Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia

In Queensland, Australia, forest areas are discriminated from non-forest by applying a threshold (∼ 12%) to Landsat-derived Foliage Projected Cover (FPC) layers (equating to ∼ 20% canopy cover), which are produced routinely for the State. However, separation of woody regrowth following agricultural clearing cannot be undertaken with confidence, and is therefore not mapped routinely by State Agencies. Using fully polarimetric C-, L- and P-band NASA AIRSAR and Landsat FPC data for forests and agricultural land near Injune, central Queensland, we corroborate that woody regrowth dominated by Brigalow (Acacia harpophylla) cannot be discriminated using either FPC or indeed C-band data alone, because the rapid attainment of a canopy cover leads to similarities in both reflectance and backscatter with remnant forest. We also show that regrowth cannot be discriminated from non-forest areas using either L-band or P-band data alone. However, mapping can be achieved by thresholding and intersecting these layers, as regrowth is unique in supporting both a high FPC (> ∼ 12%) and C-band SAR backscatter (> ~ − 18 dB at HV polarisation) and low L-band and P-band SAR backscatter (e.g. < =∼ 14 dB at L-band HH polarisation). To provide a theoretical explanation, a wave scattering model based on that of Durden et al. [Durden, S.L., Van Zyl, J.J. & Zebker, H.A. (1989). Modelling and observation of radar polarization signature of forested areas. IEEE Trans. Geoscience and Remote Sensing, 27, 290-301.] was used to demonstrate that volume scattering from leaves and small branches in the upper canopy leads to increases in C-band backscattering (particularly HV polarisations) from regrowth, which increases proportionally with FPC. By contrast, low L-band and P-band backscatter occurs because of the lack of double bounce interactions at co-polarisations (particularly HH) and volume scattering at HV polarisation from the stems and branches, respectively, when their dimensions are smaller than the wavelength. Regrowth maps generated by applying simple thresholds to both FPC and AIRSAR L-band data showed a very close correspondence with those mapped using same-date 2.5 m Hymap data and an average 73.7% overlap with those mapped through time-series comparison of Landsat-derived land cover classifications. Regrowth mapped using Landsat-derived FPC from 1995 and JER-1 SAR data from 1994-1995 also corresponded with areas identified within the time-series classification and true colour stereo photographs for the same period. The integration of Landsat FPC and L-band SAR data is therefore expected to facilitate regrowth mapping across Queensland and other regions of Australia, particularly as Japan's Advanced Land Observing System (ALOS) Phase Arrayed L-band SAR (PALSAR), to be launched in 2006, will observe at both L-band HH and HV polarisations.     

Analysis of multi-frequency and multi-polarization SAR data for wetland mapping in Hamoun-e-Hirmand wetland

ABSTRACT

The complex, dynamic and narrow boundaries between vegetation types make wetland mapping challenging. Hereafter the case study of the Hamoun-e-Hirmand wetland is considered by analysing eight Synthetic Aperture Radar (SAR) Images acquired in dry and wet periods with three wavelengths (X-band ~ 3 cm, C-band ~ 6 cm, and L-band ~ 25 cm), three polarizations (HH, VV and VH), and four incidence angles (22°, 30°, 34° and 53°). Then, the Support Vector Machine (SVM) classification method was applied to classify TerraSAR-X, Sentinel-1, and ALOS-PALSAR images. The final wetland land cover map was created by combining the classification results obtained from each sensor. In the case in question, results show that TerraSAR-X (X-band, HH-53°) and Sentinel-1 data (C-band, VV-34°) were useful for determining the flooded vegetation area in the wet period. This is crucial for the conservation of water bird habitats since flooded vegetation is an ideal environment for the nesting and feeding of water birds. PALSAR data (L-band in both HH and VH polarizations, 30°) were capable of separating the classes of vegetation density in the wetland. In the dry period, Sentinel-1 (VV and VH, 34°) and TerraSAR-X (HH, 22° and 53°) had higher potential in land cover mapping than PALSAR (HH and VH, 30°). Based on these results, Sentinel-1 in VV and VH provides the highest ability to discriminate between dry and green plants. TerraSAR-X is better for separating meadow and bare land. The results obtained in this paper can reduce the ambiguity in selecting satellite data for wetland studies. The results can also be used to produce more accurate data from satellite images and to facilitate wetland investigation, conservation and restoration.     

