Oil thickness in oil spills involving sea ice is a key parameter required for an effective oil spill response; however, quantifying it from radar backscatter data remains a difficult task. We investigated a possible solution for estimating oil slick thickness by using electromagnetic forward and inverse scattering models of oil-covered newly formed sea ice (NI). Our forward model employs a first-order approximation of a multilayered small perturbation method to predict two copolarization C-band radar backscatters of NI covered by an oil slick with thicknesses ranging from 0–7 mm. The results showed that the backscatter decreases as slick thickness increases, which we attributed to signal attenuation within the saline-oil layer. Our inverse model relies on the particle swarm optimization algorithm to determine the slick thickness on NI using synthetic backscatter data, and it requires the input of several important ice and oil physical parameters (thickness, dielectrics, and roughness). Moreover, the estimated slick thickness was validated using scatterometer data from an oil-on-ice experiment at the University of Manitoba’s Sea-ice Environmental Research Facility. With synthetic data, the 5 mm oil slick thickness was overestimated by 25%, while with experimental data, it was overestimated by 8%. Overall, our findings have laid the groundwork for future inversion studies to identify the thickest oil spill zone from current and future C-band radar satellites for immediate response.

New techniques for automated retrieval of ocean surface wind speed from the RADARSAT Constellation mission (RCM) without input wind direction have been developed and tested. We collected a large number of collocated and coincided ocean buoy wind measurements and RCM co- and cross-polarization observations acquired over the West and East coasts of Canada from February 1, 2020 to July 25, 2021: 1190 data points for RCM ScanSAR 50 m (SC50M) VV–VH, 3819 points for SC50M HH–HV, and 397 points for ScanSAR Low Noise (SCLN) HH–HV images. The observations captured a wide range of wind conditions with the maximum wind speeds of 22.3, 23.8, and 19.7 m/s for SC50M VV–VH, SC50M HH–HV, and SCLN HH–HV, respectively. For these three types of RCM data, the ocean surface wind speed was modeled as a function of the co-polarization (VV or HH) and cross-polarization backscatter (VH or HV) as well as the noise floor and the incidence angle. The models were tested on completely independent subsets of data, and their performance was compared against CMOD5.N (for VV) and CMODH (for HH) models which require input wind direction. The root-mean square errors (RMSEs) for the proposed models in the testing subsets are 1.20, 1.52, and 1.26 m/s, while CMOD models showed 1.37, 1.91, and 3.06 m/s for SC50M VV–VH, SC50M HH–HV, and SCLN HH–HV, respectively. The proposed models are being integrated in various Environment and Climate Change Canada applications including ice and wind data assimilation systems and the National SAR Winds program.

Synthetic aperture radar (SAR) sea ice imagery is a promising source of data for sea ice data assimilation. Classification of SAR sea ice imagery into ice and water is of particular relevance due to its relationship with ice concentration, a key variable in sea ice data assimilation systems. With increasing volumes of SAR data, automated methods to carry out these classifications are of particular importance. Although several automated approaches have been proposed, none look at the impact of including an estimate of uncertainty of the model parameters and input features on the classification output. This article uses an established database of SAR image features to train a multilayer perceptron (MLP) neural network to classify pixel locations as either ice, water, or unknown. The classification accuracies are benchmarked using a recently developed logistic regression approach for the same database. The two methods are found to be comparable. The MLP approach is then enhanced to allow uncertainty to be estimated at each pixel location. Following methods proposed in the deep learning community, two kinds of uncertainty are considered. The first, epistemic uncertainty, is that due to uncertainty in the MLP weights. The second kind of uncertainty, aleatoric uncertainty, is that which cannot be explained by the model, and is therefore associated with the input data. It is found that including these uncertainties in the MLP models reduces their accuracies slightly, but also reduces misclassification rates. This is of particular importance for data assimilation applications, where misclassifications could severely degrade the analysis.

