Lutz Weihermüller

Analysis of air-launched ground-penetrating radar techniques to measure the soil surface water content

We analyze the common surface reflection and full-wave inversion methods to retrieve the soil surface dielectric permittivity and correlated water content from air-launched ground-penetrating radar (GPR) measurements. In the full-wave approach, antenna effects are filtered out from the raw radar data in the frequency domain, and full-wave inversion is performed in the time domain, on a time window focused on the surface reflection. Synthetic experiments are performed to investigate the most critical hypotheses on which both techniques rely, namely, the negligible effects of the soil electric conductivity (?) and layering. In the frequency range 1–2 GHz we show that for ? > 0.1 Sm?1, significant errors are made on the estimated parameters, e.g., an absolute error of 0.10 in water content may be observed for ? = 1 Sm?1. This threshold is more stringent with decreasing frequency. Contrasting surface layering may proportionally lead to significant errors when the thickness of the surface layer is close to one fourth the wavelength in the medium, which corresponds to the depth resolution. Absolute errors may be >0.10 in water content for large contrasts. Yet we show that full-wave inversion presents valuable advantages compared to the common surface reflection method. First, filtering antenna effects may prevent absolute errors >0.04 in water content, depending of the antenna height. Second, the critical reference measurements above a perfect electric conductor (PEC) are not required, and the height of the antenna does not need to be known a priori. This averts absolute errors of 0.02–0.09 in water content when antenna height differences of 1–5 cm occur between the soil and the PEC. A laboratory experiment is finally presented to analyze the stability of the estimates with respect to actual measurement and modeling errors. While the conditions were particularly well suited for applying the common reflection method, better results were obtained using full-wave inversion.

Electromagnetic induction calibration using apparent electrical conductivity modelling based on electrical resistivity tomography

Electromagnetic parameters of the subsurface such as electrical conductivity are of great interest for non-destructive determination of soil properties (e.g., clay content) or hydrologic state variables (e.g., soil water content). In the past decade, several non-invasive geophysical methods have been developed to measure subsurface parameters in situ . Among these methods, electromagnetic (EM) induction appears to be the most efficient one that is able to cover large areas in a short time. However, this method currently does not provide absolute values of electrical conductivity due to calibration problems, which hinders a quantitative analysis of the measurement. In this study, we propose to calibrate EM induction measurements with electrical conductivity values measured with electrical resistivity tomography (ERT). EM induction measures an apparent electrical conductivity at the surface, which represents a weighted average of the electrical conductivity distribution over a certain depth range, whereas ERT inversion can provide absolute values for local conductivities as a function of depth. EM induction and ERT measurements were collected along a 120-metre-long transect. To reconstruct the apparent electrical conductivity measured with EM induction, the inverted ERT data were used as input in an electromagnetic forward modelling tool for magnetic dipoles over a horizontally layered medium considering the frequencies and offsets used by the EM induction instruments. Comparison of the calculated and measured apparent electrical conductivities shows very similar trends but a shift in absolute values, which is attributed to system calibration problems. The observed shift can be corrected for by linear regression. This new calibration strategy for EM induction measurements now enables the quantitative mapping of electrical conductivity values over large areas.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Brightness Temperature and Soil Moisture Validation at Different Scales During the SMOS Validation Campaign in the Rur and Erft Catchments, Germany

The European Space Agencys Soil Moisture and Ocean Salinity (SMOS) satellite was launched in November 2009 and delivers now brightness temperature and soil moisture products over terrestrial areas on a regular three-day basis. In 2010, several airborne campaigns were conducted to validate the SMOS products with microwave emission radiometers at L-band (1.4 GHz). In this paper, we present results from measurements performed in the Rur and Erft catchments in May and June 2010. The measurement sites were situated in the very west of Germany close to the borders to Belgium and The Netherlands. We developed an approach to validate spatial and temporal SMOS brightness temperature products. An area-wide brightness temperature reference was generated by using an area-wide modeling of top soil moisture and soil temperature with the WaSiM-ETH model and radiative transfer calculation based on the L-band Microwave Emission of the Biosphere model. Measurements of the airborne L-band sensors EMIRAD and HUT-2D on-board a Skyvan aircraft as well as ground-based mobile measurements performed with the truck mounted JÜLBARA L-band radiometer were analyzed for calibration of the simulated brightness temperature reference. Radiative transfer parameters were estimated by a data assimilation approach. By this versatile reference data set, it is possible to validate the spaceborne brightness temperature and soil moisture data obtained from SMOS. However, comparisons with SMOS observations for the campaign period indicate severe differences between simulated and observed SMOS data.

