A physics-based microwave emission model for layered vegetation (MEMLV) is developed to simulate the vegetation optical depth (VOD) and scattering albedo of coniferous forests from P- to Ka-band (0.4 GHz–37 GHz). This study aims to use physics-based forward modeling to guide and support multifrequency VOD retrieval. The MEMLV consists of three major components: 1) the single-layer discrete scatter model (SL-DSM) that calculates the VOD and single scattering albedo of a single layer; 2) a new Tree Structure Model (TSM) that represents forests by stratified multilayer media; and 3) the Two-Stream microwave emission model (2S-MEM) that combines SL-DSM and TSM to calculate the brightness temperature of forests and their corresponding effective VOD,$\tau _{\text {eff}}$, and effective scattering albedo,$\omega _{\text {eff}}$. Simulation of an exemplary coniferous forest shows that$\tau _{\text {eff}}$increases as frequency increases until reaching saturation at K-band. Meanwhile,$\omega _{\text {eff}}$increases rapidly at low frequencies until reaching a peak at S-band, then decreases until reaching X-band, and finally increases monotonically as frequency increases. Notably,$\omega _{\text {eff}}$is negligible at P-band for most cases. Sensitivity analyses demonstrate the saturation of$\tau _{\text {eff}}$when canopy height is substantial, and the decreasing trend of$\omega _{\text {eff}}$as canopy height or the areal fraction of canopy gap increases. The MEMLV has the potential to improve the parameterization of retrieval algorithms and to enhance the understanding of the retrieved VOD over a wide frequency range.

Seasonal snow cover is an important environmental component, as its characteristics affect energy and gas exchange between ground and the atmosphere. The mass of seasonal snow cover, or Snow Water Equivalent (SWE), describes the available freshwater stored in snow. When present, snow cover affects the microwave signature of the scene and should therefore be considered in any microwave based remote sensing model and retrieval algorithm. Microwave remote sensing of snow and its properties has a long history, and in particular SWE has traditionally been retrieved by using passive microwave observations at 19 and 37 GHz [1] , [2] .

The future of agriculture is moving towards a more technological and efficient way to produce food for the world’s population. One key resource for that is water. A radiometer design is introduced that will be used to determine the feasibility of an 800g C-Band (6.8 GHz) radiometer for fixed-wing UAVs, giving agronomists a new cutting-edge tool to measure soil and kernel moisture in cereal grain pods. Simulation tools and computer-aided optimization were used to develop multiple design iterations, which were manufactured and measured to assess the performance of the proposed radiometer. A custom HEMT LNA was designed using ADS EM co-simulation, resulting in a 10dB gain and 1dB noise figure. The proposed system design has a theoretical NEΔT of 0.28 K and a preliminary measured NEΔT of 0.535K at 36.9ms integration time, respectively.

A second-generation portable L-band radiometer is presented for use in turfgrass management applications and golf course soil moisture mapping. The "turfRad" has been optimized for ease-of-use and for the conditions in operational turfgrass management. This is the first known use of a vehicle-mounted L-band radiometer for turfgrass soil moisture mapping for irrigation decision-making. Preliminary results, including dual-polarization brightness temperatures and retrieved soil moisture, are presented. The single-parameter soil moisture retrieval using the Tau-Omega emission model with constant vegetation optical depth is chosen due to the high level of vegetation uniformity in the fairway areas.

This study focuses on modelling the scattering albedo of a vegetation canopy, which can be used in vegetation opacity depth (VOD) retrieval, over a wide frequency range (1-37 GHz). In this study, boreal tree canopy has been used as an example. A discrete scatter model has been implemented to calculate single scattering albedo and vegetation opacity depth (VOD) of a single-layer canopy consisting of a variety type of scatterers. For a more realistic parameterization, a novel tree structure model has been developed to quantify the vertical structure of a forest. For the first time, we combined the discrete scatter model with the multi-layer 2Stream model to calculate the brightness temperature of the tree canopy based on its vertical structure. This research provides a comprehensive forward modelling tool that can be parameterized with different vegetation types (e.g. tree, crops and grass) and parameters (e.g. height, density, soil condition and vertical water content distribution). The model can be used in retrieval algorithm to find effective scattering albedo and VOD over a wide range of frequencies.

This article experimentally investigates relationships between copol backscattering at a wide range of frequencies (L- to Ka-bands) and snow–ground state parameters (SPs) in different evolution phases during the full winter cycle of 2019/2020. Backscattering coefficients from 1 to 40 GHz,in situsnow–ground SPs, and meteorological data are measured at the Davos-Laret Remote Sensing Field Laboratory (Switzerland). Relative strengths of the snow–ground system’s three primary scattering elements (air–snow interface, snow volume, and snow–ground interface) on backscattering are assessed. An anticorrelation between reasonably high snow wetness and backscattering coefficient is found, especially at higher microwave frequencies. For small amounts of snow wetness, backscatter coefficients at L- and S-bands are intensified via increasing snow volume and snow surface scattering. Snow–ground SPs influence backscattering according to their characteristic time scales of temporal evolution. Under dry snow conditions and at low and intermediate frequencies, ground permittivity is the major influencer of backscatter at a time scale of roughly two weeks. Snowfall is the major influencer of backscatter at a time scale of a few hours to a few days. The findings of this article are valuable to the development of retrieval algorithms using machine learning while maintaining a grasp on the ongoing physical processes. Another key message is that multifrequency active microwave measurements are critical to maximize the number of retrievable SPs and their estimation accuracy. For example, while Ka-band performs well in the detection of snow cover, L-band measurements are more responsive to changes of snow water equivalent (SWE) under moist or wet snow conditions.