A new technique for automated retrieval of ice concentration from RADARSAT-2 dual-polarization HH-HV ScanSAR Wide images for subsequent assimilation in ice numerical models is presented. First, we extended our previously introduced ice and water detection approach operating at a 2.05 km x 2.05 km spatial scale to a set of 19 different spatial scales ranging from 2.05 km (41 pixels) down to 0.25 km (5 pixels). As the spatial resolution was increased, the overall accuracy of ice and water detection stayed at a very high level across all scales (between 99.5% and 99.8%), but the number of water retrievals substantially dropped. Second, we designed an approach for estimating ice concentration in a 2 km × 2 km (40 × 40 pixels) area consisting of 64 5 × 5 pixel blocks. The 5 × 5 pixel blocks which are initially classified as unknowns are iteratively combined in clusters with effective spatial scales larger than 5 pixels. The clusters are further classified as ice or water using the ice probability model corresponding to the effective spatial scale. The 40 x 40 pixel area becomes populated with high-resolution (5 x 5 pixels) ice and water retrievals, and the ice concentration is estimated based on the number of ice and water retrievals. The proposed approach produces a much better agreement with the Canadian Ice Service Image Analysis ice concentrations (rootmean-square error (RMSE) = 2.2%) compared to the original 2-km ice/water detection approach (RMSE = 19.9%). The developed technique will be adapted to the RADARSAT Constellation Mission data for data assimilation in Environment and Climate Change Canada Regional Ice-Ocean Prediction System.

In this study, we performed a model-based analysis of scattering from oil-contaminated sea ice. Actual physical measurements of oil-contaminated sea ice were obtained from an experiment in 2017. We used a dielectric mixture model approach to create a multi-layered dielectric profile that represents the sea ice. For the first time, our multi-layered scattering model based on the small perturbation theory, was used to simulate scattering from oil-contaminated sea ice. Modeling results demonstrate that the method can be used for these conditions and show promise for further detailed model studies for detecting oil spills in a sea ice environment.

In this study, we present a new technique for automated retrieval of ice concentration from RADARSAT-2 dual-polarization HH-HV ScanSAR images. We extended our previously introduced ice and water detection approach (based on more than 15,000 SAR images) operating at 2.05 km x 2.05 km spatial scale to a set of 19 retrieval scales ranging from 2.05 km (41 pixel) down to 0.25 km (5 pixel). Then we designed a technique for estimating ice concentration in 2 km x 2 km (40 x 40 pixel) areas using ice and water retrievals derived at multiple spatial scales. We demonstrated that the proposed approach shows a very good agreement with the Canadian Ice Service (CIS) Image Analysis ice concentrations (RMSE=2.2%, R2 = 0.996). The developed technique will be adapted to the data stream from the RADARSAT Constellation Mission (RCM) for data assimilation in Environment and Climate Change Canada (ECCC) Regional Ice-Ocean Prediction System (RIOPS).

In this article, we evaluate the impact of assimilating spaceborne synthetic aperture radar (SAR) data in an Arctic regional ice analysis system over a year cycle. Ice and water information was automatically extracted from more than 7000 RADARSAT-2 HH-HV ScanSAR Wide images acquired over the Canadian Arctic and adjacent waters throughout the entire year 2013. A quality-control procedure was specifically developed and applied to reduce the number of erroneous SAR retrievals. To assess the impact of SAR ice and water retrievals on the Environment and Climate Change Canada (ECCC) Regional Ice-Ocean Prediction System (RIOPS) ice concentration analyses, we designed a set of data assimilation experiments with and without the inclusion of SAR retrievals. Our verification results suggest that the assimilation of SAR-derived retrievals considerably improves ice concentration analyses in the situations where high spatial resolution is important (e.g., near land and over small inland lakes). Furthermore, SAR retrievals are particularly useful over the areas where the Canadian Ice Service's (CIS) manually derived ice products (such as Image Analyses, daily and weekly ice charts) are not available or have limited coverage. The three-satellite RADARSAT Constellation Mission (RCM) launched in June 2019 will significantly increase the temporal frequency of SAR data. According to the most recent CIS estimate, more than 54 000 RCM images a year will be acquired over the CIS areas of interest. Therefore, the assimilation of SAR retrievals from RCM should further enhance automated ice concentration analyses products.

