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Matthias Drusch, Eric F. Wood, and Thomas J. Jackson

Abstract

A radiative transfer model and data from the Southern Great Plains 1997 Hydrology Experiment were used to analyze the dependency of surface emissivity retrieval at 19 GHz on atmospheric and vegetative effects. Volumetric soil moisture obtained from ground measurements in the Central Facility area that show a dynamic range of 25% was highly correlated with the corresponding L-band electronically steered thinned array radiometer (ESTAR) 1.4-GHz and Special Sensor Microwave Imager 19-GHz brightness temperatures. For the Little Washita area, only the ESTAR measurements were well correlated with volumetric soil moisture. Atmospheric corrections, which were calculated from collocated radiosonde measurements, did not improve the soil moisture retrieval significantly. However, a sensitivity study at 19 GHz using a larger dataset of 241 radiosonde ascents indicates that the variability in integrated atmospheric water vapor introduces variations of 0.023 in surface emissivity. This value is ∼36% of the variability caused by changes in soil moisture. Therefore, atmospheric corrections should generally improve the soil moisture retrieval at 19 GHz. Different water vapor absorption schemes and absorption by nonraining clouds do not affect this result. Even for sparse vegetation (vegetation water content of 0.33 kg m−2), the effect on soil emissivity retrieval is significant. Because of the lack of appropriate data for vegetation cover and single scattering albedo, the effects of the vegetation had to be estimated. Within a reasonable parameter range they were comparable to the effects caused by soil moisture changes. To quantify the effect of surface emissivity changes on integrated water vapor retrieval, brightness temperatures were modeled using actual soil and atmospheric parameters. The radiative transfer equation was then inverted with respect to the atmospheric contribution using an average value for the surface emissivity. An uncertainty of 5% in volumetric soil moisture caused an error of 30 kg m−2 in integrated water vapor.

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Garry L. Schaefer, Michael H. Cosh, and Thomas J. Jackson

Abstract

Surface soil moisture plays an important role in the dynamics of land–atmosphere interactions and many current and upcoming models and satellite sensors. In situ data will be required to provide calibration and validation datasets. Therefore, there is a need for sensor networks at a variety of scales that provide near-real-time soil moisture and temperature data combined with other climate information for use in natural resource planning, drought assessment, water resource management, and resource inventory. The U.S. Department of Agriculture (USDA)–Natural Resources Conservation Service (NRCS)–National Water and Climate Center has established a continental-scale network to address this need, called the Soil Climate Analysis Network (SCAN). This ever-growing network has more than 116 stations located in 39 states, most of which have been installed since 1999. The stations are remotely located and collect hourly atmospheric, soil moisture, and soil temperature data that are available to the public online in near–real time. New stations are located on benchmark soils when possible. Future plans for the network include increasing the number of stations, improving on user-friendly data summaries, increasing data quality, and scaling the stations to the surrounding region.

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Dongryeol Ryu, Wade T. Crow, Xiwu Zhan, and Thomas J. Jackson

Abstract

Hydrologic data assimilation has become an important tool for improving hydrologic model predictions by using observations from ground, aircraft, and satellite sensors. Among existing data assimilation methods, the ensemble Kalman filter (EnKF) provides a robust framework for optimally updating nonlinear model predictions using observations. In the EnKF, background prediction uncertainty is obtained using a Monte Carlo approach where state variables, parameters, and forcing data for the model are synthetically perturbed to explicitly simulate the error-prone representation of hydrologic processes in the model. However, it is shown here that, owing to the nonlinear nature of these processes, an ensemble of model forecasts perturbed by mean-zero Gaussian noise can produce biased background predictions. This ensemble perturbation bias in soil moisture states can lead to significant mass balance errors and degrade the performance of the EnKF analysis in deeper soil layers. Here, a simple method of bias correction is introduced in which such perturbation bias is corrected using an unperturbed model simulation run in parallel with the EnKF analysis. The proposed bias-correction scheme effectively removes biases in soil moisture and reduces soil water mass balance errors. The performance of the EnKF is improved in deeper layers when the filter is applied with the bias-correction scheme. The interplay of nonlinear hydrologic processes is discussed in the context of perturbation biases, and implications of the bias correction for real-data assimilation cases are presented.

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Thomas J. Jackson, Ann Y. Hsu, and Peggy E. O'Neill

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 observations was implemented and tested for the SGP region. A technique for extracting algorithm parameters from the observations was developed and tested. The algorithm was then used to produce soil moisture maps of the region for the two study periods.

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Anthony T. Cahill, Marc B. Parlange, Thomas J. Jackson, Peggy O’Neill, and T. J. Schmugge

Abstract

The use of remotely sensed near-surface soil moisture for the estimation of evaporation is investigated. Two widely used parameterizations of evaporation, the so-called α and β methods, which use near-surface soil moisture to reduce some measure of potential evaporation, are studied. The near-surface soil moisture is provided by a set of L- and S-band microwave radiometers, which were mounted 13 m above the surface. It is shown that soil moisture measured with a passive microwave sensor in combination with the β method yields reliable estimates of evaporation, whereas the α method is not as robust.

