Advanced Synthetic Aperture Radar Images Research Articles

On the Effects of the Incidence Angle on the L-Band Multi-Polarisation Scattering of a Small Ship

The monitoring of ships is of paramount importance for ocean and coastal area surveillance. The synthetic aperture radar is shown to be a key sensor to provide effective and continuous observation of ships due to its unique imaging capabilities. When advanced synthetic aperture radar imaging systems are considered, the full scattering information is available that was demonstrated to be beneficial in developing improved ship detection and classification algorithms. Nonetheless, the capability of polarimetric synthetic aperture radar to observe marine vessels is significantly affected by several imaging and environmental parameters, including the incidence angle. Nonetheless, how changes in the incidence angle affect the scattering of ships still needs to be further investigated since only a sparse analysis, i.e., on different kinds of ships of different sizes observed at multiple incidence angles, has been performed. Hence, in this study, for the first time, the polarimetric scattering of the same ship, i.e., a small fishing trawler, which is imaged multiple times under the same sea state conditions but in a wide range of incidence angles, is analysed. This unique opportunity is provided by a premium L-band UAVSAR airborne dataset that consists of five full-polarimetric synthetic aperture radar scenes collected in the Gulf of Mexico. Experimental results highlight the key role played by the incidence angle on both coherent, i.e., co-polarisation signature and pedestal height, and incoherent, i.e., multi-polarisation and total backscattering power, polarimetric scattering descriptors. Experimental results show that: (1) the polarised scattering component is more sensitive to the incidence angle with respect to the unpolarised one; (2) the co-polarised channel under horizontal polarisation dominated the polarimetric backscattering from the fishing trawler at lower angles of incidence, while both co-polarised channels contribute to the polarimetric backscattering at higher incidence angles; (3) the HV polarisation provides the largest target-to-clutter ratio at lower incidence angles, while the HH polarisation should be preferred at higher angles of incidence.

An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning

As one of the main sources of remote sensing big data, synthetic aperture radar (SAR) can provide all-day and all-weather Earth image acquisition. However, speckle noise in SAR images brings a notable limitation for its big data applications, including image analysis and interpretation. Deep learning has been demonstrated as an advanced method and technology for SAR image despeckling. Most existing deep-learning-based methods adopt supervised learning and use synthetic speckled images to train the despeckling networks. This is because they need clean images as the references, and it is hard to obtain purely clean SAR images in real-world conditions. However, significant differences between synthetic speckled and real SAR images cause the domain gap problem. In other words, they cannot show superior performance for despeckling real SAR images as they do for synthetic speckled images. Inspired by recent studies on self-supervised denoising, we propose an advanced SAR image despeckling method by virtue of Bernoulli-sampling-based self-supervised deep learning, called SSD-SAR-BS. By only using real speckled SAR images, Bernoulli-sampled speckled image pairs (input–target) were obtained as the training data. Then, a multiscale despeckling network was trained on these image pairs. In addition, a dropout-based ensemble was introduced to boost the network performance. Extensive experimental results demonstrated that our proposed method outperforms the state-of-the-art for speckle noise suppression on both synthetic speckled and real SAR datasets (i.e., Sentinel-1 and TerraSAR-X).

Semantic Labeling of Globally Distributed Urban and Nonurban Satellite Images Using High-Resolution SAR Data

While the analysis and understanding of multispectral (i.e., optical) remote sensing images have made considerable progress during the last decades, the automated analysis of synthetic aperture radar (SAR) satellite images still needs some innovative techniques to support nonexpert users in the handling and interpretation of these big and complex data. In this article, we present a survey of existing multispectral and SAR land cover image datasets. To this end, we demonstrate how an advanced SAR image analysis system can be designed, implemented, and verified that is capable of generating semantically annotated classification results (e.g., maps) as well as local and regional statistical analytics such as graphical charts. The initial classification is made based on Gabor features and followed by class assignments (labeling). This is followed by inclusion. This can be accomplished by the inclusion of expert knowledge via active learning with selected examples, and the extraction of additional knowledge from public databases to refine the classification results. Then, based on the generated semantics, we can create new topic models, find typical country-specific phenomena and distributions, visualize them interactively, and present significant examples including confusion matrices. This semiautomated and flexible methodology allows several annotation strategies, the inclusion of dedicated analytics procedures, and can generate broad as well as detailed semantic (multi-)labels for all continents, and statistics or models for selected countries and cities. Here, we employ knowledge graphs and exploit ontologies. These components could already be validated successfully. The proposed methodology can also be adapted to other SAR instruments with different resolutions as well as to multispectral images.

