisture variations with vegetation being the most important. It serves as an attenuating layer which can totally obscure the surface. Research indicates that it is possible to obtain five or more levels of moisture discrimination and that a mature corn crop is the limiting vegetation situation.

Schmugge, T. J.↗

NASA NTRS1984

Microwave remote sensing of soil moisture

Schmugge, T. J.↗

NASA NTRS2001

Ground-Based Passive Microwave Remote Sensing Observations of Soil Moisture at S and L Band with Insight into Measurement Accuracy

Accurate estimates of spatially heterogeneous algorithm variables and parameters are required in determining the spatial distribution of soil moisture using radiometer data from aircraft and satellites. A ground-based experiment in passive microwave remote sensing of soil moisture was conducted in Huntsville, Alabama from July 1-14, 1996 to study retrieval algorithms and their sensitivity to variable and parameter specification. With high temporal frequency observations at S and L band, we were able to observe large scale moisture changes following irrigation and rainfall events, as well as diurnal behavior of surface moisture among three plots, one bare, one covered with short grass and another covered with alfalfa. The L band emitting depth was determined to be on the order of 0-3 or 0-5 cm below 0.30 cubic centimeter/cubic centimeter with an indication of a shallower emitting depth at higher moisture values. Surface moisture behavior was less apparent on the vegetated plots than it was on the bare plot because there was less moisture gradient and because of difficulty in determining vegetation water content and estimating the vegetation b parameter. Discrepancies between remotely sensed and gravimetric, soil moisture estimates on the vegetated plots point to an incomplete understanding of the requirements needed to correct for the effects of vegetation attenuation. Quantifying the uncertainty in moisture estimates is vital if applications are to utilize remotely-sensed soil moisture data. Computations based only on the real part of the complex dielectric constant and/or an alternative dielectric mixing model contribute a relatively insignificant amount of uncertainty to estimates of soil moisture. Rather, the retrieval algorithm is much more sensitive to soil properties, surface roughness and biomass.

Laymon, Charles A.↗

NASA NTRS2019

Optimization of a Remote Sensing Energy Balance Method over Different Canopy Applied at Global Scale

Parameterization methods which calculate turbulent heat and water fluxes with thermal remote sensing data were evaluated in the revised remote sensing surface energy balance system (SEBS) model (Chen et al., 2013). The model calculates sensible heat (H) based on the Monin-Obukhov similarity theory (MOST) and determines latent heat (LE) as the residual of energy balance. We examined the uncertainties of H and LE in the SEBS model due to five key parameters at the local station point scale. Observations at 27 flux towers located in seven land cover types (needle-leaf forest, broad leaf forest, shrub, savanna, grassland, cropland, and sparsely vegetated land) and an artificial intelligence particle swarm optimization (PSO) algorithm was combined to calibrate the five parameters (leaf drag coefficient, leaf heat transfer coefficients, roughness length for soil, and two parameters for ground heat calculation) in the SEBS model. The root-mean-square error at the site scale was reduced by 9 W/sq.m for H, and 92 W/sq.m for LE, and their correlation coefficients were increased by 0.07 (H) and 0.11 (LE) after using the calibrated parameters. The updated model validation was further conducted globally for the remotely sensed evapotranspiration (ET) calculations. Overestimation of SEBS global ET was significantly improved by using the optimized values of the parameters. The results suggested PSO was able to consistently locate the global optimum of the SEBS model, and appears to be capable of solving the ET model optimization problem.

Chen, Xuelong↗

NASA NTRS1993

Classification of Simple Vegetation Types Using POLSAR Image Data

Freeman, A.↗

NASA NTRS1995

Measuring soil moisture with imaging radars

An empirical model was developed to infer soil moisture and surface roughness from radar data. The accuracy of the inversion technique is assessed by comparing soil moisture obtained with the inversion technique to in situ measurements. The effect of vegetation on the inversion is studied and a method to eliminate the areas where vegetation impairs the algorithm is described.

Dubois, Pascale C.↗

DOE OSTI2025

Uniting Surface Properties With Hydrodynamic Roughness in Shallow Overland Flow Models

Describing flow resistance from the properties of an underlying surface is a challenge in surface hydrology. Runoff models must specify a resistance formulation or “roughness scheme”—describing the functional relationship between flow resistance and flow depth/velocity—and its parameters. Uncertainty in runoff predictions derives from both the selected roughness scheme (e.g., Darcy Weisbach, Manning's, or laminar flow equations), and its parameterization with a roughness coefficient (e.g., Manning's n). Both choices are informed by model calibration to data, usually discharge, and, if available, velocity. In this study, a Saint Venant Equation-based runoff model is calibrated to discharge and velocity data from 112 rainfall simulator experiments. The results are used to identify the optimal roughness scheme among four widely-used options for each experiment, and to explore whether surface properties can be used to select the optimal roughness scheme and its coefficient. Among the tested roughness schemes, a transitional flow equation provided the best fit to the plurality of experiments. The most suitable roughness scheme for a given experiment was not related to measured surface properties. Regression models predicted the calibrated roughness coefficients with adjusted values between 0.48 and 0.54, depending on the roughness scheme used. Litter cover was the best predictor of the roughness coefficient, followed by soil cover and average canopy gap size. The results suggest that selection of an optimal roughness scheme based on surface properties alone remains difficult, but that once a scheme is selected, roughness coefficients can be estimated from surface properties.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1974