水面船只SAR探测的极化方式研究

利用SIR-C SAR的C和L波段全极化数据,分析水面船只的极化散射特性和船只与背景海面雷达后向散射的信噪比特性,研究水面船只SAR探测的最优极化方式。结果显示,二面角散射是水面船只SAR成像的主要机理。线性极化中,HV极化具有最大的船只与背景海面雷达后向散射信噪比。与线性极化相比,圆极化的雷达后向散射信噪比更优。C波段和L波段的水面船只的极化散射特性存在较大的差异,L波段的信噪比大于C波段的信噪比。水面船只的雷达后向散射特性表明,L波段的圆极化是水面船只探测的最优极化方式。     

Automatic extraction of flood inundation areas from SAR images: a case study of Jilin,China during the 2017 flood disaster

Flood is one of the most frequent and widespread natural hazards globally, which can cause tremendous economic damage and human casualties. As such, flood event monitoring is essential, for which Synthetic Aperture Radar (SAR), with high spatial resolution as well as all-weather and all-time capabilities, can provide high-quality data support. However, algorithms for automatic flood inundation mapping that do not require ancillary data are limited. In this study, we propose a hybrid methodology that combines automatic thresholds selection, pixel- and object-based classification, and bidirectional region growing method for extracting flood inundation areas. This is a fully automatic approach that does not require the assistance of ancillary data. Firstly, the gamma distribution is used to estimate the probability density function (PDF) of ‘open water’ and to set thresholds. Then, we introduce a two-step classification approach, applying the pixel- and object-based classifications; the former is easy to implement with low computational complexity and stable performance, whereas the latter can reduce noise pixels and is less sensitive to SAR intrinsic speckle. The two-step classification is employed to yield core flooded and non-flooded regions that are used as seeds for region growing. Furthermore, we propose a bidirectional region growing approach that grows regions for flooded and non-flooded regions simultaneously to eliminate areas of uncertainty, while minimizing under- and over-detection. We verified the proposed approach by applying it to real flood events that occurred in Jilin, China on 13 July 2017 and 20 July 2017. The experimental results demonstrate the effectiveness and reliability of the proposed approach.     

Assessment of an analytical model for sea surface wind speed retrieval from spaceborne SAR

An analytical model based on radar backscatter theory was utilized to retrieve sea surface wind speeds from C-band satellite synthetic aperture radar (SAR) data at either vertical (VV) or horizontal (HH) polarization in transmission and reception. The wind speeds were estimated from several ENVISAT Advanced SAR (ASAR) images in Hong Kong coastal waters and from Radarsat-1 SAR images along the west coast of North America. To evaluate the accuracy of the analytical model, the estimated wind speeds were compared to coincident buoy measurements, as well as winds retrieved by C-band empirical algorithms (CMOD4, CMOD_IRF2 and CMOD5). The comparison shows that the accuracy of the analytical model is comparable to that of the C-band empirical algorithms. The results indicate the capability of the analytical model for sea surface wind speed retrieval from SAR images at both VV and HH polarization.     

Multi-polarization Envisat-ASAR images as a function of leaf area index (LAI) of White Poplar and Desert Date plantations

The relationship between leaf area index (LAI) of plantations and multi-polarization Synthetic Aperture Radar (SAR) data (Envisat-ASAR) was investigated for White Poplar (Populus tomentosa Carr) and Desert Date (Elaeagnus angustifolius) in Heihe district, northwest china. The study showed that, for homogeneous White Poplar plantations, HH and HV polarization data (where H and V represent horizontal and vertical polarizations, respectively, and the first of the two letters refers to the transmission polarization and the second to the received polarization) were sensitive to LAI and the r² (logistic relationship fits) values between HH polarization and LAI, 0.56 and 0.58 on 25 and 28 June images respectively, was much higher than that for the other polarizations of VV, VH and HV. For Desert Date plantations, the heterogeneity of the forests results in a more complex backscattering than that for White Poplar. Incidence angle also plays an important role in SAR backscattering, so a suitable SAR mode should be chosen to avoid scattering saturation when the LAI and incidence angle exceed certain values. The logistic polarization ratios of HH/HV and VV/VH showed varying correlation with LAI over White Poplar plantations, probably due to incidence angle.     

