This study assesses the accuracy of Spinning Enhanced Visible and Infrared Imager (SEVIRI) geostationary sensor-derived evapotranspiration (ET) estimates from Meteosat Second Generation (MSG) satellites across Europe. Evaluation encompasses seven dimensions: diurnal cycle, daily, intra-annual, inter-annual, ecosystem, and climate zone. Using in situ measurements from 54 eddy covariance (EC) sites spanning 2004 to 2018, SEVIRI actual ET products (diurnal and daily SEVIRI-ETa) and reference ET (daily SEVIRI-ET0) were examined. Results indicate varying accuracies based on diurnal and daily assessments, with seasonal fluctuations. Notably, SEVIRI-ETademonstrated better accuracy during summer and midday. Intra-annual accuracy for daily SEVIRI-ETashowed improvement during mid-year. Peat and grassland ecosystems exhibited higher accuracy than cropland ecosystems. SEVIRI-ET0mirrored similar patterns with the highest accuracy in crop ecosystems. Overall, the SEVIRI-ET products showed stable accuracy trends across different spatial domains, effectively capturing both inter-annual and spatial variations. This study comprehensively evaluates SEVIRI diurnal and daily ET products in Europe, offering insights for optimized product selection.

The retrieval of (almost) daily soil moisture from C-band Sentinel-1 Synthetic Aperture Radar (SAR) records is still influenced by incidence angle effects and vegetation dynamics. In this study we present a method to reduce the effects of both methods on the alpha approximation methods, a time series approach assuming changes in backscattering signals between two consecutive observations are related to a change in soil moisture. By implementing a Fourier series for incidence angle normalization and a linear regression to co-polarized backscatter for a vegetation adaption, the alpha approximation method has been extended to gain high temporal resolution soil moisture time series. The approach was tested in the Rur catchment, Germany and the Apulian Tavoliere, Italy.

Unmanned Aircraft Systems (UAS) are being used more often in agriculture to provide estimations of important metrics such as biomass because of the potential for improved temporal and spatial resolutions. More recently LiDAR sensor technology has advanced enabling more compact sizes that can be integrated with UAS platforms. Being an active sensor, LiDAR signals are capable of penetrating through the vegetation canopy providing more information on plant structure. Commonly, LiDAR data is used to derive only height information. However, newer studies have shown the retrieval of additional information from the spatial distribution and intensity of LiDAR signals. This study takes a unique look at combining these types of informative products, that are particular to LiDAR, for making biomass estimation with winter wheat.

As unmanned aircraft systems (UAS) remote sensing technology has advanced, providing unprecedented resolution, crop status at the individual plant level has become popular. Often plant detection is performed using high resolution RGB cameras that utilize algorithms and machine learning methods centered around trained pixel patterns of object textures. Similar methods with UAS LiDAR are not as explored considering their more recent UAS adaptation and the significantly larger price tag. Methods that have been created center around individual tree detection using crown delineations utilizing the height information and local maximum filtering. This study explores if this methodology can be used in a similar way for crops such as maize.

For high-resolution crosshole ground-penetrating radar (GPR) tomography, a wide-range of ray path angles are required, including transmitter-receiver pairs with high-angles. However, artifacts have been observed in the inverted GPR tomograms when high-angle data were incorporated in ray-based inversion (RBI) tomography due to not well-understood increasing apparent velocities for increasing ray-angles. To reduce these artifacts, it is common practice to limit the angular aperture to a threshold between 30° and 50°, which reduces the spatial resolution. We apply 3-D finite-difference time-domain GPR modeling including borehole fluid and resistive loaded finite-length antenna (FLA) models to study the increase of apparent velocity with increasing ray path angle. This study shows that the strong refraction of the electromagnetic waves at the borehole interface between water and subsurface is one of the reasons for these not well-understood phenomena. We introduce a novel borehole-fluid effect correction (BFEC) that relocates the transmitter and receiver positions to the location, where the refraction is occurring to remove any influence of the borehole such that the remaining traveltimes can be inverted using an RBI. BFEC improves the estimated apparent-velocity (relative permittivity) values and enables the incorporation of wide-angle ray paths resulting in more accurate tomograms. We verify the BFEC with a simple layered subsurface model and a realistic synthetic model. By applying curved-ray RBI without and with the BFEC, the subsurface structures are reconstructed with more details for the BFEC data and the average relative error model reduced from 13% to below 8% for the high-resolution inhomogeneous model.

