-
[show abstract]
[hide abstract]
ABSTRACT: In the near future, data from two microwave remote sensors at L-band will enable estimation of near-surface soil moisture. The European Space Agency's Soil Moisture and Salinity Mission (SMOS) launched in November 2009, and NASA is developing a new L-band soil moisture mission named Soil Moisture Active/Passive (SMAP). Soil moisture retrieval theory is well-established, but many details of its application, including the effects of spatial scale, are still being studied. To support these two L-band missions, studies are needed to improve our understanding of the various error sources associated with retrieval of soil moisture from satellite sensors. The purpose of this study is to quantify the magnitude of the scaling error created by the existence of sub-footprint scale variability in soil and vegetation properties, which have nonlinear relationships with emitted microwave energy. The scaling error is related to different functional relationships between surface microwave emissivity and soil moisture that exist for different soils and land cover types within a satellite footprint. We address this problem using single-frequency, single-polarization passive L-band microwave simulations for an Upper Midwest agricultural region in the United States. Making several simplifying assumptions, the analysis performed here helps provide guidance and define limits for future mission requirements by indicating hydrological and landscape conditions under which large errors are expected, and other conditions that are more conducive to accurate soil moisture estimates. Errors associated with spatial aggregation of highly variable land surface characteristics within 40 km satellite ?footprints? were found to be larger than the baseline mission requirements of 0.04-0.06 Volumetric Soil Moisture (VSM) over much of the study area. Soil moisture estimation errors were especially large and positive over portions of the domain characterized by mixtures of forests, wetlands, and open wate-
r or mixtures of forest and pasture. However, by eliminating from the analysis areas with high vegetation water content or substantial surface water fractions, conditions that have well-documented adverse effects on soil moisture retrieval, we obtained errors that are in line with these mission requirements. We developed a parameterization for effective optical depth (?<sub>eff</sub>) based on the standard deviation of optical depth (?<sub>?</sub>) within a footprint in order to improve soil moisture retrieval in the presence of highly variable vegetation density. Use of the resulting parameterized optical depth in retrievals eliminated almost all of the soil moisture biases in our simulated setting. Operationally, the empirical relationship between ?<sub>eff</sub> and ?<sub>?</sub> would need to be determined a priori based on intensive measurements from ground-based instrumentation networks or via tuning of the algorithm. Due to this issue and other confounding factors, results are not expected to be as good as in the simulated cases presented here. However, the relationship found in this study is likely to be consistent across landscapes, so any correction following this functional form would very likely lead to large improvements over retrievals based simply on weighted mean properties.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 04/2010; · 1.49 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: In advance of two upcoming soil moisture satellite missions, it is important to improve our understanding of the error sources associated with satellite-based soil moisture retrievals. Uncertainties in remotely-sensed soil moisture estimates can be attributed to many factors, including errors in measured brightness temperatures and input parameters, deficiencies of the radiative transfer scheme and variability of surface conditions within a satellite footprint. The purpose of this study is to quantify the magnitude of scaling errors attributable to heterogeneity within a satellite footprint of soil and vegetation properties and to present a method to reduce these errors. We address this problem using simulated single-frequency, single-polarization passive L-band microwave observations for an agricultural region of the United States.
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International; 08/2008
-
[show abstract]
[hide abstract]
ABSTRACT: Among passive microwave frequencies, sensors operating at C- and X-band frequencies have been used with some success to estimate near-surface soil moisture from aircraft and satellite platforms. The objective of this paper is to quantify the sensitivities of soil moisture retrieved via a single-channel single-polarization algorithm to the observed brightness temperature and to retrieval algorithm parameters of surface roughness, vegetation B parameter, and single-scattering albedo. Examination of the regions within the parameter space that produce accurate soil moisture retrievals reveals that reasonably accurate retrievals can be made over a range of conditions using a fixed set of input parameters. Retrievals with horizontally polarized brightness temperature observations are more consistent than with vertically polarized observations. At horizontal polarization, sensitivity to the input parameters is much greater for wet soils than for dry soils, whereas for vertical polarization the moisture dependence is much weaker. At vertical polarization, sensitivities to variations in all parameters are much lower. To ensure that retrieval accuracy specifications are consistently met, high soil moisture conditions should be used in defining parameter accuracy requirements. Given the spatial and temporal variability of vegetation and soil conditions, it seems unlikely that, for regions with substantial rapidly growing vegetation, the accuracy requirements for model parameters in a single-frequency, single-polarization retrieval algorithm can be met with current satellite products. For such conditions, any soil moisture retrieval algorithm using parameterizations similar to those of this study may require multiple frequencies, polarizations, or look angles to produce stable, reliable soil moisture estimates.
