L-band Radiometer Measurements Research Articles

On sea ice emission modeling for MOSAiC's L-band radiometric measurements

Abstract The retrieval of sea ice thickness using L-band passive remote sensing requires robust models for emission from sea ice. In this work, measurements obtained from surface-based radiometers during the MOSAiC expedition are assessed with the Burke, Wilheit and SMRT radiative transfer models. These models encompass distinct methodologies: radiative transfer with/without wave coherence effects, and with/without scattering. Before running these emission models, the sea ice growth is simulated using the Cumulative Freezing Degree Days (CFDD) model to further compute the evolution of the ice structure during each period. Ice coring profiles done near the instruments are used to obtain the initial state of the computation, along with Digital Thermistor Chain (DTC) data to derive the sea ice temperature during the analyzed periods. The results suggest that the coherent approach used in the Wilheit model results in a better agreement with the horizontal polarization of the in situ measured brightness temperature. The Burke and SMRT incoherent models offer a more robust fit for the vertical component. These models are almost equivalent since the scattering considered in SMRT can be safely neglected at this low frequency, but the Burke model misses an important contribution from the snow layer above sea ice. The results also suggest that a more realistic permittivity falls between the spheres and random needles formulations, with potential for refinement, particularly for L-band applications, through future field measurements.

Fostering the Need of L-Band Radiometer for Extreme Oceanic Wind Research

Ocean surface winds from space-borne radiometers and scatterometers are crucial inputs for numerical models for the operational weather and oceanic sea state forecasts. The rainfall, associated mainly with deep convective clouds, influences the wind retrievals from these sensors during extreme winds conditions (higher than 30 m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{s}^{-1}$ </tex-math></inline-formula> ), as the signal strongly gets affected by the intervening atmosphere, mainly the precipitation. This study emphasizes the importance of the winds from L-band radiometric measurements from Soil Moisture Active Passive (SMAP) satellite compared to the operational Advanced Scatterometer (ASCAT) (C-band) and SCATSAT-1 (Ku-band) scatterometers, National Centers for Environmental Prediction (NCEP) final analysis, and European Centre for Medium-Range Weather Forecasts reanalysis 5 (ERA5) reanalysis wind products. Investigation at global scale suggests that except SMAP, no other selected data are able to capture wind speed more than 56 m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{s}^{-1}$ </tex-math></inline-formula> , and large underestimations are found in presently available scatterometers and reanalysis. These high wind speed errors are more prominent when verified with Joint Typhoon Warning Center (JTWC) best track data for global storms. Moreover, the high winds near storms from scatterometers are generally flagged as rainy pixels and not used for operational applications. These limitations of capturing high winds in scatterometers have been addressed here using histogram corrections approach using SMAP retrieved winds. Moreover, the regional model simulations for a case study suggest that the modified scatterometer winds have larger impact on tropical storm prediction mainly on track and intensity.

Objective Analysis of SMOS and SMAP Sea Surface Salinity to Reduce Large-Scale and Time-Dependent Biases from Low to High Latitudes

AbstractTen years of L-band radiometric measurements have proven the capability of satellite sea surface salinity (SSS) to resolve large-scale-to-mesoscale SSS features in tropical to subtropical ocean. In mid-to-high latitudes, L-band measurements still suffer from large-scale and time-varying errors. Here, a simple method is proposed to mitigate the large-scale and time-varying errors. First, an optimal interpolation using a large correlation scale (~500 km) is used to map independently Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) level-3 (L3) data. The mapping is compared with the equivalent mapping of in situ observations to estimate the large-scale and seasonal biases. A second mapping is performed on adjusted SSS at the scale of SMOS/SMAP spatial resolution (~45 km). This procedure merges both products and increases the signal-to-noise ratio of the absolute SSS estimates, reducing the root-mean-square difference of in situ satellite products by about 26%–32% from mid- to high latitudes, respectively, in comparison with the existing SMOS and SMAP L3 products. However, in the Arctic Ocean, some issues on satellite retrieved SSS related to, for example, radio frequency interferences, land–sea contamination, and ice–sea contamination remain challenging to reduce given the low sensitivity of L-band radiometric measurements to SSS in cold water. Using the International Thermodynamic Equation Of Seawater—2010 (TEOS-10), the resulting level-4 SSS satellite product is combined with satellite-microwave SST products to estimate sea surface density, spiciness, and haline contraction and thermal expansion coefficients. For the first time, we illustrate how useful these satellite-derived parameters are to fully characterize the surface ocean water masses at large mesoscale.