The Extraction of Ocean Tidal Loading from ASAR Differential Interferograms

The spatiotemporal crustal non-tectonic deformation caused by ocean tidal loading (OTL) can reach the centimeters scale in coastal land areas. The temporal variation of the site OTL displacements can be estimated by the global positioning system (GPS) technique, but its spatial variation needs to be further determined. In this paper, in order to analyze the spatial characteristics of the OTL displacements, we propose a multi-scale decomposition method based on signal spatial characteristics to derive the OTL displacements from differential interferometric synthetic aperture radar (D-InSAR) measurements. The method was tested using long-term advanced synthetic aperture radar (ASAR) data and GPS reference site data from the Los Angeles Basin in the United States, and we compared the results with the FES2014b tide model. The experimental results showed that the spatial function of the OTL displacements in an ASAR image can be represented as a higher-order polynomial function, and the spatial trends of the OTL displacements determined by the InSAR and the GPS techniques are basically consistent with the FES2014b tide model. The root-mean-square errors of the differences between the spatial OTL displacements of these two methods and the FES2014b tide model are less than 0.8 mm. The results indicate that the OTL displacement extracted from InSAR data can accurately reflect the spatial characteristics of the OTL effect, which will help to improve the spatial resolution and accuracy of the OTL displacement in coastal areas.

Joint Monitoring of Ground and Sky for Cereal Crops Based on Scatterometer Measurement and ASAR Images

The joint monitoring of the ground and sky for cereal crops based on microwave data has become a popular method for researches on earth surface objects. Focused on the sensitivity of backscatter from the scatterometer measurement and advanced synthetic aperture radar (ASAR) images to cereal parameters of rice, nine acquisitions, including rice parameters related eco-physiological variables and scattering coefficients, have been carried over the paddy field corresponding to rice growth stages. This paper analyzes the relationship between the corresponding backscatter to the cereal parameters based on the measurement at the interesting bands, polarizations, and incidence angels. Further, a modified water cloud model is built based on the ground measurement and advanced integrated equation model (AIEM), and then cereal parameters from ASAR images are retrieved and verified. The research results show that the sensitivity of backscatter to cereals from the sensor of the radar scatterometer could be helpful to build the retrieve model for synthetic aperture radar (SAR) images, which can achieve the scientific goals of the joint monitoring of ground and sky for cereal crops.

An adaptive machine learning approach to improve automatic iceberg detection from SAR images

Iceberg distribution, dispersion and melting patterns are fundamental aspects in the balance of heat and freshwater in the Southern Ocean; yet these features are not fully understood. This lack of understanding is, in part, due to the difficulties in accurately identifying icebergs in different environmental conditions. To improve the understanding, reliable iceberg detection tools are necessary to achieve a detailed picture of iceberg drift and disintegration patterns, an thus to gain further information on the freshwater input into the Southern Ocean. Here, we present an accurate automatic large-scale iceberg detection method using an alternative machine learning architecture applied to high resolution Synthetic Aperture Radar (SAR) images. Our method is based on the concept of adaptability and focuses on improving the performance of identifying icebergs in ambiguous environmental contexts with wide radiometric, textural, size and shape variability. The fundamentals of the method are centred on superpixel segmentation, ensemble learning and incremental learning. The method is applied to a dataset containing 586 ENVISAT Advanced SAR images acquired during 2003–2005 (Weddell Sea region) and to the Radarsat-1 Antarctic Mapping Project (RAMP) mosaic, covering the Antarctic wide near-coastal zone. These images cover scenes under heterogenous backscattering signatures for all seasons with variable meteorological, oceanographic and acquisition parameters (e.g. band, polarization). Our method is highly adaptable to distinguish icebergs from ambiguous objects hidden in the images. The average false positive rate and miss rate are 2.3 ± 0.4% and 3.3 ± 0.4%, respectively. Overall, 9512 icebergs with sizes varying from 0.1 to 4567.82 km2 are detected with average classification accuracy of 97.5 ± 0.6%. The results confirm that the method presented here is robust for widespread iceberg detection in the Antarctic seas.