IEEE Transactions on Geoscience and Remote Sensing 01/2006; · 2.89 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Over the past two decades, successful estimation of soil moisture has been accomplished using L-band microwave radiometer data. However, remaining uncertainties related to surface roughness and the absorption, scattering, and emission by vegetation must be resolved before soil moisture retrieval algorithms can be applied with known and acceptable accuracy using satellite observations. Surface characteristics are highly variable in space and time, and there has been little effort made to determine the parameter estimation accuracies required to meet a given soil moisture retrieval accuracy specification. This study quantifies the sensitivities of soil moisture retrieved using an L-band single-polarization algorithm to three land surface parameters for corn and soybean sites in Iowa, United States. Model sensitivity to the input parameters was found to be much greater when soil moisture is high. For even moderately wet soils, extremely high sensitivity of retrieved soil moisture to some model parameters for corn and soybeans caused the retrievals to be unstable. Parameter accuracies required for consistent estimation of soil moisture in mixed agricultural areas within retrieval algorithm specifications are estimated. Given the spatial and temporal variability of vegetation and soil conditions for agricultural regions it seems unlikely that, for the single-frequency, single-polarization retrieval algorithm used in this analysis, the parameter accuracy requirements can be met with current satellite-based land surface products. We conclude that for regions with substantial vegetation, particularly where the vegetation is changing rapidly, any soil moisture retrieval algorithm that is based on the physics and parameterizations used in this study will require multiple frequencies, polarizations, or look angles to produce stable, reliable soil moisture estimates.
IEEE Transactions on Geoscience and Remote Sensing 08/2005; · 2.89 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: This investigation is aimed at using a coupled hydrologic/radiobrightness model to validate remotely sensed brightness temperatures measured from aircraft and the satellite and derived moisture. The advantage of this approach is that the model can bridge the discontinuities in space and time among many observations at disparate scales and provide estimates of measurement uncertainty. This effort was focused on data generated during the Soil Moisture Experiments in 2002. Results are preliminary at this time and have served to raise numerous questions that are directing current and future research.
Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International; 08/2003
-
[show abstract]
[hide abstract]
ABSTRACT: This paper compares microwave brightness temperature
(T<sub>B</sub>) estimated by two radiobrightness models: a multilayer
coherent radiative transfer (CRT) model and a single-layer Fresnel
reflectance model. Two dielectric mixing schemes were used along with
the models to calculate permittivity (real part of the dielectric
constant). Model T<sub>B</sub> and permittivity estimates were compared
and validated against Huntsville, AL 1998 field experiment measurements.
Model differences can be attributed to the mixing scheme, the
radiobrightness model, or the vertical profile representation. Two sets
of simulations were performed to quantify the sources of variation, one
using observed son temperature and moisture profiles as input, and
another using uniform profiles. Using uniform profiles, systematic
differences in permittivity estimated by the mixing schemes resulted in
T<sub>B</sub> differences as large as 15 K. However, for uniform
profiles, differences in T<sub>B</sub> estimated by the radiobrightness
models for a given permittivity value were less than 2 K. For cases
using observed profiles, near-surface drying of the profiles resulted in
T<sub>B</sub> values from the CRT model 6-10 K higher than estimates
from the Fresnel model, which determines T<sub>B</sub> based on 0-5 cm
mean moisture and temperature. Therefore, the major sources of T<sub>B
</sub> variations were the dielectric mixing scheme and the shape of the
near-surface moisture profile. No radiobrightness/mixing scheme
combination exhibited superiority across all plots and times
IEEE Transactions on Geoscience and Remote Sensing 02/2002; · 2.89 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Currently hydrological models are being developed that can be used to predict soil moisture conditions. However, these models suffer from drift due to nonlinearities in the dynamic system being modeled and due to roundoff errors in the computer hardware. We want use remotely sensed information to update the hydrological model. In order to sufficiently penetrate the soil to yield any useful information about the soil moisture of all but the very surface layer (< 1 cm) we need to choose from long wavelength microwave bands. Given the finite aperture of the antennas, this gives us a very low resolution. The problem we need to solve is how to match the low spatial resolution of the microwave sensor with the high resolution of the hydrological model. We developed an artificial neural network that is input the low-resolution remote sensor data along with information about the soil type, vegetation, and precipitation history at high resolution. The output is soil moisture information at high resolution. We can then use a Kalman filter to update the hydrological model.
