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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Soil Moisture Limitations on Monitoring Boreal Forest Regrowth Using Spaceborne L-Band SAR Data

A study was carried out to investigate the utility of L-band SAR data for estimating aboveground biomass in sites with low levels of vegetation regrowth. Data to estimate biomass were collected from 59 sites located in fire-disturbed black spruce forests in interior Alaska. PALSAR L-band data (HH and HV polarizations) collected on two dates in the summer/fall of 2007 and one date in the summer of 2009 were used. Significant linear correlations were found between the log of aboveground biomass (range of 0.02 to 22.2 t ha-1) and (L-HH) and (L-HV) for the data collected on each of the three dates, with the highest correlation found using the LHV data collected when soil moisture was highest. Soil moisture, however, did change the correlations between L-band and aboveground biomass, and the analyses suggest that the influence of soil moisture is biomass dependent. The results indicate that to use L-band SAR data for mapping aboveground biomass and monitoring forest regrowth will require development of approaches to account for the influence that variations in soil moisture have on L-band microwave backscatter, which can be particularly strong when low levels of aboveground biomass occur

Kasischke, Eric S.↗
NASA NTRS1978

Soil moisture modeling review

A determination of the state of the art in soil moisture transport modeling based on physical or physiological principles was made. It was found that soil moisture models based on physical principles have been under development for more than 10 years. However, these models were shown to represent infiltration and redistribution of soil moisture quite well. Evapotranspiration has not been as adequately incorporated into the models.

Hildreth, W. W.↗
NASA NTRS1983

Effects of vegetation cover on the microwave radiometric sensitivity to soil moisture

The reduction in sensitivity of the microwave brightness temperature to soil moisture content due to vegetation cover is analyzed using airborne observations made at 1.4 and 5 GHz. The data were acquired during six flights in 1978 over a test site near Colby, Kansas. The test site consisted of bare soil, wheat stubble, and fully mature corn fields. The results for corn indicate that the radiometric sensitivity to soil moisture S decreases in magnitude with increasing frequency and with increasing angle of incidence (relative to nadir). The sensitivity reduction factor, defined in terms of the radiometric sensitivities for bare soil and canopy-covered conditions Y = 1 - Scan/Ss was found to be equal to 0.65 for normal incidence at 1.4 GHz, and increases to 0.89 at 5 GHz. These results confirm previous conclusions that the presence of vegetation cover may pose a serious problem for soil moisture detection with passive microwave sensors.

Ulaby, F. T.↗
NASA NTRS1992

Development of soil moisture retrieval algorithm for L-band SAR measurements

A study of algorithm development and testing for soil moisture retrieval for bare fields using L-band synthetic aperture radar (SAR) imagery is reported. First-order surface scattering models predict that the copolarization ratio is sensitive to soil moisture but not to surface roughness. All possible ratios of the co-polarization signals and their linear combinations are evaluated. The best sensitivity to soil moisture is achieved from measurements as predicted by the first-order surface scattering model. The effects of system noise and volume scattering of soil are evaluated. To minimize the effect of the volume scattering, an algorithm which includes both the surface and volume scattering has been developed and tested using Jet Propulsion Laboratory Airborne SAR (JPL AIRSAR) data. The results show that the estimation of soil moisture can be improved after removing the system noise and including the volume scattering effect at large incidence angles.

Shi, Jiancheng↗
NASA NTRS1975

Soil moisture detection from Skylab

An investigation was designed for the Skylab satellite to determine the feasibility of remote sensing of the soil moisture content of the surface from various microwave sensors. Skylab data for the experiment were collected during passes 5, 10, 16, and 38 across the two test sites selected in eastern Kansas and western Texas. Pass 38 covered both test sites giving five data sets for the analysis. As Skylab data were being taken the moisture content of the soil was sampled by ground crews for each 2.5 centimeter depth from the surface to 15 centimeters at interval of about six kilometers along two different routes along the test sites. This resulted in a total of 2250 soil moisture samples corresponding to different locations and six different depths. Skylab data were collected by passive microwave radiometers at wavelengths of 2.1 and 21 centimeters by the S193 and S194 microwave sensors. An active microwave system also collected scatterometer data at a wavelength of 2.1 centimeters. The analysis of microwave data has revealed that the longer wavelength L-Band passive radiometer gives the best correlation with soil moisture content of the upper 2.5 centimeter depth of soil.

