Southern Great Plains Hydrology Experiment Research Articles

Time stability and variability of Electronically Scanned Thinned Array Radiometer soil moisture during Southern Great Plains hydrology experiments

AbstractVariability and time‐stability analysis for field‐scale (800 m) Electronically Scanned Thinned Array Radiometer soil moisture within a satellite scale footprint (∼ 50 km) were quantified using observations from the Southern Great Plains Hydrology Experiment 1997 and 1999 (SGP97 and SGP99). The pixels' time‐stability properties were examined with respect to soil, vegetation and topographic parameters in order to determine which physical parameters can be used to identify good candidate observation locations for validating soil moisture from satellite observations and global‐scale model output. The results show that the time‐stability concept remains valid at the satellite scale. The root mean square error values were 1·47, 1·51, 1·93 and 2·32% for the 1st, 2nd, 50th and 100th most stable fields, respectively. The most stable locations had sand and clay percentages consistent with sandy loam soils and moderate to high normalized difference vegetation index values. Neither land cover nor topography properties could be used to identify potentially stable fields in the study region. Copyright © 2010 John Wiley & Sons, Ltd.

Impacts of spatial resolutions and data quality on soil moisture data assimilation

The resolution of near‐surface soil moisture imagery derived from passive remote sensing sources is expected to be on the order of 30 km in the foreseeable future. While analogous active remote sensing retrievals may be derived at finer scales (∼100 m), these have been shown to be prone to error and increased uncertainties in the presence of vegetation and/or dominant surface roughness effects. In this study, we resort to an extension of multiscale Kalman filtering (MKF) for assimilation of near‐surface soil moisture retrievals derived during the Southern Great Plains Hydrology experiment of 1997 (SGP97) into the Three‐Layer Variable Infiltration Capacity (VIC‐3L) land surface model. Our first objective is to evaluate impacts of resolution of the passive remotely sensed soil moisture imagery (from 0.78 km to 25 km) on the consistency of the assimilation algorithm and the effectiveness of assimilation with regards to improving the prediction of soil moisture states and energy fluxes from VIC‐3L. Motivated by recent evidence suggesting that temporal ratios of near‐surface soil moisture retrievals from active remote sensing sources may retain much of the soil moisture signal, we additionally assess the value of synthetically derived fine (0.78 km) scale temporal ratios of near‐surface soil moisture when assimilated in conjunction with coarse (25 km) resolution retrievals of near‐surface soil water content. Temporal ratios of soil moisture may, in general, be thought of as a noisy data set conveying information on the spatial organization of near‐surface soil moisture as opposed to reliable estimates of specific volumetric water content values. We find that the joint assimilation of synthetic temporal ratios of soil moisture and coarse volumetric soil moisture retrievals leads to the recovery of relevant spatial features not captured in the 25‐km data set or by VIC‐3L.

A Case Study on the Effects of Heterogeneous Soil Moisture on Mesoscale Boundary-Layer Structure in the Southern Great Plains, U.S.A. Part II: Mesoscale Modelling

The importance of soil moisture inputs and improved model physics in the prediction of the daytime boundary-layer structure during the Southern Great Plains Hydrology Experiment 1997 (SGP97) is investigated using the non-hydrostatic fifth-generation Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model MM5. This is Part II of a two-part study examining the relationship of surface heterogeneity to observed boundary-layer structure. Part I focuses on observations and utilizes a simple model while Part II uses observations and MM5 modelling. Soil moisture inputs tested include a lookup table based on soil type and season, output from an offline land-surface model (LSM) forced by atmospheric observations, and high-resolution (\(\approx\) 800 m) airborne microwave remotely sensed data. Model physics improvements are investigated by comparing an LSM directly coupled with the MM5 to a simpler force-restore method at the surface. The scale of land surface heterogeneities is compared to the scale of their effects on boundary-layer structure.

