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Snow avalanches cause a sudden change of snow properties making them detectable with synthetic aperture radar (SAR). However, steep alpine terrain combined with the slant view geometry of SAR sensors complicates detection: the avalanche brightness depends on the incidence angle and the observed area is limited by radar layover and shadow. Likewise, the spatial resolution varies strongly with the local incidence angle relative to the terrain. To increase the avalanche brightness and to improve the imaging coverage and resolution we apply local resolution weighting (LRW) on Sentinel-1 (S1) backscatter images from ascending and descending orbits. LRW merges acquisitions by averaging them, weighted with the local ground-range resolution. To analyze the relative avalanche brightness with respect to the local incidence angle and polarization, and to quantify the benefit of LRW, we created a dataset of 914 manually drawn avalanche outlines based on S1 imagery of an extreme avalanche event on January 4, 2018 in the Swiss Alps. We show that avalanches appear brightest at slopes facing away from the radar at local incidence angles of 55±20∘ ; such slopes are weighted considerably stronger through LRW. With a processing pipeline for avalanche segmentation using a fixed threshold on the backscatter difference we obtain a higher F1 score (0.75) with LRW compared to an unweighted orbit average (F1 = 0.68) or single orbit acquisitions (F1 = 0.5). For automatic segmentation, we used the manually drawn dataset for training and testing of a deep neural U-Net and achieved a F1 score of 0.81 on LRW backscatter differences., IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, ISSN:1939-1404, ISSN:2151-1535

This article describes the analysis of changes in time and look angle series of TanDEM-X (TDX) and TerraSAR-X (TSX) staring spotlight images over the regions around Kjeller near Oslo in Norway and the town Nikel in Russia near the Norwegian border. It is shown that change detection may be done with TDX/TSX with shorter intervals than the repeat cycle using time series of geocoded images with varying look angles. Using look angle series of geocoded images can help to identify changes due to look angle variation. Accurate science orbit data and very good overlap of scenes both in time and look angle make the TDX/TSX data very attractive for both coherent and incoherent change detection.

Surface soil moisture retrievals were performed by inverting physical scattering models for forests over $\text{30}^\circ$ to $\text{50}^\circ$ incidence angle range and 0.05 to 0.40 m $^3$ /m $^3$ soil moisture range using $L$ -band airborne synthetic aperture radar (SAR) data during a 28-day period. The forward models implemented single-scattering of discrete elements of trees and were validated at $F$ 2 site within 1.5 dB rmse of observation for VV-pol, which was enabled by introducing the gaps between trees. The physical forward models were inverted using the time-series SAR data to retrieve soil moisture and soil surface roughness, which were validated with in situ data at four sites $F$ 1, $F$ 2, $F$ 3, and $F$ 5. Retrievals using VV input over the full dynamic ranges of wetness are accurate to 0.044 m $^3$ /m $^3$ unbiased rmse with correlations of 0.71 to 0.84, which is very encouraging for retrieval under forest canopy. The conditions of these results are the vegetation water content varied from 7.3 to 25.6 kg/m $^2$ and the sensitivity of VV to soil moisture changes ranged from 0.64 to 2.27 dB/(0.1 m $^3$ /m $^3$ ). When both HH and VV were used as inputs to retrieval, the performance did not improve because the benefit of the multichannels was offset by the larger uncertainties in HH modeling. Due to the short duration of in situ data, the efficacy of commonly used relative change index retrieval is diminished. In comparison, the inversion of the scattering model is found to be an effective way to estimate forest soil moisture, it is capable of systematic correction of vegetation effect, and offers accurate retrieval of the dynamic ranges of soil moisture values in terms of unbiased rmse. The retrieval with the physical forward model provides a first step toward global application for the future NISAR satellite.

Both synthetic aperture radar tomography (TomoSAR) profiles and light detection and ranging (LiDAR) waveforms are the responses of a 3-D canopy structure. Relative height (RH) metrics are extensively applied for forest biophysical parameters [i.e., the forest height and above-ground biomass (AGB)] estimation in full-waveform LiDAR studies. However, the use of RH metrics to forest biophysical estimation with TomoSAR profiles is limited due to the estimation error in the ground peak. To overcome this problem, RH metrics were redefined to avoid directly estimating the ground peak in this article. The redefined RH metrics were examined based on the L- and X-bands multibaseline (MB) SAR data simulated by LandSAR, which was a coherent backscattering model of 3-D forest canopies with the capability of MB data simulation at a landscape scale. First, the performance of LandSAR in modeling the tomographic features was verified over mountainous areas using the reference DSM and LiDAR digital terrain model acquired in 2012 in the frame of the Daxinganling campaign. Subsequently, the tomographic profiles were retrieved from the simulated MB data by using the Capon method. Additionally, redefined RH metrics were derived and then applied for retrieving the forest height. The highest performance corresponded to the combination of the L-band redefined RH metrics, with a correlation of R2 = 0.863. The X-band redefined RH metrics performed worst due to limited penetration. Finally, the estimated forest height was used for the AGB retrieval with a height-to-biomass allometry. The R2 and root-mean-square error of the forest AGB estimated using the combined model were 0.814 and 28.566 t/ha, respectively, compared with the reference forest AGB. All findings demonstrated that the redefined RH metrics had the potential of forest height and AGB retrieval using low frequencies TomoSAR data.

Soil moisture ( Mv ) estimation and monitoring over agricultural areas using Synthetic Aperture Radar (SAR) are often affected by vegetation cover during the growing season. Volume scattering and vegetation attenuation can complicate the received SAR backscatter signal when microwave interacts with the vegetation canopy. To address the existing problems, this article employed the model-based polarimetric decomposition method considering the two-way attenuation to remove the volume scattering and vegetation attenuation. A deorientation process of SAR data was applied to remove the influence of randomly distributed target orientation angles before the polarimetric decomposition. To parameterize the two-way attenuation, Radar Vegetation Index derived from the SAR intensity images was adopted. The Dubois model was used to describe backscattering from the underlying bare soil. Since the soil roughness parameters are difficult to measure under vegetation cover, the optimum surface roughness method was used to parameterize the Dubois model. This soil moisture retrieval algorithm was applied to the polarimetric multitemporal RADARSAT-2 SAR data over soybean fields. The validation indicates the root-mean-square error of 9.2 vol.% and 8.2 vol.% at HH and VV polarization, respectively, over the entire soybean growing period, suggesting that the proposed method is capable of reducing the effect of vegetation cover for soil moisture monitoring over the soybean field.