Active and passive microwave characteristics of diurnal soil freeze-thaw transitions and their relationships are crucial for developing retrieval algorithms of the soil liquid water content ($\theta _{\mathrm {liq}}$) and freeze/thaw state, which, however, have been less explored. This study investigates these microwave characteristics and relationships via analysis of ground-based measurements of brightness temperature ($T_{B}$) and backscattering coefficients ($\sigma ^{0}$) in combination with simulations performed with the Tor Vergata discrete radiative transfer model. Both an L-band (1.4 GHz) radiometer ELBARA-III and a wide-band (1–10 GHz) scatterometer are installed in a seasonally frozen Tibetan meadow ecosystem to measure diurnal variations of$T_{B}$and copolarized$\sigma ^{0}$at both hh ($\sigma _{\mathrm {hh}}^{0}$) and vv ($\sigma _{\mathrm {vv}}^{0}$) polarizations. Analysis of measurements collected between December 2017 and March 2018 shows that 1) diurnal cycles are observed in both$T_{B}$and$\sigma ^{0}$due to the change in surface$\theta _{\mathrm {liq}}$caused by diurnal soil freeze-thaw transitions; 2) a negatively linear relationship is found between$e$and$\sigma ^{0}$regardless of frequency, polarization combinations, and observation angles; 3) slopes ($\beta$) of linearly fit equations between$e^{H}$and$\sigma _{\mathrm {hh}}^{0}$decrease with increasing observation angles of ELBARA-III, while the ones between$e^{V}$and$\sigma _{\mathrm {vv}}^{\mathrm {0 {}}}$increase with increasing observation angles; and 4) correlations between$e$and$\sigma ^{0}$increase with decreasing microwave frequency of$\sigma ^{0}$measurements and ELBARA-III observation angles, and magnitudes of diurnal$\sigma ^{0}$cycles also increase with decreasing microwave frequency. Moreover, the calibrated Tor Vergata model shows capability to reproduce both diurnal$e$and$\sigma ^{\mathrm {0 {}}}$variations as well as to quantify their relationships at different frequencies and observation angles.

An unexplored aspect of L-band microwave emission is the impact of soil moisture and soil temperature (SMST) profile dynamics on diurnal brightness temperature ( TB) signatures of frozen soil. This study investigates this effect by comparing the TBsimulations of layered ( TB,l) and uniform ( TB,u) soils using a newly developed integrated land emission model. The multilayer Wilheit model and the single-layer Fresnel model are adopted to compute the smooth soil reflectivity for the layered and uniform soils, respectively. A four-phase dielectric mixing model is used to calculate the soil permittivity ( εs). A data set of concurrent ELBARA-III TBand SMST profile measurements performed in a seasonally frozen Tibetan meadow ecosystem is used for the analysis. The simulated TB,lconsidering SMST profile information captures well the ELBARA-III measurements with low biases (≤6 K) and high correlations ( R2≥ 0.88). TB,uproduced based on the Fresnel model using the soil moisture of 2.5 cm is more consistent with the TB,l. The sensitivity test of averaging SMST profile below 2.5 cm leads to maximum differences of 2 K in TB,lsimulations, indicating that the TBvariations are primary dominated by the SMST dynamics at the surface layer. A sensitivity test of the Wilheit model to different εsparameterizations shows that the dielectric model of Zhang et al. is comparable to the four-phase dielectric model in simulating TB,l, while the Mironov et al. 's model demonstrates larger biases for frozen soil with, on average, 2.2% clay content, 49.7% sand content, and a bulk density of 1 g·cm-3.

We examine melt over Greenland using two passive microwave-based melt detection algorithms. A recently developed L-band (1.4 GHz) liquid water quantification method, and a well known method based on 19 and 37 GHz brightness temperatures known as the cross polarization gradient ratio (XPGR). We discuss spatial patterns of total number of melt days, and temporal trends in total melt area. The L-band method provides quantified liquid water as opposed to melt flagging, but for this study we simplify the retrievals to a binary melt flag for the sake of comparison with the XPGR. We find similar spatial patterns between the two methods, but some notable differences such as melt on the Jacobsavn Isbrae is more apparent in the L-band retrievals as opposed to the XPGR.