The upcoming launch of the L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) will enable multi-frequency SAR observations when combined with existing C-band satellite missions (e.g., Sentinel-1). Due to the different penetration depths of the SAR signals, multi-frequency SAR offers great potential for field-scale agricultural monitoring and the estimation of soil and plant parameters. The SARSense campaign, conducted between June and August 2019 at the Selhausen agricultural test site near Jülich, Germany, has yielded a comprehensive dataset that includes both air- and space-borne C- and L-band SAR data, extensive in-situ field measurements of soil and plant parameters as well as unmanned aerial systems (UAS)-based multispectral and thermal infrared measurements and cosmic neutron sensing observations. The study provides both, an insight into the strengths and limitations of the acquired dataset as well as an analysis of the different behaviour of C- and L-band backscattering on changing soil moisture and plant parameters for taproot crops and cereals.

Droughts in recent years increased tree mortality in Europe. Especially the spruce, with its shallow root system, can not endure extreme events and loses resilience to infestations such as the bark beetle. We developed a simple method to transfer official forest statistics of a reference year to larger areas to locate affected spruce stands and retrieve actual statistics of a target year by implementing Sentinel-2 data. Google Earth Engine (GEE) was used to implement the approach and to investigate the impact of elevation, as a key environmental factor, on spatial patterns of spruce mortality.

Wildfire is an important process that affects nature environment and living organisms. In this study, we applied a causal network discovery method called PCMCI which performs in a PC condition selection stage to select relevant conditions and a MCI conditional independent test stage to control false positive rate, to detect casual relationships and time lags between wildfire burned area and weather/drought and vegetation conditions. The results show that for grassland, weather and aridity conditions are dominant indicators to BA (burned area). For shrub land, FWI (fire weather index) is dominant. For sparsely vegetated land cover, which is water or fuel limited region, vegetation growth and health conditions are dominant. For broad leaf forests, radiation is the most important indicator. While for needle leaf forests, temperature is dominant.

The Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup has been established to coordinate the development of standardized validation across the satellite-derived products from different platforms, sensors, and algorithms with reference measurements from the in situ networks. Soil moisture exhibits a high variability in space that challenges the in situ validation. One of the main drivers for this variability is the characteristic heterogeneity in the soil texture. By the machine learning methods using the soil profile measurements and the remotely sensed predictors, spatially continuous maps of basic soil properties such as soil texture and bulk density are available. Those can be used to estimate soil moisture variability within a satellite product grid cell, here exemplarily shown for the Soil Moisture Active Passive (SMAP) 36-km product. The soil moisture standard deviation is described as a function of the mean soil moisture, whereby the approach needs the mean and standard deviation of the hydraulic parameters as input. The resulting global data set helps identifying the number of in situ stations necessary to validate the coarse soil moisture products. For most SMAP grid cells, three to four stations are adequate to estimate the mean soil moisture for validation; however, also regions were identified where 80 stations are necessary.

In summer 2019 the SARSense campaign was held in Jülich, Germany, to provide insights into the potentials and specifications of the ESA Copernicus candidate mission ROSE-L (Radar Observation System for Europe). ROSE-L will consist of two satellites that carry a polarimetric L-band SAR. Since the L-band signal can penetrate through many natural materials such as vegetation, dry snow and ice, the mission will provide additional information that cannot be gathered by the Copernicus Sentinel-1 C-band SAR mission. The overall objective of the SARSense 2019 campaign is to analyze the mission design concerning its potential for agricultural monitoring services including target applications such as soil moisture monitoring, irrigation management, crop type discrimination, food security and precision farming. The SARSense in situ measurements of soil moisture, soil temperature, vegetation properties, UAS-based multispectral and thermal mapping, as well as the airborne SAR observations are presented as well as strategies for soil moisture retrieval and first analysis.

Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. We use this information for the prediction of the sub-grid soil moisture variability for each SMOS, SMAP, and ASCAT grid cell. The approach is based on a method that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean, available at https://doi.org/10.1594/PANGAEA.878889. The resulting data set helps identify adequate regions to validate coarse scale soil moisture products by providing a measure of representativeness of small-scale measurements for the coarse grid cell.