Monitoring soybean growth using L-, C-, and X-band scatterometer data

A ground-based fully polarimetric scatterometer operating at multiple frequencies was used to continuously monitor soybean growth over the course of a growing season. Polarimetric backscatter data at L-, C-, and X-bands were acquired every 10 min. We analysed the relationships between L-, C-, and X-band signatures, and biophysical measurements over the entire soybean growth period. Temporal changes in backscattering coefficients for all bands followed the patterns observed in the soybean growth measurements (leaf area index (LAI) and vegetation water content (VWC)). The difference between the backscattering coefficients for horizontally transmitted horizontally received (HH) and vertically transmitted vertically received (VV) polarizations at the L-band was apparent after the R2 stage (DOY 224) due to the double-bounce scattering effect. Results indicated that L-, C-, and X-band radar backscatter data can be used to detect different soybean growth stages. The results of correlation analyses between the backscattering coefficient for specific bands/polarizations and soybean growth data showed that L-band HH-polarization had the highest correlation with the vegetation parameters LAI (r = 0.98) and VWC (r = 0.97). Prediction equations for estimation of soybean growth parameters from the L-HH were developed. The results indicated that L-HH could be used for estimating the vegetation biophysical parameters considered here with high accuracy. These results provide a basis for developing a method to retrieve crop biophysical properties and guidance on the optimum microwave frequency and polarization necessary to monitor crop conditions. The results are directly applicable to systems such as the proposed NASA Soil Moisture Active Passive (SMAP) satellite.     

Towards operational monitoring of a northern wetland using geomatics-based techniques

This paper presents a geomatics-based approach for the operational monitoring of spatio-temporal changes in a northern wetland. It demonstrates how valuable, and otherwise unattainable, spatially distributed and timely baseline data can be obtained using basic remote sensing techniques. It further shows how these data can be combined to retrieve information pertaining to the hydro-ecological relationships in the wetland. The study was conducted in the Peace-Athabasca Delta (PAD), which is a large wetland complex in northeastern Alberta, Canada. Combinations of Radarsat Synthetic Aperture Radar (SAR) and optical satellite images (Landsat or SPOT multispectral) were used to generate a time-series of flood maps for the six-year period, 1996 to 2001. These maps clearly depict the extent of the 1996 and 1997 overland floods and the subsequent water level draw down. A flood duration map that shows how long each image cell was inundated was generated by combining the series of flood maps. The flood duration map highlights regions where the duration of flooding appears to be highest or lowest. Such maps are invaluable for any ecological change detection protocols that may be developed for this region. The general vegetation patterns were also mapped using multi-temporal SPOT-4 images from the summer season (May and August) of 2001 to an accuracy of 86%. By comparing the vegetation and flood duration maps, the relationship between vegetation patterns and duration of flooding could be examined. Results indicated that basins inundated for longer periods (3-5 years) were dominated by relatively more productive graminoid (grass-like) vegetation, whereas, areas flooded for less than two years were characterised by less productive shrub vegetation. Airborne scanning LiDAR (Light Detection and Ranging) data from the summer (June) of 2000 were also used to generate a Digital Elevation Model (DEM) of selected non-flooded areas. LiDAR DEM accuracy was satisfactory (Root Mean Square Error of 0.24 m) and it proved to be sufficiently detailed to detect the subtle topographic patterns in this relatively flat region. Comparison of the vegetation map to the DEM demonstrated that the shrubs were located in areas that were, on average, between 0.5 m (in south) and 0.73 m (in north) higher than the graminoid covered regions. Notwithstanding other parameters that influence the distribution of vegetation, these results indicate that flood duration and elevation are two important factors. The usefulness of these spatial databases recommends the timely generation of flood and vegetation maps in the continued monitoring of the changes and relationships in this delta.     

Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island

Multi-temporal TerraSAR-X, ASAR/ENVISAT and PALSAR SAR data acquired at various incidence angles and polarizations were analyzed to study the potential of these new spaceborne SAR systems for monitoring sugarcane crops. The sensitivity of different radar parameters (wavelength, incidence angles, and polarization) to sugarcane growth stages was analyzed to determine the most suitable radar configuration for better characterisation of sugarcane fields and in particular the monitoring of sugarcane harvest.Correlation between backscattered signals and crop height was also carried out. Radar signal increased quickly with sugarcane height until a threshold height, which depended on radar wavelength and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. The threshold height is higher with longer wavelengths (L-band in comparison with C- and X-bands) and higher incidence angles (~ 40° in comparison with ~ 20°).The radar backscattering coefficients (σ°) were also compared to the Normalized Difference Vegetation Index (NDVI) calculated from SPOT-4/5 images. Results showed a high correlation between the behaviors of σ° and NDVI as a function of sugarcane crop parameters. A decrease in NDVI for fully mature sugarcane fields due to drying of the sugarcane (water stress) was also observed in the radar signal. This decrease in radar signal was of the same order as the decrease in radar signal after the sugarcane harvest. In general, it is more suitable to monitor the sugarcane harvest using high incidence angles regardless of the radar wavelength. SAR data in L- and C-bands showed an ambiguity between the signals of ploughed fields and those of fields in vegetation because of the high sensitivity of the radar signal at these wavelengths to surface roughness of bare soils. Indeed, sometimes the radar signal of ploughed fields was of the same order as that of harvested or mature sugarcane fields. Results showed better discrimination between ploughed fields and sugarcane fields in vegetation (sugarcane canopy) when using TerraSAR-X data (X-band).Concerning the influence of radar polarization, results showed that the co-polarizations channels (HH and VV) were well correlated, but had slightly less potential than cross-polarization channels (HV and VH) for the detection of the sugarcane harvest. Finally, SAR data at high spatial resolution were shown to be useful and necessary for better analysis of SAR images when the fields were of small size.     

ALOS/PALSAR InSAR数学模型研究

日本ALOS卫星携带的相控阵型L波段合成孔径雷达(PALSAR),因其较长的波长使得相同时间间隔内地面具有较高的相干性,因而极具InSAR应用潜力。本文首先介绍ALOS PALSAR,进而详细分析该数据在InSAR数学模型(包括距离向频谱、干涉临界基线距、模糊高度、差分相位对形变的敏感度)中的特点,并与常见的ERS SAR数据进行比较。     

Development of polarization ratio model for sea surface wind field retrieval from TerraSAR-X HH polarization data

In this article, the polarization ratio (PR) of TerraSAR-X (TS-X) vertical–vertical (VV) and horizontal–horizontal (HH) polarization data acquired over the ocean is investigated. Similar to the PR of C-band synthetic aperture radar (SAR), the PR of X-band SAR data also shows significant dependence on incidence angle. The normalized radar cross-section (NRCS) in VV polarization data is generally larger than that in HH polarization for incidence angles above 23°. Based on the analysis, two PR models proposed for C-band SAR were retuned using TS-X dual-polarization data. A new PR model, called X-PR hereafter, is proposed as well to convert the NRCS of TS-X in HH polarization to that in VV polarization. By using the developed geophysical model functions of XMOD1 and XMOD2 and the tuned PR models, the sea surface field is retrieved from the TS-X data in HH polarization. The comparisons with in situ buoy measurements show that the combination of XMOD2 and X-PR models yields a good retrieval with a root mean square error (RMSE) of 2.03 m s^–1 and scatter index (SI) of 22.4%. A further comparison with a high-resolution analysis wind model in the North Sea is also presented, which shows better agreement with RMSE of 1.76 m s^–1 and SI of 20.3%. We also find that the difference between the fitting of the X-PR model and the PR derived from TS-X dual-polarization data is close to a constant. By adding the constant to the X-PR model, the accuracy of HH polarization sea surface wind speed is further improved with the bias reduced by 0.3 m s^–1. A case acquired at the offshore wind farm in the East China Sea further demonstrates that the improvement tends to be more effective for incidence angles above 40°.     