In this paper, we present a new technique for automated detection of ice and open water from sequential RADARSAT-2 ScanSAR dual-polarization HH-HV images. The technique is based on combining a previously developed approach to ice and water detection applied to single synthetic aperture radar (SAR) images with the ice motion information derived from sequential SAR images. To evaluate the new approach, it was applied to 736 SAR image pairs acquired in 2013. Compared with the previous approach, the new approach produced an increase in the fraction of correctly classified water samples from 57.7% to 72.6% while the fraction of correctly classified ice samples did not change appreciably. The overall accuracy stayed at a high level exceeding 99.8%, when compared against the Canadian Ice Service Image Analysis pure ice and water samples. Verification results for different regions and months showed that the detection accuracy exceeds 99.5% for the most regions and months. The proposed approach can also assign enhanced quality to ice and water retrievals found in the reference image. The results are particularly relevant in light of the upcoming Canadian RADARSAT Constellation Mission which will significantly increase the amount and frequency of SAR observations over the Arctic region.

This paper presents a new technique for automated detection of multi-year (MY) and first-year (FY) sea ice from RADARSAT-2 dual-polarization HH–HV ScanSAR Wide images under cold environmental conditions. The approach is applied to 2.05 km$\times2.05$km ($41 \times 41$pixels) spatial window in the situation where the area is labeled as ice by our recently introduced ice and open water detection approach. The probability of the presence of MY ice is modeled as a function of the two selected predictor parameters computed over each spatial window: the HV/HH polarization ratio and the standard deviation of the HV signal. The proposed MY ice probability model was built based on thousands of synthetic aperture radar (SAR) images and corresponding Canadian Ice Service (CIS) Image Analysis products covering the 2010–2016 time period, not including 2013. Our verification results for the independent testing subset for the year 2013 against the CIS Image Analysis products suggest that approximately 50% of pure MY and FY ice samples were classified with an accuracy of 98.2%. The incidence angle correction of HH and HV backscatter does not improve MY and FY ice detection results in the space of the selected predictor parameters. The proposed technique will be used as part of the Environment and Climate Change Canada Regional Ice-Ocean Prediction System in support of assimilation of ice thickness retrievals from Cryosat-2 and Soil Moisture and Ocean Salinity mission data.

In this letter, we introduce adaptive probability thresholding in addition to our previously developed technique for automated detection of ice and open water from RADARSAT-2 ScanSAR dual-polarization HH–HV images. Situations where the probability threshold needs to be modified were identified based on the analysis of misclassified ice and water samples when the static probability threshold of 0.95 is applied. We found that with the use of the proposed approach, the fraction of misclassified ice samples decreased from 0.98% to 0.24% and the fraction of misclassified water samples decreased from 0.35% to 0.09% in the most clean verification scenario against Canadian Ice Service Image Analysis pure ice and water data, while the fraction of correctly classified ice and water samples did not decrease appreciably, from 72.2% to 65.4%. The developed approach will be implemented as a part of the data assimilation component of the operational Environment and Climate Change Canada Regional Ice-Ocean Prediction System.

In this study, we present a technique for automated detection of ice and open water based on ice motion information derived from sequential RADARSAT-2 images and an ice probability model applied to both SAR images. We investigate how the use of sequential synthetic aperture radar (SAR) images could increase the number of ice/water retrievals compared to the ice/water detection applied to a single SAR image only. The proposed technique was run for 736 image pairs and the ice/water retrieval results were verified against Canadian Ice Service Image Analysis products. Our results suggest that the fraction of water samples classified correctly has significantly increased from 65% (in the case of using a single SAR image) to 81% (in the case of using sequential SAR), while the detection accuracy stayed at approximately the same high level exceeding 99%. The developed approach is recommended to be implemented as part of the data assimilation component of the operational Environment and Climate Change Canada Regional Ice-Ocean Prediction System. The results are particularly important in light of the upcoming Canadian RADARSAT constellation mission which will significantly increase the amount and frequency of SAR observations over the Arctic region.

We present model and measurement results for time-series C-band normalized radar cross-sections (NRCS) over first-year snow-covered sea ice during a winter-spring transition period. Experimental scatterometer and physical data were collected near Cambridge Bay, Nunavut, Canada between May 20 and May 28, 2014 covering a severe storm event on May 25. We observed good agreement between model and experimental HH and VV NRCS. Before the storm, the large-scale surface scattering and volume scattering components dominated. After the storm, the large-scale scattering contribution increased, while the volume scattering contribution considerably dropped. Surface scattering from the small-scale component of the air-snow interface became significant at high incidence angles. We attribute these effects to the increase in surface roughness and snow moisture content during the post-storm period. Our results provide a physical basis for interpretation of time-series SAR images over sea ice at the winter-spring transition.