Great Aletsch Glacier (Grosser Aletschgletscher), the largest glacier in the European Alps, contains 20% of the entire Swiss ice mass. Therefore, it has been selected as a super-testsite for the TanDEM-X satellite mission. Dense time series with a repeat interval down to 11 d were acquired between 2011 and 2019 using two polarizations (HH and VV) and across-track baselines of 0–1 km. To evaluate the use of interferometric single-pass synthetic aperture radar mission for glaciological applications, we implemented a processing pipeline in interactive data language (IDL) and computed 130 digital elevation models (DEMs) from bistatic radar interferograms. We present a method to circumvent a common pitfall during orthorectification of radar DEM differences. Regression analysis of DEM time series shows a height loss of up to 8 m a$^{-1}$ on the tongue and 1.5 m a$^{-1}$ when averaged over the whole glacier area. In spring 2013, we observed X-band penetration depths of 4$\pm$2 m in the accumulation area. For strongly crevassed areas, the coherence drops already to 0.5 for across-track baselines $\mathbf {B_\perp }>$ 200 m. With patch-based incoherent offset tracking, we obtained an almost complete 200 m resolution velocity map. Velocities reach up to 0.8 m d$^{-1}$, show a seasonal variability of $\pm$0.05 m d$^{-1}$, and agree within 0.04 m d$^{-1}$(root mean square error) with field measurements. Copolar phase differences suggest an approximation of snow accumulation. We demonstrate orthorectification of the backscatter intensity using simultaneously acquired TanDEM-X interferograms, which allows for the decoupling of horizontal velocity estimates from phase-center height changes due to penetration and ice melt.

The scatterometer onboard the China-France Oceanography Satellite (CFOSAT) is the first rotating fan beam scatterometer (CSCAT) in operation. It is used to measure the ocean surface wind fields over a large observation swath. Geolocation is an important part of the preprocessing of scatterometer data. The accuracy of geolocation has a large impact on the accuracy of wind field retrieval. It is necessary to estimate and analyze the geolocation processing of CSCAT. The challenge is understanding how to estimate the geolocation errors of CSCAT. In this study, the coastline inflection method is used to estimate the geolocation errors of CSCAT based on the distribution characteristics of the backscatter coefficients at both the ocean and the land. The coastline inflection points are obtained using the gradient changes of backscatter coefficients near the coastline. The locations of these points are in line with the trend of the high-precision coastline data. The geolocation errors are estimated by comparing these points with the high-precision coastline. The results show that the current geolocation errors of CSCAT are about 7.31 km, which meets the requirements of 25 × 25 km wind field retrieval. In addition, because the geolocation of scatterometer data is affected by terrain, the geolocation errors of CSCAT are corrected based on an elevation correction algorithm using high-precision digital elevation model data. The results verify the effectiveness of the algorithm.

The particulate backscattering coefficient (bbp) plays an important role in the underwater light field. However, it is difficult to accurately estimate bbp(λ) in turbid inland water with complex optical properties. To accurately estimate the backscattering coefficients in inland water, a simple classification method based on the shape of remote sensing reflectance was first proposed to distinguish two water types (i.e., water type 1 and water type 2) with different backscattering characteristics. Then, trigonometric functions were developed to simulate the backscattering coefficients at all bands in water type 1 and the backscattering coefficients in the visible band of water type 2, whereas a linear function was built to estimate the backscattering coefficients in the near-infrared band of water type 2. The proposed algorithm was compared with four state-of-the-art methods and validated by an independently measured dataset of three lakes in the middle and lower reaches of the Yangtze River in 2020. The results showed that the proposed algorithm performed well in inland waters, with all mean absolute percentage errors < 40% and root-mean-square errors < 0.25 m−1. Finally, the algorithm was applied to Ocean and Land Color Instrument images from 2016 to 2020 in Lake Taihu and Lake Hongze. It was found that the backscattering coefficients in Lake Taihu and Lake Hongze showed opposite seasonal variation trends, and the bbp(676) in Lake Hongze began to decrease since 2017, whereas no obvious interannual variation was observed in Taihu Lake in recent five years.

Data from TanDEM-X in single-pass and bistatic interferometric mode together with the interferometric water cloud model (IWCM) can provide estimates of forest height and stem volume (or the related above-ground biomass) of boreal forests with high accuracy. We summarize results from two boreal test sites using two approaches, i.e., 1) based on model calibration using reference insitu stands, and 2) based on minimization of a cost function. Both approaches are based on inversion of IWCM, which models the complex coherence and backscattering coefficient of a homogeneous forest layer, which includes gaps where free-space wave propagation is assumed. A digital terrain model of the ground is also needed. IWCM is used to estimate forest height or stem volume, since the two variables are assumed to be related through an allometric equation. A relationship between the fractional area of gaps, the area-fill, and stem volume is also required to enable model inversion. The accuracy of the stem volume estimate in the two sites varies between 16% and 21% for height of ambiguity

Being able to accurately estimate inherent optical properties (IOPs) at long time scales is key to comprehending the aquatic biological and biogeochemical responses to long-term global climate change. We employed the near-infrared band and combined it with four “common bands” at visible wavelengths (around 443, 490, 551, and 670 nm) to adjust the IOPs data processing system, IDASv2. We applied the IDASv2 algorithm further to correct for the residual error in images of turbid waters. We evaluated the performance of the IDASv2 algorithm using datasets covering a wide range of natural water types from clear open ocean to turbid coastal and inland waters. Due to the water-leaving signals’ sensitivity to the optically significant constituents of highly turbid waters, the near-infrared band was very important for retrieving IOPs from those waters. In our analysis, we found that the IDASv2 algorithm provided IOPs data with Rrs) data because of the strong absorption of pure water. We tested the IDASv2 algorithm with numerically simulated and satellite observed data of turbid water. After applying IDASv2, the IOPs data were accurately determined from Rrs data contaminated by the residual error. Furthermore, the mean intermission difference between Medium Resolution Spectral Imager 2 and Visible Infrared Imaging Radiometer Rrs data at 443 and 551 nm decreased from 8%–25% to 1%–9%. These results suggest that we can accurately estimate IOPs data for natural waters including naturally clear and turbid waters.