We introduce a recently developed cost effective, low mass, portable L-band radiometer for aerial applications on drones or for ground-based applications. The radiometer has been tested on a multicopter drone and for a winter seasons measuring snow at the Davos-Laret Remote Sensing Field Site in Switzerland. The radiometer measures dual-linear polarizations, has a brightness temperature resolution of about 0.15 K for 1 sec integration time, and the antenna has a half-power full beamwidth of 37 degrees. The mass of the combined system is 3.8 kg and the power consumption is less than 3 W. We present the antenna and radiometer instrument, and initial results from these test phases.

We demonstrate an application of the L-band specific Microwave Emission Model of Layered Snowpacks (LS-MEMLS) to snow over the Greenland Ice Sheet to retrieve density and snow wetness from SMOS satellite data. Retrievals of these parameters have previously been demonstrated using ground-based L-band radiometry over snow covered ground in alpine conditions. Existing passive microwave data products over ice sheets include empirical and threshold-based techniques but until recently have not used radiative-transfer physics-based modelling. We discuss the retrieval method based on inversion of LS-MEMLS and present a comparison between our retrievals and in-situ weather station air temperature data. We also mention ongoing work investigating the applicability of the novel retrieval method for mapping the ablation zone of the Greenland Ice Sheet.

Microwave coaxial resonator sensors are used to retrieve snow complex dielectric permittivity εS= εSi·εSfrom sensors frequency response. In addition to the conventional analytical approach, electromagnetic simulation software HFSS is used to devise a unified εSretrieval approach applicable to all resonator sensors based on the simulation of their frequency response. εSretrievals are performed using sensor measurements, conducted with a portable Vector Network Analyzer (VNA), in Davos-Laret Remote Sensing Field Laboratory and six locations over the Greenland ice sheet. Results indicate improved εSretrieval accuracy achieved using the simulation-based approach. The use of VNA technology together with the simulation-based εSretrieval approach allows for more reliable electromagnetic characterization of snow cover over large areas, which is of critical importance for remote sensing applications.

An Emission Models (EM) adequate for a retrieval algorithm requires being simple while still capturing the responses of brightness temperatures TBp,θto the retrieval parameters. The objective of this study is to explore the benefits of the multiple-scattering Two-Stream (2S) EM over the "Tau-Omega" (TO) EM to retrieve soil Water Content WC and vegetation optical depth τ from L-band TBp,θ. For sparse and low-scattering vegetation TB,EMp,θsimulated with EM = TO and EM = 2S converge, which is not the case for dense and strongly scattering vegetation. WCRCand τRCare retrieved with Retrieval Configurations RC = {TO, 2S} from TBp,θ: i) from a tower within a deciduous forest, and ii) by the "Soil Moisture and Ocean Salinity" (SMOS) mission. Using 2S EM instead of TO EM resulted in marginally lower WCRCretrievals while τRCretrievals are reduced more considerably. With respect to in-situ WCin-situ, retrievals WC2Sderived from tower-based TBp,θperformed better than forest soil water-content WCTOretrieved via the inversion of the "reference" TO EM. Likewise, SMOS based WC2Sretrievals revealed better agreement with ECMWF WC simulations than WCTOachieved with the "reference" RC = TO. In short, our study provides clear evidence that it is meaningful to replace TO EM used for current SMOS and SMAP land retrieval with 2S EM.Further advantages of the 2S EM over the TO EM are outlined in this study.

The aim of the ESA SnowLab project is to provide a comprehensive multi-frequency, multi-polarisation, multi-temporal dataset of active microwave measurements over snow-covered grounds to investigate the relationship between effective snow- and ground parameters and the resultant signals detected by microwave radar. An important part for the development of microwave models is the microstructural characterisation. This characterisation can only be done by repeated measurements by SnowMicroPen and more completely, but also much more expensive, by X-ray micro-tomography. Within this project we complemented the microwave measurements of Alpine snow in Switzerland with extensive effective snow- and ground parameters and meteorological data. Microwave backscatter measurements were conducted using the 9 - 18 GHz ESA SnowScat instrument and since December 2018 the recently built ESA WBScat instrument. WBScat allows to extend the spectral coverage to 1 - 40 GHz.

This letter presents the results evaluating retrievals of liquid water content (θliq) performed with a zero-order radiative transfer (τ-ω) model under frozen and thawed soil conditions from Soil Moisture Active Passive (SMAP) and ELBARA-III brightness temperature (TBp) measurements collected over a Tibetan meadow ecosystem. A good agreement is found between time series of the SMAP and ELBARA-III measured TBpresulting in a Pearson product-moment coefficient (R) larger than 0.87. Differences noted between the two data sets can be associated with discrepancies in θliqmeasured in the specific footprints, whereby the SMAP measurements are best explained by the in situ θliq. Furthermore, the in situ θliqhas a better agreement with the horizontally polarized SMAP and ELBARA-III measurements (THB ) in the cold season, whereas the vertically polarized measurements (TVB ) are1111better correlated with θliqin the warm season. With the implementation of new vegetation and surface roughness parameterizations for the τ-ω model, the dynamics of in situ θliqis better reproduced by corresponding retrievals for both frozen and thawed soil conditions, leading to the reduction in the unbiased root-mean-square error (ubRMSE) by more than 31% in comparison with these retrievals using SMAP default parameterizations. Notably, the single-channel algorithm configured with the new parameterizations using SMAP TVB measured during the ascending overpass provides the best θliqretrievals with a ubRMSE of 0.035 m3·m-3that is well within the SMAP mission requirements.