Effects of the Near-Surface Soil Moisture Profile on the Assimilation of L-band Microwave Brightness Temperature

Abstract The impact of model and observation errors in the European Land Data Assimilation System (ELDAS) data assimilation system on the analyzed surface variables has been studied using the Southern Great Plains Hydrology Experiment (SGP) 1997 and 1999 datasets. The model error for soil moisture was derived from an error propagation experiment based on perturbed rainfall forcing data. It was found that the errors for the top three model layers are 0.010, 0.010, and 0.0015 m3 m−3, respectively. Data assimilation experiments based on screen-level variables (2-m temperature and humidity) and L-band brightness temperature observations from SGP97 with this error distribution result in improved soil moisture forecasts when compared to model runs with a vertically constant model error of 0.005 m3 m−3. In the second part of this study, the effect of the vertical soil moisture distribution—which can hardly be resolved by large-scale hydrological models—in the assimilation system has been quantified using SGP99 data. The vertical profile has a significant impact on the modeled brightness temperatures. Based on the time elapsed between a rainfall event and the observation, a correction scheme has been developed that can be applied in observation space. The assimilation of brightness temperatures led to more accurate predictions of soil moisture and surface fluxes when the correction scheme was used.

ASSESSMENT OF SAMPLING ERROR ASSOCIATED WITH SOIL MOISTURE ESTIMATION DESIGNS1

ABSTRACT:A spectral formalism was developed and applied to quantify the sampling errors due to spatial and/or temporal gaps in soil moisture measurements. A design filter was developed to compute the sampling errors for discrete measurements in space and time. This filter has as its advantage a general form applicable to various types of sampling design. The lack of temporal measurements of the two‐dimensional soil moisture field made it difficult to compute the spectra directly from observed records. Therefore, the wave number frequency spectra of soil moisture data derived from stochastic models of rainfall and soil moisture were used. Parameters for both models were estimated using data from the Southern Great Plains Hydrology Experiment (SGP97) and the Oklahoma Mesonet. The estimated sampling error of the spatial average soil moisture measurement by airborne L‐band microwave remote sensing during the SGP97 hydrology experiment is estimated to be 2.4 percent. Under the same climate conditions and soil properties as the SGP97 experiment, equally spaced ground probe networks at intervals of 25 and 50 km are expected to have about 16 percent and 27 percent sampling error, respectively. Satellite designs with temporal gaps of two and three days are expected to have about 6 percent and 9 percent sampling errors, respectively.

Optimal multiscale Kalman filter for assimilation of near‐surface soil moisture into land surface models

We undertake an alternative and novel approach to assimilation of near‐surface soil moisture into land surface models by means of an extension of multiscale Kalman filtering (MKF). While most data assimilation studies rely on the assumption of spatially independent near‐surface soil moisture observations to attain computational tractability in large‐scale problems, MKF allows us to explicitly and very efficiently model the spatial dependence and scaling properties of near‐surface soil moisture fields. Furthermore, MKF has the appealing ability to cope with model predictions and observations made at different spatial scales. Yet another essential feature of our approach is that we resort to the use of the expectation maximization (EM) algorithm in conjunction with MKF so that the statistical parameters inherent to MKF may be optimally determined directly from the data at hand and allowed to vary over time. This constitutes a significant advantage since these parameters (e.g., observation and model error noise variances) essentially determine the performance of the assimilation approach and have so far been most commonly prescribed heuristically and not allowed to evolve in time. We test our approach by assimilating the near‐surface soil moisture fields derived from electronically scanned thinned array radiometer (ESTAR) during the Southern Great Plains Hydrology experiment of 1997 (SGP97) into the three‐layer variable infiltration capacity (VIC‐3L) land surface model. The results show that assimilation significantly improves the short‐term predictions of soil moisture and energy fluxes from VIC‐3L, especially with regard to capturing the spatial structure of these state variables. Additionally, we find that allowing the statistical parameters of the assimilation algorithm to evolve in time allows for an adequate representation of the time‐varying uncertainties in land surface model predictions.

Soil moisture retrieval during the Southern Great Plains Hydrology Experiment 1999: A comparison between experimental remote sensing data and operational products