Correcting Sea Surface Temperature Spurious Effects in Salinity Retrieved From Spaceborne L-Band Radiometer Measurements

Earlier studies have pointed out systematic differences between sea surface salinity retrieved from L-band radiometric measurements and measured in situ, which depend on sea surface temperature (SST). We investigate how to cope with these differences given existing physically based radiative transfer models. In order to study differences coming from seawater dielectric constant parametrization, we consider the model of Somaraju and Trumpf (2006) (ST) which is built on sound physical bases and close to a single relaxation term Debye equation. While ST model uses fewer empirically adjusted parameters than other dielectric constant models currently used in salinity retrievals, ST dielectric constants are found close to those obtained using the Meissner and Wentz (2012) (MW) model. The ST parametrization is then slightly modified in order to achieve a better fit with seawater dielectric constant inferred from SMOS data. Upgraded dielectric constant model is intermediate between KS and MW models. Systematic differences between SMOS and in situ salinity are reduced to less than +/-0.2 above 0 °C and within +/-0.05 between 7 °C and 28 °C. Aquarius salinity becomes closer to in situ salinity, and within +/-0.1. The order of magnitude of remaining differences is very similar to the one achieved with the Aquarius version 5 empirical adjustment of wind model SST dependence. The upgraded parametrization is recommended for use in processing the SMOS data. Further assessment or improvement using new laboratory measurements should consider keeping the physics-based formulation by ST that has been shown here to be very efficient.

New insights into SMOS sea surface salinity retrievals in the Arctic Ocean

Since 2010, the Soil Moisture and Ocean Salinity (SMOS) satellite mission monitors the earth emission at L-Band. It provides the longest time series of Sea Surface Salinity (SSS) from space over the global ocean. However, the SSS retrieval at high latitudes is a challenge because of the low sensitivity L-Band radiometric measurements to SSS in cold waters and to the contamination of SMOS measurements by the vicinity of continents, of sea ice and of Radio Frequency Interferences. In this paper, we assess the quality of weekly SSS fields derived from swath-ordered instantaneous SMOS SSS (so called Level 2) distributed by the European Space Agency. These products are filtered according to new criteria. We use the pseudo-dielectric constant retrieved from SMOS brightness temperatures to filter SSS pixels polluted by sea ice. We identify that the dielectric constant model and the sea surface temperature auxiliary parameter used as prior information in the SMOS SSS retrieval induce significant systematic errors at low temperatures. We propose a novel empirical correction to mitigate those sources of errors at high latitudes.Comparisons with in-situ measurements ranging from 1 to 11 m depths spotlight huge vertical stratification in fresh regions. This emphasizes the need to consider in-situ salinity as close as possible to the sea surface when validating L-band radiometric SSS which are representative of the first top centimeter.SSS Standard deviation of differences (STDD) between weekly SMOS SSS and in-situ near surface salinity significantly decrease after applying the SSS correction, from 1.46 pss to 1.28 pss. The correlation between new SMOS SSS and in-situ near surface salinity reaches 0.94. SMOS estimates better capture SSS variability in the Arctic Ocean in comparison to TOPAZ reanalysis (STDD between TOPAZ and in-situ SSS = 1.86 pss), particularly in river plumes with very large SSS spatial gradients.

Improved SMAP Dual-Channel Algorithm for the Retrieval of Soil Moisture

The soil moisture active passive (SMAP) mission was designed to acquire L-band radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSD of not more than 0.04 $\text{m}^{3}/\text{m}^{3}$ volumetric accuracy in the top 5 cm for vegetation with a water content of less than 5 kg/ $\text{m}^{2}$ . Single-channel algorithm (SCA) and dual-channel algorithm (DCA) are implemented for the processing of SMAP radiometer data. The SCA using the vertically polarized brightness temperature (SCA-V) has been providing satisfactory SM retrievals. However, the DCA using prelaunch design and algorithm parameters for vertical and horizontal polarization data has a marginal performance. In this article, we show that with the updates of the roughness parameter $h$ and the polarization mixing parameters $Q$ , a modified DCA (MDCA) can achieve improved accuracy over DCA; it also allows for the retrieval of vegetation optical depth (VOD or $\tau$ ). The retrieval performance of MDCA is assessed and compared with SCA-V and DCA using four years (April 1, 2015 to March 31, 2019) of in situ data from core validation sites (CVSs) and sparse networks. The assessment shows that SCA-V still outperforms all the implemented algorithms.

