Niko Verhoest

On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar

Synthetic Aperture Radar has shown its large potential for retrieving soil moisture maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of soil moisture is an ill-posed problem when using single configuration imagery. Unless accurate surface roughness parameter values are available, retrieving soil moisture from radar backscatter usually provides inaccurate estimates. The characterization of soil roughness is not fully understood, and a large range of roughness parameter values can be obtained for the same surface when different measurement methodologies are used. In this paper, a literature review is made that summarizes the problems encountered when parameterizing soil roughness as well as the reported impact of the errors made on the retrieved soil moisture. A number of suggestions were made for resolving issues in roughness parameterization and studying the impact of these roughness problems on the soil moisture retrieval accuracy and scale.

The importance of the spatial patterns of remotely sensed soil moisture in the improvement of discharge predictions for small-scale basins through data assimilation

Abstract In this paper, we investigate to which degree information concerning the spatial patterns of remotely sensed soil moisture data are needed in order to improve discharge predictions from hydrological models. For this purpose, we use the TOPMODEL-based Land–Atmosphere Transfer Scheme (TOPLATS). The remotely sensed soil moisture values are determined using C-band backscatter data from the European Space Agency (ESA) European Remote Sensing (ERS) Satellites. A baseline run, without soil moisture assimilation, is established for both the distributed and lumped versions of the land–atmosphere scheme. The modeled discharge matches the observations slightly better for the distributed model than for the lumped model. The remotely sensed soil moisture data are assimilated into the distributed version of the model through the ‘nudging to individual observations’ method, and the ‘statistical correction assimilation’ method. The remotely sensed soil moisture data are also assimilated into the lumped version of the model through the ‘statistical correction assimilation’ method. The statistical correction assimilation method leads to similar, and improved, discharge predictions for both the distributed and lumped models. The nudging to individual observations method leads, for the distributed model, to only slightly better results than the statistical correction assimilation method. As a consequence, it is suggested that it is sufficient to assimilate the statistics (spatial mean and variance) of remotely sensed soil moisture data into lumped hydrological models when one wants to improve hydrological model-based discharge predictions.

Some analytical solutions of the linearized Boussinesq equation with recharge for a sloping aquifer

Subsurface flow from a hillslope can be described by the hydraulic groundwater theory as formulated by the Boussinesq equation. Several attempts have been made to solve this partial differential equation, and exact solutions have been found for specific situations. In the case of a sloping aquifer, Brutsaert [1994] suggested linearizing the equation to calculate the unit response of the hillslope. In this paper we first apply the work of Brutsaert by assuming a constant recharge to the groundwater table. The solution describes the groundwater table levels and the outflow in function of time. Then, an analytical expression is derived for the steady state solution by allowing time to approach infinity. This steady state water table is used as an initial condition to derive another analytical solution of the Boussinesq equation. This can then be used in a quasi steady state approach to compute outflow under changing recharge conditions. | Subsurface flow from a hillslope can be described by the hydraulic groundwater theory as formulated by the Boussinesq equation. Several attempts have been made to solve this partial differential equation, and exact solutions have been found for specific situations. In the case of a sloping aquifer, Brutsaert [1994] suggested linearizing the equation to calculate the unit response of the hillslope. In this paper we first apply the work of Brutsaert by assuming a constant recharge to the groundwater table. The solution describes the groundwater table levels and the outflow in function of time. Then, an analytical expression is derived for the steady state solution by allowing time to approach infinity. This steady state water table is used as an initial condition to derive another analytical solution of the Boussinesq equation. This can then be used in a quasi steady state approach to compute outflow under changing recharge conditions.

A comparison between soil roughness statistics used in surface scattering models derived from mechanical and laser profilers

The objective of this paper is to quantify the impact of the type of surface-profiling instrument on the roughness measurements in radar remote sensing studies. Particularly, the use of mechanical profilers as compared to more precise laser profilers is investigated. The motivations for this study are twofold. First, simple and inexpensive mechanical profilers will probably still be used extensively for in situ ground measurements in the next few years, e.g., to investigate the use of multipolarization, multiincidence angle satellite data, i.e., Advanced Synthetic Aperture Radar (ASAR) onboard the European Space Agency Environmental Satellite. Second, a great amount of roughness data have been acquired in the past by means of mechanical profilers and, to date, a quantification of the error budget affecting these measurements is still missing. The paper focuses on modeling and quantifying the measurement errors associated with profiles a few meters long. To determine the errors, we compare soil roughness measurements obtained using laser and mechanical profilers over agricultural surfaces with different roughness characteristics. The analyzed datasets consist of roughness measurements acquired over the Matera site (Italy) and the Marestaing site, near Toulouse (France), in 1998 and 2000, respectively. Analytical expressions for first and second statistical moments of roughness parameters as a function of different sources of measurement errors are derived and compared to experimental values. The results show that mechanical measurements, once appropriately calibrated, are in overall good agreement with laser measurements. Practical indications of the most appropriate profiler length and number of independent measurements to be recorded are also derived in the paper.

Parameterization of tillage-induced single-scale soil roughness from 4-m profiles

Soil roughness greatly affects the scattering process of microwaves to the soil surface. Previous studies showed that the values of roughness parameters increase asymptotically with increasing profile length. In this paper, 25-m profiles are used to study the influence of profile length on the roughness parameters and on the shape of the autocorrelation function. It is further investigated whether correct soil roughness parameters, as obtained from long surface roughness profiles, can be determined from 4-m-long profiles. Therefore, the extrapolation of an empirical relationship between roughness parameters and profile length is investigated, for three different roughness classes. The technique yields parameter values which are comparable to the 25-m roughness parameters.