Soil moisture images for the Southern Great Plains Hydrology Experiment (SGP99) were derived from airborne passive microwave measurements obtained from the electronically scanned thinned array radiometer (ESTAR) and the polarimetric scanning radiometer (PSR) at L‐band and C‐band, respectively. In order to mimic operational products, a robust retrieval method is chosen and the corrections for vegetation and surface roughness are based on average values from the literature. Validation against field measurements results in root‐mean‐square errors and biases of 2.9% and 4.2% in volumetric soil moisture for the ESTAR data set and 4.6% and 6.0% for the PSR product. For this sparsely vegetated experiment region, the quality of the C‐band derived soil moisture is therefore comparable to the corresponding L‐band product. The accuracies of the operational data sets, namely, the European Centre for Medium‐Range Weather Forecasts (ECMWF) 40 year reanalysis product (ERA40) and the European Remote Sensing Satellite (ERS) scatterometer derived soil moisture, are quantified based on the high‐resolution PSR soil moisture images for the SGP99 region. The ERA40 reanalysis comprises soil moisture data for four soil layers at the T159 spectral resolution. The top 7 cm layer soil moisture product is in reasonable agreement with the C‐band derived soil moisture data sets. Temporal and spatial patterns are well represented, but a significant wet bias of up to 14.4% (ERA40) is present in dry conditions. In order to evaluate the quality of the operational data sets on a longer temporal timescale, in situ soil moisture measurements at the Little Washita National Oceanic and Atmospheric Administration/Atmospheric Turbulence and Diffusion Division (NOAA/ATDD) site are used to analyze time series for June 1997 to December 1998. The temporal evolution of soil moisture is captured reasonably well by the ERA40 product and the ERS scatterometer derived surface soil moisture data set. RMS errors were found to be 5.6% and 5.7%, respectively. The study shows that passive microwave remote sensing has the potential to improve operational products.

Using a Microwave Emission Model to Estimate Soil Moisture from ESTAR Observations during SGP99

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.

A downscaling framework for L band radiobrightness temperature imagery

In this paper we introduce a general downscaling framework and apply it to L band microwave radiobrightness temperature fields retrieved from electronically scanned thinned array radiometer (ESTAR). The gist of the downscaling scheme presented in this paper is the statistical characterization of scale‐invariant properties of the wavelet coefficients or fluctuations from long memory 1/f processes. We test the proposed downscaling framework with the radiobrightness temperature images collected during the Southern Great Plains hydrology experiment of 1997. We produce realizations of radiobrightness temperature at 800‐m resolution given a mean‐area value at approximately 30‐km resolution (the near‐future expected operational scale). The results obtained evince that the proposed downscaling methodology is capable of accurately preserving the variability and overall structure of spatial dependence of the observed radiobrightness temperature fields.

A stochastic modeling approach for characterizing the spatial structure of L band radiobrightness temperature imagery

This study focuses on the statistical characterization of the spatial structure of L band microwave radiobrightness temperature fields retrieved during the Southern Great Plains hydrology experiment of 1997 (SGP97). It is found that the radiobrightness temperature observations of interest can be considered nonstationary scaling processes that exhibit persistence or long memory. This implies that the spatial dependence of observations decays very slowly at large separation distances such that models with exponentially decaying autocorrelation (e.g., autoregressive moving average models or exponential models commonly used in geostatistics) are not appropriate. It is further shown that the radiobrightness temperature fields retrieved during SGP97 exhibit distinct scaling behaviors along the horizontal (west to east) and vertical (south to north) directions. A two‐dimensional implementation of the fractionally integrated moving average (FIMA) time series model is shown capable of capturing the spatial autocovariance structure of the observations. The results presented evince that the FIMA paradigm allows for robust estimation of distinct scaling exponents along the horizontal and vertical directions both in stationary and nonstationary situations. Comparisons to alternative heuristic methods for determining the scaling exponent(s) further demonstrate that FIMA models yield estimates with superior accuracy. Additionally, within the FIMA framework it is possible to jointly and accurately model the spatial dependence of radiobrightness temperature for observations separated by short and long distances.

Correcting Land Surface Model Predictions for the Impact of Temporally Sparse Rainfall Rate Measurements Using an Ensemble Kalman Filter and Surface Brightness Temperature Observations

Current attempts to measure short-term (<1 month) rainfall accumulations using spaceborne radiometers are characterized by large sampling errors associated with low observation frequencies for any single point on the globe (from two to eight measurements per day). This degrades the value of spaceborne rainfall retrievals for the monitoring of surface water and energy balance processes. Here a data assimilation system, based on the assimilation of surface L-band brightness temperature (TB) observations via the ensemble Kalman filter (EnKF), is introduced to correct for the impact of poorly sampled rainfall on land surface model predictions of root-zone soil moisture and surface energy fluxes. The system is evaluated during the period from 1 April 1997 to 31 March 1998 over two sites within the U.S. Southern Great Plains. This evaluation includes both a data assimilation experiment, based on synthetically generated TB measurements, and the assimilation of real TB observations acquired during the 1997 Southern Great Plains Hydrology Experiment (SGP97). Results suggest that the EnKF-based assimilation system is capable of correcting a substantial fraction (>50%) of model error in root-zone (40 cm) soil moisture and latent heat flux predictions associated with the use of temporally sparse rainfall measurements as forcing data. Comparable gains in accuracy are demonstrated when actual TB measurements made during the SGP97 experiment are assimilated.

