A.Y. Hsu

Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data

An algorithm based on a fit of the single-scattering integral equation method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quad-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by the Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well-managed watershed in southwest Oklahoma. Prior to its application for soil moisture inversion, a good agreement was found between the single-scattering IEM simulations and the L-band measurements of SIR-C and AIRSAR over a wide range of soil moisture and surface roughness conditions. The sensitivity of soil moisture variation to the co-polarized signals were then examined under the consideration of the calibration accuracy of various components of SAR measurements. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively.

An observing system simulation experiment for hydros radiometer-only soil moisture products

Based on 1-km land surface model geophysical predictions within the United States Southern Great Plains (Red-Arkansas River basin), an observing system simulation experiment (OSSE) is carried out to assess the impact of land surface heterogeneity, instrument error, and parameter uncertainty on soil moisture products derived from the National Aeronautics and Space Administration Hydrosphere State (Hydros) mission. Simulated retrieved soil moisture products are created using three distinct retrieval algorithms based on the characteristics of passive microwave measurements expected from Hydros. The accuracy of retrieval products is evaluated through comparisons with benchmark soil moisture fields obtained from direct aggregation of the original simulated soil moisture fields. The analysis provides a quantitative description of how land surface heterogeneity, instrument error, and inversion parameter uncertainty impacts propagate through the measurement and retrieval process to degrade the accuracy of Hydros soil moisture products. Results demonstrate that the discrete set of error sources captured by the OSSE induce root mean squared errors of between 2.0% and 4.5% volumetric in soil moisture retrievals within the basin. Algorithm robustness is also evaluated for the case of artificially enhanced vegetation water content (W) values within the basin. For large W(>3 kg/spl middot/m/sup -2/), a distinct positive bias, attributable to the impact of sub- footprint-scale landcover heterogeneity, is identified in soil moisture retrievals. Prospects for the removal of this bias via a correction strategy for inland water and/or the implementation of an alternative aggregation strategy for surface vegetation and roughness parameters are discussed.

Soil moisture and TRMM microwave imager relationships in the Southern Great Plains 1999 (SGP99) experiment

Satellite data collected by the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) and the special sensor microwave/imager (SSM/I) were compared to soil moisture observations as part of the Southern Great Plains (SGP) 1999 Experiment. SGP99 was conducted to address significant gaps in the knowledge base on the microwave remote sensing of soil moisture. Satellite, aircraft and ground based data collection were conducted between July 8, 1999 and July 20, 1999, during which an excellent sequence of meteorological conditions occurred. Cross calibration of the SSM/I data to the same TMI channels showed nearly identical brightness temperatures, 19 GHz SSM/I data and soil moisture relationships were similar to those observed in previous experiments in this region. Comparison studies of the SSM/I and TMI channels revealed that only sampling areas with adequate spatial domains should be used for soil moisture validation. Analyses of the TMI 10 GHz data provide new information on potential improvements that this channel can provide for soil moisture estimation. Soil moisture maps of the region were derived for dates of coverage.

Surface Soil Moisture Retrieval and Mapping Using High-Frequency Microwave Satellite Observations in the Southern Great Plains

Abstract Studies have shown the advantages of low-frequency (<5 GHz) microwave sensors for soil moisture estimation. Although higher frequencies have limited soil moisture retrieval capabilities, there is a vast quantity of systematic global high-frequency microwave data that have been collected for 15 yr by the Special Sensor Microwave Imager (SSM/I). SSM/I soil moisture studies have mostly utilized antecedent precipitation indices as validation, while only a few have employed limited ground observations, which were typically not optimal for this particular type of satellite data. In the Southern Great Plains (SGP) hydrology experiments conducted in 1997 and 1999, ground observations of soil moisture were made over an extended region for developing and validating large-scale mapping techniques. Previous studies have indicated the limitations of both the higher-frequency data and models for soil moisture retrieval. Given these limitations, an alternative retrieval technique that utilizes multipolarization...

A modified IEM model for: scattering from soil surfaces with application to soil moisture sensing

The IEM surface scattering model is generalized to include the possible existence of a dielectric profile such as observed in drying conditions after rain. It is shown that for such a case, the generalized signal model including this improvement in reflectivity may give better agreement with backscatter measurements for some data sets. Hence, such a model may be applied either directly for estimating soil moisture or used to generate training patterns for statistical estimators.