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William P. Kustas, Thomas J. Jackson, Andrew N. French, and J. Ian MacPherson

Abstract

The 1997 Southern Great Plains Hydrology Experiment (SGP97) was designed and conducted to extend surface soil moisture retrieval algorithms based on passive microwave observations to coarser resolutions, larger regions with more diverse conditions, and longer time periods. The L-band Electronically Scanned Thinned Array Radiometer (ESTAR) on an airborne platform was used for daily mapping of surface soil moisture over an area of approximately 40 km × 260 km for a 1-month period. Results showed that the soil moisture retrieval algorithm performed the same as in previous investigations, demonstrating consistency of both the retrieval and the instrument. This soil moisture product at 800-m pixel resolution together with land use and fractional vegetation cover information is used in a remote sensing model for computing spatially distributed fluxes over the SGP97 domain. Validation of the model output is performed at the patch scale using tower-based measurements and at regional scale using aircraft flux observations. Comparisons at the patch scale yielded discrepancies between model- and tower-based sensible and latent heat fluxes of 40% and 20%, respectively. At regional scales, differences between modeled and aircraft-based sensible and latent heat fluxes were less, on the order of 30% and 15%, respectively. A preliminary comparison of regional average energy fluxes with a model using remotely sensed temperatures was conducted and yielded good agreement. The utility of spatially distributed energy flux and model-simulated surface temperature maps over the SGP97 region is discussed.

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Wade T. Crow, George J. Huffman, Rajat Bindlish, and Thomas J. Jackson

Abstract

Over land, remotely sensed surface soil moisture and rainfall accumulation retrievals contain complementary information that can be exploited for the mutual benefit of both product types. Here, a Kalman filtering–based tool is developed that utilizes a time series of spaceborne surface soil moisture retrievals to enhance short-term (2- to 10-day) satellite-based rainfall accumulation products. Using ground rain gauge data as a validation source, and a soil moisture product derived from the Advanced Microwave Scanning Radiometer aboard the NASA Aqua satellite, the approach is evaluated over the contiguous United States. Results demonstrate that, for areas of low to moderate vegetation cover density, the procedure is capable of improving short-term rainfall accumulation estimates extracted from a variety of satellite-based rainfall products. The approach is especially effective for correcting rainfall accumulation estimates derived without the aid of ground-based rain gauge observations. Special emphasis is placed on demonstrating that the approach can be applied in continental areas lacking ground-based observations and/or long-term satellite data records.

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Huilin Gao, Eric F. Wood, Matthias Drusch, Wade Crow, and Thomas J. Jackson

Abstract

The 1999 Southern Great Plains Hydrology Experiment (SGP99) provides comprehensive datasets for evaluating microwave remote sensing of soil moisture algorithms that involve complex physical properties of soils and vegetation. The Land Surface Microwave Emission Model (LSMEM) is presented and used to retrieve soil moisture from brightness temperatures collected by the airborne Electronically Scanned Thinned Array Radiometer (ESTAR) L-band radiometer. Soil moisture maps for the SGP99 domain are retrieved using LSMEM, surface temperatures computed using the Variable Infiltration Capacity (VIC) land surface model, standard soil datasets, and vegetation parameters estimated through remote sensing. The retrieved soil moisture is validated using field-scale and area-averaged soil moisture collected as part of the SGP99 experiment, and had a rms range for the area-averaged soil moisture of 1.8%–2.8% volumetric soil moisture.

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Jun Wen, Thomas J. Jackson, Rajat Bindlish, Ann Y. Hsu, and Z. Bob Su

Abstract

The potential for soil moisture and vegetation water content retrieval using Special Sensor Microwave Imager (SSM/I) brightness temperature over a corn and soybean field region was analyzed and assessed using datasets from the Soil Moisture Experiment 2002 (SMEX02). Soil moisture retrieval was performed using a dual-polarization 19.4-GHz data algorithm that requires the specification of two vegetation parameters—single scattering albedo and vegetation water content. Single scattering albedo was estimated using published values. A method for estimating the vegetation water content from the microwave polarization index using SSM/I 37.0-GHz data was developed for the region using extensive datasets developed as part of SMEX02. Analyses indicated that the sensitivity of the brightness temperature to soil moisture decreased as vegetation water content increased. However, there was evidence that SSM/I brightness temperatures changed in response to soil moisture increases resulting from rainfall during the later stages of crop growth. This was partly attributed to the lower soil and vegetation thermal temperatures that typically followed a rainfall. Comparisons between experimentally measured volumetric soil moisture and SSM/I-retrieved soil moisture indicated that soil moisture retrieval was feasible using SSM/I data, but the accuracy highly depended upon the levels of vegetation and atmospheric precipitable water; the standard error of estimate over the 3-week study period was 5.49%. The potential for using this approach on a larger scale was demonstrated by mapping the state of Iowa. Results of this investigation provide new insights on how one might operationally correct for vegetation effects using high-frequency microwave observations.

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Keith D. Hutchison, Barbara D. Iisager, Thomas J. Kopp, and John M. Jackson

Abstract

A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust, smoke, volcanic ash, and industrial pollution over both land and ocean backgrounds in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This new methodology relies strongly upon data collected in the 0.412-μm bandpass, where smoke has a maximum reflectance in the VIIRS bands while dust simultaneously has a minimum reflectance. The procedures benefit from the VIIRS design, which is dual gain in this band, to avoid saturation in cloudy conditions. These new procedures also exploit other information available from the VIIRS cloud mask algorithm in addition to cloud confidence, including the phase of each cloudy pixel, which is critical to identify water clouds and restrict the use of spectral tests that would misclassify ice clouds as heavy aerosols. Comparisons between results from these new procedures, automated cloud analyses from VIIRS heritage algorithms, manually generated analyses, and MODIS imagery show the effectiveness of the new procedures and suggest that it is feasible to identify and distinguish between clouds and heavy aerosols in a single cloud mask algorithm.

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