In this paper, we present a new technique for automated detection of ice and open water from RADARSAT-2 ScanSAR dual-polarization HH-HV images. Probability of the presence of ice within 2.05 km$\times2.05$km areas is modeled using a form of logistic regression as a function of the difference between the wind speeds estimated from synthetic aperture radar (SAR) data and those obtained from numerical weather prediction short-term forecasts, the spatial correlation between HH and HV backscatter signals, and the spatial standard deviation of the wind speed estimated from SAR. The resulting ice probability model was built based on thousands of SAR images and corresponding Canadian Ice Service (CIS) Image Analysis products covering all seasons and all Canadian and adjacent Arctic regions being monitored by CIS. Extensive verification of the proposed technique was conducted for an entire year (2013) against independent Image Analysis products and Interactive Multisensor Snow and Ice Mapping System ice extent products. Using a probability threshold of 0.95, 72.2% of the retrievals were classified as either ice or open water with an accuracy of 99.2% in the most clean verification scenario against Image Analysis pure ice and water data. The ability to obtain such a large number of retrievals with a very high accuracy makes it feasible to assimilate the resulting retrievals in an ice prediction system. Consequently, the developed ice/water retrieval technique will be implemented as a part of the data assimilation component of the operational Environment and Climate Change Canada Regional Ice-Ocean Prediction System.

In this paper, we present model and measurement results for time-series angular dependencies of C-band nn and VV normalized radar cross-sections (NRCS) over first-year snow-covered sea ice during a winter-spring transition period. Experimental scatterometer and physical data were collected near Cambridge Bay, Nunavut, Canada, between May 20 and May 28, 2014, covering a severe storm event on May 25. We use the small perturbation scattering theory to model small-scale surface scattering, the Mie scattering theory to estimate the level of volume scattering in snow, and the Kirchhoff physical optics model to compute the large-scale surface scattering component. We observed good agreement between the model and experimental nn and VV NRCS. Before the storm, R2between model and experimental NRCS was 0.88 and 0.82 for VV and nn, respectively. After the storm, R2was 0.81 and 0.78 for VV and nn, respectively. Our model results suggest an overall increase in surface roughness after the storm event, supported by LiDAR measurements of the snow surface topography. Before the storm, the large-scale and small-scale surface scattering from the air-snow interface as well as volume scattering components dominated. After the storm, the large- and small-scale scattering contributions increased, while the volume scattering component considerably dropped. We attribute these effects to the increase in surface roughness and snow moisture content during the poststorm period. Our results could aid in interpretation of timeseries synthetic aperture radar images with respect to physical properties of snow and ice during the winter-spring transition period.

The dielectric properties of sea ice are important for both passive and active microwave remote sensing of sea ice. In this paper, we present a new technique for dielectric measurements of artificially grown sea ice in the frequency range between 0.3 and 12 GHz using an open-ended coaxial probe. To provide a solid contact between the probe and ice, we slightly submerge and then freeze the probe's flange in sea water in a cold laboratory with a preset temperature. Once the ice is formed, we conduct a measurement of the complex reflection coefficient in the cold room using a vector network analyzer. To calibrate the system, we propose a set of measurements from air, shorting block (short), and pure methanol to be conducted immediately after. Both the real and imaginary parts of the complex dielectric constant as functions of frequency are then derived using a coaxial probe inverse model fed by these data. X-ray microtomography analysis of our samples revealed that the ice formed under the described conditions has completely isotropic microstructure typical for the frazil layer of natural first-year sea ice. To evaluate the experimental system's accuracy, we conducted extensive test measurements of standard materials (saline water, methanol, butanol, and pure ice). We also demonstrate that our sea ice dielectric measurements are close to corresponding values previously reported in the literature. The proposed measurement technique is valuable for developing a sea ice dielectric mixture model at microwave frequencies for different temperatures and salinities.

This paper investigates the inversion of the time-series single-frequency normalized radar cross section (NRCS) data collected from first-year snow-covered sea ice in Cambridge Bay, Nunavut, Canada. The discrepancy between the reconstructed profiles and the true profiles, obtained by in situ measurements, are speculated to be due to the discrepancy between the model used in the inversion algorithm, and the actual electromagnetic wave interactions with the profiles.