The microwave scatterometer onboard the China-France Oceanography Satellite (CFOSAT Scatterometer, CSCAT) is the first rotating fan-beam scatterometer (RFSCAT) in operation. It is used to obtain the ocean surface wind field by measuring the backscattering coefficient over a large observation swath. To estimate the backscattering coefficient, the scatterometer needs to measure the scattered signal accurately. The process of removing noise from the echo signal (scattered signal plus noise) is called noise subtraction. Noise subtraction has a great influence on the measurement of the backscattering coefficient and subsequent wind field retrieval. Therefore, accurate noise subtraction is necessary and important. In this article, an improved noise subtraction method for CSCAT is proposed. According to the distribution characteristics of backscattering coefficients, noise correction factors are introduced to improve the noise subtraction of CSCAT. The ocean surface wind fields are obtained using the improved backscattering coefficient data, and the deviation of wind speed and direction are calculated by comparison with the European Centre for Medium Range Weather Forecasts wind fields. The results show that the improved noise subtraction method can lead to improvement of the quality of wind field effectively. In addition, based on the improved method of noise subtraction, a method of estimating the noise correction factor is proposed at a large incidence angle, furtherly improving the noise subtraction method. The results show that the proposed method is effective.

In this article, seven filter algorithms were compared. The Lee sigma method was more suitable for estimating soil moisture (SM) than the other filtering methods under different land cover types. First, we used a combination of roughness and the dual-polarized Sentinel-1A backscattering coefficients (VV and VH) to estimate SM in bare soil areas. Second, we employed water cloud model (WCM) to remove the influence of vegetation signals on the land surface backscattering and estimate SM in vegetation-covered areas. SM was also retrieved by modified soil moisture monitoring index (MSMMI) and modified perpendicular drought index (MPDI) of Sentinel-2A images. The results show that MSMMI can more accurately monitor SM in bare soil areas, which was slightly better than synthetic aperture radar (SAR) results. The SAR backscattering coefficients after the removal of vegetation influence by WCM can more precisely estimate SM in vegetation-covered areas, which is significantly better than MSMMI and MPDI, especially in high vegetation-covered areas. Optics and SAR differ in their abilities to estimate SM under different land cover, but the powerful fitting ability of machine learning can make full use of their advantages. We employed the generalized regression neural network (GRNN), support vector regression (SVR), random forest regression (RFR), and deep neural network (DNN) algorithms to estimate SM combining Sentinel-1A with Sentinel-2A images. The estimation accuracies of SM by regression algorithms were higher than those by the semiempirical SAR and optical models. The accuracy of estimated SM by DNN was higher than that of GRNN and RFR, which were better than SVR.

Persistent scatterer (PS) techniques are a set of important time-series tools for interferometric synthetic aperture radar (InSAR) that enable deformation analysis in highly decorrelated terrain. Detailed knowledge of the statistics of persistent scatterers in InSAR images is critical for the design of better techniques that will enable both the extraction of deformation in traditionally difficult regions as well as develop a better understanding of how performance of these algorithms relates to important system parameters. In this article, we characterize the backscatter statistics of both persistent and distributed scatterers over wavelength using data from X -band (COSMO-SkyMed), C -band (Sentinel-1), and L -band (ALOS) sensors. We show that popular distributions that have previously been used to fit SAR backscatter can effectively capture the returns from both PS and clutter, with the $\text {G}^0$ distribution being the most applicable across wavelength and scatterer type. Thus, our work paves the way for improved detection algorithms to be designed based on these distributions and also builds an initial foundation for developing a greater theoretical understanding of PS statistics.

In the presence of abrupt change events, multitemporal synthetic aperture radar (SAR) data represent a precious supporting tool for quantifying changes, in particular in urban areas. A large amount of SAR data also exists at very high resolution (VHR). Over urban areas, the introduction of the VHR imagery moves the analysis down to the single building scale. However, VHR imagery is also characterized by a large heterogeneity and a more complex representation of the building. In this work, we propose a geometrical model for describing partially destroyed buildings and derive the corresponding multitemporal backscattering signature by applying the ray-tracing method. The model is integrated into an unsupervised automatic approach for the detection of both fully and partially destroyed buildings. The strategy considers a hierarchical structure of the changes. Experimental results conducted on two multitemporal VHR SAR datasets show a large robustness of the approach and good accuracy in the detection of the classes for damaged buildings with different severity levels.

Global navigation satellite system reflectometry (GNSS-R) has found many applications in the field of Earth observation including ocean wind-speed detection, ice altimetry, soil moisture monitoring, and more. The main focus of GNSS-R research to date has been on forward-scattered reflections, but theoretical work has proposed a backscattering regime and associated new application opportunities, including marine target detection. This article discusses the methods and results of processing the U.K. TechDemoSat-1 raw data collections in a backscattering regime for the first time, with initial results from sea ice datasets presented. The research has also identified a key problem with the backscatter method-for certain geometries the power from the specular point (forward scattered) may contaminate the data. The theory behind this and a method for predicting such occurrences is also discussed.

Soil moisture is a key factor in the reclamation of wetland habitats. Understanding the distribution and relative amount of water can be critical in reintroducing trees and grasses to disturbed soils. Soil moisture is also one of the main factors affecting microwave radar backscatter from the ground; while there are other factors determining backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, high resolution, multitemporal synthetic aperture radar (SAR). In this work, relative soil moisture indicators are derived from a time series of Sentinel-1 SAR data over previously mined oil sands in Alberta, Canada. The algorithm provides a relative assessment of soil moisture and requires calibration over wet and dry periods. An evaluation of the soil moisture product is validated using in situ measurements at multiple sites with observations showing agreement from May to August. Comparisons with precipitation records show that SAR derived surface soil moisture is influenced by discreet precipitation events; that is, rainfall that is coincident with the satellite observation reduces the effectiveness of the measurement. The resulting algorithm controls for rain events by including local weather records to adjust estimates based on the known precipitation.

The normalized radar cross section (NRCS) of microwave backscattering from a rough sea surface can be described by a composite Bragg scattering (CBS) model, which is a combination of a two-scale backscatter model and a geometric optics model. For small local radar incidence angles that are smaller than a given angle setting, the two-scale backscattering mechanism of the sea surface is replaced by a geometric optics solution for specular reflection. In this study, an optimized setting of 18 degrees for the small incidence angle is determined by comparing the NRCSs between 52 RADARSAT-2 (R2) synthetic aperture radar (SAR) images and the CBS model with simultaneous sea surface wind derived by meteorological mooring buoys located in the northern South China Sea. By using the CBS model with the optimized incidence angle setting of 18° and the nearly simultaneous wind directions of the Advanced Scatterometer (ASCAT) as the input, sea surface wind speeds are retrieved from 46 R2 SAR images at C-band and from 2 COSMO-SkyMed (CSK) SAR images at X-band for the application study. The average root mean square error of the C-band R2 SAR retrieval results validated against nearly simultaneous measurements of ASCAT is 1.5 m/s, while the same value is 1.6 m/s for the X-band CSK SAR image. The results also indicate that the optimized small incidence angle setting of the CBS model found in this study is relatively reliable under sea surface wind speed conditions less than 14.0 m/s.

