Charles A. Laymon

Polarimetric scanning radiometer C and X band microwave observations during SMEX03

Soil Moisture Experiments 2003 (SMEX03) was the second in a series of field campaigns using the NOAA Polarimetric Scanning Radiometer (PSR/CX) designed to validate brightness temperature data and soil moisture retrieval algorithms for the Advanced Microwave Scanning Radiometer on the Aqua satellite. Data from the TRMM Microwave Imager were also used for X-band comparisons. The study was conducted in different climate/vegetation regions of the US (Alabama, Georgia, Oklahoma). In the current investigation, more than one hundred flightlines of PSR/CX data were extensively processed to produce gridded brightness temperature products for the four study regions. Variations associated with soil moisture were not as large as hoped for due to the lack of significant rainfall in Oklahoma. Observations obtained over Alabama include a wide range of soil moisture and vegetation conditions. Comparisons were made between the PSR and AMSR for all sites

Parameter sensitivity of soil moisture retrievals from airborne C- and X-band radiometer measurements in SMEX02

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.

Ground-based passive microwave remote sensing observations of soil moisture at S-band and L-band with insight into measurement accuracy

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//cm/sup 3/ 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.

Comparison of two microwave radiobrightness models and validation with field measurements

This paper compares microwave brightness temperature (T/sub B/) 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/ 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/ differences as large as 15 K. However, for uniform profiles, differences in T/sub B/ 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/ values from the CRT model 6-10 K higher than estimates from the Fresnel model, which determines T/sub B/ based on 0-5 cm mean moisture and temperature. Therefore, the major sources of T/sub B/ 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.

Impacts of Spatial Scaling Errors on Soil Moisture Retrieval Accuracy at L-Band

In the near future, data from two microwave remote sensors at L-band will enable estimation of near-surface soil moisture. The European Space Agencys 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 water 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 (?eff) based on the standard deviation of optical depth (??) 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 ?eff and ?? 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.

Soil moisture variability on the landscape as a function of land use: implication for remote sensing of surface soil moisture

Knowledge of the spatial and temporal distribution of soil moisture under a variety of landscapes and soil conditions is essential for the proper management and utilization of available water. The spatial distribution of soil moisture in the field is often related to the heterogeneity of soil hydraulic and other physical properties. During the Southern Great Plains Hydrology Experiment (1997) in Oklahoma, time domain reflectometry (TDR) measurements were made on a full-section wheat field, open rangeland, and a field dominated with sodic soils along 7 transects spaced at 100 meters. The spacing between sampling points along the transects was also 100 m. Six rainfall events occurred during the 24-day measurement period. Soil moisture content ranged from 0.3 to 0.70 cm/sup 3//cm/sup 3/ in the field during the experiment. Time series analysis showed no significant difference in surface soil moisture between the rangeland and harvested wheat fields. They were consistently drier than the sandy, bare sodic soils. The standard deviation decreased with decreasing soil moisture in the sodic soils and cut wheat fields. The coefficient of variation decreased with increasing soil water content. Results of this study will aid in the development of hydrological and land surface models.

Sensitivity of microwave remotely-sensed soil moisture to soil properties

Surface soil moisture is important in a number of disciplines including agricultural scheduling, water resource management, and weather forecasting. While conventional methods of surface soil moisture determination are labor intensive and of limited spatial scale, ground- and aircraft-based microwave remote sensing systems provide repetitive measurements over large areas. However, it is unclear how the remotely-sensed soil moisture reflects the variations in soil parameters used as input variables in soil moisture retrieval algorithms. Data from a multi-frequency passive microwave remote sensing experiment were used to study the sensitivity of an algorithm to soil texture, bulk density and surface roughness. The algorithm was relatively insensitive to texture, slightly sensitive to bulk density and highly sensitive to surface roughness (RMS). For the most part, the deviations in volumetric soil moisture from the baseline were more pronounced under wet conditions than under dry conditions, and these deviations were also independent of wavelength. This study implies that areal average values of texture variables and bulk density can serve as convenient inputs into soil moisture algorithms.

Defining the range of uncertainty associated with remotely sensed soil moisture estimates with microwave radiometers

We evaluate the response of a passive microwave soil moisture retrieval algorithm to errors in the estimation of input variables and parameters. The model is run varying one parameter at a time within a specified range to quantify the effects individual parameters have on soil moisture retrieval. Although errors in the estimation of most parameters yield total variations in soil moisture of less than about 4% volumetric water content (vwc), variations in the estimates of vegetation water content, vegetation b parameter, percent clay, and surface roughness yield the greatest total variations in calculated soil moisture. The effects of these parameter variations on calculated soil moisture are greater for wetter soils (above 25% vwc) and can result in total variations in soil moisture retrieval up to 24% vwc. These same parameters have a compound effect on calculated soil moisture when they vary collectively; variations in soil moisture retrieval with errors in vegetation water content and surface roughness may be as high as 38% vwc (-12%, +26%). Even over more common conditions between 10% and 25% vwc, errors in vegetation water content, percent clay, and surface roughness result in total soil moisture variations of 9% to 15% (plus or minus 4.5% to plus or minus 7.5%), which are unacceptably high for many applications. When random errors are imposed on these three parameters of the Southern Great Plains 1997 (SGP97) Hydrology Experiment data set, the macrostructure of the soil moisture distribution remains intact compared to the original calculations, but the moisture field is significantly more heterogeneous. It is demonstrated that the distribution (plus or minus 2(sigma) ) of soil moisture for given values of brightness temperature ranges between plus or minus 5% vwc from random errors imposed on the same three parameters. Improvements in parameter estimation in SGP97 contributed to a decrease in the soil moisture uncertainty ((alpha) equals 0.05) by about 67% to plus or minus 3% vwc.

Validation of aircraft and satellite remote sensing of brightness temperature and derived soil moisture using a hydrologic/radiobrightness model

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.

Effects of noise on optimal deconvolution accuracy in microwave observations

Due to large footprints of remotely sensed microwave brightness temperatures, accuracy of microwave observations in areas of large surface heterogeneity has always been a technological challenge. Microwave observations in areas dominated by waterbodies typically exhibit observed brightness temperature several tens of kelvins lower than areas having no surface water. The non-linearity between brightness temperature and other geophysical quantities such as soil moisture makes the accuracy of microwave observations a critical element for accurate estimation of these quantities. In retrieving soil moisture estimates, an error of 1 K in remotely sensed microwave brightness temperatures results in about 0.5–1% error in volumetric soil moisture. Large uncertainties in the observed brightness temperatures make such observations unusable in areas of large brightness temperature contrast. In this article, we discuss a deconvolution method to improve accuracy using the overlap in the adjacent microwave observations. We have shown that the method results in improved accuracy of 40% in brightness temperature estimation in regions of high brightness temperature contrast.