The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using Ensemble Kalman filtering: a case study based on ESTAR measurements during SGP97

An Ensemble Kalman filter (EnKF) is used to assimilate airborne measurements of 1.4 GHz surface brightness temperature ( T B) acquired during the 1997 Southern Great Plains Hydrology Experiment (SGP97) into the TOPMODEL-based Land–Atmosphere Transfer Scheme (TOPLATS). In this way, the potential of using EnKF-assimilated remote measurements of T B to compensate land surface model predictions for errors arising from a climatological description of rainfall is assessed. The use of a real remotely sensed data source allows for a more complete examination of the challenges faced in implementing assimilation strategies than previous studies where observations were synthetically generated. Results demonstrate that the EnKF is an effective and computationally competitive strategy for the assimilation of remotely sensed T B measurements into land surface models. The EnKF is capable of extracting spatial and temporal trends in root-zone (40 cm) soil water content from T B measurements based solely on surface (5 cm) conditions. The accuracy of surface state and flux predictions made with the EnKF, ESTAR T B measurements, and climatological rainfall data within the Central Facility site during SGP97 are shown to be superior to predictions derived from open loop modeling driven by sparse temporal sampling of rainfall at frequencies consistent with expectations of future missions designed to measure rainfall from space (6–10 observations per day). Specific assimilation challenges posed by inadequacies in land surface model physics and spatial support contrasts between model predictions and sensor retrievals are discussed.

Spatial characterization of soil moisture estimates from the Southern Great Plain (SGP 97) hydrology experiment

The Southern Great Plains Hydrology experiment (SGP'97) was conducted on an area approximately 12 times larger than that of previous field experiments (e.g. Washita'92), and thus the statistical structure of soil moisture can be examined using large-scale remotely sensed images (40 km×250 km). Here, empirical scaling analysis was conducted to elucidate the link-ages between the spatial and temporal variability of soil moisture and the succession of wetting and drying cycles in the landscape. The results show that the soil moisture fields exhibit multiscaling behavior varying with the scales of observation and hydrometeorological conditions. During inter-storm periods the spatial evolution of soil moisture is closely associated with the spatial variability of soil permeability, and by vegetation after it reaches values near field-capacity. Topography appears to dominate the spatial structure of soil moisture during and immediately after storm events, while evapotranspiration increasingly dominates the evolution of soil moisture fields as the landscape dries. Specifically, the multiscaling behavior is consistent with the scaling behavior of soil hydraulic properties as described by soil texture parameters such as sand and clay percentage. Finally, a break in statistical symmetry (multiple scaling behavior) was identified, which separates the spatial and temporal evolution of the statistical structure of soil moisture fields for wavelengths below and above 10 km, the α- and β-scale ranges respectively.

Impact of Soil Moisture Aggregation on Surface Energy Flux Prediction During SGP'97

The nonlinear nature of the point‐scale soil moisture‐transpiration relationship, combined with the highly heterogeneous spatial structure of soil moisture fields and the limited horizontal resolution of spaceborne microwave radiometers, has generated considerable interest in the soil moisture aggregation problem. Specifically, understanding the impact of aggregating soil moisture information up to regional grid‐scales (∼103 km2) on the grid‐scale prediction of surface water and energy fluxes within the land surface component of a weather prediction model. Using data from the 1997 Southern Great Plains Hydrology Experiment (SGP'97), a conceptual link is presented between the impact of soil moisture aggregation on surface energy flux prediction and the spatial scaling properties of soil moisture fields.

Downscaling of soil moisture fields using a modified fractal interpolation scheme

The scaling analysis of the soil moisture from the Southern Great Plains Hydrology experiment (SGP'97) showed that the scaling behavior of soil moisture fields violates the scale invariance at large-scale and this characteristic can be explained by field parameters related water holding capacity. Therefore, ancillary data were incorporated in to the fractal interpolation method as vertical scaling parameters to replicate the spatial characteristic of soil moisture from the existing characteristics of field parameters. Even though previous works related random fractal downscaling method can generate more deterministion output as the number of observations is increased, the output is a single realization of many. Therefore, they have limitation for practical application. A modified fractal interpolation method using a contraction mapping approach was used to construct unique fractal interpolated surfaces. We applied the modified fractal interpolation method for downscaling the retrieved soil moisture from the SGP'97 experiment. The results demonstrate that the downscaling model can capture all the basic statistics of soil moisture at higher-resolution as well the dual-scaling behavior for spatial scales above and below 10 km for a wide range of environmental conditions including very wet and dry soils.