The potential use of the interferometric coherence measured with Sentinel-1 satellites as input feature for crop classification is explored in this study. A one-year time series of Sentinel-1 images acquired over an agricultural area in Spain, in which 17 crop species are present, is exploited for this purpose. Different options regarding temporal baselines, polarisation, and combination with radiometric data (backscattering coefficient) are analysed. Results show that both radiometric and interferometric features provide notable classification accuracy when used individually (overall accuracy lies between 70% and 80%). It is found that the shortest temporal baseline coherences (6 days) and the use of all available intensity images perform best, hence proving the advantage of the 6-day revisit time provided by the Sentinel-1 constellation with respect to longer revisit times. It is also shown that dual-pol data always provide better classification results than single-pol ones. More importantly, when both coherence and backscattering coefficient are jointly used, a significant increase of accuracy is obtained (greater than 7% in overall accuracy). Individual accuracies of all crop types are increased, and an overall accuracy above 86% is reached. This proves that both features provide complementary information, and that the combination of interferometric and radiometric radar data constitute a solid information source for this application. This work was supported in part by the European Space Agency via the ESA SEOM Program ITT under Grant AO/1-8306/15/I-NB “SEOM-S14SCI Land,” and in part by the Spanish Ministry of Science, Innovation and Universities, the State Agency of Research (AEI), and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P.

This article presents a novel ocean vector wind (OVW) retrieval algorithm for remote sensing of ocean winds, using the precipitation radar (PR) and tropical rainfall measurement mission (TRMM) microwave imager (TMI) that operated for >17 years on the TRMM observatory. In this study, the wind speed and anisotropic wind direction (WD) characteristics of the ocean backscatter and brightness temperature (Tb) were empirically characterized to produce geophysical model functions for 49 PR beam positions and three TMI frequency channels (10, 19, and 37 GHz). For passive TMI measurements, the modified second stokes parameter (weighted difference of V and H-pol Tb's) was used to cancel the atmosphere contribution, which enhances the WD signal. Thus, combining linearly polarized Tb measurements with the anisotropic backscatter signature of the Ku-band PR, the feasibility of retrieving ocean surface wind vector is demonstrated. The OVW retrieval algorithm is validated by comparing retrievals with collocated numerical weather models wind vectors, and the statistics of OVW retrieval accuracy is presented.

The main objective of this article is to develop a physically constrained support vector machine (SVM) to predict C-band backscatter over snow-covered terrain as a function of geophysical inputs that reasonably represent the relevant characteristics of the snowpack. Sentinel-1 observations, in conjunction with geophysical variables from the Noah-MP land surface model, were used as training targets and input datasets, respectively. Robustness of the SVM prediction was analyzed in terms of training targets, training windows, and physical constraints related to snow liquid water content. The results showed that a combination of ascending and descending overpasses yielded the highest coverage of prediction (15.2%) while root mean square error (RMSE) ranged from 2.06 to 2.54 dB and unbiased RMSE ranged from 1.54 to 2.08 dB, but that the combined overpasses were degraded compared with ascending-only and descending-only training target sets due to the mixture of distinctive microwave signals during different times of the day (i.e., 6 A.M. versus 6 P.M. local time). Elongation of the training window length also increased the spatial coverage of prediction (given the sparsity of the training sets), but resulted in introducing more random errors. Finally, delineation of dry versus wet snow pixels for SVM training resulted in improving the accuracy of predicted backscatter relative to training on a mixture of dry and wet snow conditions. The overall results suggest that the prediction accuracy of the SVM was strongly linked with the first-order physics of the electromagnetic response of different snow conditions. ispartof: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING vol:14 pages:2403-2417 ispartof: location:United States status: published

This article presents an electromagnetic backscatter model of iceberg and compares the modeled scattering behavior with C -band RADARSAT-2 synthetic aperture radar (SAR) images. It also explores iceberg SAR signature variability over various ocean parameters. Three-dimensional (3-D) profiles of icebergs were captured in a field study off the coast of Bonavista, NL, Canada, in June 2017 at the time of an SAR satellite overpass. The 3-D profiles were captured from a vessel, using a LiDAR and multibeam sonar. The SAR image and 3-D profiles were captured within hours of one another. Simulated SAR images of the icebergs were generated in a simulator called GRECOSAR with the satellite, target orientation, and ocean parameters that closely mimic the real SAR scene. A new ocean model was introduced to model an ocean backscatter at satellites’ lower incidence angle beam mode. Comparison between real and simulated SAR images of the icebergs shows good agreement in terms of SAR signature, total radar cross section, and polarimetric decomposition. Wind direction was varied over 90° extent to observe icebergs’ backscatter variability in the simulator. Furthermore, simulated SAR images were generated for low and high wind conditions. Our study finds that the macrostructure of the melt iceberg dominates its polarimetric behavior of its backscatter. Large variability of iceberg SAR signature over varying ocean parameters was also observed. A mathematical model that considers the melting condition of iceberg suggested that significant backscattering can reflect from top surface when the melt water layer could be as little as 0.1 mm.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2019-01-07, 資料番号: PA1910037000

This paper illustrates a fusion approach to jointly exploit Sentinel-1 (S1) and Sentinel-2 (S2) data to detect urban areas. The proposed procedure is designed to automatically and effectively exploit the specific characteristics of synthetic aperture radar (SAR) and multispectral data, so that it can be safely applied to different urban environments with satisfying results. To this aim, it starts from two previously developed algorithms for urban extent extraction designed for multispectral or SAR spaceborne data. They are first adapted to use S1 and S2 datasets, and then coupled to a novel procedure that merges the two processing chains with the goal to exploit the spectral properties of man-made materials as well as the double bounce backscatter effect that is common in built-up environments. Experimental results in urban areas all around the world show that the proposed approach is successful at combining S1 and S2 information by reducing the errors of the two original techniques.