The centimeter-scale roughness of natural surfaces, such as soil and sea ice, influences both microwave scattering and turbulent exchanges of heat and momentum between the surface and atmosphere. In this paper, we present a technique for determining surface roughness parameters from high-resolution terrestrial Light Detection and Ranging (LiDAR) data. Field tests demonstrate that the two-dimensional roughness parameters obtained are considerably more precise than parameters determined from traditional one-dimensional profiling techniques. However, laboratory experiments show that the accuracy of the measured roughness parameters is limited by the high inclination scanning angle of the LiDAR system. Results from a numerical model are used to determine a set of calibration functions which can be used to easily correct the LiDAR measurements for the inclination angle effects.

In this paper, we present model and measurement results for C-band HH and VV normalized radar cross-sections (NRCS) from winter snow-covered first-year sea ice with average snow thicknesses of 16, 4, and 3 cm. The brine content in snow pack was low in all three case studies, which is typical for cold winter conditions. We used the first-order approximation of the small perturbation theory accounting for surface scattering from the air-snow and snow-ice rough interfaces and continuously layered snow and sea ice. The experimental data were collected during the Circumpolar Flaw Lead system study in the winter of 2008 in the southern Beaufort Sea from the research icebreaker Amundsen. Good agreement between the model and experimental data were observed for all three case studies. The model results revealed that the scattering at the snow-ice rough interface is usually stronger than that at the air-snow interface. Furthermore, both model and experimental NRCS values (at VV and HH polarizations) were considerably higher for thin-snow cover compared with the thick-snow-cover case. We associate this effect with the lower attenuation of the propagated wave within the thin-snow pack in comparison to the thick-snow pack. We also demonstrated that different brine volume contents in snow with close thicknesses of 4 and 3 cm did not affect the backscattering coefficients at certain incidence angles and polarization. Our findings provide the physical basis for winter snow thickness retrieval and suggest that such retrievals may be possible from radar observations under particular scattering conditions.

Microwave scattering from sea ice is partially controlled by the ice surface roughness. In this paper, we propose a technique for calculating 2-D centimeter-scale surface roughness parameters, including the rms height, correlation length, and form of autocorrelation function, from 3-D terrestrial light detection and ranging data. We demonstrate that a single scale of roughness can be extracted from complex sea ice surfaces, incorporating multiple scales of topography, after sophisticated 2-D detrending, and calculate roughness parameters for a wide range of artificial and natural sea ice surface types. The 2-D technique is shown to be considerably more precise than standard 1-D profiling techniques and can therefore characterize surface roughness as a stationary single-scale process, which a 1-D technique typically cannot do. Sea ice surfaces are generally found to have strongly anisotropic correlation lengths, indicating that microwave scattering models for sea ice should include surface spectra that vary as a function of the azimuthal angle of incident radiation. However, our results demonstrate that there is no fundamental relationship between the rms height and correlation length for sea ice surfaces if the sampling area is above a threshold minimum size.

Terrestrial light detection and ranging (LiDAR) offers significant advantages over conventional techniques for measuring the centimeter-scale surface roughness of natural surfaces, such as sea ice. However, the laser scanning technique is inherently limited, principally by the following: 1) the high inclination scanning angle of the sensor with respect to nadir; 2) the precision of the laser ranging estimate; and 3) the beam divergence of the laser. In this paper, we introduce a numerical model that has been designed to simulate the acquisition of LiDAR data over a regular rough surface. Results from the model compare well (r2= 0.97) with LiDAR observations collected over two experimental surfaces of known roughness that were constructed from medium-density fibreboard using a computer numerical control three-axis router. The model demonstrates that surface roughness parameters are not sensitive to minor variations in the LiDAR sensor's range and laser beam divergence, but are slightly sensitive to the precision of the ranging estimate. The model also demonstrates that surface roughness parameters are particularly sensitive to the inclination angle of the LiDAR sensor. The surface RMS height is underestimated, and the correlation length is overestimated as either the inclination angle of the sensor or the true roughness of the surface increases. An isotropic surface is also increasingly observed as an anisotropic surface as either the inclination angle or the true surface roughness increases. Based on the model results, we propose a set of calibration functions that can be used to correct in situ LiDAR measurements of surface roughness.