Decomposition of polarimetric synthetic aperture radar backscatter from upland and flooded forests

The goal of this research was to decompose polarimetric Synthetic Aperture Radar (SAR) imagery of upland and flooded forests into three backscatter types: single reflection, double reflection, and cross-polarized backscatter. We used a decomposition method that exploits the covariance matrix of backscatter terms. First we applied this method to SAR imagery of dihedral and trihedral corner reflectors positioned on a smooth, dry lake bed, and verified that it accurately isolated the different backscatter types. We then applied the method to decompose multi-frequency Jet Propulsion Laboratory (JPL) airborne SAR (AIRSAR) backscatter from upland and flooded forests to explain scattering components in SAR imagery from forested surfaces. For upland ponderosa pine forest in California, as SAR wavelength increased from C-band to P-band, scattering with an odd number of reflections decreased and scattering with an even number of reflections increased. There was no obvious trend with wavelength for cross-polarized scattering. For a bald cypress-tupelo floodplain forest in Georgia, scattering with an odd number of reflections dominated at C-band. Scattering power with an even number of reflections from the flooded forest was strong at L-band and strongest at P-band. Cross-polarized scattering may not be a major component of total backscatter at all three wavelengths. Various forest structural classes and land cover types were readily distinguishable in the imagery derived by the decomposition method. More importantly, the decomposition method provided a means of unraveling complex interactions between radar signals and vegetated surfaces in terms of scattering mechanisms from targets. The decomposed scattering components were additions to the traditional HH and V V backscatter. One cautionary note: the method was not well suited to targets with low backscatter and a low signal-to-noise ratio.     

Properties of X-, C- and L-band repeat-pass interferometric SAR coherence in Mediterranean pine forests affected by fires

Synthetic Aperture Radar (SAR) data has been investigated to determine the relationship between burn severity and interferometric coherence at three sites affected by forest fires in a hilly Mediterranean environment. Repeat-pass SAR images were available from the TerraSAR-X, ERS-1/2, Envisat ASAR and ALOS PALSAR sensors. Coherence was related to measurements of burn severity (Composite Burn Index) and remote sensing estimates expressed by the differenced normalized burn ratio (dNBR) index. In addition, the effects of topography and weather on coherence estimates were assessed. The analysis for a given range of local incidence angle showed that the co-polarized coherence increases with the increase of burn severity at X- and C-band whereas cross-polarized coherence was practically insensitive to burn severity. Higher sensitivity to burn severity was found at L-band for both co- and cross-polarized channels. The association strength between coherence and burn severity was strongest for images acquired under stable, dry environmental conditions. When the local incidence angle is accounted for the determination coefficients increased from 0.6 to 0.9 for X- and C-band. At L-band the local incidence angle had less influence on the association strength to burn severity.     

Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data

Sentinel-1A synthetic aperture radar (SAR) data present an opportunity for acquiring crop information without restrictions caused by weather and illumination conditions, at a spatial resolution appropriate for individual rice fields and a temporal resolution sufficient to capture the growth profiles of different crop species. This study investigated the use of multi-temporal Sentinel-1A SAR data and Landsat-derived normalized difference vegetation index (NDVI) data to map the spatial distribution of paddy rice fields across parts of the Sanjiang plain, in northeast China. The satellite sensor data were acquired throughout the rice crop-growing season (May–October). A co-registered set of 10 dual polarization (VH/VV) SAR and NDVI images depicting crop phenological development were used as inputs to Support Vector Machine (SVM) and Random Forest (RF) machine learning classification algorithms in order to map paddy rice fields. The results showed a significant increase in overall classification when the NDVI time-series data were integrated with the various combinations of multi-temporal polarization channels (i.e. VH, VV, and VH/VV). The highest classification accuracies overall (95.2%) and for paddy rice (96.7%) were generated using the RF algorithm applied to combined multi-temporal VH polarization and NDVI data. The SVM classifier was most effective when applied to the dual polarization (i.e. VH and VV) SAR data alone and this generated overall and paddy rice classification accuracies of 91.6% and 82.5%, respectively. The results demonstrate the practicality of implementing RF or SVM machine learning algorithms to produce 10 m spatial resolution maps of paddy rice fields with limited ground data using a combination of multi-temporal SAR and NDVI data, where available, or SAR data alone. The methodological framework developed in this study is apposite for large-scale implementation across China and other major rice-growing regions of the world.     

