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Results for “soil moisture microwave remote sensing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Theory for passive microwave remote sensing of near-surface soil moisture

The theory of microwave thermal emission from a nonscattering half-space medium is developed for application to regions with nonuniform subsurface soil-moisture and temperature variations. A coherent stratified model is presented which is valid for nonuniform temperature profiles and rapidly varying moisture profiles, under which conditions the commonly used emissivity and radiative-transfer approaches become inaccurate. For naturally occurring profiles the stratified model gives more accurate results than the other approaches at frequencies below about 4 GHz. Experimental results from ground-based radiometric observations of a controlled target area compare systematically with brightness temperatures predicted from the theoretical model to within approximately 10 K. Results of dielectric-constant measurements of the sand are given at seven frequencies in the microwave range and for moisture contents in the range from 0% to 30% by volume. By using this model, the thermal microwave emission spectrum is computed for a number of representative moisture and temperature profiles in the frequency range from 0.25 to 25 GHz.

Njoku, E. G.↗
NASA NTRS2018

Polarization Decomposition and Temperature Bias Resolution for SMAP Passive Soil Moisture Retrieval Using Time Series Brightness Temperature Observations

In passive microwave remote sensing of soil moisture, the tau-omega (τ-ω) model has often been used to provide soil moisture estimates at a spatial scale representative of the satellite footprint dimensions. For modeling simplicity, model parameters such as the single scattering albedo (ω) and vegetation opacity (τ) that go into the geophysical inversion process are often assumed to be independent of polarizations. Although this absence of polarization dependence can often be justified in special cases as in low-frequency remote sensing or under dense vegetation conditions, it is not a robust assumption in general. Additional model parameterization errors arising from this assumption are possible, leading to degradation in soil moisture estimation accuracy. In this paper, we propose a time series approach to try to resolve the polarization dependence of several τ-ω model parameters as well as the temperature bias arising from the ancillary temperature data. The Version 4 of the Soil Moisture Active Passive (SMAP) Level 1B brightness temperature time series observations were used to illustrate the mechanics of this approach, with an emphasis on a comparison between resulting satellite soil moisture retrievals and in situ data collected at several core validation sites. It was found that this time series approach resulted in significant reduction of the dry bias exhibited in the current SMAP passive soil moisture data products, while retaining the same performance in other metrics of the current baseline passive soil moisture retrieval algorithm.

time series↗
NASA NTRS1984

Remote sensing of soil moisture

Four major objectives are proposed: (1) to study the sensitivity of active and passive microwave remote sensing approaches to soil moisture variations; (2) to investigate the effect of vegetation cover on microwave backscatter and emission; (3) to test theoretical models of microwave backscatter and emission from a natural terrain against the observations obtained from SIR-B and aircraft radiometer flights; and (4) to estimate vegetation biomass with airborne visible and infrared sensors.

Wang, J. R.↗
NASA NTRS1999

Estimates of Long Term Surface Soil Moisture in the Midwestern U.S. Derived from Satellite Microwave Observations

Soil moisture is a key component of the water and energy balances of the Earth's surface, and has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. However, soil moisture is often somewhat difficult to measure accurately in both space and time, especially at large spatial scales. Soil moisture is highly variable, and while point measurements are typically quite accurate, subsequent areal averaging of these measurements often leads to large errors. Since remotely sensed land surface observations are already a spatially averaged or areally integrated value, they are a logical input parameter to regional or larger scale land process models. A database of long-term soil moisture was compared to satellite microwave observations over test sites in the Midwestern United States. Ground measurements of average volumetric surface soil moisture in the top ten cm were made bimonthly at 19 locations throughout the state of Illinois. Nighttime microwave brightness temperatures were observed at a frequency of 6.6 GHz, by the Scanning Multichannel Microwave Radiometer (SMMR), onboard the Nimbus 7 satellite. The life of the SMMR instrument spanned from Nov. 1978 to Aug. 1987. At 6.6 GHz, the instrument provided a spatial resolution of approximately 150 km, and a temporal frequency over the test area of about 3 nighttime orbits per week. Vegetation radiative transfer characteristics, such as the canopy transmissivity, were estimated from vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the 37 GHz Microwave Polarization Difference Index (MPDI). Because the time of satellite coverage does not always coincide with the ground measurements of soil moisture, the existing ground data were used to calibrate a water balance for the top IO cm surface layer in order to interpolate daily surface moisture values. Such a climate-based approach is often more appropriate for estimating large-area average soil moisture because meteorological data are generally more spatially representative than isolated point measurements of soil moisture, Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface soil moisture at global scales. Real time estimates should improve weather and climate modelling efforts, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and global change studies.