A focused study on the C-band polarimetric scattering and physical characteristics of frost-flower-covered sea ice was conducted at the Sea-Ice Environmental Research Facility over a three day period. Sea ice was grown in an outdoor pool outfitted with automated sensors to monitor environmental conditions. C-band polarimetric scattering measurements were conducted continuously at a range of incidence angles, and surface roughness statistics were obtained at discrete times using a laser scanner system LiDAR. Four stages of development were identified that exhibited notably different physical and scattering characteristics: 1) initial formation; 2) surface brine expulsion; 3) frost flower growth; and 4) decimation. An optimal polarization and incidence angle is not readily apparent for the purposes of identifying the frost flower development Stages I-III; however, the lower incidence angles (25° and 35°) appear to be most sensitive to the surface brine expulsion. Only the dual-polarization measurements at low incidence angles (e.g., 25°) could be used to identify the onset of the decimation stage. Backscatter increased rapidly during the initial formation, with a local maximum corresponding to ~ 80% areal coverage of frost flowers, followed by a local minimum when the surface was covered by a brine-rich surface layer, connoting that surface brine expulsion may be identified using polarimetric scatterometry.

A new sea ice motion tracking algorithm that operates with two sequential synthetic aperture radar (SAR) RADARSAT-2 ScanSAR images is presented. The feature tracking approach is based on the combination of the phase-correlation and cross-correlation methods. An algorithm for selecting control points, a matching technique, an approach for filtering out error vectors, and a confidence levels setting for output drift vectors were specifically developed in order to increase the system's robustness and accuracy. We evaluated ice motion tracking results derived from HH and HV channels of RADARSAT-2 ScanSAR imagery and formulated a condition where the HV channel is more reliable than the HH channel for ice tracking. Furthermore, we found that the ice motion tracking from the HV channel is not affected by noise floor stripes, which are prominent in the cross-polarization RADARSAT-2 ScanSAR images. The developed sea ice tracking technology was implemented at the Canadian Ice Service, Environment Canada for operational use. The system was successfully run to provide operational support of field work in the Arctic Ocean in compliance with the United Nations Convention on the Law of the Sea in the spring of 2010.

Two new models for wind speed retrieval from C-band synthetic aperture radar (SAR) data have been developed, based on a large body of statistics on buoy observations collocated and coinciding with RADARSAT-1 and -2 ScanSAR images. The first model's independent variables are co-polarization (HH) normalized radar cross-section (NRCS), and antenna beam incidence angle. The second model's predictors are HH NRCS, cross-polarization (HV) NRCS, instrument noise floor, and incidence angle. The latter model has better accuracy than the first because of using an additional HV variable. Furthermore, we found that the proposed models without wind direction input demonstrated a better accuracy than CMOD_IFR2 and CMOD5.N models in combination with the SAD HH co-polarization ratio (VV/HH), which require wind direction input. These results were confirmed on a large independent subset of collected data. The developed wind speed retrieval models, in conjunction with our previously developed ice motion tracking algorithm, can be a useful tool for studying sea ice dynamics processes in the marginal ice zone. The developed models have been integrated into a quasi-operational system at the Meteorological Service of Canada.

In this study we used our automated sea ice motion tracking algorithm - that operates with two sequential Synthetic Aperture Radar (SAR) images - to investigate capabilities of HH and HV channels of RADARSAT-2 ScanSAR imagery for ice motion detection. We analyzed ice motion tracking results derived from 10 dual-polarization RADARSAT-2 images and formulated a condition where the HV channel is more reliable than the HH channel for ice tracking. Furthermore, we found that the ice motion tracking from the HV channel is not affected by noise floor stripes, which are prominent in the cross-polarization RADARSAT-2 ScanSAR images.

In this paper, experimental studies for bare soil radiobrightnesses at frequency of 6.9 GHz were carried out at a test site with meteorological parameters being registered. On the other hand, temperature and moisture profiles in soil were simulated using the land surface processes. As a result, the temperature and moisture profiles were determined. With these data the radiobrightness patterns could be simulated using the radiation transfer theory. The value of moist soil complex dielectric constant as a function of depth was calculated using the spectroscopic soil dielectric model with the soil bound water influence being taken into account. Finally, diurnal variations of radiobrightnesses were simulated and the results of that simulation were compared with the measured data. The observed and simulated diurnal trends of radiobrightness were found to be in a good agreement, thus proving the land surface processes radiobrightness model coupled with the spectral dielectric model to be an efficient instrument for developing data processing algorithms in radiothermal remote sensing.