Effective methods of sea ice classification are crucial to regional ice type mapping using spaceborne synthetic aperture radar (SAR), especially in ScanSAR mode. However, in TerraSAR-X ScanSAR images of ocean scenes, the scalloping and interscan banding (ISB) artifacts are usually visible. Though these two artifacts could be reduced to a certain degree where residual artifacts do not impede the visual interpretation of SAR images, this is not the case when it comes to sea ice classification. The difficulty mainly comes from the involvement of texture features in sea ice classification. Texture features, especially gray level co-occurrence matrix (GLCM) are useful bases for sea ice classification using SAR data in single or dual polarization. When GLCM is applied to TerraSAR-X ScanSAR data, however, scalloping and ISB artifacts are found to be intensified, further affecting the classification result. How to largely eliminate the effects of scalloping and ISB on sea ice classification has not been studied thoroughly yet. In this paper, an approach combining Kalman filter, GLCM, and support vector machine is proposed. An independent testing shows that this approach is effective at the removal of scalloping and ISB's effects on sea ice classification using TerraSAR-X ScanSAR data, with the overall accuracy of 88.26%. Besides, convolutional neural network is implemented to compare with the proposed approach.

Remote sensing inversion of vegetation-covered soil moisture is often affected by crop canopy, surface roughness, and other factors. In order to eliminate the scattering influence of vegetation effectively, this paper developed a modified vegetation backscattering model to retrieve vegetation-covered soil moisture based on multi-temporal RADARSAT-2 data and field measurements. This model combined the advantages of optical and radar methods by considering scattering contributions of underlying bare soil and vegetation canopy. Vegetation coverage was used to separate the scattering mechanism of the vegetation from bare soil component in a pixel. In addition, advanced integral equation method was presented to define the scattering of underlying bare soil. PROSAIL optical model was applied to calculate crown water content, which is an important variable associated with the scattering of vegetation canopy. Results demonstrated that the modified model on March 29, 2014 performed better in soil moisture retrieval than that at other growth stages with R 2 of 0.806 and root-mean-square error of 0.043 m3·m−3, respectively. Soil moisture can be effectively retrieved by using the modified model in an agricultural region where the surface type is ranging from relatively sparse to full cover. Overall, the modified model provides an insight into extensive application of vegetation-covered soil moisture retrieval in agricultural regions.

The accurate estimation of grid-scale fluxes of water, energy, and carbon requires consideration of subgrid spatial variability in root-zone soil moisture (RZSM). The NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission represents the first systematic attempt to repeatedly map high-resolution RZSM fields using airborne remote sensing across a range of biomes. Here, we compare 3-arc-sec (∼100 m) spatial resolution AirMOSS RZSM retrievals from P -band radar acquisitions over nine separate North American study sites with analogous RZSM estimates generated by the Flux-Penn State Integrated Hydrologic Model (Flux-PIHM). The two products demonstrate comparable levels of accuracy when evaluated against ground-based soil moisture products and a significant level of temporal cross correlation. However, relative to the AirMOSS RZSM retrievals, Flux-PIHM RZSM estimates generally demonstrate much lower levels of spatial and temporal variability, and the spatial patterns captured by both products are poorly correlated. Nevertheless, based on a discussion of likely error sources affecting both products, it is argued that the spatial analysis of AirMOSS and Flux-PIHM RZSM fields provides meaningful upper and lower bounds on the potential range of RZSM spatial variability encountered across a range of natural biomes.

The polarimetric L-band imaging synthetic aperture radar (PLIS) is a high spatial resolution (better than 6 m) airborne synthetic aperture radar system that has been dedicated to scientific research into civilian applications since 2010. The weight of PLIS is ∼38 kg, allowing it to be installed aboard small low-cost aircraft, with two antennas used to measure the full backscatter matrix for a swath between 15° and 50° on each side of the flight direction. Calibration based on a total of 96 calibration points and a homogeneous forest during the two recent soil moisture active passive experiments (SMAPEx-4 and 5) showed an overall radiometric accuracy of 0.58 dB (root-mean-square error) over trihedral passive radar calibrators. Independent evaluation based on polarimetric active radar calibrators showed an amplitude imbalance of 0.17 dB with a standard deviation of 0.15 dB and a phase imbalance of 3.87° with a standard deviation of 2.86°. Two calibrated phased-array L-Band synthetic aperture radar-2 (PALSAR-2) images with different observation modes (ScanSAR and Stripmap) were compared with the calibrated PLIS images. The agreement between PALSAR-2 Stripmap and PLIS had a root mean square difference of 1.27 dB and a correlation coefficient of 0.87. Further comparisons over different landcover types confirmed that homogeneous forest and grassland areas constitute optimal targets for cross-validation and/or calibration.

The successor to the current series of Metop advanced scatterometers (ASCATs), the Metop-SG SCA, will be able to record data in dual-polarization, at C -band. The aim of this study is to investigate if the information contained in the added cross-polarization measurements can improve the vegetation parameterization for the estimation of the soil moisture content. In case of the operational Hydrology Satellite Application Facility Metop ASCAT soil moisture product, vegetation dynamics are characterized by the relationship between radar backscattering intensity and the incidence angle, the so-called SLOPE parameter. Building on findings from previous studies, the assumption is that the polarization ratio, i.e., VH/VV, could improve this characterization. To verify this assumption, flexible approaches, able to integrate a combination of ASCAT VV data and AQUARIUS (NASA) VH/VV data were required. Two machine learning methods were chosen: Support-vector-regression and artificial-neural-networks, and one statistical approach, the Bayesian-Regression. Each of these methods were used to derive models with different input configurations, with and without characterization of vegetation. The results show that the information contained in the SLOPE parameter and in PR are similar. Based on a global average, almost identical SMC retrieval accuracies were achieved. Despite that, analysis of the temporal dynamics of SLOPE and PR revealed certain location specific differences, which affect the spatial distribution of SMC retrieval accuracies. As a result, improvements based on the combination of the two parameters are minor overall, but they can be significant locally.

Understanding the impact of soil and plant parameter changes in agriculture on Synthetic Aperture Radar (SAR) measurements is of great interest when it comes to monitor the temporal evolution of agricultural crops by means of SAR. In this regard, specific transitions between phenological stages in corn, barley, and wheat have been identified associated to certain dielectric and geometric changes, based on a time series of fully polarimetric multibaseline SAR data and in situ measurements. The data have been acquired in the frame of DLR's CROPEX campaign on six dates between May and July in 2014. The experiments reported in this paper address the sensitivity of X-, C-, and L-band to phenological transitions exploiting the availability of multiple baselines on each acquisition date. The application of tomographic techniques enables the estimation of the three-dimensional (3-D) backscatter distribution and the separation of ground and volume scattering components. Tomographic parameters have been derived at different frequencies, namely the center of mass of the profiles of the total and of the volume-only 3-D backscatter, and the ground and volume powers. Their sensitivity and ability to detect changes occurring on the ground and in the vegetation volume have been evaluated focusing on the added value provided by the 3-D resolution at the different frequencies and polarizations available.