Owe, M.↗
NASA NTRS1980

Soil moisture sensing with microwave techniques

Microwave approaches for the remote sensing of soil moisture are discussed, with the advantages described as follows: (1) the all-weather capability, (2) the greater penetration depth into the soil and through vegetation than with optical or infrared sensors, and (3) the large changes in the dielectric properties of soil produced by changes in water content. Both active and passive microwave approaches are discussed. The dependence of the relationship between microwave response and soil moisture on such things as soil texture, surface roughness, vegetative cover and nonuniform moisture and temperature profiles is analyzed from both the experimental and theoretical viewpoints. The dielectric properties of the soil are analyzed quantitatively, as these control the reflective and emissive properties of the soil surface, and a model for estimating a soil's dielectric properties from its texture and moisture content is also presented. Emissivity is calculated using the Fresnel equation of electromagnetic theory, and reflectivity is shown to be decreased by surface roughness, while the backscatter coefficient increases. It is demonstrated, that microwave radiometers are sensitive to soil moisture for a wide range of surface conditions, and that the longer wavelengths are best for soil moisture sensing.

Schmugge, T.↗
NASA NTRS1997

Monitoring Land Surface Soil Moisture from Space with in-Situ Sensors Validation: The Huntsville Example

Based on recent advances in microwave remote sensing of soil moisture and in pursuit of research interests in areas of hydrology, soil climatology, and remote sensing, the Center for Hydrology, Soil Climatology, and Remote Sensing (HSCARS) conducted the Huntsville '96 field experiment in Huntsville, Alabama from July 1-14, 1996. We, researchers at the Global Hydrology and Climate Center's MSFC/ES41, are interested in using ground-based microwave sensors, to simulate land surface brightness signatures of those spaceborne sensors that were in operation or to be launched in the near future. The analyses of data collected by the Advanced Microwave Precipitation Radiometer (AMPR) and the C-band radiometer, which together contained five frequencies (6.925,10.7,19.35, 37.1, and 85.5 GHz), and with concurrent in-situ collection of surface cover conditions (surface temperature, surface roughness, vegetation, and surface topology) and soil moisture content, would result in a better understanding of the data acquired over land surfaces by the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer (AMSR), because these spaceborne sensors contained these five frequencies. This paper described the approach taken and the specific objective to be accomplished in the Huntsville '97 field experiment.

Wu, Steve Shih-Tseng↗
NASA NTRS2018

Assimilation of Satellite Derived Soil Moisture Profiles into a Crop Modeling System for Robust Yield Estimates

Soil Moisture Measurement - Remote Sensing. - Microwave (MW) Remote Sensing: Physically based and quantitative in nature; Based on difference in dielectric constant; Coarse spatial resolution 25-40 km; Shallow SM estimation depth 0-5 cm (approx.); All weather capabilities (e.g. Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP) etc.) - Thermal Infrared (TIR) Remote Sensing: Indirect SM retrieval through energy flux estimations; Relatively better spatial resolution 1-10 km; Root-zone moisture retrieval capability; Can not penetrate through clouds, hence data gaps (e.g. Surface Energy Balance Algorithm for Land (SEBAL), Atmospheric Land Exchange Inverse (ALEXI) etc.)