The present study demonstrates the successful use of L-band brightness temperatures TBpto retrieve snow liquid water Wsfrom multi-angular measurements TBp(θk). The emission model employed was developed from parts of the “microwave emission model of layered snowpacks” (MEMLS) coupled with components adopted from the “L-band microwave emission of the biosphere” (L-MEB) model. Two types of snow liquid water retrievals were performed based on TBp(θk) measured over i) areas with a metal reflector placed on the ground (“reflector area”-TpB,R), and ii) natural snow-covered ground (“natural area”-TpB,N). The reliable representation of temporal variations of snow liquid water is demonstrated for both types of retrievals. This is verified by the fact that both types of Ws-retrievals indicate a dry snowpack throughout the “cold winter period” with frozen ground and air temperatures well below freezing, and synchronously respond to snowpack moisture variations during the “early spring period”. Snow liquid water retrievals based on TpB,Nare achieved in a two-step retrieval procedure using exclusively L-band brightness temperatures, without the need for in-situ measurements such as ground permittivity CG and snow mass-density ρs. The latter two are estimated in the first retrieval-step employing the well-established two-parameter (ρs, εG) retrieval scheme. The proposed and investigated two-step retrieval approach opens up the possibility of using airborne or spaceborne L-band radiometry to estimate (ρs, εG, Ws).

Ground permittivity and snow density retrievals are performed using the tower-based experimental L-band radiometry data from the winter 2016/2017 campaign at the Davos-Laret Remote Sensing Field Laboratory. The performance of multi-angle two-parameter retrieval algorithms to estimate snow density and ground permittivity PRM=(ρSRM,εGRM) is assessed using in-situ measured εGand ρS. Additionally, a synthetic retrieval sensitivity analysis is conducted which studies impacts of “melting effects” on retrievals (ρSRM,εGRM) in the form of snow liquid water. Experimental and synthetic analyses show that snow liquid water noticeably disturbs the retrievals and result in increased correlation R2(ρSRM,εGRM) between them. The strength of this correlation is used as a quality-indicator flag to filter out highly correlated retrieval pairs. It is demonstrated that this filtering significantly improves the accuracy of both ground permittivity and snow density retrievals compared to corresponding reference in-situ data. Our analysis shows that retrievals PV=(ρsV,εGV) using vertical polarization only (RM = V) are predominantly least prone to snow liquid water. The presented experimental results indicate that retrievals match in-situ observations best for the “snow-free period” and the “cold winter period” when snow liquid water is at minimum.

A synergistic method for obtaining soil permittivity and soil frost was developed. The method incorporates a semiempir-ical backscattering model for forested land. Soil permittivity is retrieved from active microwave observations using least squares inversion method. Bayesian assimilation scheme can be applied to combine the active retrieval with a permittivity estimate from a passive instrument. Soil frost can be determined from permittivity estimates using a threshold method. The synergistic method was tested on boreal forest site in Northern Finland using ASAR for active and SMOS for passive observations. Satellite retrievals were compared to in situ soil permittivity, temperature and frost measurements. The results show that high resolution SAR data (e.g., ASAR, Sentinel) can be used to downscale coarse resolution SMOS estimates and that synergistic method reduces variability and biases of the ASAR retrieval.

The Soil Moisture Active Passive (SMAP) satellite mission launched in January 2015 provides worldwide soil moisture (SM) monitoring based on L-band brightness temperature (TBp) measurements at horizontal (TBH) and vertical (TBV) polarizations. This paper presents a performance assessment of SMAP soil emission model and SM retrieval algorithms for a Tibetan desert ecosystem. It is found that the SMAP emission model largely underestimates the SMAP measured THB(≈ 15 K), and the TBVis underestimated during dry-down episodes. A cold bias is noted for the SMAP effective temperature due to underestimation of soil temperature, leading to the TBpunderestimation (>5 K). The remaining TBHunderestimation is found to be related to the surface roughness parameterization that underestimates its effect on modulating the TBpmeasurements. Further, the topography and uncertainty of soil information are found to have minor impacts on the TBpsimulations. The SMAP baseline SM products produced by single-channel algorithm (SCA) using the TBVmeasurements capture the measured SM dynamics well, while an underestimation is noted for the dry-down periods because of TBVunderestimation. The products based on the SCA with TBHmeasurements underestimate the SM due to underestimation of TBH, and the dual-channel algorithm overestimates the SM. After implementing a new surface roughness parameterization and improving the soil temperature and texture information, the deficiencies noted above in TBpsimulation and SM retrieval are greatly resolved. This indicates that the SMAP SM retrievals can be enhanced by improving both surface roughness and adopted soil temperature and texture information for Tibetan desert ecosystem.