Horizontally stratified media are commonly used to represent naturally occurring and man-made structures, such as soils, roads, and pavements, when probed by ground-penetrating radar (GPR). Electromagnetic (EM) wave scattering from such multilayered media is dependent on the roughness of the interfaces. In this paper, we developed a closed-form asymptotic EM model considering random rough layers based on the scalar Kirchhoff-tangent plane approximation (SKA) model that we combined with planar multilayered media Green's functions. In order to validate our extended SKA model, we conducted simulations using a numerical EM solver based on the finite-difference time-domain (FDTD) method. We modeled a medium with three layers-a base layer of perfect electric conductor (PEC) overlaid by two layers of different materials with rough interfaces. The reflections at the first and at the second interface were both well reproduced by the SKA model for each roughness condition. For the reflection at the PEC surface, the extended SKA model slightly overestimated the reflection, and this overestimation increased with the roughness amplitude. Good agreement was also obtained between the FDTD simulation input values and the inverted root mean square (rms) height estimates of the top interface, while the inverted rms heights of the second interface were slightly overestimated. The accuracy and the performances of our asymptotic forward model demonstrate the promising perspectives for simulating rough multilayered media and, hence, for the full waveform inversion of GPR data to noninvasively characterize soils and materials.

Low resolution satellite products are often validated by point-scale in-situ soil moisture measurements from TDR/FDR devices, where the scale mismatch limits the quality of validation efforts in heterogeneous regions. In Cosmic Ray Neutron Probes (CRNP) provide area-average soil moisture within a 150-250 m radius footprint, so that they are able to fill the scale gap between both systems. In this study we evaluate differences and communalities between CRNP observations, and surface soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the METOP-A/B Advanced Scatterometer (ASCAT), the Soil Moisture Active and Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), as well as simulations from the Global Land Data Assimilation System Version 2 (GLDAS2). CRNPs within the Rur catchment in Germany have been selected for comparison. Standard validation scores identified SMAP to provide a high accuracy soil moisture product with low noise or uncertainties as compared to CRNPs.

The objective of the NASA Soil Moisture Active Passive (SMAP) mission is to provide global measurements of soil moisture and freeze/thaw states. SMAP integrates L-band radar and radiometer instruments as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments are a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer and the active L-band system F-SAR of DLR were flown simultaneously on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e., the main land cover types and all experimental monitoring sites. Here, we used the obtained data sets as a test bed for the analysis of three active-passive fusion techniques: 1) estimation of soil moisture by passive sensor data and subsequent disaggregation by active sensor backscatter data; 2) disaggregation of passive microwave brightness temperature by active microwave backscatter and subsequent inversion to soil moisture; and 3) fusion of two single-source soil moisture products from radar and radiometer. Results indicate that the regression parameters β are dependent on the radar vegetation index. The best performance was obtained by the fusion of radiometer brightness temperatures and radar backscatter, which was able to reach the same accuracy as single-source coarse-scale radiometer soil moisture retrieval but on a higher spatial resolution.

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 Green's 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.

The objective of the NASA Soil Moisture Active & Passive (SMAP) mission is to provide global measurements of soil moisture and its freeze/thaw state. The SMAP measurement approach is to integrate L-band radar and radiometer as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments will be a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer (PLMR2) and the active L-band system DLR F-SAR were flown on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e. all types of land cover and experimental monitoring sites. The obtained data sets are used as a test-bed for the analysis of existing and development of new active-passive fusion techniques.

Ground-penetrating radar (GPR) uses the recording of electromagnetic waves and is increasingly applied for a wide range of applications. Traditionally, the main focus was on the analysis of the medium permittivity since estimates of the conductivity using the far-field approximation contain relatively large errors and cannot be interpreted quantitatively. Recently, a full-waveform inversion (FWI) scheme has been developed that is able to reliably estimate permittivity and conductivity values by analyzing reflected waves present in on-ground GPR data. It is based on a frequency-domain solution of Maxwell's equations including far, intermediate, and near fields assuming a 3-D subsurface. Here, we adapt the FWI scheme for on-ground GPR to invert the direct ground wave traveling through the shallow subsurface. Due to possible interference with the airwaves and other reflections, an automated time-domain filter needed to be included in the inversion. In addition to the obtained permittivity and conductivity values, also the wavelet center frequency and amplitude return valuable information that can be used for soil characterization. Combined geophysical measurements were carried out over a silty loam with significant variability in the soil texture. The obtained medium properties are consistent with Theta probe, electromagnetic resistivity tomography, and electromagnetic induction results and enable the formulation of an empirical relationship between soil texture and soil properties. The permittivities and conductivities increase with increasing clay and silt and decreasing skeleton content. Moreover, with increasing permittivities and conductivities, the wavelet center frequency decreases, whereas the wavelet amplitude increases, which is consistent with the radiation pattern and the antenna coupling characteristics.

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.