SERVIR↗
NASA NTRS1977

Microwave remote sensing and its application to soil moisture detection

The author has identified the following significant results. Experimental measurements were utilized to demonstrate a procedure for estimating soil moisture, using a passive microwave sensor. The investigation showed that 1.4 GHz and 10.6 GHz can be used to estimate the average soil moisture within two depths; however, it appeared that a frequency less than 10.6 GHz would be preferable for the surface measurement. Average soil moisture within two depths would provide information on the slope of the soil moisture gradient near the surface. Measurements showed that a uniform surface roughness similar to flat tilled fields reduced the sensitivity of the microwave emission to soil moisture changes. Assuming that the surface roughness was known, the approximate soil moisture estimation accuracy at 1.4 GHz calculated for a 25% average soil moisture and an 80% degree of confidence, was +3% and -6% for a smooth bare surface, +4% and -5% for a medium rough surface, and +5.5% and -6% for a rough surface.

Newton, R. W.↗
NASA NTRS1983

1983 International Geoscience and Remote Sensing Symposium (IGARSS '83), San Francisco, CA, August 31-September 2, 1983, Digest. Volumes 1 & 2

Various papers on remote sensing are presented. The general topics addressed include: remote sensing of the biosphere, geophysical inversion, science and archaeology, image processing, sensing the atmosphere with microwaves, analysis of scattering for remote sensing, advanced sensors and technology, archaeology, data processing, fiber optics, Landsat-4 and TM. Also discussed are: remote sensing and microwave instrumentation, soil moisture and hydrology, measurement techniques for ocean surface waves, geological remote sensing, radar millimeter wave propagation through the atmosphere, and remote sensing in planetary science, geophysics, hydrology, oceanography, and surface vegetation.

Source record↗
NASA NTRS2021

Assimilation of SMAP Brightness Temperature Observations in the GEOS Land–Atmosphere Data Assimilation System

Errors in soil moisture adversely impact the modeling of land–atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This article investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the GEOS weakly coupled land–atmosphere data assimilation system (LADAS) during boreal summer 2017. The SMAP Tb analysis improves the correlation of LADAS surface and root-zone soil moisture versus in situ measurements by ∼0.1–0.26 over that of ADAS estimates; the unbiased root-mean-square error of LADAS soil moisture is reduced by 0.002–0.008 m^3 /m^3 from that of ADAS. Furthermore, the global land average RMSE versus in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2m_max ) is reduced by 0.05 g/kg and 0.04 K, respectively, for LADAS compared to ADAS estimates. Regionally, the RMSE of LADAS q2m and T2m_max is improved by up to 0.4 g/kg and 0.3 K, respectively. Improvement in LADAS specific humidity extends into the lower troposphere (below ∼700 mb), with relative improvements in bias of 15–25%, although LADAS air temperature bias slightly increases relative to that of ADAS. Finally, the root mean square of the LADAS Tb observation-minus-forecast residuals is smaller by up to ∼0.1 K than in a land-only assimilation system, corroborating the positive impact of the Tb analysis on the modeled land–atmosphere coupling.

microwave remote sensing↗
NASA NTRS1979

Comparison of 2.8- and 21-cm microwave radiometer observations over soils with emission model calculations

An airborne experiment was conducted under NASA auspices to test the feasibility of detecting soil moisture by microwave remote sensing techniques over agricultural fields near Phoenix, Arizona at midday of April 5, 1974 and at dawn of the following day. Extensive ground data were obtained from 96 bare, sixteen hectare fields. Observations made using a scanning (2.8 cm) and a nonscanning (21 cm) radiometer were compared with the predictions of a radiative transfer emission model. It is shown that (1) the emitted intensity at both wavelengths correlates best with the near surface moisture, (2) surface roughness is found to more strongly affect the degree of polarization than the emitted intensity, (3) the slope of the intensity-moisture curves decreases in going from day to dawn, and (4) increased near surface moisture at dawn is characterized by increased polarization of emissions. The results of the experiment indicate that microwave techniques can be used to observe the history of the near surface moisture. The subsurface history must be inferred from soil physics models which use microwave results as boundary conditions.