Soil freeze–thaw transition monitoring is essential for quantifying climate change and hydrologic dynamics over cold regions, for instance, the Third Pole. We investigate the L-band (1.4 GHz) microwave emission characteristics of soil freeze–thaw cycle via analysis of tower-based brightness temperature ($T_{{{{\mathrm {B}}}}}^{p}$) measurements in combination with simulations performed by a model of soil microwave emission considering vertical variations of permittivity and temperature. Vegetation effects are modeled using Tor Vergata discrete emission model. The ELBARA-III radiometer is installed in a seasonally frozen Tibetan grassland site to measure diurnal cycles of L-band$T_{{{{\mathrm {B}}}}}^{p}$every 30 min, and supporting micrometeorological as well as volumetric soil moisture ($\theta $) and temperature profile measurements are also conducted. Soil freezing/thawing phases are clearly distinguished by using$T_{{{{\mathrm {B}}}}}^{p}$measurements at two polarizations, and further analyses show that: 1) the four-phase dielectric mixing model is appropriate for estimating permittivity of frozen soil; 2) the soil effective temperature is well comparable with the temperature at 25 cm depth when soil liquid water is freezing, while it is closer to the one measured at 5 cm when soil ice is thawing; and 3) the impact on$T_{{{{\mathrm {B}}}}}^{p}$caused by diurnal changes of ground permittivity is dominating the impact of changing ground temperature. Moreover, the simulations performed with the integrated Tor Vergata emission model and Noah land surface model indicate that the$T_{{{{\mathrm {B}}}}}^{p}$signatures of diurnal soil freeze–thaw cycle is more sensitive to the liquid water content of the soil surface layer than thein situmeasurements taken at 5 cm depth.

Recent theoretical and experimental studies have indicated the feasibility of passive microwave L-band observationsfor observing dry snow cover characteristics, namely snow density in the lower approx.. 10 cm of the snowpack. The sensitivity of L-band emission to snow density is based on the dual influence of refraction and impedance matching on observed brightness temperature with changing effective snow permittivity. The permittivity of pure, dry snow, on the other hand, depends largely on snow density. In this study, we expand the theoretical and experimental results of retrieving dry snow density to passive L-band satellite observations. Such retrievals could be appealing in the context of improving satellite based retrievals of e.g. Snow Water Equivalent (SWE) using other sensors. Retrievals are applied to both multi-angular observations from the ESA SMOS mission, and observations of the NASA SMAP radiometer on a single angle of observation. While in theory the multi-angular approach is preferable, improved RFI mitigation in SMAP provides more spatially and temporally more stable retrievals. The applied dual-parameter retrieval scheme produces also an estimate of ground permittivity; experimental data showed dry snow cover to have a clear influence on ground permittivity retrievals, implicating that even dry snow cover is non-negligible also in retrievals of soil moisture from L-band observations.

Dry snow is conventionally considered as having minimal effect on microwave radiation at long wavelengths (such as L-band). However, dry snow affects observed microwave signatures even at these wavelengths through changes in impedance matching between soil and the overlying media, as well as through changes in the refraction angle at the soil interface. Exploiting these effects, the multi-angular, dual-polarized L-band observations of e.g. the European Space Agency's SMOS (Soil Moisture and Ocean Salinity) mission have the potential to derive snow properties, such as the density of the lowest layers of the snowpack in contact with the ground. This in turn, would have the potential to inform retrieval schemes of snow cover based on EO-data from other sensors. In addition, the theoretical studies demonstrate that the effect of dry snow on retrieval of other geophysical variables, such as soil moisture, is not negligible. In this study, we demonstrate the simultaneous retrieval of snow density and ground permittivity in dry snow conditions, using a multi-year dataset of tower-based L-band observations. We show that following predictions of the theoretical studies, the retrieved snow density matches that of the density measured for the lowest snow layers; dry snow cover is also shown to affect retrievals of ground permittivity by up to 40 %.

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.

In this paper, roughness parameterizations providing best retrievals of soil moisture (SM) at L-band were evaluated. Different parameterizations were tested to find the best correlation R, bias and ubRMSE when comparing retrieved SM and in situ SM measurements carried out at the VAS (Valencia Anchor Station) over a vineyard field. Roughness measurements were always performed after the agricultural practices in the vineyard. These in situ data was used as input of the L-MEB (L-band Microwave Emission of the Biosphere) model, which permits the retrieval of SM and TAU (vegetation optical depth). In addition, a simplified method consisting on the retrieval of a parameter which combines the effects of roughness and TAU was tested. Significantly higher correlation (R=0.86) for SM was found using this method, while the absolute bias (-0.062) and RMSE (0.069) were slightly higher than for other roughness parameterizations.

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.

Radiative properties at 1.4 GHz of vine vegetation are investigated by measuring brightness temperatures with the ETH L-band Radiometer II (ELBARA II) operated on a tower at the Mediterranean Ecosystem L-band Characterisation Experiment III (MELBEX III) field site in Spain. To this aim, experiments with and without a reflecting foil placed under the vines were performed for the vegetation winter and summer states, respectively, to provide prevailingly information on vegetation transmissivities. The resulting parameters, which can be considered as “ground truth” for the MELBEX III vineyard, were retrieved from brightness temperature at horizontal and vertical polarization measured at observation angles between 30° and 60°. These MELBEX III “ground-truth” values are representative for the Mediterranean Soil Moisture and Ocean Salinity (SMOS) Valencia Anchor Station (VAS) and therefore valuable for the corresponding calibration and validation activities over the VAS site. Likewise, quantifying the uncertainties of the measured brightness temperatures was also important, particularly as several equivalent ELBARA II instruments are currently operative in ongoing SMOS-related field campaigns.

