Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

Tracking seasonal dynamics of evapotranspiration (ET) across global biomes and along seasonal time periods using remote sensing is vital for monitoring ecosystem health and indicating early signals of drought. In this study, we assess the potential of adding weather and illumination-independent signals from active and passive microwave remote sensing (SAR backscatter & vegetation optical depth, VOD) to the established set of ET products, like from optical/thermal remote sensing (MODIS, SEVIRI) and reanalysis (ERA-5 land, GLDAS) data.Our study covers a four-year period (2017-2020), including dry (2018 & 2019) and wet (2017) years. The study was conducted over eight ICOS sites across Europe. These sites are predominantly forested with a low biomass dynamic over the observation period.We find that the ET products from in situ Eddy Covariance (EC), MODIS, and GLDAS deviate relatively minor along the seasons (< 1 [mm/day]), but differ between years. Here, the years (2017-2020) indicate a slightly different ET rate between in situ measurements (EC) and derived products (MODIS & GLDAS), which is currently being investigated. The microwave-based indicators (backscatter & VOD) are proxies by their nature and serve as first-order indicators of relative dynamics allowing the identification of seasonal patterns of ET as well as their spatio-temporal anomalies along both dry and wet years.

In literature, a variety of models, representations and formalisms exist that describe electromagnetic waves and their interaction with media. The choice of model often depends on the required properties of the electromagnetic wave, e.g. the degree of polarization or whether the modeled electromagnetic wave is described coherently (amplitude and phase) or incoherently (only amplitude). In this study, we propose a more unified theoretical framework that can represent all cases of coherent, non-coherent, fully polarized, partially polarized, and non-polarized electromagnetic waves. The novel framework also represents them in such a way that the principles of energy conservation and conservation of polarization states are already manifested in the equations.

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.

Climate change increases the severity and frequency of drought events. Over the globe, different ecosystems show different reactions to drought stress. By using two state of the art remote sensing techniques; vegetation optical depth and chlorophyll fluorescence, we can get a hand on reactions to drought stress at the ecosystem scale. The vegetation optical depth serves as a proxy for the total amount of water in the vegetation, while the sun-induced chlorophyll fluorescence serves as a proxy for the photosynthetic activity. Plant drought reactions break down into two strategies; isohydric behaviour and anisohydric behaviour. Isohydric plants tend to regulate their stomata, allowing them to reduce water losses during drought events. Anisohydric plants tend to be less strict in their stomatal regulation, allowing them to keep up their vegetation growth during drought events. In ecosystems dominated by isohydric plants, such as tropical rainforests, sun-induced chlorophyll fluorescence is a good indicator for the vegetation water status. In anisohydric regions, such as croplands, the vegetation optical depth provides better information on the vegetation water status, as these plants tend to show only little reactivity in their photosynthetic activity to drought stress. Combining the information of both metrics is expected to provide a more complete estimate of the plant water status.

Unmanned Aircraft Systems (UAS), with the ability to fly close to the ground and under clouds, make it possible to collect data at unprecedented spatial and temporal resolutions. LiDAR systems are more commonly being used on UAS platforms as these sensors become smaller and more accessible. Within the field of precision farming, UAS LiDAR is often used for height calculations that takes advantage of its ability to penetrate through the canopy to the ground but the rate at which these signals pass through can provide important metrics on crop structure and (vegetation) density. These can be related to well known (or classically used) vegetation indices such as Leaf Area Index (LAI) which often plays a major contributor in monitoring plant health and predicting crop yield. This study exploits UAS LiDAR advantages and investigates its ability to estimate LAI for crops such as winter wheat. It was found that LiDAR LAI spatial patterns were consistent with other forms of data while providing estimates similar to ground measurements.