Time series of Leaf Area Index (LAI) is of utmost importance for various disciplines of bio- and geosciences where satellite remote sensing makes LAI estimation possible for large areas. Remote sensing LAI, validated against in situ LAI (LAIinsitu), is used as base for calculating LAI for large areas e.g. on catchment scale. Various vegetation indices (NDVI, SAVI, both with and without substituting the red band with red-edge band) were applied for better estimates of LAIrapideye. SAVI (Soil Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index) present same correlation between remote sensing based predicted LAI (LAIrapideye) against LAIinsituin winter wheat fields. Both NDVI and SAVI with red-edge band showed improved correlation of remote sensing based VI and in situ measurements. Prior to vegetation indices calculation, radiometric normalization was applied to the time series of RapidEye data. To test the impact of radiometric normalization for calculating vegetation indices on a time series of satellite images, pre- and post-radiometric normalization LAIrapideyewas compared. More precise and high resolution estimation of LAI for large areas is of vital importance for improving evapotranspiration and soil moisture.

Correct parameterization of radiative transfer models is very important for high accuracy soil moisture retrievals from spaceborne L-band passive microwave sensors, such as the ESA Soil Moisture and Ocean Salinity (SMOS) Mission. In order to investigate the characteristics of radiative transfer parameters such as vegetation opacity and soil surface roughness, a dual state and parameter update data assimilation system has been developed. By assimilating SMOS brightness temperatures into the L-band Microwave Emission of the Biosphere (L-MEB) model, respective parameters can be estimated. The data assimilation system makes use of a temporal smoothing algorithm based on the Sampling Importance Resampling Particle Filter. The new approach, namely the Sampling Importance Resampling Particle Smoother, makes use of a particle weighting function valid not for a single time step, but for a specific time period. The resulting parameters estimated are more stable throughout the whole period under investigation, where the possibility to vary with time is still given. This is important to cover the seasonality of e.g. vegetation parameters.

The European Space Agency's 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.

In this study, we experimentally analyze the feasibility of estimating the soil hydraulic properties from L-band radiometer and ground-penetrating radar (GPR) data. L-band radiometer and ultrawideband off-ground GPR measurements were performed above a sand box in hydrostatic equilibrium with a water table located at different depths. The results of the inversions showed that the radar and radiometer signals contain sufficient information to estimate the soil water retention curve and its related hydraulic parameters with a relatively good accuracy compared to time-domain reflectometry estimates. However, an accurate estimation of the hydraulic parameters was only obtained by considering the saturated water content parameter as known during the inversion.

Global measurements of soil moisture, the key variables in the water cycle, are provided by spaceborne radiometer based on the long wavelength detection. As one potentially critical factor, topography will induce soil moisture retrieval error over mountain areas from space. To explore the mechanism of relief effects on L-band, the imitated landscapes are generated underlying Gaussian surfaces, and an improved microwave radiative transfer model to simulate relief effects is proposed. Based on the model, the significance of soil moisture and land surface temperature to relief effects in these terrain scenes are analyzed respectively, and the impact of topography on brightness temperature and soil moisture retrieval is predicted. It is shown that the maximum fractional error of soil moisture retrieval arisen by topography compared to soil moisture in the flat terrain at L band is 77.6%.

The objective of the NASA Soil Moisture Active & Passive (SMAP) mission is to provide global measurements of soil moisture by fused active and passive L-band microwave measurements. With an airborne campaign conducted in the Rur catchment, Germany, in 2012, an active and passive L-band microwave data set is generated. The passive Polarimetric L-band Multi-beam Radiometer (PLMR2) and the active L-band system DLR F-SAR were installed on the DLR Dornier DO 228 aircraft. Despite of the differences between the airborne and the future spaceborne sensors, the data set will serve as a test bed for the analysis of existing and development of enhanced future active/passive data fusion techniques. Moreover, the derivation of (vegetation) parameters for the fusion approaches is planned.

ASCAT and SMOS soil moisture products were validated for the year 2010 in the Rur and Erft catchments in the west of Germany. In situ data of three test sites of the TERENO initiative were used to calibrate the hydrological model WaSiM-ETH, which was applied to generate a soil moisture reference for the whole study area. Comparison of SMOS soil moisture with the reference displayed a high dry bias and low to moderate correlations. ASCAT soil moisture showed higher correlations and no bias. A temporal stability analysis exhibits low stability of the SMOS data and higher stability of ASCAT data. Generally, the performance of ASCAT is well, while there are still some problems with soil moisture retrieval and RFI in the SMOS soil moisture product.