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.

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.

In a combined experimental and model study, we investigated the thermal L-band signatures of a sandy soil with periodic topography (furrows) with dimensions close to the observation wavelength of 21 cm. Measurements were carried out with a radiometer mounted on a tower and aimed at a soil box with an artificially prepared furrowed soil surface. Corresponding reflectivities were derived from brightness temperature measurements performed under dry and moist conditions, with the furrow direction either along or perpendicular to the plane of incidence. Results showed that the furrows had a pronounced effect on the reflectivity, depending on the polarization of the observed radiance, the direction of the furrows, and the soil moisture. A physical reflectivity model for dielectric periodic surfaces was used to explain the soil reflectivities measured for the different furrow directions and soil-water contents. Using this model improved the agreement between the measured and modeled reflectivities considerably compared to the Fresnel reflectivities. The observed dependence of soil reflectivity on furrow orientation and soil moisture could be reproduced by the reflectivity model. The quantitative agreement with the observed reflectivities was further improved by using a simple empirical approach to consider the small-scale heterogeneity of the top soil layer.

We investigated the influence of a wire grid above a paved ground on thermal brightness at 1.4 GHz. Reflectivities were derived from dual-polarized brightness temperature measurements for different grid orientations and wire spacings between 2.5 and 20 cm. For spacings larger than a quarter of a wavelength (≈5 cm), the grid had no impact on the observed reflectivities. A physical model was used to analyze the experimental results.

The emission of bare soils at microwave L-band (1-2 GHz) frequencies is known to be correlated with surface soil moisture. Roughness plays an important role in determining soil emissivity although it is not clear which roughness length scales are most relevant. Small-scale (i.e., smaller than the resolution limit) inhomogeneities across the soil surface and with soil depth caused by both spatially varying soil properties and topographic features may affect soil emissivity. In this paper, roughness effects were investigated by comparing measured brightness temperatures of well-characterized bare soil surfaces with the results from two reflectivity models. The selected models are the air-to-soil transition model and Shi's parameterization of the integral equation model (IEM). The experimental data taken from the Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) consist of surface profiles, soil permittivities and temperatures, and brightness temperatures at 1.4 GHz with horizontal and vertical polarizations. The types of correlation functions of the rough surfaces were investigated as required to evaluate Shi's parameterization of the IEM. The correlation functions were found to be clearly more exponential than Gaussian. Over the experimental period, the diurnal mean root mean square (rms) height decreased, while the correlation length and the type of correlation function did not change. Comparing the reflectivity models with respect to their sensitivities to the surface rms height and correlation length revealed distinct differences. Modeled reflectivities were tested against reflectivities derived from measured brightness, which showed that the two models perform differently depending on the polarization and the observation angle.

Recent studies of passive L-band observations over forests have shown that the average canopy transmissivity in temperate coniferous and deciduous forests is on the order of 0.4-0.5. Although the canopy would therefore be expected to transmit a reasonable amount of ground emission, the total emission observed above the canopy shows very little variation with varying soil moisture content. Moist litter present on the forest floor is known to obscure the soil emission. Therefore, more knowledge on the L-band radiative properties of litter and understory layers is needed to better understand the emission of the whole forest system. In order to contribute toward this issue, a field experiment was conducted in a pine forest in southwest France. Radiometric observations were done on the canopy and on different configurations of the forest floor, following sequential stripping of each forest floor layer. In combination with a long-term data set of above-canopy observations, this resulted in emissivity values of bare soil, soil-litter, soil-litter-grass, and soil-litter-grass-canopy configurations for a range of soil and litter moisture values. Calculations involved the use of the Wilheit and L-MEB models. The sensitivity to soil moisture was found to be substantially suppressed by the presence of a grass understory and litter. This corroborates the low correlation between soil moisture and L-band brightness temperature measured above the canopy. Several results of recent modeling and laboratory studies are also confirmed by this paper, which is, to our knowledge, the first to useinsituexperimental data in this context.