Burke, W. J.↗
NASA NTRS2022

Thermal Hydraulic Disaggregation of SMAP Soil Moisture Over the Continental United States

Thermal Hydraulic disaggregation of Soil Moisture (THySM) algorithm was implemented to downscale NASA’s Soil Moisture Active Passive (SMAP) Enhanced soil moisture (SM) product to 1 km over the continental United States (CONUS). This algorithm was developed by combining thermal inertia theory with a soil hydraulic-based approach that considers fine-scale SM spatial distribution driven by both heat fluxes and hydraulic conductivity in soils. Relative soil wetness values were estimated using land surface temperature and normalized difference vegetation index for the thermal inertia model and using soil properties for the hydraulic model. The relative soil wetness values at 1 km from both models were then combined by using weighting functions whereby the spatial distribution of SM was governed more by thermal fluxes during times of strong heat transport and infiltration during moisture abundant soil conditions. THySM values were evaluated using in situ SM measurements from SMAP Core Validation Sites (CVS), the USDA Soil Climate Analysis Network, and the NOAA Climate Reference Network over CONUS. THySM shows higher accuracy than the SMAP / Sentinel-1 (SPL2SMAP_S) 1 km SM product when compared to in situ measurements. The accuracy of THySM is 0.048 m3/m3 based on unbiased root mean square error (ubRMSE), outperforming SPL2SMAP_S by 0.01-0.02 m3/m3. The ubRMSE of THySM 1km SM over the SMAP grassland/rangeland-dominated CVS sites is better than 0.04 m3/m3, which meets the SMAP mission SM accuracy requirement applied at 9 and 36 km.

SMAP↗
NASA NTRS1985

1985 International Geoscience and Remote Sensing Symposium (IGARSS '85), University of Massachusetts, Amherst, October 7-9, 1985, Digest. Volumes 1 & 2

The present conference on remote sensing instrumentation considers topics in water resources research, planetary remote sensing and mathematical geophysics, sea ice behavior in view of the Cold Regions Research and Engineering Laboratories pond measurements, Shuttle Imaging Radar-B (SIR-B) system performance and calibration results, geological applications of remote sensing, and microwave scattering from vegetation. Further attention is given to atmospheric probing with lasers, image processing for remote sensing, forest inventory and condition assessment, atmospheric remote sensing, SIR-B geological results, the reflectance, emission, and scattering characteristics of vegetation, sea ice sensing, multipolarization SAR results, the theoretical modeling of surfaces and volumes, crop condition assessment and productivity estimates, and SIR-B results for vegetation cover. Also considered are the microwave remote sensing of soil moisture, advanced sensors, advanced information extraction, wind and wave remote observations, results from the ESA Remote Sensing satellite, large area crop and land cover statistics, SIR-B results for directional ocean wave spectra, seasonal snow cover, ice sheets, and lake ice, fundamental research in terrain remote sensing, image classification methods, vegetation stress detection, remote sensing of coastal processes, SAR systems and calibration, electromagnetic geophysical methods and signal processing, image processing techniques, and SAR internal wave measurements.

Carver, K. R.↗
NASA NTRS2000

Future Challenges for Microwave Remote Sensing in Support of Earth Sciences

The presentation will include an overview of leading Earth Science scientific problems that can be addressed using microwave remote sensing, including soil moisture, precipitation, sea salinity, sea surface winds, atmospheric profiling, etc. Using this basis of scientific measurement, the presentation will outline current technological impediments to implementing new measurement system, concentrating on a few example approaches to new technology, such as the conceptual design tradeoffs and capability improvements represented by a fleet of inexpensive nano-satellites, versus geostationary large aperture sensing systems. The outlook for measurement capabilities will be traded against the expected technological hurdles.

Hildebrand, Peter H.↗