Mapping Field-Scale Soil Moisture With L-Band Radiometer and Ground-Penetrating Radar Over Bare Soil

Accurate estimates of surface soil moisture are essential in many research fields, including agriculture, hydrology, and meteorology. The objective of this study was to evaluate two remote-sensing methods for mapping the soil moisture of a bare soil, namely, L-band radiometry using brightness temperature and ground-penetrating radar (GPR) using surface reflection inversion. Invasive time-domain reflectometry (TDR) measurements were used as a reference. A field experiment was performed in which these three methods were used to map soil moisture after controlled heterogeneous irrigation that ensured a wide range of water content. The heterogeneous irrigation pattern was reasonably well reproduced by both remote-sensing techniques. However, significant differences in the absolute moisture values retrieved were observed. This discrepancy was attributed to different sensing depths and areas and different sensitivities to soil surface roughness. For GPR, the effect of roughness was excluded by operating at low frequencies (0.2-0.8 GHz) that were not sensitive to the field surface roughness. The root mean square (rms) error between soil moisture measured by GPR and TDR was 0.038 m3·m-3. For the radiometer, the rms error decreased from 0.062 (horizontal polarization) and 0.054 (vertical polarization) to 0.020 m3·m-3 (both polarizations) after accounting for roughness using an empirical model that required calibration with reference TDR measurements. Monte Carlo simulations showed that around 20% of the reference data were required to obtain a good roughness calibration for the entire field. It was concluded that relatively accurate measurements were possible with both methods, although accounting for surface roughness was essential for radiometry.

Inverse determination of heterotrophic soil respiration response to temperature and water content under field conditions

Heterotrophic soil respiration is an important flux within the global carbon cycle. Exact knowledge of the response functions for soil temperature and soil water content is crucial for a reliable prediction of soil carbon turnover. The classical statistical approach for the in situ determination of the temperature response (Q10 or activation energy) of field soil respiration has been criticised for neglecting confounding factors, such as spatial and temporal changes in soil water content and soil organic matter. The aim of this paper is to evaluate an alternative method to estimate the temperature and soil water content response of heterotrophic soil respiration. The new method relies on inverse parameter estimation using a 1-dimensional CO2 transport and carbon turnover model. Inversion results showed that different formulations of the temperature response function resulted in estimated response factors that hardly deviated over the entire range of soil water content and for temperature below 25°C. For higher temperatures, the temperature response was highly uncertain due to the infrequent occurrence of soil temperatures above 25°C. The temperature sensitivity obtained using inverse modelling was within the range of temperature sensitivities estimated from statistical processing of the data. It was concluded that inverse parameter estimation is a promising tool for the determination of the temperature and soil water content response of soil respiration. Future synthetic model studies should investigate to what extent the inverse modelling approach can disentangle confounding factors that typically affect statistical estimates of the sensitivity of soil respiration to temperature and soil water content.

Characterization of unsaturated porous media by high‐field and low‐field NMR relaxometry

[1] A comparison study of nuclear magnetic resonance relaxometry at high and low magnetic field (7 and 0.1 T) has been initiated for investigating the influence of the magnetic field strength, variable clay content, and different degrees of saturation on the relaxometric properties of four ideal porous media. The samples consisted of medium sand with increasing fractions of kaolin clay ranging from 0 to 15%. Six different volumetric water contents between saturation and θ = 0.05 were used. Changes in water content of the samples were achieved by slow evaporation. T 2 relaxation curves were monitored by the Carr-Purcell-Meiboom-Gill sequence and were further analyzed by inverse Laplace transformation, yielding T 2 distribution functions. Sand shows a slight continuous shift with decreasing water content of a bimodal distribution function of T 2 to faster relaxation at high and low magnetic field. Sand-clay mixtures show broad, bimodal distribution functions for both magnetic field intensities which shift slightly with decreasing water content. Signal amplitude behavior with variation of saturation degree was also monitored. An expected proportionality of the total signal amplitude with water content was observed for all samples at 0.1 T, whereas at 7 T deviations occurred for samples with a clay content higher than 5%, which are assigned to loss of signal in the first echo periods. The relaxivity in unsaturated clay-based porous media is mostly surface dominated, as the weak and comparable dependence of 1/T 2 on T E at both field strengths shows. Nevertheless, for a reliable determination of water content in mixed systems with varying texture and saturation the employment of multiecho sequences at low magnetic field strength are preferable.