Eagleman, J. R.↗
NASA NTRS2006

Impact of Rainfall on the Retrieval of Soil Moisture Using AMSR-E Data

Rainfall leads to errors and limitations on the soil moisture retrieval using satellite radiometry. To understand the impact of rainfall, we examined the temporal and spatial correlations between rainfall and soil moisture using AMSR-E (Advanced Microwave Scanning Radiometer-EOS) data. Scan by scan (swath basis) analyses were conducted to find the short time scale relationship between the two physical parameters. The retention of soil moisture after rainfall in different climatic regimes (e.g. humid and arid regions) was also examined.

rainfall↗
NASA NTRS2018

An Assessment of the Differences Between Spatial Resolution and Grid Size for the SMAP Enhanced Soil Moisture Product over Homogeneous Sites

Satellite-based passive microwave remote sensing typically involves a scanning antenna that makes measurements at irregularly spaced locations. These locations can change on a day to day basis. Soil moisture products derived from satellite-based passive microwave remote sensing are usually resampled to a fixed Earth grid that facilitates their use in applications. In many cases the grid size is finer than the actual spatial resolution of the observation, and often this difference is not well understood by the user. Here, this issue was examined for the Soil Moisture Active Passive (SMAP) enhanced version of the passive-based soil moisture product, which has a grid size of 9-km and a nominal spatial resolution of 33-km. In situ observations from core validation sites were used to compute comparison metrics. For sites that satisfied the established reliability and scaling criteria, the impact of validating the 9-km grid product with in situ data collected over a 9-km versus a 33-km domain was very small for the sites studied (0.039 cu. m/cu. m unbiased root mean square difference for the 9-km case versus 0.037 cu. m/cu. m for the 33-km case). This result does not mean that the resolution of the product is 9-km but that for the conditions studied here the soil moisture estimated from in situ observations over 9-km is a close approximation of the soil moisture estimated from in situ observations over the 33-km resolution. The implication is that using the enhanced SMAP product at its grid resolution of 9-km should not introduce large errors in most applications.

Soil moisture↗
NASA NTRS2010

Utilization of Airborne and in Situ Data Obtained in SGP99, SMEX02, CLASIC and SMAPVEX08 Field Campaigns for SMAP Soil Moisture Algorithm Development and Validation

Field experiment data sets that include coincident remote sensing measurements and in situ sampling will be valuable in the development and validation of the soil moisture algorithms of the NASA's future SMAP (Soil Moisture Active and Passive) mission. This paper presents an overview of the field experiment data collected from SGP99, SMEX02, CLASIC and SMAPVEX08 campaigns. Common in these campaigns were observations of the airborne PALS (Passive and Active L- and S-band) instrument, which was developed to acquire radar and radiometer measurements at low frequencies. The combined set of the PALS measurements and ground truth obtained from all these campaigns was under study. The investigation shows that the data set contains a range of soil moisture values collected under a limited number of conditions. The quality of both PALS and ground truth data meets the needs of the SMAP algorithm development and validation. The data set has already made significant impact on the science behind SMAP mission. The areas where complementing of the data would be most beneficial are also discussed.

Soil moisture↗
NASA NTRSDate not supplied

Non-Gaussian Ensemble Filtering and Adaptive Inflation for Soil Moisture Data Assimilation

The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally distributed single-catchment locations for a 10-yr experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF consistently increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone, and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results additionally demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors.

Rolf Reichle↗
NASA NTRS2001

A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval Using the Microwave Polarization Difference Index

A methodology for retrieving surface soil moisture and vegetation optical depth from satellite microwave radiometer data is presented. The procedure is tested with historical 6.6 GHz brightness temperature observations from the Scanning Multichannel Microwave Radiometer over several test sites in Illinois. Results using only nighttime data are presented at this time, due to the greater stability of nighttime surface temperature estimation. The methodology uses a radiative transfer model to solve for surface soil moisture and vegetation optical depth simultaneously using a non-linear iterative optimization procedure. It assumes known constant values for the scattering albedo and roughness. Surface temperature is derived by a procedure using high frequency vertically polarized brightness temperatures. The methodology does not require any field observations of soil moisture or canopy biophysical properties for calibration purposes and is totally independent of wavelength. Results compare well with field observations of soil moisture and satellite-derived vegetation index data from optical sensors.