Magnetic properties of Apollo samples and implications for regolith formation

The magnetic properties of a number of Apollo 17 samples have been measured and confirm that regoliths of mare sites (Apollo 11, 12, 15 valley, and 17 valley) differ markedly from those of highland sites (Apollo 14, 16, and 17 massif) in the ratio of content of metallic to ferrous iron and in the grain size of metallic iron. The ratio of metallic to ferrous iron is correlated with mean particle size, a parameter representing maturity, for soils of Apollo 16 and roughly correlated with the age of the sites for soils of different sites. It is suggested that the ratio of metallic to ferrous iron may be an effective indicator of relative soil maturity for any one site and of the age of the soil material for any sites.

Pearce, G. W.↗

NASA NTRS1996

On Soil Moisture Retrieval and Target Decomposition

In an earlier study, an empirical model was developed to infer soil moisture and surface roughness from radar data. The inversion technique was extensively tested over bare surfaces by comparing the estimated soil moisture to in situ measurements. The overall root mean square (RMS) error in the soil moisture estimate was found to be about 3.5% and the RMS error in the RMS height estimate was less than 0.35 cm absolute for bare or slightly vegetated surfaces. However, inversion results indicate that significant amounts of vegetation cause the algorithm to underestimate soil moisture and overestimate RMS height. Among the areas over which the inversion cannot be applied, the areas with intermediate vegetation cover are of particular interest as both the vegetation and the underlying bare surface affect the backscatter. This paper concentrates mostly on these areas. Using the full polarimetric information and the Cloude target decomposition approach, three different components of the target backscattering can be isolated. One of these three components can be identified as the surface component in the case of intermediate vegetation cover. Once the surface component of the scattering is isolated, the bare surface inversion can then be applied.

Dubois, Pascale C.↗

NASA NTRS1996

On Soil Moisture Retrieval and Target Decomposition

In an earlier study, an empirical model was developed to infer soil moisture and surface roughness from radar data. The inversion technique was extensively tested over bare surfaces by comparing the estimated soil moisture to in situ measurements. The overall RMS error in the soil moisture estimate was found to be 3.5% and the RMS error in the RMS height estimate was less than 0.35 cm absolute for bare or slightly vegetated surfaces. However, inversion results indicate that significant amounts of vegetation cause the algorithm to underestimate soil moisture and overestimate RMS height. Among the areas over which the inversion cannot be applied, the areas with intermediate vegetation cover are of particular interest as both the vegetation and the underlying bare surface affect the backscatter. This paper concentrates mostly on these areas. Using the full polarimetric information and the Cloude target decomposition approach. Three different components of the target backscattering can be isolated. One of these three components can be identified as the surface component in the case of intermediate vegetation cover. Once the surface component of the scattering is isolated, the bare surface inversion can then be applied.

Dubois, Pascale C.↗

NASA NTRS2017

HydroCube Mission Concept: P-Band Signals of Opportunity for Remote Sensing of Snow and Root Zone Soil Moisture

We have developed the HydroCube mission concept with a constellation of small satellites to remotely sense Snow Water Equivalent (SWE) and Root Zone Soil Moisture (RZSM). The HydroCube satellites would operate at sun-synchronous 3-day repeat polar orbits with a spatial resolution of about 1-3 Km. The mission goals would be to improve the estimation of terrestrial water storage and weather forecasts. Root-zone soil moisture and snow water storage in land are critical parameters of the water cycle. The HydroCube Signals of Opportunity (SoOp) concept utilizes passive receivers to detect the reflection of strong existing P-band radio signals from geostationary Mobile Use Objective System (MUOS) communication satellites. The SWE remote sensing measurement principle using the P-band SoOp is based on the propagation delay (or phase change) of radio signals through the snowpack. The time delay of the reflected signal due to the snowpack with respect to snow-free conditions is directly proportional to the snowpack SWE. To address the ionospheric delay at P-band frequencies, the signals from both MUOS bands (360-380 MHz and 250-270 MHz) would be used. We have conducted an analysis to trade off the spatial resolution for a space-based sensor and measurement accuracy. Through modeling analysis, we find that the dual-band MUOS signals would allow estimation of soil moisture and surface roughness together. From the two MUOS frequencies at 260 MHz and 370 MHz, we can retrieve the soil moisture from the reflectivity ratio scaled by wavenumbers using the two P-band frequencies for MUOS. A modeling analysis using layered stratified model has been completed to determine the sensitivity requirements of HydroCube measurements. For mission concept demonstration, a field campaign has been conducted at the Fraser Experimental Forest in Colorado since February 2016. The data acquired has provided support to the HydroCube concept.

Derksen, Chris↗