Sun-induced chlorophyll fluorescence has a mechanistic link to photosynthesis. It is therefore sensitive to subtle, stress-induced changes in photosynthetic activity. This study shows the evolution of the emission of sun-induced chlorophyll fluorescence (SIF) by a yellow mustard stand grown in plant boxes under varying water supply and under varying meteorological conditions, causing changes in the evaporative demand. Affected by both leaf biochemical processes and by changes in the plant canopy structure, we combined the fluorescence measurements with reflectance measurements to assess the effect of both components. A first result of this study shows that the biochemical component of the fluorescence emission decreases because of either a reduction in the water supply or an increase in the evaporative demand by the atmosphere.

The attenuation of microwave emissions through the canopy is quantified by the vegetation optical depth (VOD), which is related to the amount of water, the biomass and the structure of vegetation. To provide microwave-derived plant water estimates, one must account for biomass/structure contributions in order to extract the water component from the VOD. This study uses Aquarius scatterometer data to build an L-band global seasonality of vegetation volume fraction (δ), representative of biomass/structure dynamics. The dynamic range of δ is adapted for its application in a gravimetric moisture (Mg) retrieval model. Results show that δ ranging from 0 to 3.35.10-4is needed for modelling physically reasonable Mg values. The global average of δ shows consistent spatial patterns across vegetation distributions, and δ seasonality is coherent with the phenology of the studied vegetation types. These findings enable the separation of information on vegetation water and biomass/structure inherent within VOD.

A retrieval methodology for forest water potential from ground-based L-band radiometry is proposed. It contains the estimation of the gravimetric and the relative water content of a forest stand and tests in situ- and model-based functions to transform these estimates into forest water potential. The retrieval is based on vegetation optical depth data from a tower-based experiment of the SMAPVEX 19–21 campaign for the period from April to October 2019 at Harvard Forest, MA, USA. In addition, comparison and validation with in situ measurements on leaf and xylem water potential as well as on leaf wetness and complex permittivity are foreseen to understand limitations and potentials of the proposed approach. As a first result the radiometer-based water potential estimates of the forest stand are concurrent in time and similar in value with their in situ (xylem) counterparts from single trees in the radiometer footprint.

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.

In this article, we present a multipolarimetric estimation approach for two model-based vegetation structure parameters (shape A and orientation distribution ψ of the main canopy elements). The approach is based on a reduced observation set of three incoherent (no phase information) polarimetric backscatter intensities (|SHH|2, |SHV|2, and |SVV|2) combined with a two-parameter (APand ψ) discrete scatterer model of vegetation. The objective is to understand whether this confined set of observations contains enough information to estimate the two vegetation structure parameters from the L-band radar signals. In order to disentangle soil and vegetation scattering influences on these signals and ultimately perform a vegetation only retrieval of vegetation shape A and orientation distribution ψ, we use the subpixel spatial heterogeneity expressed by the covariation of co- and cross-polarized backscatter ΓPP-PQof the neighboring cells and assume it is indicative for the amount of a vegetation-only co-to-cross-polarized backscatter ratio μPP-PQ. The ratio-based retrieval approach enables a relative (no absolute backscatter) estimation of the vegetation structure parameters which is more robust compared to retrievals with absolute terms. The application of the developed algorithm on global L-band Soil Moisture Active Passive (SMAP) radar data acquired from April to July 2015 indicates the potential and limitations of estimating these two parameters when no fully polarimetric data are available. A focus study on six different regions of interest, spanning land cover from barren land to tropical rainforest, shows a steady increase in orientation distribution toward randomly oriented volumes and a continuous decrease in shape arriving at dipoles for tropical vegetation. A comparison with independent data sets of vegetation height and above-ground biomass confirms this consistent and meaningful retrieval of APand ψ. The retrieved shapes and orientation distributions represent the main vegetation elements matching the literature results from model-based decompositions of fully polarimetric L-band data at the SMAP spatial resolution. Based on our findings, APand ψ can be directly applied for parameterizing the vegetation scattering component of model-based polarimetric decompositions. This should facilitate decomposition into ground and vegetation scattering components and improve the retrieval of soil parameters (moisture and roughness) under vegetation.

The fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4 and -5) were conducted at the beginning of the SMAP operational phase, May and September 2015, to: 1) evaluate the SMAP microwave observations and derived soil moisture (SM) products and 2) intercompare with the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions over the Murrumbidgee River Catchment in the southeast of Australia. Airborne radar and radiometer observations at the same microwave frequencies as SMAP were collected over SMAP footprints/grids concurrent with its overpass. In addition, intensive ground sampling of SM, vegetation water content, and surface roughness was carried out, primarily for validation of airborne SM retrieval over six ~ 3 km × 3 km focus areas. In this study, the SMAPEx-4 and -5 data sets were used as independent reference for extensively evaluating the brightness temperature and SM products of SMAP, and intercompared with SMOS and Aquarius under a wide range of SM and vegetation conditions. Importantly, this is the only extensive airborne field campaign that collected data while the SMAP radar was still operational. The SMAP radar, radiometer, and derived SM showed a high agreement with the SMAPEx-4 and -5 data set, with a root-mean-squared error (RMSE) of ~3 K for radiometer brightness temperature, and an RMSE of ~ 0.05 m3 for the radiometer-only SM product. The SMAP radar backscatter had an RMSE of 3.4 dB, while the retrieved SM had an RMSE of 0.11 m3/m3 when compared with the SMAPEx-4 data set.

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.

A considerable amount of water can be stored in vegetation, especially in regions experiencing large quantities of precipitation (mid-latitudes). In this context, an accurate estimate of the actual water status of the vegetation could lead to an improved understanding of the effect of plant water on the water budget. In this study, we developed and validated a novel approach to retrieve the vegetation volume fraction (δ) (i.e., volume percentage of solid plant material of a canopy in air) and the gravimetric vegetation water content (mg) (i.e., amount of water per wet biomass) for winter wheat. The estimation was based on the attenuation of L-band microwave measurements through vegetation (vegetation optical depth, (τ)-parameter). Ground-based L-band microwave measurements over an entire growing cycle together with in situ measured vegetation characteristics have been used for this purpose. Retrieved δ- and mg-values revealed to be comparable to literature and in situ measurements (i.e., δ was within the range between 0 and 0.01 and the retrieved mghad a mean value of 0.58 (0.55 (in situ) and 0.54 (literature))). Finally, we also tested the sensitivity of the δ- and mg-retrievals to their input-values to investigate their possible mutual dependencies. The analysis showed that already small changes in the input-mgor -δ result in relatively large changes in the retrieved δ or mg.

A method of physical integration of electromagnetic (EM) interaction models is presented here to estimate the backscattering coefficient (BSC) for L-band, brightness temperature (TB) for L- and C-Band and the reflectance for visible (VIS) and near-infrared (NIR) region for dynamic vegetated terrain. The SPIN (Spectrum Invariant Interaction) model is obtained by solving vector radiative transfer (VRT) equations kernel-based and therefore for different wave interaction mechanisms. To demonstrate its application for the microwave region, the measurements during the growing cycle of corn from the Eleventh Microwave, Water, and Energy Balance Experiment (MicroWEX-11) have been used. For the optical part the results are compared with the PROSAIL model. By applying the SPIN model in the radar regime, it could be shown that the modeled backscattering coefficients (BSC) correlate strongly with the vertical polarization measurements (Pearson 0.83, R20.69) and are less correlated with the horizontal measurements (Pearson 0.45, R20.20). In addition, the modeled brightness temperatures (L- and C-band) in both polarization states are also correlated with the MicroWEX-11 measurements (L-band: Pearson 0.755, R20.57; C-band: Pearson 0.73, R20.53). Finally, the optical results are consistent with the results of other standard optical models (Pearson 0.99, R20.98), like PROSAIL.