Imaging water fluxes in porous media by magnetic resonance imaging using D2O as a tracer

In this study, we investigate the usefulness of D(2)O as a conservative tracer for monitoring water flux by MRI in a heterogeneous sand column. The column consisted of a cylindrical 3x9-cm packing of fine sand in which an 8-mm diameter cylindrical obstacle was placed. Constant steady-state flux densities between J(w)=0.07 and 0.28 cm min(-1) corresponding to mean pore flow velocities between 0.20 and 0.79 cm min(-1) were imposed at the top of the sand column, and a constant hydraulic head of -39 cm was maintained at the lower boundary. We injected pulses of 0.01 M NiCl(2) and 55% D(2)O and monitored the motion of the tracer plumes by MRI using a fast spin echo sequence over a period of 20 min. We observed that the center of gravity of all plumes moved with the mean pore flow velocity, which showed that D(2)O behaves as a conservative tracer. The motion of the tracer plume at J(w)=0.14 cm min(-1) was validated by a numerical simulation using HYDRUS2D, which reproduced the experimentally observed behavior very satisfactorily.

Estimation of Hydraulic Properties of a Sandy Soil Using Ground-Based Active and Passive Microwave Remote Sensing

In this paper, we experimentally analyzed the feasibility of estimating soil hydraulic properties from 1.4 GHz radiometer and 0.8-2.6 GHz ground-penetrating radar (GPR) data. Radiometer and GPR measurements were performed above a sand box, which was subjected to a series of vertical water content profiles in hydrostatic equilibrium with a water table located at different depths. A coherent radiative transfer model was used to simulate brightness temperatures measured with the radiometer. GPR data were modeled using full-wave layered medium Greens functions and an intrinsic antenna representation. These forward models were inverted to optimally match the corresponding passive and active microwave data. This allowed us to reconstruct the water content profiles, and thereby estimate the sand water retention curve described using the van Genuchten model. Uncertainty of the estimated hydraulic parameters was quantified using the Bayesian-based DREAM algorithm. For both radiometer and GPR methods, the results were in close agreement with in situ time-domain reflectometry (TDR) estimates. Compared with radiometer and TDR, much smaller confidence intervals were obtained for GPR, which was attributed to its relatively large bandwidth of operation, including frequencies smaller than 1.4 GHz. These results offer valuable insights into future potential and emerging challenges in the development of joint analyses of passive and active remote sensing data to retrieve effective soil hydraulic properties.

Measurement and Simulation of Topographic Effects on Passive Microwave Remote Sensing Over Mountain Areas: A Case Study From the Tibetan Plateau

Knowledge about the surface soil water content is essential because it controls the surface water dynamics and land-atmosphere interaction. In high mountain areas in particular, soil surface water content controls infiltration and flood events. Although satellite-derived surface soil moisture data from passive microwave sensors are readily available for most regions globally, mountainous areas are often excluded from these data (or at least flagged as biased) due to the strong topographic influence on the retrieved signal. Even though a substantial volume of literature is available dealing with topographic effects on spaceborne brightness temperature, no systematic analysis has been reported. Therefore, we present a comprehensive analysis of topographic effects on brightness temperature at C-band using a two-step approach. First, a well-controlled field experiment is carried out using a mobile truck-mounted C-band radiometer to analyze the impact of geometric and adjacent effects on the radiometer signal. Additionally, a comprehensive radiative transfer model is developed accounting for both effects and tested on the ground-based data. Second, recorded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data over the Tibetan Plateau were used to analyze the error due to the impact of topography using the developed model. The results of the field experiment clearly show that the geometric effect of a single hill has a much larger impact on brightness temperature compared to the adjacent effect of multiple hills, whereby, due to the geometric effect, the bias is up to +20 K for horizontal and -13 K for vertical polarization. For the adjacent effect, the bias is less than 3 K for both polarizations. Additionally, the developed radio transfer model was able to reproduce both effects with high accuracy. For the AMSR-E data, the model shows that the brightness temperature recorded is biased in the same way as the ground-based measurements and that uncertainties induced by the wide existence of atypical mountain regions in the Tibetan Plateau will have a great impact on the retrieving error (maximum 30%). The largest impact on the retrieval error, on the other hand, is calculated for the soil moisture with a maximum relative error of 44%. The negligible impact can be attributed to false parameterization of the soil texture, soil surface temperature, and sky temperature. Finally, the overall absolute error in the estimated water content is quantified on average with 4%, whereby single pixels indicate a maximum absolute error of up to 16%. In conclusion, we show that recorded spaceborne brightness temperatures are highly biased by topographic effects in mountainous regions using a comprehensive radiative transfer model. Additionally, we suggest using this model to invert the effective surface emissivity of mountain areas for standard processing of higher level data products such as surface soil water content.