Automation Congress, 2002 Proceedings of the 5th Biannual World; 02/2002
-
[show abstract]
[hide abstract]
ABSTRACT: A ground-based experiment in passive microwave remote sensing of
soil moisture was conducted in Huntsville, AL, from July 1-14, 1996. The
goal of the experiment was to evaluate the overall performance of an
empirically-based retrieval algorithm at S-band and L-band under a
different set of conditions and to characterize the site-specific
accuracy inherent within the technique. With high temporal frequency
observations at S-band and L-band, the authors 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 cm<sup>3</sup>/cm<sup>3</sup> with an indication that it is
less at higher moisture values. The S-band emitting depth was not
readily distinguishable from L-band. The uncertainty in remotely sensed
soil moisture observations due to surface heterogeneity and temporal
variability in variables and parameters was characterized by imposing
random errors on the most sensitive variables and parameters and
computing the confidence limits on the observations. Discrepancies
between remotely sensed and gravimetric soil moisture estimates appear
to be larger than those expected from errors in variable and parameter
estimation. This would suggest that a vegetation correction procedure
based on more dynamic modeling may be required to improve the accuracy
of remotely sensed soil moisture
IEEE Transactions on Geoscience and Remote Sensing 10/2001; · 2.89 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The authors' point-scale Land Surface Process/Radiobrightness
(LSP/R) model for a prairie grassland in the northern Great Plains was
adapted to winter wheat-stubble within the region of the Southern Great
Plains 1997 (SGP'97) Hydrology Experiment. The model maintains running
estimates of near-surface soil moisture and stored water in soil and
vegetation when forced by weather, and predicts the microwave brightness
of the terrain. LSP/R model predictions were compared with the field
observations recorded during SGP'97. The model captures canopy and soil
temperatures very well, with the maximum mean and variance of the
difference between the model and field temperatures being 1.06 K and
3.28 K<sup>2</sup>, respectively. It yields reasonable predictions for
the moisture in deeper layers of the soil, but its predictions for the
moisture in the upper layers are low by ~2.3% by volume. These
underpredictions of near-surface soil moisture result in higher H-pol
brightnesses at 19 GHz than those observed
IEEE Transactions on Geoscience and Remote Sensing 10/1999; · 2.89 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: A multifrequency passive microwave remote sensing system was
deployed during a ground-based experiment. Conducted near Huntsville,
Alabama in 1996. The Sand L-band microwave radiometers were able to
detect both the large scale moisture changes due to the irrigation and
rainfall events and also diurnal fluctuations in surface soil moisture
content. Using a two frequency system provides much more information
about near-surface soil water dynamics than does a single
frequency
Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. 1998 IEEE International; 08/1998
-
[show abstract]
[hide abstract]
ABSTRACT: The effect of vegetation on passive microwave remote sensing of soil moisture is studied. The radiative transfer modeling work of Njoku and Kong is applied to a stratified medium of which the upper layer is treated as a layer of vegetation. An effective dielectric constant for this vegetation layer is computed using estimates of the dielectric constant of individual components of the vegetation layer. The horizontally-polarized brightness temperature is then computed as a function of the incidence angle. Model predictions are used to compare with the data obtained in the Huntsville '96, remote sensing of soil moisture experiment, and with predictions obtained using a correction procedure of Jackson and Schmugge.
03/1998;