Investigation of the accuracy of soil moisture inversion using microwave data and its impact on watershed hydrological modeling

During 1992 and 1994 NASA/GSFC, USDA, and Princeton University conducted hydrology field experiments in the Little Washita River watershed near Chickasha, Oklahoma, with a goal of characterizing the spatial and temporal variability of soil moisture using microwave sensors from ground, aircraft, and space platforms. A major objective of these activities included the subsequent incorporation of the microwave-derived soil moisture patterns in models of larger scale water balance and partial area hydrology. While work is continuing to improve the accuracy of microwave soil moisture inversion algorithms for both bare and vegetated soils, the impact of errors in estimated soil moisture on hydrological modeling of the watershed has yet to be addressed. In this study a coupled water and energy balance model operating within a topographic framework was used to predict surface soil moisture fields for the Little Washita watershed for an eight-day period in June, 1992 which covered a wide range of soil moisture conditions. The model was first driven by meteorological forcing data, and the model-generated soil moisture fields are compared in space and time to those produced for the watershed by the airborne passive microwave ESTAR sensor for the same time period. In a second analysis, the model was initialized by the remote sensing data, and subsequent model predictions of soil moisture are compared to measured values.

SAR terrain correction for improved soil moisture estimation in a mountain watershed

The spatial and temporal distribution of soil moisture can be viewed as a key descriptor of hydrologic processes in a watershed. If SAR is to be a useful tool for studying and identifying these hydrologic processes, then the effects of topography must be separable from the response due to soil moisture within any microwave data set. This is especially true in mountainous terrain where much stronger reflections are observed from radar-facing slopes than from slopes facing away from the radar. To investigate the impact of topography on the accuracy of SAR-derived soil moisture estimates, AIRSAR data were analyzed from a field experiment conducted in the mountainous semiarid Reynolds Creek watershed near Boise, Idaho during 1991. Reynolds Creek watershed is almost entirely covered with sparse sagebrush vegetation which is essentially transparent at L-band wavelengths. In an attempt to correct for topography, a USGS 30 m DEM of Reynolds Creek was registered to the AIRSAR data, and the changes in radar cross-section area and antenna pattern for each pixel due to slopes in the range and azimuth directions were computed using JPLs POLCAL 4.0 software. Initial results indicate that the 30 m resolution of the USGS DEM may be too coarse for terrain correction of the AIRSAR data for this area. Use of higher resolution DEM for sub-watersheds within Reynolds Creek produced improved calibration of the terrain effect, and increased the correlation between radar backscatter and soil moisture.<<ETX>>

An observing system simulation experiment for hydros radiometer-only soil moisture and freeze-thaw products

Abstract : An important issue in the development of a dedicated space borne soil moisture sensor has been concern over the reliability of soil moisture retrievals in densely vegetated areas and the global extent over which retrievals will be possible. Errors in retrieved soil moisture can originate from a variety of sources within the measurement and retrieval process. In addition to instrument error, three key contributors to retrieval error are the masking of the soil microwave signal by vegetation, the interplay between nonlinear retrieval physics and the relatively poor spatial resolution of space borne sensors, and retrieval parameter uncertainty. Quantification of these errors requires the realistic specification of land surface soil moisture heterogeneity and spatial vegetation patterns. Since detailed soil moisture patterns are currently difficult to obtain from direct observations, an attractive alternative is the application of an observing system simulation experiment (OSSE) in which simulated land surface states are propagated through the sensor measurement and retrieval process to investigate and constrain expected levels of retrieval error. This manuscript describes results from an OSSE designed out to simulate the impact of land surface heterogeneity, instrument error, and retrieval parameter uncertainty on radiometer-only soil moisture products derived from the NASA ESSP Hydrosphere State (Hydros) mission.

Comparison of soil moisture retrieval algorithms using simulated HYDROS brightness temperatures

The HYDROS mission objective is to collect global scale measurements of the Earths soil moisture and land surface freeze/thaw conditions, using a combined L band radiometer and radar system operating at 1.41 and 1.26 GHz, respectively. In order to examine how HYDROS soil moisture retrieval will be performed and how the retrieval accuracy will be impacted by vegetation water content and surface heterogeneity, an observing system simulation experiment (OSSE) was conducted using a modeled geophysical domain in the south-central United States centered on the Arkansas-Red River basin for a one-month period in 1994. Three separate radiometer retrieval algorithms were evaluated: (1) a single-channel algorithm (H polarization), (2) a two-channel iterative algorithm, and (3) a two-channel reflectivity ratio algorithm. Analysis indicates that the HYDROS accuracy goal of 4% volumetric soil moisture can be met anywhere in the test basin except woodland areas. Nonlinear scaling of higher resolution ancillary vegetation data can adversely affect algorithm retrieval accuracies, especially in heavy tree areas on the east side of the basin

Estimating surface soil moisture from SIR-C measurements over the Little Washita watershed

For more than two decades radar backscattering signals at a frequency range of 1-10 GHz have been demonstrated by a number of investigators to depend on surface soil moisture. Most of these past studies were limited to the forward problem in that the measured backscatter was directly correlated to the ground-measured soil moisture. Attempts to invert the experimentally measured radar backscattering signals to soil moisture values occurred only recently. The authors examined three out of these four attempts with respect to the SIR-C observations over a test site some 60 km southwest of Oklahoma City, Oklahoma. The results showed some promise, but also suggested the need of a close scrutiny of these inversion algorithms.