The accuracy of hydrological model simulations is dependent on the reliability of model input data like, for example, meteorological information or land cover and soil information. Uncertainties of simulations of soil water fluxes are hereby directly related to the accuracy of available precipitation data. As precipitation is characterized by small temporal and spatial correlation lengths, the uncertainties in precipitation data increase with decreasing density of available precipitation gauges. As soil moisture directly depends on precipitation dynamics, its variation can be used as a proxy for precipitation variability. Remote sensing techniques allow for monitoring of surface soil moisture dynamics at different spatiotemporal scales. In particular, low-frequency microwave data are most sensitive to soil moisture dynamics. This paper investigates the potential of integrating L-band (1-2 GHz) microwave radiometer data into a simple model for soil wetness to compensate for uncertainties inaprioriinformation of precipitation. The study is based on a short-term ground-based L-band radiometer data set over grassland. A high correlation between the microwave signature and surface soil moisture was found, which is consistent with previous findings. An analytical data assimilation scheme for the integration of that information into a soil wetness model, based on an antecedent precipitation index (API), was established. The results revealed that the data assimilation filter adds or removes an amount of water partially compensating for the actual precipitation error. The correlation coefficient between the filter update and the actual precipitation error was found to be 0.6 lesrles 0.8, and the model simulations did show a better coincidence withinsitusoil moisture records when integrating the microwave data. The results indicate high potential for use of L-band microwave data to compensate for uncertainties in precipitation data.

L-band (1-2 GHz) microwave radiometry is an important remote sensing technique to monitor soil moisture over land surfaces at the global scale. The ESA L-Band SMOS radiometer mission aims at providing asglobal maps of soil moisture, with accuracy better than 0.04 m3m-3every 3 days, with a spatial resolution better than 50 km. To improve the models used and for specifying the accuracy of the aimed global soil moisture data product, ground based radiometer campaigns before launch, during the commissioning phase and during the operative SMOS mission are important. Furthermore, the availability of ground-based L-band data measured simultaneously with the over-flying SMOS satellite are required for calibration and validation purposes. To address these needs three ELBARA II radiometers are being constructed for ESA. They rely on the proven architecture of the ELBARA radiometer [1] with improvements on the user interface, the mechanics, and the microwave electronics. Especially the development of an Active Cold Load (ACL) [2] as cold reference is expected to improve the radiometric accuracy.

In this paper, the L-band Microwave Emission of the Biosphere (L-MEB) model used in the Soil Moisture and Ocean Salinity (SMOS) Level 2 Soil Moisture algorithm is calibrated using L-band (1.4 GHz) microwave measurements over a coniferous (pine) and a deciduous (mixed/beech) forest. This resulted in working values of the main canopy parameters optical depth (tau), single scattering albedo (omega), and structural parameterstt(H) andtt(V), besides the soil roughness parametersHRandNR. Using these calibrated values in the forward model resulted in a root mean-square error in brightness temperatures from 2.8 to 3.8 K, depending on data set and polarization. Furthermore, the relationship between canopy optical depth and leaf area index is investigated for the deciduous site. Finally, a sensitivity study is conducted for the focus parameters, temperature, soil moisture, and precipitation. The results found in this paper will be integrated in the operational SMOS Level 2 Soil Moisture algorithm and used in future inversions of the L-MEB model, for soil moisture retrievals over heterogeneous, partly forested areas.

The microwave Forest Soil Moisture Experiment (FOSMEX) was performed at a deciduous forest site at the Research Centre Julich (Germany). An L- and an X-band radiometer were mounted 100 m above ground and directed to the canopy. The measurements consist of dual- and single-polarized L- and X-band data and simultaneously recorded ground moisture, temperature, and meteorological data. The canopy L-band transmissivity was estimated from a subset of the FOSMEX data, where the ground was masked with a metalized foil. For the foliage-free canopy, the reflecting foil diminished the L-band brightness by ap24 K, whereas brightness increased by ap14 K when the foil was removed from below the foliated canopy. Depending on the assumption made on the scattering albedo of the canopy, the transmissivities were between 0.2 and 0.51. Furthermore, the contribution of the foliage was quantified. Although, the evaluation revealed the semitransparency of the canopy for L-band frequencies, the brightness sensitivity with respect to ground moisture was substantially reduced for all foliation states. The effect of ground surface moisture was explored in an irrigation experiment. The L-band measurements were only affected for a few hours until the water drained through the litter layer. This emphasizes the significance of the presence of litter for soil moisture retrieval from remotely sensed L-band brightness data. The FOSMEX database serves for further testing and improving radiative transfer models used for interpreting microwave data received from future spaceborne L-band radiometers flying over areas comprising a considerable fraction of deciduous forests.

The crown vegetation of a deciduous forest is known to be semitransparent at low microwave frequencies, and leaf litter covering the forest soil has been recognized to have a significant impact on ground emission. The proposed approach for modeling the L-band radiative transfer through leaf litter consists of an isotropic effective medium approach for the litter permittivities, a coherent radiative transfer model for computing the coherent reflectivities from dielectric depth profiles, and an averaging procedure for computing the reflectivities determining the field-scale brightness temperatures. Evaluations were performed for the case of leaf litter on top of a conducting wire grid (litter-grid formation) and for litter on underlying soil (litter-soil formation). A model sensitivity analysis was performed with respect to parameters characterizing litter thickness variations and boundary roughness. For the litter-soil formation, the model was rather sensitive to local irregularities at the air-to-litter boundary. Modeled microwave signatures reproduced the major features of the measurements performed on a site comprising a litter-grid formation. Under dry conditions, the investigated litter layer was nearly ldquoinvisible.rdquo When the same litter layer was wetted, it acted as an important radiation source to be taken into account for the quantitative remote soil moisture detection of forested areas. Under certain conditions, the simulations revealed an increasing brightness when the litter is wetted prior to the underlying soil. Further wetting of the litter-soil system then resulted in a decreasing brightness as expected for increased moisture. Such effects are important to know to avoid misleading interpretations of L-band signatures.