Radar imaging of Kelvin arms of ship wakes

The wave pattern generated by a moving ship is formed by two dominant features: the turbulent wake and a 'V'-shaped pattern trailing the ship, consisting of the two Kelvin arms. In this paper we investigate the radar imaging mechanism of Kelvin arms, which are formed by the cusp waves. A composite surface model for the radar backscattering at the ocean surface is used. The radar signatures of Kelvin arms can be attributed to tilt and hydrodynamic modulation of Bragg waves by the cusp waves. The proposed model allows the computation of the normalized radar backscattering cross-section (NRCS) as a function of radar frequency, polarization, incidence angle, wind speed and direction, and wavelength, direction, and slope of the cusp waves. By using this imaging model, radar signatures of cusp waves are calculated for several spaceborne Synthetic Aperture Radars (SARs): (1) the SEASAT L-band HH-polarized SAR, (2) the ERS-1/-2 VV-polarized SAR, (3) the RADARSAT C-band HH-polarized SAR, and (4) the X-, C- and L-band multipolarization SARs of the Space Radar Laboratory flown on the space shuttle during the SIRC/X-SAR mission in 1994. The results of the simulations are compared with SEASAT and SIR-C/X-SAR imagery of ship wake patterns. It is shown that the dependence of the observed radar signatures of Kelvin arms on radar look direction is consistent with the proposed imaging theory and that the measured relative mean NRCS values induced by Kelvin arms can be fairly well reproduced by the proposed model. The simulations indicate that ship wake signatures should be more clearly visible on SEASAT L-band SAR than on ERS-1/-2 or RADARSAT C-band SAR images. The radar signatures of Kelvin arms are strongest at low wind speeds and are not very sensitive to wind direction.     

Island connected sea bed signatures observed by multi-frequency synthetic aperture radar

Multifrequency X-, C-, and L-band synthetic aperture radar (SAR) images of the northern sea area off the isle of Heligoland in the German Bight of the North Sea have been analysed. The data were collected during the SAR and X-band Ocean Nonlinearities Research Platform North Sea Experiment (SAXON-FPN) which was carried out in November, 1990. Different oceanographic phenomena are visible on simultaneously obtained SAR images. Wind streaks and a vortex street can be identified only on the X- and C-band SAR images. Elongated streaks of predominantly low radar return are related to nearshore reefs and are imaged on all available radar scenes. The imaging mechanism of these submarine ridges is investigated and discussed using with some modifications the simple Bragg relaxation model proposed by Alpers and Hennings. The improved model differs from the original version in the representation of the unperturbed wave-energy spectral density. Also the advection term and the phase modulation (velocity bunching) have been included in the model. Due to the improvements it is now possible to simulate the radar cross-section modulation with the same order of magnitude at 0.4 GHz (X-band) and 5.3GHz (C-band) as well as at 1.3 GHz (L-band). However, the simulated radar cross-section modulation is still underestimated compared with the SAR data, but the phase of the calculated radar cross-section modulation agrees quite well with the SAR measurements.     

Laboratory and field measurements of the modification of radar backscatter by sand

Over the last two decades, the use of synthetic aperture radar (SAR) to address geologic problems has expanded as new applications for radar have been developed. One of the earliest and perhaps most surprising results from orbital SAR images of the Sahara was that, under certain conditions, radar signals penetrated up to several meters of sand to reveal subsurface features such as ancient river channels. Subsequent studies of radar penetration of arid sand deposits have dealt with factors that govern the ability of radar to penetrate a sand cover. This paper presents results from a laboratory experiment in which radar backscatter from a surface of rocks was measured under controlled conditions as a function of frequency, polarization, incidence angle, and sand cover thickness. The sand used in the experiment had a moisture content of 0.28 vol.% and caused calculated average attenuations of 4.2±1 dB/m for C-band and ∼11±2 dB/m for X-band. Results from the experiment were compared to field measurements of sand thickness during acquisition of airborne radar images. In AIRSAR images, the extent of dry sand in a dune field appears best in C-band because longer wavelength L- and P-band signals penetrate thinner sand deposits. Images of wet sand (4.9 vol.%) suggest that L-band was able to penetrate thin sand even though that sand was wet. Together, these laboratory and field measurements contribute towards a better understanding of how a sand cover modifies the radar backscatter of a surface.     