Timing and spatial variability of fall soil freezing in boreal forest and its effect on SMAP L-band radiometer measurements

This paper investigates the potential for the Soil Moisture Active Passive (SMAP) L-band radiometer to estimate the percentage of frozen soil inside a pixel during fall periods. To evaluate the spatial and temporal variability of the autumn freeze in northeastern Canada boreal forest, a network of compact and self-recording temperature sensors (iButton) along transects averaging 10 km was deployed on the soil surface at two different sites. Results show important spatial variability in soil freezing timing with maximums, for the two sites, spanning from 7.5 to 9.5 weeks to reach a frozen state. It is known that L-band radiometry such as Soil Moisture Active Passive (SMAP) can monitor freeze/thaw (F/T) seasonality at low spatial resolution (~40 km). However, the possible strong surface variability within a 40 km × 40 km pixel can lead to spatial delay in fall freezing. Simulations of brightness temperature (TB) weighted by spatially distributed iButton F/T records and using the ω-τ vegetation model show good agreement with SMAP satellite observations during the fall transition period with an average RMSE of 3.5 K. A new intra-pixel frozen soil percentage retrieval algorithm using only SMAP observations and air temperature is proposed. The percentage of frozen soil in falls 2015 and 2016 from the new algorithm and the in situ measurements are in good agreement with a coefficient of determination (R2) ranging between 0.63 and 0.88.

Sampling depth of L-band radiometer measurements of soil moisture and freeze-thaw dynamics on the Tibetan Plateau

Knowing the exact sampling depth of microwave radiometry is essential for quantifying the performance and appreciation of the applicability of satellite soil moisture products. We investigate in this study the sampling depth (δSM) of the L-band microwave emission under frozen and thawed soil conditions on the Tibetan Plateau. Two years of diurnal brightness temperature (TBp) measurements at a time interval of 30 min are collected by the ELBARA-III radiometer deployed at a Tibetan meadow site. Vertical profiles of soil temperature and volumetric liquid water content (θliq) are measured simultaneously at soil depths up to 1 m below the surface. The impact of the θliq measured at different depths on the microwave emission simulations is assessed using the τ-ω emission model, whereby the permittivity of frozen and thawed soil is estimated by the four-phase dielectric mixing model. It is found that: 1) the sampling depth for the effective temperature depends on the magnitude of θliq, and is estimated to be, on average, about 50 and 15 cm for the cold dry and wet warm period, respectively, because of the seasonality in θliq; 2) the δSM is determined at 2.5 cm for both frozen and thawed soil conditions during both cold and warm periods, which is shallower than the commonly used θliq measurement depth (i.e. 5 cm) adopted for the in-situ monitoring networks across the globe; 3) the TBp simulations performed with the θliq measurements taken at the estimated δSM of 2.5 cm result in lower unbiased root mean squared errors, about 14% (3.16 K) and 22% (3.36 K) for the horizontal and vertical polarizations respectively, in comparison to the simulations with the θliq measurements taken from 5 cm soil depth; and 4) the θliq retrieved with the single channel algorithm from the ELBARA-III measured vertically polarized TBp are in better agreement with the θliq measured at 2.5 cm than the one measured at 5 cm. These findings are crucial for developing strategies for the calibration/validation as well as the application of satellite based soil moisture products relying on the L-band radiometry.

Vegetation Optical Depth and Soil Moisture Retrieved from L-Band Radiometry over the Growth Cycle of a Winter Wheat