Soil moisture retrieval from passive microwave remote sensing over densely vegetated areas, such as forests, has become a pertinent subject in the light of ESA’s upcoming Soil Moisture and Ocean Salinity (SMOS) mission. Even though it is probably not possible to retrieve soil moisture over forested areas with the accuracy required by SMOS, understanding forest radiative transfer properties at L-band (1.1-1.7 GHz) is still important for soil moisture retrieval in partly forested pixels. The forward model to be used by the SMOS Level 2 Soil Moisture algorithm is “L-MEB”, from “L-band Microwave Emission of the Biosphere”. This paper shows the results of L-MEB model calibration over two forest types in terms of various soil and vegetation parameters, together with an assessment of model performance in both forward and inverse mode. While, as expected, the retrieval of soil moisture over forest areas does not give results up to the standard required by SMOS, the forward model does result in an acceptable root mean square error in terms of “reconstructed” brightness temperatures.

This paper compares L-band measurements from three different experiments in areas covered by grass. The main objective is to assess soil moisture retrievals based on the L-band Microwave Emission of the Biosphere model (L-MEB) used by the Soil Moisture and Ocean Salinity mission (SMOS). Results indicate that over grass the vegetation is isotropic to the microwave propagation at horizontal polarisation, while at vertical polarisation non-zero scattering is observed for all the grass data sets. Surface soil moisture is retrieved with enough accuracy for all data sets as long as the soil roughness and litter emission are calibrated beforehand. The study also highlights the importance of detecting strong attenuation by wet vegetation and litter due to rainfall interception. We show that strong rainfall interception can be flagged using a microwave polarisation index.

This paper describes a model which simulates the emission of forests at L band. In particular, the problem of soil emission attenuated by vegetation is considered. Results of comparisons with experimental data collected by the upward looking ELBARA radiometer are presented and discussed.

A field experiment with an L-band radiometer at 1.4 GHz was performed from May-July 2004 at an experimental site near Zurich, Switzerland. Before the experiment started, clover grass was seeded. Thermal infrared, in situ temperature, and time-domain reflectometer (TDR) measurements were taken simultaneously with hourly radiometer measurements. This setup allowed for investigation of the microwave optical depths and mode opacities (parallel and perpendicular to the soil surface) of the clover grass canopy. Optical depths and opacities were determined by in situ analysis and remotely sensed measurements using a nonscattering radiative transfer model. Due to the canopy structure, optical depth and opacity depend on the polarization and radiometer direction, respectively. A linear relation between vegetation water-mass equivalent and polarization-averaged optical depth was observed. Furthermore, measured and modeled radiative transfer properties of the canopy were compared. The model is based on an effective-medium approach considering the vegetation components as ellipsoidal inclusions. The effect of the canopy structure on the opacities was simulated by assuming an anisotropic orientation of the vegetation components. The observed effect of modified canopy structure due to a hail event was successfully reproduced by the model. It is demonstrated that anisotropic vegetation models should be used to represent the emission properties of vegetation. The sensitivity of radiometer measurements to soil water content was investigated in terms of the fractional contribution of radiation emitted from the soil to total radiation. The fraction of soil-emitted radiation was reduced to approximately 0.3 at the most developed vegetation state. The results presented contribute toward a better understanding of the interaction between L-band radiation and vegetation canopies. Such knowledge is important for evaluating data generated from future satellite measurements.

We report on field-measured microwave emission in a period of frost penetration into a grassland soil. The measurements were recorded with a high temporal resolution using an L-band radiometer mounted on a 7-m high tower. The observation period (December 2002 to March 2003) included two cycles of soil freezing and thawing with maximum frost depth of 25 cm. In situ soil temperature and liquid water content were measured at five depths down to 45 cm. Soil moisture profiles were calculated using the COUP numerical soil water and heat model in combination with measured soil properties and meteorological data monitored at the site. The L-band radiation data clearly showed the penetration and thawing of seasonal soil frost. We calculated soil reflectivities based on in situ measured and modeled soil moisture profiles by applying a coherent radiative transfer model. The calculated reflectivities were compared with the radiometrically determined soil reflectivities. It was demonstrated that the quantitative consistency between these reflectivities was significantly improved by applying an impedance matching approach accounting for surface effects. In this particular case, the dielectric structure of the uppermost soil horizon was largely influenced by soil roughness, vegetation, and snow cover. The radiometrically measured soil reflectivities were fitted using a radiative transfer model in combination with a roughness model assuming a soil surface roughness of 25 mm. The analysis during a period of frost penetration shows coherent behavior of the soil reflectivity. Temporal oscillation of the measured L-band radiation appears to be a coherent effect. This effect has the potential to be used for estimating the frost penetration velocity.