Soil moisture variability and scale-dependency of nonlinear parameterizations in coupled land–atmosphere models

Many of the relationships used in coupled land–atmosphere models to describe interactions between the land surface and the atmosphere have been empirically parameterized and thus are inherently dependent on the observational scale for which they were derived and tested. However, they are often applied at scales quite different than the ones they were intended for due to practical necessity. In this paper, a study is presented on the scale-dependency of parameterizations which are nonlinear functions of variables exhibiting considerable spatial variability across a wide range of scales. For illustration purposes, we focus on parameterizations which are explicit nonlinear functions of soil moisture. We use data from the 1997 Southern Great Plains Hydrology Experiment (SGP97) to quantify the spatial variability of soil moisture as a function of scale. By assuming that a parameterization keeps its general form the same over a range of scales, we quantify how the values of its parameters should change with scale in order to preserve the spatially averaged predicted fluxes at any scale of interest. The findings of this study illustrate that if modifications are not made to nonlinear parameterizations to account for the mismatch of scales between optimization and application, then significant systematic biases may result in model-predicted water and energy fluxes.

Verification of Patch- and Regional-Scale Energy Balance Estimates Derived from Microwave and Optical Remote Sensing during SGP97

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 ...

Multitemporal monitoring of soil moisture with RADARSAT SAR during the 1997 Southern Great Plains hydrology experiment

Multitemporal RADARSAT Synthetic Aperture Radar (SAR) imagery was evaluated for monitoring soil moisture during the Southern Great Plains 1997 Hydrology Experiment (SGP97). Over the 1-month experiment a total of 10 RADARSAT (C-band; 5.3GHz; HH polarized) acquisitions were obtained. After co-registration of the images, the dependence of the backscattering coefficient (σ) on incidence angle (θ) was evaluated. An angular correction scheme was applied to the data and the correlation of a values with volumetric soil moisture measurements was determined. A high positive correlation between the two was observed for wheat stubble fields, whereas no correlation could be observed for pasture fields. A change detection approach yielded a correlation of soil moisture changes to σ changes for wheat stubble fields, indicating the potential of this method for soil moisture monitoring.

Vegetative and Atmospheric Corrections for the Soil Moisture Retrieval from Passive Microwave Remote Sensing Data: Results from the Southern Great Plains Hydrology Experiment 1997

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.

Influence of near-surface soil moisture on regional scale heat fluxes: model results using microwave remote sensing data from SGP97

During the 1997 Southern Great Plains Hydrology Experiment (SGP97), passive microwave observations using the L-band electronically scanned thinned array radiometer (ESTAR) were used to extend surface soil moisture retrieval algorithms to coarser resolutions and larger regions with more diverse conditions. This near-surface soil moisture product (W) at 800 m pixel resolution together with land use and fractional vegetation cover (f/sub c/) estimated from normalized difference vegetation index (NDVI) was used for computing spatially distributed sensible (H) and latent (LE) heat fluxes over the SGP97 domain (an area /spl sim/40/spl times/260 km) using a remote sensing model (called the two-source energy Balance-soil moisture, TSEB/sub SM/, model). With regional maps of W and the heat fluxes, spatial correlations were computed to evaluate the influence of W on H and LE. For the whole SGP97 domain and full range in f/sub c/, correlations (R) between W and LE varied from 0.4 to 0.6 (R/spl sim/0.5 on average), while correlations between W and H varied from -0.3 to -0.7 (R/spl sim/-0.6 on average). The W-LE and W-H correlations were dramatically higher when variability due to f/sub c/ was considered by using NDVI as a surrogate for f/sub c/ and computing R between heat fluxes and corresponding W values under similar fractional vegetation cover conditions. The results showed a steady decline in correlation with increasing NDVI or f/sub c/. Typically, |R|/spl gsim/0.9 for data sorted by NDVI having values /spl lsim/0.5 or f/sub c//spl lsim/0.5, while |R|/spl lsim/0.5 for the data sorted under high canopy cover where NDVI/spl gsim/0.6 or f/sub c//spl gsim/0.7.