An algorithm for the retrieval of sea surface wind fields using X-band TerraSAR-X data

TerraSAR-X (TS-X) is a new, fully polarized X-band synthetic aperture radar (SAR) satellite, which is a successor of the Spaceborne Imaging Radar X-band Synthetic Aperture Radar (SIR-X-SAR) and the SRTM. TS-X has provided high-quality image products over land and oceans for scientific and commercial users since its launch in June 2007. In this article, a new geophysical model function (GMF) is presented to retrieve sea surface wind speeds at a height of 10 m (U ₁₀) based on TS-X data obtained with VV polarization in the ScanSAR, StripMap and Spotlight modes. The X-band GMF was validated by comparing the retrieved wind speeds from the TS-X data with in situ observations, the high-resolution limited area model (HIRLAM) and QuikSCAT scatterometer measurements. The bias and root mean square (RMS) values were 0.03 and 2.33 m s^?1, respectively, when compared with the co-located wind measurements derived from QuikSCAT. To apply the newly developed GMF to the TS-X data obtained in HH polarization, we analysed the C-band SAR polarization models and extended them to the X-band SAR data. The sea surface wind speeds were retrieved using the X-band GMF from pairs of TS-X images obtained in dual-polarization mode (i.e. VV and HH). The retrieved results were also validated by comparing with QuikSCAT measurements and the results of the German Weather Service (DWD) atmospheric model. The obtained RMS was 2.50 m s^?1 when compared with the co-located wind measurements derived from the QuikSCAT, and the absolute error was 2.24 m s^?1 when compared with DWD results.     

Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry

Forest canopy height is a critical parameter in better quantifying the terrestrial carbon cycle. It can be used to estimate aboveground biomass and carbon pools stored in the vegetation, and predict timber yield for forest management. Polarimetric SAR interferometry (PolInSAR) uses polarimetric separation of scattering phase centers derived from interferometry to estimate canopy height. A limitation of PolInSAR is that it relies on sufficient scattering phase center separation at each pixel to be able to derive accurate forest canopy height estimates. The effect of wavelength-dependent penetration depth into the canopy is known to be strong, and could potentially lead to a better height separation than relying on polarization combinations at one wavelength alone. Here we present a new method for canopy height mapping using dual-wavelength SAR interferometry (InSAR) at X- and L-band. The method is based on the scattering phase center separation at different wavelengths. It involves the generation of a smoothed interpolated terrain elevation model underneath the forest canopy from repeat-pass L-band InSAR data. The terrain model is then used to remove the terrain component from the single-pass X-band interferometric surface height to estimate forest canopy height. The ability of L-band to map terrain height under vegetation relies on sufficient spatial heterogeneity of the density of scattering elements that scatter L-band electromagnetic waves within each resolution cell. The method is demonstrated with airborne X-band VV polarized single-pass and L-band HH polarized repeat-pass SAR interferometry using data acquired by the E-SAR sensor over Monks Wood National Nature Reserve, UK. This is one of the first radar studies of a semi-natural deciduous woodland that exhibits considerable spatial heterogeneity of vegetation type and density. The canopy height model is validated using airborne imaging LIDAR data acquired by the Environment Agency. The rmse of the LIDAR canopy height estimates compared to theodolite data is 2.15 m (relative error 17.6%). The rmse of the dual-wavelength InSAR-derived canopy height model compared to LIDAR is 3.49 m (relative error 28.5%). From the canopy height maps carbon pools are estimated using allometric equations. The results are compared to a field survey of carbon pools and rmse values are presented. The dual-wavelength InSAR method could potentially be delivered from a spaceborne constellation similar to the TerraSAR system.     

Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2/PALSAR-2 imagery

This article presents for the first time the combination of dual-polarimetric C-band Sentinel-1 synthetic aperture radar (SAR) data and quad-polarimetric L-band ALOS-2/PALSAR-2 imagery for mapping of flooded areas with a special focus on flooded vegetation. L-band SAR data is well suited for mapping of flooded vegetation, while C-band enables an accurate extraction open water areas. Polarimetric decomposition-based unsupervised Wishart classification is combined with object-based post-classification refinement and the integration of spatial contextual information and global auxiliary data. In eight different scenarios, focusing on single datasets or fusion of classification results of several ones, respectively, different polarimetric decomposition and classification principles, including the entropy/anisotropy/alpha and the Freeman–Durden–Wishart classification, were investigated. The helix scattering component of the Yamaguchi decomposition, derived from ALOS-2 imagery, showed high suitability to refine the Sentinel-1-based detection of flooded vegetation. A test site at the Evros River (Greek/Turkish border region) was chosen, which was affected by a flooding event that occurred in spring 2015. The validation was based on high spatial resolution optical WorldView-2 imagery acquired with short temporal delay to the SAR data.