L-band radiometer measurements were performed at the Selhausen remote sensing field laboratory (Germany) over the entire growing season of a winter wheat stand. L-band microwave observations were collected over two different footprints within a homogenous winter wheat stand in order to disentangle the emissions originating from the soil and from the vegetation. Based on brightness temperature (TB) measurements performed over an area consisting of a soil surface covered by a reflector (i.e., to block the radiation from the soil surface), vegetation optical depth (τ) information was retrieved using the tau-omega (τ-ω) radiative transfer model. The retrieved τ appeared to be clearly polarization dependent, with lower values for horizontal (H) and higher values for vertical (V) polarization. Additionally, a strong dependency of τ on incidence angle for the V polarization was observed. Furthermore, τ indicated a bell-shaped temporal evolution, with lowest values during the tillering and senescence stages, and highest values during flowering of the wheat plants. The latter corresponded to the highest amounts of vegetation water content (VWC) and largest leaf area index (LAI). To show that the time, polarization, and angle dependence is also highly dependent on the observed vegetation species, white mustard was grown during a short experiment, and radiometer measurements were performed using the same experimental setup. These results showed that the mustard canopy is more isotropic compared to the wheat vegetation (i.e., the τ parameter is less dependent on incidence angle and polarization). In a next step, the relationship between τ and in situ measured vegetation properties (VWC, LAI, total of aboveground vegetation biomass, and vegetation height) was investigated, showing a strong correlation between τ over the entire growing season and the VWC as well as between τ and LAI. Finally, the soil moisture was retrieved from TB observations over a second plot without a reflector on the ground. The retrievals were significantly improved compared to in situ measurements by using the time, polarization, and angle dependent τ as a priori information. This improvement can be explained by the better representation of the vegetation layer effect on the measured TB.

Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment

Organic soils play a key role in global warming because they store large amount of soil carbon which might be degraded with changing soil temperatures or soil water contents. There is thus a strong need to monitor these soils and, in particular, their hydrological characteristics using, for instance, space-borne L-band brightness temperature observations. However, there are still open issues with respect to soil moisture retrieval techniques over organic soils. In view of this, organic soil blocks with their vegetation cover were collected from a heathland in the Skjern River catchment in western Denmark and then transported to a remote sensing field laboratory in Germany where their structure was reconstituted. The controlled conditions at this field laboratory made it possible to perform tower-based L-band radiometer measurements of the soils over a period of two months. Brightness temperature data were inverted using a radiative transfer (RT) model for estimating the time variations in the soil dielectric permittivity and the vegetation optical depth. In addition, the effective vegetation scattering albedo parameter of the RT model was retrieved based on a two-step inversion approach. The remote estimations of the dielectric permittivity were compared to in situ measurements. The results indicated that the radiometer-derived dielectric permittivities were significantly correlated with the in situ measurements, but their values were systematically lower compared to the in situ ones. This could be explained by the difference between the operating frequency of the L-band radiometer (1.4 GHz) and that of the in situ sensors (70 MHz). The effective vegetation scattering albedo parameter was found to be polarization dependent. While the scattering effect within the vegetation could be neglected at horizontal polarization, it was found to be important at vertical polarization. The vegetation optical depth estimated values over time oscillated between 0.10 and 0.19 with a mean value of 0.13. This study provides further insights into the characterization of the L-band brightness temperature signatures of organic soil surface layers and, in particular, into the parametrization of the RT model for these specific soils. Therefore, the results of this study are expected to improve the performance of space-borne remote sensing soil moisture products over areas dominated by organic soils.

Uncertainty Estimates in the SMAP Combined Active–Passive Downscaled Brightness Temperature

NASA's Soil Moisture Active Passive (SMAP) mission objective is global mapping of surface volumetric soil moisture at 10-km resolution every two to three days and with accuracy of 0.04 cm3 cm−3 (one sigma). In order to achieve this resolution and accuracy, the SMAP utilizes L-band radar and L-band radiometer measurements. The instruments share a rotating 6-m mesh reflector antenna that scans across a 1000-km swath in order to meet the required data refresh rate. The Level-2 Active–Passive soil moisture product (L2_SM_AP) at 9 km is retrieved from the disaggregated/downscaled brightness temperature obtained by merging of active and passive L-band observations. The baseline L2_SM_AP algorithm disaggregates the coarse-resolution (∼36 km) brightness temperatures of the SMAP L-band radiometer using the high-resolution (∼3 km) backscatter data from the SMAP L-band radar with unfocused synthetic aperture processing. The inversion of brightness temperature to estimate surface soil moisture is more mature when compared with inversions of radar backscatter. This is the primary driver of the brightness temperature disaggregation approach to the combined active–passive surface soil moisture product. Furthermore, this approach allows some consistency with the coarse-resolution radiometer-only surface soil moisture product since the disaggregated brightness temperatures sums to the radiometer measurement. The disaggregated brightness temperature contains instrument errors (∼0.7 dB for co-pol backscatter and ∼1.0 dB for cross-pol backscatter, and ∼1.3 K in brightness temperature) inherent in the radar and radiometer. Furthermore, the algorithm has two critical parameters that add uncertainty. Finally, correction of the land brightness temperature (used in the inversion) for water body contributions is a source of uncertainty. In this paper, we introduce analytical expressions for the SMAP downscaled brightness temperature due to all these sources of uncertainty. The expressions allow estimation of uncertainty (in kelvin) for each data granule of the SMAP L2_SM_AP product. Since the uncertainties depend on the given ground conditions, e.g., existing water body fraction and local algorithm parameters that depend on vegetation cover and landscape heterogeneity, it is necessary to evaluate the uncertainty for each data granule. In this paper, we show that the uncertainty expressions closely match Monte Carlo simulations with an overall difference of only ∼0.1 K. Whereas Monte Carlo estimates of uncertainty can only be afforded for a nominal case (such as those typically reported in Algorithm Theoretical Basis Documents as uncertainty tables), the analytical expressions allow uncertainty estimates for every data granule. The expressions are now used to provide uncertainty standard deviation of downscaled brightness temperature at 9 km in the SMAP L2_SM_AP product. These standard deviations are useful for the following: 1) guidance on the expected level of error in the estimate brightness temperature due to the downscaling process and 2) observation error in direct radiance data assimilation.