There are only a few examples in technical literature about the use of Synthetic Aperture Radar (SAR) dataset to extract human settlement extents. This paper shows how it is possible to extract a global urban layer from multiyear ENVISAT Advanced SAR (ASAR) backscatter measurements, overcoming some of the drawbacks of a preliminary release. Additionally, this research shows that the same technique can be applied with minor changes to SAR data from the new Sentinel-1A platform. The current ASAR urban layer shows improvements with respect to the preliminary version in the test sites of the 2011 Round Robin (RR) on urban mapping promoted by the European Space Agency (ESA). For Sentinel-1, results are similarly validated on the test sites of the 2015 edition of the RR on urban mapping. Results prove that UEXT can be used for SAR-based urban extent extraction using multiple sensors.

This paper describes the development of, and the methodology for, a new, consolidated Brigham Young University (BYU)/National Ice Center (NIC) Antarctic iceberg tracking database. The new database combines daily positional data from the original BYU daily iceberg tracking database derived from scatterometers, and the NIC's weekly Antarctic iceberg tracking database derived mostly from optical and infrared sensors. Interpolation methods and statistical analyses of iceberg locations are discussed. A new, automated method of using positional data and scatterometer backscatter images to estimate sizes and rotational patterns of icebergs is also developed. This information is included in the new database.

Equipped with an L-band radiometer, SMOS, and Aquarius provide an unprecedented sea surface salinity (SSS) dataset of the global oceans in days. The sensitivity of L-band brightness temperature (TB) to SSS variation is about 0.3–0.8 K/psu, which means the salinity signal in TB is very weak. Enormous efforts are devoted to the development, evaluation, and improvement of the SSS retrieval algorithm especially under some unfavorable conditions, i.e., the rain. Rain drops inducing freshening and roughness effects on the sea surface have made the SSS retrieval challenging for years. This paper describes a new method to separate the freshening and roughness effects of rainfall based on the combined active/passive observations of Aquarius. The dependence of the sea surface emissivity (sensitive to both roughness and freshening) on the backscatter (only sensitive to roughness) is obtained and the rain-induced roughness is corrected. The method is applied to the salinity retrieval under rain. The retrieval results ( $\text{SSS}_{rc}$ ) are compared with HYCOM data corrected by the rain impact model ( $\text{SSS}_{\text{HYCOM}\_\text{RIM}}$ ). The bias of $\text{SSS}_{\text{rc}}$ shows no clear dependence on rain rate. However, the bias of the standard product of Aquarius ( $\text{SSS}_{\text{ADPS}}$ , V4.0) rises sharply with rain rate. Furthermore, the standard deviation of $\text{SSS}_{\text{rc}}$ is about 0.5 psu, which is also superior to $\text{SSS}_{\text{ADPS}}$ (0.9 psu). The above results confirm the feasibility of this new retrieval algorithm for the SSS remote sensing in rainy weather.

Synthetic aperture radar (SAR) data with polarization diversity and multibaseline acquisition has the capability to resolve the structural information of the target. Three-dimensional (3-D) imaging using SAR tomography is an advance and recent technology that focuses on backscattered echoes not only in the 2-D plane defined by the azimuth and range coordinates, but also in cross-range direction. Forests act as a semitransparent media for radar wavelengths, and thus allow us to gather the vertical distribution of this volumetric scatterer. This paper investigated the potential of polarimetric SAR tomography (PolTomSAR) to retrieve the scatterer distribution of forest area using C-band Radarsat-2 spaceborne SAR dataset. Teak patch of Haldwani forest was chosen as the test site to validate the robustness of TomoSAR algorithms. Fourier, beamforming, Capon, Capon like, and polarimetric capon were implemented in this study and backscatter power variations with their corresponding vertical profile for a particular azimuth bin were derived and analyzed. It was observed that Fourier suffered from high sidelobes that were reduced by beamforming and Capon. Limitations of beamformer were highlighted and well resolved by Capon. However, both beamforming and Capon yielded low backscattering power at different height levels. The Capon-like algorithm substantially improved the backscatter intensity at different forest height levels. Polarimetric capon surpassed the Capon algorithm and was able to estimate better heights by incorporating various polarization channels. Height map was generated for fully polarimetric Capon and validated with the ground truth data.

Satellite-based synthetic aperture radar (SAR) has been proven to be an effective tool for ship monitoring. Offshore platforms monitoring is a key topic for both safety and security of the maritime domain. However, the scientific literature oriented to the observation of offshore platforms using SAR imagery is very limited. This study is mostly focused on the analysis and understanding of the multipolarization behavior of platforms’ backscattering using dual-polarization X-band SAR imagery. This study is motivated by the fact that under low incidence angle and moderate wind conditions, copolarized channels may fail in detecting offshore platforms even when fine-resolution imagery is considered. This behavior has been observed on both medium- and high-resolution TerraSAR-X/TanDEM-X SAR imagery, despite the fact that platforms consist of large metallic structures. Hence, a simple multipolarization model is proposed to analyze the platform backscattering. Model predictions are verified on TerraSAR-X/TanDEM-X SAR imagery, showing that for acquisitions under low incidence angle, the platforms result in a reduced copolarized backscattered intensity even when fine resolution imagery is considered. Finally, several solutions to tackle this issue are proposed with concluding remark that the performance of offshore observation.

Observing a target from different look and incidence angles is one of the key features of the Advanced Scatterometer (ASCAT) on-board the series of Metop satellites. The incidence angle dependency of backscatter plays an important role in extracting useful information for the retrieval of geophysical parameters. The TU Wien change detection algorithm exploits the multiangle measurement capabilities of ASCAT to retrieve relative surface soil moisture content. In the TU Wien algorithm, the incidence angle dependence of backscatter is characterized with a second-order polynomial and its coefficients are estimated from several years of data due to robustness. Recently, however, it has been shown by Melzer [1] , that a kernel smoother (KS) holds promise to characterize the polynomial coefficients on an interannual basis. In this study, we tested the performance and robustness of the KS globally, by comparing the results obtained from ASCAT on-board Metop-A and Metop-B independently. Overall, a good agreement has been found between Metop-A and Metop-B confirming a robust interannual estimation of the incidence angle dependence of backscatter using the KS. However, in two cases, the prevailing conditions on the ground complicated the estimation: areas with very low signal variation and sandy deserts. An analysis of Hovmoller diagrams provided insight into seasonal variations, also revealing small remaining biases between the instruments. The dynamic characterization of the incidence angle dependence of backscatter allows to study the temporal evolution in more detail and, at the same time, moving a step further on the vegetation correction in the TU Wien soil moisture algorithm.