Owe, Manfred↗
NASA NTRS2019

Soil Moisture Data Assimilation to Estimate Irrigation Water Use

Knowledge of irrigation is essential to support food security, manage depleting water resources, and comprehensively understand the global water and energy cycles. Despite the importance of understanding irrigation, little consistent information exists on the amount of water that is applied for irrigation. In this study, we develop and evaluate a new method to predict daily to seasonal irrigation magnitude using a particle batch smoother data assimilation approach, where land surface model soil moisture is applied in different configurations to understand how characteristics of remotely sensed soil moisture may impact the performance of the method. The study employs a suite of synthetic data assimilation experiments, allowing for systematic diagnosis of known error sources. Assimilation of daily synthetic soil moisture observations with zero noise produces irrigation estimates with a seasonal bias of 0.66% and a correlation of 0.95 relative to a known truth irrigation. When synthetic observations were subjected to an irregular overpass interval and random noise similar to the Soil Moisture Active Passive satellite (0.04 cm(exp 3) cm(exp -3)), irrigation estimates produced a median seasonal bias of <1% and a correlation of 0.69. When systematic biases commensurate with those between NLDAS‐2 land surface models and Soil Moisture Active Passive are imposed, irrigation estimates show larger biases. In this application, the particle batch smoother outperformed the particle filter. The presented framework has the potential to provide new information into irrigation magnitude over spatially continuous domains, yet its broad applicability is contingent upon identifying new method(s) of determining irrigation schedule and correcting biases between observed and simulated soil moisture, as these errors markedly degraded performance.

R Abolafia-Rosenzweig↗
NASA NTRS1998

The Influence of Soil Moisture and Wind on Rainfall Distribution and Intensity in Florida

Land surface processes play a key role in water and energy budgets of the hydrological cycle. For example, the distribution of soil moisture will affect sensible and latent heat fluxes, which in turn may dramatically influence the location and intensity of precipitation. However, mean wind conditions also strongly influence the distribution of precipitation. The relative importance of soil moisture and wind on rainfall location and intensity remains uncertain. Here, we examine the influence of soil moisture distribution and wind distribution on precipitation in the Florida peninsula using the 3-D Goddard Cumulus Ensemble (GCE) cloud model Coupled with the Parameterization for Land-Atmosphere-Cloud Exchange (PLACE) land surface model. This study utilizes data collected on 27 July 1991 in central Florida during the Convection and Precipitation Electrification Experiment (CaPE). The idealized numerical experiments consider a block of land (the Florida peninsula) bordered on the east and on the west by ocean. The initial soil moisture distribution is derived from an offline PLACE simulation, and the initial environmental wind profile is determined from the CaPE sounding network. Using the factor separation technique, the precise contribution of soil moisture and wind to rainfall distribution and intensity is determined.

Baker, R. David↗
NASA NTRSDate not supplied

Evaluation of Extreme Soil Moisture Patterns over the Sahel during the 2020 Growing Season

The African Sahel is an ecologically and climatically sensitive region, and thus is a valuable test case for examination of climate extremes. Above-average rainfall during the 2020 growing season (June-October) led to flooding in the West, Central and East Sahel, with implications for infrastructure, agriculture and disease outbreaks. In this study, we evaluate soil moisture patterns in the region during 2020 to assess and quantify the extremeness of the event. The primary tool is the NASA Soil Moisture Active Passive (SMAP) Level 4 surface soil moisture data. Daily, monthly, and seasonal anomalies are computed relative to SMAP’s long-term mean (2015-2021). Additional comparisons are made with longer-time-series data sets, including surface soil moisture from NASA’s Modern-Era Retrospective analysis for Research and Applications, Version 2(MERRA-2; 1981-present) and precipitation from the African Rainfall Climatology, Version 2 (ARC2; 1983-present).Possible drivers of the extreme wet event are examined, including potential links to the concurrent 2020-21 La Niña event. Finally, we explore the connections between the extreme soil moisture and vector-borne disease outbreaks in the region in2020, namely, Rift Valley Fever in Mauritania and Chikungunya in Chad.