Readily available soil moisture data will help farmers to better optimize their irrigation scheduling and minimize water consumption. Consequently, there is a large and accelerating interest in using sensing technologies in precision agriculture worldwide. Among them, passive microwave sensing technology has been considered as the most accurate in retrieving soil moisture. This study compares the performance of an L-band radiometer system at two different platforms: airborne and near-surface (buggy). Field experiments have been conducted across an agricultural site in Tasmania, Australia for three consecutive days for. Ground sampling was also conducted in order to evaluate the accuracy of both L-band radiometer systems. Brightness temperature data from the buggy showed a larger temporal variation on individual days than the aircraft data, likely due to the irrigation activity during the longer period required for data collection by the buggy than for the aircraft. In terms of the relationship between brightness temperature and soil moisture, both platforms showed similar results while the buggy-based data showed a slightly better correlation with soil moisture.

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.

Various approaches exist to model scattering of a vegetation canopy above ground in terms of optical and radar wavelengths. Due to the different scattering properties these two spectral regions are modelled separately for visible/ infrared bands and for microwave regions. The newly developed RadOptics model (RO-M) integrates these two spectral regions semi-physically into one radiative transfer (RT)-based model framework, resting on the law of Beer-Bougert-Lambert. Due to the integrative nature of RO-M, it can calculate/simulate the canopy and soil reflectances for the optical and radar spectrum using a single unified model architecture. By Applying RO-M in radar domain (ROR-M) it is shown that the observed dependence of Backscattering coefficient on Leaf Area Index (LAI), soil moisture content and frequency can be simulated consistently with results in literature. The results of the RO-M within the optical domain (ROO-M) present an equivalent trend of reflectance and band ratio values with LAI compared to studies in literature.

This paper describes the first results of a field experiment conducted at the Selhausen remote sensing field laboratory (Germany) over the entire growing season of a winter wheat. Brightness temperature measurements were performed with the passive microwave L-band radiometer ELBARA-II. The data were collected above two different footprints within a homogeneous winter wheat stand in order to disentangle between the radiation originating from the soil and the radiation originating from the vegetation. In a first step, the brightness temperature (TB) data collected above the first plot with the soil surface covered by a metal grid (i.e., blocking the radiations from the ground) were used to retrieve the vegetation optical depth ( τ). In a second step, TB data collected above the second plot without the metal grid on the soil surface were used to retrieve the soil surface moisture using several inverse modeling approaches. All modeling investigations were performed with the simple zero-order τ-ω model. The results show that using τ data derived from the first plot as a priori information, which showed to be time, polarization, and angle dependent, allows us to improve the soil moisture retrieval, due to a better representation of the vegetation canopy effect on the measured TB. Furthermore, the correlations between τ and different vegetation indices were analyzed and highlight the potential of τ in terms of vegetation monitoring for, e.g., all-weather measurements compared to optical measurements.

L-Band microwave radiometry over land mainly focuses on observations of horizontally (H) and vertically (V) polarized brightness temperatures. However, it has been demonstrated that measurements of the full Stokes vector [1] are sensitive to additional environmental properties, e.g. azimuthal plant row orientation. Furthermore, model simulations show that also a smooth surface with a periodic permittivity pattern can cause azimuthal dependencies of the Stokes parameters. The objective of this paper is to present fully polarimetric Lband measurement results from observations of a striped wood and styrodur target, when rotated 360° in small steps. Measurement results of a striped soil and open water target are also reported. Finally, measurements are compared to model simulations, with very good agreement within the validity range of the model (stripe thickness <; λ/2).

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.

Microwave remote sensing instruments are an adequate way to provide soil moisture information at large scale. Microwave remote sensing data are linked to the electromagnetic properties of soil. In that context, the objective of this study is to develop an integrated approach to estimate effective electromagnetic properties of soils layers at different scale using ground-penetrating radar (GPR), L-band radiometer, dielectric laboratory measurements, modelling approaches and in situ measurements of essential state variables.

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.

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.

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.