Calibration of the L-MEB Model for Croplands in HiWATER Using PLMR Observation

The Soil Moisture and Ocean Salinity (SMOS) mission was initiated in 2009 with the goal of acquiring global soil moisture data over land using multi-angular L-band radiometric measurements. Specifically, surface soil moisture was estimated using the L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model. This study evaluated the applicability of this model to the Heihe River Basin in Northern China for specific underlying surfaces by simulating brightness temperature (BT) with the L-MEB model. To analyze the influence of a ground sampling strategy on the simulations, two resampling methods based on ground observations were compared. In the first method, the simulated BT of each point observation was initially acquired. The simulations were then resampled at a 1 km resolution. The other method was based on gridded data with a resolution of 1 km averaged from point observations, such as soil moisture, soil temperature, and soil texture. The simulated BTs at a 1 km resolution were then obtained using the L-MEB model. Because of the large variability in soil moisture, the resampling method based on gridded data was used in the simulation. The simulated BTs based on the calibrated parameters were validated using airborne L-band data from the Polarimetric L-band Multibeam Radiometer (PLMR) acquired during the HiWATER project. The root mean square errors (RMSEs) between the simulated results and the PLMR data were 6 to 7 K for V-polarization and 3 to 5 K for H-polarization at different angles. These results demonstrate that the model effectively represents agricultural land surfaces, and this study will serve as a reference for applying the L-MEB model in arid regions and for selecting a ground sampling strategy.

Errors in SMOS Sea Surface Salinity and their dependency on a priori wind speed

The wind speed (WS) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to initialize the retrieval process of WS and Sea Surface Salinity (SSS) obtained by the Soil Moisture and Ocean Salinity (SMOS) mission. This process compensates for the lack of onboard instrument providing a measure of ocean surface WS independent of the L-band radiometer measurements. The SMOS-retrieved WS in the center of the swath (±300km) is adjusted regarding to its a priori estimate. The quality of the SMOS-retrieved SSS (SSSSMOS) is better at the center of the swath than at the edge of the swatch because the larger number of brightness temperature measurements available at the center of the swath reduces the effects of noise and because the greater variety of incidence angles provides more scope for adjusting the WS. This highlights the advantage of using a multi-parameter retrieval with respect to a SSS-only retrieval in which the WS would be entirely prescribed. Systematic inconsistencies between the atmospheric WS modeled using ECMWF and the WS sensed by radiometers are observed. These inconsistencies in the WS are reduced by the retrieval scheme but they still lead to residual biases in the SSSSMOS, especially in the eastern equatorial Pacific ocean if the ECMWF WS is used as an a priori estimate.

Analysis of coincident L-band radiometer and radar measurements with respect to soil moisture and vegetation conditions

Active and passive microwave remote sensing data offer complementary information on the properties of the observed scene. This study investigates the relationship of L-band radiometer and radar signals to vegetation and soil during four field campaigns conducted in United States between 1999 and 2008. The study shows complex relationship between radiometer observed reflectivity and radar observed backscatter over various landscapes, as expected. Vegetation classification was attempted with different in situ and remote sensing parameters but only a purely empirical combination of Vegetation Water Content and cross- polarized backscatter was able to successfully divide the observations into broad categories of high and low vegetation. The study suggests that a combination of radar and radiometer signal is necessary for establishing a parameter that can fully describe the vegetation state.