We combined the radiative transfer approach to simulate L band brightness temperatures at 1.4 GHz with a hydrological simulator (HYDRUS ID) and a global optimization routine SCE-UA to estimate the Mualem van Genuchten (MVG) soil hydraulic parameters from time lapse L-band brightness temperatures and in situ soil moisture measurements at different depths. The measurements were collected from a bare soil plot, prepared after ploughing. First, we briefly described the coupled inversion procedure and compared the results with measured brightness temperatures and in-situ soil moisture data. Second, estimated soil hydraulic parameters were compared with laboratory derived ones. The results suggest that the proposed method is promising for the effective characterization of the soil hydraulic properties and the determination of soil moisture within the top soil layer.

The objective of this study was to evaluate two remote-sensing methods for mapping the surface 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. 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. For the radiometer, the effect of roughness was accounted for using an empirical model that required calibration with the reference TDR measurements. The root mean square (RMS) error between soil moisture measured by GPR and TDR was 0.038 m3m-3while the RMS error between radiometer (horizontal and vertical polarizations)and TDR-derived soil water content was 0.020 m3m-3. These results suggest that both remote-sensing techniques are promising for field-scale mapping of surface soil moisture over bare soils.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been 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 the activities performed in the Rur and Erft catchment, which is situated in the very west of Germany close to the borders to Belgium and The Netherlands. Measurements of the L-band sensors EMIRAD and HUT-2D on board a Skyvan aircraft as well as ground- based mobile measurements with the JULBARA radiometer mounted on a truck are analyzed in a qualitative comparison for different crop stands. These data can be used for validation of the SMOS sensor by giving valuable information about parameters for the radiative transfer modeling.

ESA provides operational routines to calculate the SMOS Level-2 product soil moisture from the radiometer Tb. But, the radiative transfer from measured Tb into soil moisture is influenced by several conditions such as soil surface roughness and vegetation opacity, which are parameterized in a general way. These cannot be easily measured at the scale of SMOS observation. The absolute values of these parameters for different land surfaces are uncertain, and the degree of this uncertainty is unknown as well. In addition, recent studies found that SMOS overestimates Tb. In this paper, we present a method to enhance the accuracy of the SMOS soil moisture product by parameter estimation using a data assimilation technique (Sampling Importance Resampling Particle Filter SIR-PF) with in-situ soil moisture observations. Therefore, we performed a synthetic study to analyze the ability of the system to track the temporal evolution of parameters such as vegetation opacity and soil surface roughness. To generate a soil moisture and soil temperature reference the hydrological model HYDRUS-1D was used. Based on this, the L-band Microwave Emission of the Biosphere (L-MEB) forward model was run and perturbed according to the measurement accuracy of MIRAS to simulate the SMOS Tb observations. L-MEB was integrated into a data assimilation framework using the SIR-PF, which is able to concurrently update L- MEB states and parameters. In addition, we investigate the ability of the proposed approach to account for the SMOS observation bias by introducing a bias factor in L-MEB. The overall advantage of the proposed sequential approach is its ability to be integrated into the operational near real time processing of the Level-2 product. The objectives of this study are: (i) to retrieve radiative transfer parameters and their temporal changes and (ii) to account for a bias in SMOS measurements.

The antenna of a zero-offset off-ground ground-penetrating radar can be accurately modeled using a linear system of frequency-dependent complex scalar transfer functions under the assumption that the electric field measured by the antenna locally tends to a plane wave. First, we analyze to which extent this hypothesis holds as a function of the antenna height above a multilayered medium. Second, we compare different methods to estimate the antenna phase center, namely, 1) extrapolation of peak-to-peak reflection values in the time domain and 2) frequency-domain full-waveform inversion assuming both frequency-independent and -dependent phase centers. For that purpose, we performed radar measurements at different heights above a perfect electrical conductor. Two different horn antennas operating, respectively, in the frequency ranges 0.2-2.0 and 0.8-2.6 GHz were used and compared. In the limits of the antenna geometry, we observed that antenna modeling results were not significantly affected by the position of the phase center. This implies that the transfer function model inherently accounts for the phase-center positions. The results also showed that the antenna transfer function model is valid only when the antenna is not too close to the reflector, namely, the threshold above which it holds corresponds to the antenna size. The effect of the frequency dependence of the phase-center position was further tested for a two-layered sandy soil subject to different water contents. The results showed that the proposed antenna model avoids the need for phase-center determination for proximal soil characterization.

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.