Satellite-Scale Snow Water Equivalent Assimilation into a High-Resolution Land Surface Model

Four methods based on the ensemble Kalman filter (EnKF) are tested to assimilate coarse-scale (25 km) snow water equivalent (SWE) observations (typical of passive microwave satellite retrievals) into finescale (1 km) land model simulations. Synthetic coarse-scale observations are assimilated directly using an observation operator for mapping between the coarse and fine scales or, alternatively, after disaggregation (regridding) to the finescale model resolution prior to data assimilation. In either case, observations are assimilated either simultaneously or independently for each location. Results indicate that assimilating disaggregated finescale observations independently (method 1D-F1) is less efficient than assimilating a collection of neighboring disaggregated observations (method 3D-Fm). Direct assimilation of coarse-scale observations is superior to a priori disaggregation. Independent assimilation of individual coarse-scale observations (method 3D-C1) can bring the overall mean analyzed field close to the truth, but does not necessarily improve estimates of the finescale structure. There is a clear benefit to simultaneously assimilating multiple coarse-scale observations (method 3D-Cm) even as the entire domain is observed, indicating that underlying spatial error correlations can be exploited to improve SWE estimates. Method 3D-Cm avoids artificial transitions at the coarse observation pixel boundaries and can reduce the RMSE by 60% when compared to the open loop in this study.

State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency

[1] An ensemble Kalman filter for state estimation and a bias estimation algorithm were applied to estimate individual soil moisture profiles in a small corn field with the CLM2.0 model through the assimilation of measurements from capacitance probes. Both without and with inclusion of bias correction, the effect of the assimilation frequency, the assimilation depth, and the number of observations assimilated per profile were studied. Assimilation of complete profiles had the highest impact on deeper soil layers, and the optimal assimilation frequency was about 1–2 weeks, if bias correction was applied. The optimal assimilation depth depended on the calibration results. Assimilation in the surface layer had typically less impact than assimilation in other layers. Through bias correction the soil moisture estimate greatly improved. In general, the correct propagation of the innovations for both the bias-blind state and bias filtering from any layer to other layers was insufficient. The approximate estimation of the a priori (bias) error covariance and the choice of a zero-initialized persistent bias model made it impossible to estimate the bias in layers for which no observations were available.

Mapping basin scale variable source areas from multitemporal remotely sensed observations of soil moisture behavior

Soil moisture is an important and highly variable component of the hydrologic cycle. Active microwave remote sensing offers the potential for frequent observation of soil moisture at basin and regional scales. Notwithstanding recent advances, the goal of obtaining accurate and reliable measurements or maps of soil moisture from these instruments remains elusive. The main difficulties for active sensors such as synthetic aperture radar (SAR) are the combined effects of soil moisture, surface roughness, and vegetation on the backscattered signal. We show that it is possible to separate soil moisture information from the other physical factors that dominate the radar backscattering, such as topography and land cover, through a principal component analysis of a time series of eight European Remote Sensing (ERS) SAR images. The soil moisture patterns observed in one of the principal components are consistent with the rainfall-runoff dynamics of a catchment and reflect the variable source areas occuring in the vicinity of the river network.

Optimization of a coupled hydrology–crop growth model through the assimilation of observed soil moisture and leaf area index values using an ensemble Kalman filter

It is well known that the presence and development stage of vegetation largely influences the soil moisture content. In its turn, soil moisture availability is of major importance for the development of vegetation. The objective of this paper is to assess to what extent the results of a fully coupled hydrology-crop growth model can be optimized through the assimilation of observed leaf area index ( LAI) or soil moisture values. For this purpose the crop growth module of the World Food Studies ( WOFOST) model has been coupled to a fully process based water and energy balance model ( TOPMODEL-Based Land-Atmosphere Transfer Scheme ( TOPLATS)). LAI and soil moisture observations from 18 fields in the loamy region in the central part of Belgium have been used to thoroughly validate the coupled model. An observing system simulation experiment ( OSSE) has been performed in order to assess whether soil moisture and LAI observations with realistic uncertainties are useful for data assimilation purposes. Under realistic conditions ( biweekly observations with a noise level of 5 volumetric percent for soil moisture and 0.5 for LAI) an improvement in the model results can be expected. The results show that the modeled LAI values are not sensitive to the assimilation of soil moisture values before the initiation of crop growth. Also, the modeled soil moisture profile does not necessarily improve through the assimilation of LAI values during the growing season. In order to improve both the vegetation and soil moisture state of the model, observations of both variables need to be assimilated.

Assessment of the operational applicability of RADARSAT-1 data for surface soil moisture estimation

The present paper focuses on the ability of currently available RADARSAT-1 data to estimate surface soil moisture over an agricultural catchment using the theoretical integral equation model (IEM). Five RADARSAT-1 scenes acquired over Navarre (north of Spain) between February 27, 2003 and April 2, 2003 have been processed. Soil moisture was measured at different fields within the catchment. Roughness measurements were collected in order to obtain representative roughness parameters for the different tillage classes. The influence of the cereal crop that covered most of the fields was taken into account using the semiempirical water cloud model. The IEM was run in forward and inverse mode using vegetation corrected RADARSAT-1 data and surface roughness observations. Results showed a great dispersion between IEM simulations and observations at the field scale, leading to inaccurate estimations. As the surface correlation length is the most difficult parameter to measure, different approaches for its estimation have been tested. This analysis revealed that the spatial variability in the surface roughness parameters seems to be the reason for the dispersion observed rather than a deficient measurement of the correlation length. At the catchment scale, IEM simulations were in good agreement with observations. The error values obtained in the inverse simulations were in the range of in situ soil moisture measuring methods (0.04 cm/sup 3//spl middot/cm/sup -3/). Taking into account the small size of the catchment studied, these results are encouraging from a hydrological point of view.