The advanced scatterometer (ASCAT) is a fan beam scatterometer carried on board the Metop series of satellites. Its primary objective is to measure ocean backscatter for the retrieval of ocean wind vectors. Two ASCAT instruments (ASCAT-A and ASCAT-B) are operational and are independently calibrated using a number of ground-based transponders. The first seven years of data from ASCAT-A have recently been processed into a climate data record. This paper describes a number of methods for cross-validating the data from the two instruments and for assessing the quality and stability of the climate data record. The methods are based on backscatter from the Amazon rainforest, mean backscatter from the open ocean, comparison of measured and modeled ocean backscatter, and ocean cone metrics. These methods show that the climate data record, which covers the period January 2007 to March 2014, has a very high stability (with trends around 0.005 dB per year), good absolute and relative calibration (better than 0.1 dB), and a good across swath calibration (peak to peak variation of less than 0.1 dB). For operational data covering the period April 2015 to March 2016, the methods indicate that ASCAT-B backscatter is around 0.1–0.2 dB higher than ASCAT-A (depending on which beam is considered). This difference is due to a combination of factors: minor changes in calibration algorithms, a minor change in the behavior of the ASCAT-A internal calibration system, and the strategy used to update calibration files in the processing system.

Observations from spaceborne radar contain considerable information about vegetation dynamics. The ability to extract this information could lead to improved soil moisture retrievals and the increased capacity to monitor vegetation phenology and water stress using radar data. The purpose of this review paper is to provide an overview of the current state of knowledge with respect to backscatter from vegetated (agricultural) landscapes and to identify opportunities and challenges in this domain. Much of our understanding of vegetation backscatter from agricultural canopies stems from SAR studies to perform field-scale classification and monitoring. Hence, SAR applications, theory, and applications are considered here too. An overview will be provided of the knowledge generated from ground-based and airborne experimental campaigns that contributed to the development of crop classification, crop monitoring, and soil moisture monitoring applications. A description of the current vegetation modeling approaches will be given. A review of current applications of spaceborne radar will be used to illustrate the current state of the art in terms of data utilization. Finally, emerging applications, opportunities and challenges will be identified and discussed. Improved representation of vegetation phenology and water dynamics will be identified as essential to improve soil moisture retrievals, crop monitoring, and for the development of emerging drought/water stress applications.

This paper compares the performance and tradeoffs of various backscatter imaging algorithms for the SeaWinds scatterometer when multiple passes over a target are available. Reconstruction methods are compared with conventional gridding algorithms. In particular, the performance and tradeoffs in conventional “drop in the bucket” (DIB) gridding at the intrinsic sensor resolution are compared to high-spatial-resolution imaging algorithms such as fine-resolution DIB and the scatterometer image reconstruction (SIR) that generate enhanced-resolution backscatter images. Various options for each algorithm are explored, including considering both linear and dB computation. The effects of sampling density and reconstruction quality versus time are explored. Both simulated and actual data results are considered. The results demonstrate the effectiveness of high-resolution reconstruction using SIR as well as its limitations and the limitations of DIB and fine-resolution DIB.

The advanced scatterometer (ASCAT) wind data processor (AWDP) produces ocean surface vector winds from radar measurements by the ASCAT on board the Metop satellites. So far, the ASCAT-coastal product with a grid size of 12.5 km has been the one with the highest resolution. Version 2.4 of AWDP, released May 2016, offers the possibility to process wind data on a 6.25 km grid. In this paper, the true spatial resolution and accuracy of that product is assessed using various methods. The crucial parameter is the radius of the area used to aggregate individual backscatter observations to a wind vector cell (WVC) level. A value of 7.5 km, half of that for ASCAT-coastal, appears to be the best compromise between resolution and accuracy. Spatial responses from multiple radar cross-section measurements are combined to cumulative responses, and show that the ASCAT-6.25 product has a spatial resolution of about 17 km, better than the 28 km found for the ASCAT-coastal product. The accuracy of the ASCAT-6.25 product is estimated using comparison with collocated buoys, triple collocation analysis, and a new method based on spatial variances. These methods show consistently that the ASCAT-6.25 product contains about ${\rm{0.2\,m}}^{2}{\rm{/ s}}^{2}$ more noise in the wind components than the ASCAT-coastal product, due to the smaller number of individual measurements contributing to the average radar cross section in a WVC. The ASCAT-6.25 product is intended for applications that demand a spatial resolution as high as possible, like the study of dynamical mesoscale phenomena.

10 pages, 15 figures, 2 tables.-- © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., With an eye on the generation of a long-term climate record of ocean winds, soil moisture, and sea ice extents across the C-band ERS and ASCAT scatterometer spans, a new calibration tool termed cone metrics has been developed. The new method is based on monitoring changes in the location and shape of the surface of maximum density of ocean backscatter measurements, also known as “the wind cone.” The cone metrics technique complements established calibration approaches, such as rain forest and NWP ocean calibration, through the characterization of linear as well as nonlinear beam offsets, the latter via wind cone deformations. Given instrument evolution, proven stability, and the monitoring by transponders, we take ASCAT-A data over 2013 as absolute calibration reference. This paper describes the new method and its application as intercalibration tool in the context of the reprocessing activities for ERS-1 and ERS-2. Cone metrics succeeds at establishing the linear and nonlinear corrections necessary to homogenize the ASCAT and ERS C-band records down to 0.05 dB

Wind scatterometers were originally developed for observation of near-surface winds over the ocean. They retrieve wind indirectly by measuring the normalized radar cross section ( $\sigma ^o$ ) of the surface, and estimating the wind via a geophysical model function relating $\sigma ^o$ to the vector wind. The $\sigma ^o$ measurements have proven to be remarkably capable in studies of the polar regions where they can map snow cover; detect the freeze/thaw state of forest, tundra, and ice; map and classify sea ice; and track icebergs. Further, a long time series of scatterometer $\sigma ^o$ observations is available to support climate studies. In addition to fundamental scientific research, scatterometer data are operationally used for sea-ice mapping to support navigation. Scatterometers are, thus, invaluable tools for monitoring the polar regions. In this paper, a brief review of some of the polar applications of spaceborne wind scatterometer data is provided. The paper considers both C-band and Ku-band scatterometers, and the relative merits of fan-beam and pencil-beam scatterometers in polar remote sensing are discussed.