Soil Moisture↗
NASA NTRS2022

Progress in Time-Series Soil Moisture Retrieval Using L- and S-Band Radar Backscatter

L- and S-band observations from NASA's Passive/Active L/S band (PALS) sensor from the SMEX02 campaign were used to estimate soil moisture. The retrieval process is based on the “alpha approximation” method. This method utilizes a time-series of normalized radar backscatter measurements as well as ancillary information to estimate soil moisture over the Walnut Creek watershed. The resulting retrieved soil moistures are compared to in-situ soil moisture measurements at multiple test sites within the watershed. The calculations show reasonable results for both L- and S-band and provide further insight into the use of L- and S-bands for the upcoming NASA/ISRO mission.

Soil moisture↗
NASA NTRS1991

Radar estimates of soil moisture over the Konza Prairie

Radar-backscatter measurements were made to estimate soil moisture. The helicopter-mounted radar was flown along selected transects that coincided with soil-moisture measurements. The radar operated at microwave frequencies of 5.3 and 9.6 GHz and at selected incidence angles between 0 and 60 degrees. Vertical polarization was used for two days and horizontal polarization was used for three days. The scattering-coefficient data from different days were grouped by frequency and antenna angles and then related to soil moisture along the flight paths using linear regression. A measure of linearity for the regression ranged between 0.9 and 0.5. The larger coefficients were for X-band measurements made at large antenna-incidence angles, and the smaller coefficients were for C-band measurements made at incidences angles near vertical.

Gogineni, S.↗
NASA NTRS2018

Observation Error Specification for Near-Surface Soil Moisture Assimilation: Does It Matter?

The observation error variances assumed in the data assimilation (DA) of remotely sensed near-surface soil moisture observations are known to have unrealistic spatial patterns. The temporal variability in the errors is also typically neglected. Soil moisture errors cannot be directly calculated at large scales, and the globally distributed errors are unknown. We apply several approaches to estimating the observation errors (R matrix) for near-surface soil moisture DA experiments, to establish whether the assumed errors are important to the DA performance, and hence whether uncertainty in the observation error specification is a likely source of uncertainty in soil moisture DA output.

Draper, Clara↗
NASA NTRS1993

Determination of microwave vegetation optical depth and single scattering albedo from large scale soil moisture and Nimbus/SMMR satellite observations

The single scattering albedo and optical depth of typical savanna vegetation in Botswana (Africa) have been determined by inverse modelling using satellite observed microwave signatures and surface soil moisture. Soil emissivity was modelled using a multi-layer radiative transfer model. The study is based on large scale surface moisture data and Nimbus/SMMR 6-6 GHz and 37 GHz dual polarized brightness temperatures over a 3-year period. As compared to the optical depths, the derived single scattering albedos displayed only minor seasonal variations, whereas the values fit well within the range reported in the literature from laboratory and field experiments. Both 6-6 and 37GHz optical depths were found to be significantly related to NDVI-values derived from NOAA/AVHRR.

Van De Griend, A. A.↗
NASA NTRS2009

Statistical and Scaling Properties of Remotely-Sensed Soil Moisture in Two Contrasting Domains in the North American Monsoon Region

Characterizing soil moisture (theta) variability is important for inferring high-resolution information from coarse estimates provided by remote sensors. In this study, we analyze the spatial variability and scale invariance of high-resolution theta estimates collected in two contrasting semiarid areas, Arizona (AZ) and Sonora (SON), during the Soil Moisture Experiment - North American Monsoon in 2004 (SMEX04- NAME). Results reveal that as the mean theta condition ( ) becomes drier, the spatial standard deviation becomes smaller in both domains. The coefficient of variation of theta decreases with in SON, but does not display a clear tendency with in AZ. We also found the presence of scale invariance and multifractality in the range of support scales from 51.2 km to 0.8 km for all soil moisture fields in the two regions. The multifractal properties of theta are clearly linked to in SON, while the relation is affected by more dispersion in AZ. We argue this is due to differences in the dynamic (rainfall) and static (vegetation) controls on theta in the two domains.

Mascaro, Giuseppe↗