Surveys and Analysis of RFI in Preparation for SMOS: Results from Airborne Campaigns and First Impressions from Satellite Data

Several soil moisture and sea salinity campaigns, including airborne L-band radiometer measurements, have been carried out in preparation for the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. The radiometer used in this context is fully polarimetric and is capable of detecting radio frequency interference (RFI) using the kurtosis method. Analyses have shown that the kurtosis method generally detects RFI in an efficient manner, particularly concerning pulsed, low duty cycle signals, but it has some shortcomings when it comes to more continuous wave signal types. Hence, other detection methods have been investigated as well. In particular, inspection of the third and fourth Stokes parameters shows promising results—possibly as a complement to the kurtosis method. The kurtosis method, however, cannot be used with SMOS data. Since SMOS is fully polarimetric, the third and fourth Stokes parameter method is an option, and a first assessment using a fully polarimetric SMOS data set looks promising. Finally, a variable incidence angle signature algorithm is introduced, and the possibility of using this as an RFI indicator is discussed.

L-Band RFI as Experienced During Airborne Campaigns in Preparation for SMOS

In support of the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission, a number of soil moisture and sea salinity campaigns, including airborne L-band radiometer measurements, have been carried out. The radiometer used in this context is fully polarimetric and has built-in radio-frequency-interference (RFI)-detection capabilities. Thus, the instrument, in addition to supplying L-band data to the geophysicists, also gave valuable information about the RFI environment. Campaigns were carried out in Australia and in a variety of European locations, resulting in the largest and most comprehensive data set available for assessing RFI at L-band. This paper introduces the radiometer system and how it detects RFI using the kurtosis method, reports on the percentage of data that are typically flagged as being corrupted by RFI, and gives a hint about geographical distribution. Also, examples of polarimetric signatures are given, and the possibility of detecting RFI using such data is discussed.

L-band radiometer measurements of soil water under growing clover grass

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.

SMOS REFLEX 2003: L-band emissivity characterization of vineyards

The goal of the Soil Moisture and Ocean Salinity mission over land is to infer surface soil moisture from multiangular L-band radiometric measurements. As the canopy affects the microwave emission of land, it is necessary to characterize different vegetation layers. This paper presents the Reference Pixel L-Band Experiment (REFLEX), carried out in June-July 2003 at the Vale/spl grave/ncia Anchor Station, Spain, to study the effects of grapevines on the soil emission and on the soil moisture retrieval. A wide range of soil moisture (SM), from saturated to completely dry soil, was measured with the Universitat Polite/spl grave/cnica de Catalunya's L-band Automatic Radiometer (LAURA). Concurrently with the radiometric measurements, the gravimetric soil moisture, temperature, and roughness were measured, and the vines were fully characterized. The opacity and albedo of the vineyard have been estimated and found to be independent on the polarization. The /spl tau/--/spl omega/ model has been used to retrieve the SM and the vegetation parameters, obtaining a good accuracy for incidence angles up to 55/spl deg/. Algorithms with a three-parameter optimization (SM, albedo albedo, and opacity) exhibit a better performance than those with one-parameter optimization (SM).

High-stability L-band radiometer measurements of saltwater

L-band radiometer brightness temperature measurements of a saltwater pond were made as a function of salinity and temperature. A precision L-band radiometer with stability better than 0.1 K per day was used for these measurements. The L-band measurements are in good agreement with three dielectric constant models over a temperature range from 8/spl deg/C to 32/spl deg/C and a salinity range from 25-40 psu. Based on this experiment, these dielectric models will provide an excellent basis for the algorithm development and design of the future National Aeronautics and Space Administration Aquarius satellite mission.

Estimation of soil-type heterogeneity effects in the retrieval of soil moisture from radiobrightness

The authors estimate the magnitude of the beam-filling error due to soil-type heterogeneity in the determination of sensor-footprint average soil moisture (/spl theta/~/sub f/) retrieved from remote L-band radiometer measurements. Sets of randomly chosen soils are given uniform initial wetness and are subjected to atmospheric drying over 15 days in a numerical model. Results indicate that soil heterogeneity contributes less than 0.7% volumetric soil-moisture error (0.007 m/sub water//sup 3//m/sub soil//sup 3/).