Using Aquarius middle beam scatterometer observations, the vegetation parameters of the water cloud model at large scale are estimated and applied to global soil moisture retrieval. Vegetation backscattering is derived using two models: Oh model is used to describe the scattering from bare soil surface, while the water cloud model is implemented to account for the effect of vegetation canopy. The vegetation parameters are estimated by minimizing the deviations between the Aquarius scatterometer observations and backscatter coefficients simulated by the water cloud model. The RMSE is less than 2 dB for both copolarizations and correlation is strong (CC > 0.6) in most areas. The vegetation parameters were used to retrieve global soil moisture from Aquarius radar data. The comparisons with the Aquarius soil moisture product derived from the Aquarius radiometer observations show low ubRMSE (0.06cm 3 /cm 3 ) and strong correlation (CC > 0.6) in most parts of the world. The impact of errors in input parameters of the water cloud model on the vegetation parameter estimation was assessed by using a Monte-Carlo simulation. The algorithm converges to the true values of the parameters when the input data is noise-free or only the radar measurement error is introduced. It was found that the errors in vegetation parameter are sensitive to the errors in input soil moisture. The errors in two vegetation parameters counteract each other to decrease the error of backscattering simulation. This study demonstrates that the water cloud model could be applied to global scatterometer observations to retrieve soil moisture if the vegetation parameters are appropriately set.

Multitemporal single (HH) and dual-polarization (i.e., HH, HV) L-band spaceborne synthetic aperture radar (SAR) scenes were evaluated under different moisture conditions caused by precipitation prior to data acquisition at varying incidence angles. The changes affecting backscattering intensity, polarimetric decomposition, backscattering mechanism, and land use/land cover classification performance were evaluated. The study area is a shifting-cultivation environment in the eastern Amazon (Brazil). Several data input scenarios were proposed in the classification scheme (i.e., backscattering intensity alone and combined with alpha/entropy decomposition parameters, band ratios, and textural parameters) using a random forest classifier framework. Integration with optical data was also examined. The classification accuracy scores were then compared with accumulated precipitation data. The results showed that the variation in both the vegetation moisture and incidence angle increases the backscattering intensity for pasture, riparian forest and young regenerated forest by at least 1 dB compared with old successional forest stages due to its more uniform vertical structure and the landscape's increased dielectric constant. The overall classification accuracy proved low for each SAR acquisition date compared with the performance of the Landsat data. Based on SAR data, misclassification occurs for the young successional forest stages and increases in scenes with higher moisture conditions. The classification performance benefits from data integration only for one SAR scene acquired in the dry season. The results highlight the importance of selecting proper temporal intervals for the different SAR polarization modes of the forthcoming SAR missions. Further investigations should address both multitemporal at a single frequency as well as multifrequency SAR approaches.

This study evaluates the potential of X-band interferometry for monitoring of agricultural grasslands. Time series of HH-polarization COSMO-SkyMed 1-day repeat-pass interferometric SAR (InSAR) pairs is analyzed in regard to detecting mowing events, and assessing vegetation height and biomass on grasslands. The time series of four InSAR pairs was analyzed in regard to the ground reference data collected during an extensive campaign covering 11 agricultural grasslands. The calculated temporal interferometric coherence was found to be inversely correlated to the vegetation height and wet above-ground biomass. It was found that grass removal increases the coherence magnitude indicating a potential use of this parameter for the detection of mowing. However, precipitation and farming activity between the acquisitions interfere with this effect. Temporal coherence was expressed as a function of the vegetation height through the random motion of scatterers in the vegetation layer. For vegetation height limited to the range between 0 and 1 m, a very strong correlation between the grass height and the linearised temporal coherence was found, with a coefficient $r=\text{0.81}$ . No significant correlation was found between the backscattering coefficient and the wet above-ground biomass as well as the height of grass. However, a strong negative correlation was found between the backscattering coefficient and the measured soil moisture.

European Space Agency's SnowScat instrument is a real aperture scatterometer which was developed by Gamma Remote Sensing AG. It operates in a continuous-wave mode, covers a frequency range of 9.15–17.9 GHz in a user-defined frequency-step and has a full polarimetric capability. The measurement campaigns were started first in February 2009 at Weissfluhjoch, in Davos, Switzerland, as an initial test of the instrument over a deep alpine snowpack. Physical characterizations of the snowpack and meteorological measurements were carried out, which formed a detailed in situ dataset. SnowScat was then moved to Sodankyla in Finland in early November 2009, a site of the Finnish Meteorological Institute in Lapland. In addition to the in situ snowpack characterizations and meteorological observations, continuous passive microwave observations were also performed. During the 2012–2013 winter period, a vertical time-domain snow profiling experiment was carried out in addition for resolving the scattering contributions from the snow layers of different physical properties. This paper summarizes the results of the SnowScat observations and initial comparisons against the in situ meteorological and snowpack data. The Sodankyla campaign data evidenced the high variability of the radar backscatter behavior of snowpack from year to year, which indicates its strong dependency on changing snow microstructure. Indeed, the snow microstructure is continuously driven by snow metamorphism, which are further affected by meteorological conditions and their interannual variability. The backscattering property of snowpack in the range X - to Ku -band for all polarizations appeared to be dominated by its microstructural morphology and underlying ground conditions, and to a lesser extent by the snow depth, or its snow-water-equivalent.

Ukraine is one of the most developed agricultural countries in the world. For many applications, it is extremely important to provide reliable crop maps taking into account diversity of cropping systems used in Ukraine. The use of optical imagery only is limited due to cloud cover, and previous studies showed particular difficulties in discriminating summer crops in Ukraine such as maize, soybeans, sunflower, and sugar beet. This paper focuses on exploring feasibility and assessing efficiency of using multitemporal satellite synthetic-aperture radar (SAR) acquired in C-band and optical images for crop classification in Ukraine. Both optical (Landsat-8/OLI) and SAR (Radarsat-2) images are used to assess the impact of adding backscattering intensity from SAR images for classification purposes. SAR intensity information is very important due to availability of Sentinel-1 imagery over Ukraine starting March 2015. Different combinations of optical and SAR images, as well as SAR modes and polarizations, are assessed for better discrimination of crops. A committee of neural networks, in particular multilayer perceptrons (MLPs), is used to improve classification accuracy compared to several standard classifiers. It is found that using backscatter coefficients from SAR images alone provides the same performance for winter crops (wheat and rapeseed) as surface reflectance from optical images. Considering the summer crops, the major impact of adding backscatter intensity information from SAR images is in better separation of sunflower, soybeans, and maize.