The life cycle of small- to medium-sized icebergs in the Amundsen Sea Embayment

An object-based method for automatic iceberg detection has been applied to Advanced Synthetic Aperture Radar images in the Amundsen Sea Embayment (ASE), Antarctica. The images were acquired between 1 January 2006 and 8 April 2012 under varying meteorological, oceanographic and sea-ice conditions. During this time period, the icebergs were counted (average 1370 ± 50) and their surface area was estimated (average 1537.5 km2). The average surface area was about 2.5 times larger than the annual calved area (620 km2), indicating that the average iceberg age in the ASE is about 2.5 years, which was confirmed by observed residence times based on drift tracks. Most of the ASE icebergs were less than 1500 m long, and almost 90% of them were smaller than 2 km2. The proportion of small- and medium-sized icebergs (84.4%) was significantly higher than in the open ocean, where large icebergs (>10 km2) account for nearly the whole iceberg surface area. The opposite was true for the freshly calved icebergs in the ASE. The data indicate that the creation of icebergs in the ASE is dominated by steady small- to medium-scale calving from ice shelves fringing the embayment. In addition, rare calving events of giant icebergs occur on a decadal timescale. There is also some import of icebergs from the Bellingshausen Sea further east along the coast, in particular after large calving events there.

Design of a Multi-mode Block Adaptive Quantizer for High-Resolution Synthetic Aperture Radar Imaging

An advanced synthetic aperture radar imaging system needs a multi-mode block adaptive quantizer (BAQ) for high-resolution image requirements. However, the Lloyd-Max quantizer inside the BAQ consists of non-uniform thresholds for comparison. Consequently, the arithmetic complexity grows exponentially if the BAQ output word-length becomes larger. In this work, a multi-mode BAQ is designed to support real-time compression for high sampling-frequency radar signals. Various output word-lengths are provided so that different compression ratios can be set depending on the applications. To solve the problem of exponential growth in complexity due to the large BAQ output word-lengths, we propose two strategies, input scaling and hybrid comparators. Significant saving in complexity is attained. From the synthesis result in 40 nm CMOS technology, the proposed design supports 14-bit ADC at 800-MHz sample frequency with output word-lengths of 2, 3, 4, 6, 8 bits. Its gate count is 45.6 K and power consumption is only 11.8 mW at 0.9 V.

Mechanisms of SAR Imaging of Shallow Water Topography of the Subei Bank

In this study, the C-band radar backscatter features of the shallow water topography of Subei Bank in the Southern Yellow Sea are statistically investigated using 25 ENVISAT (Environmental Satellite) ASAR (advanced synthetic aperture radar) and ERS-2 (European Remote-Sensing Satellite-2) SAR images acquired between 2006 and 2010. Different bathymetric features are found on SAR imagery under different sea states. Under low to moderate wind speeds (3.1~6.3 m/s), the wide bright patterns with an average width of 6 km are shown and correspond to sea surface imprints of tidal channels formed by two adjacent sand ridges, while the sand ridges appear as narrower (only 1 km wide), fingerlike, quasi-linear features on SAR imagery in high winds (5.4~13.9 m/s). Two possible SAR imaging mechanisms of coastal bathymetry are proposed in the case where the flow is parallel to the major axes of tidal channels or sand ridges. When the surface Ekman current is opposite to the mean tidal flow, two vortexes will converge at the central line of the tidal channel in the upper layer and form a convergent zone over the sea surface. Thus, the tidal channels are shown as wide and bright stripes on SAR imagery. For the SAR imaging of sand ridges, all the SAR images were acquired at low tidal levels. In this case, the ocean surface waves are possibly broken up under strong winds when propagating from deep water to the shallower water, which leads to an increase of surface roughness over the sand ridges.

A Model of Radar Backscatter of Rain-Generated Stalks on the Ocean Surface

In this paper, a model of radar backscattering from rain-generated stalks on the ocean surface in a rain field is proposed. In the model, stalks in the rain field form an array and are considered as finite water cylinders standing out of an infinite water surface. The radar backscattering coefficient from these stalks is derived. Both incoherent and coherent backscattering mechanisms from the stalks are considered. The model shows that the radar backscattering intensity is a function of the average distance between stalks on the water surface, the radar wave frequency, and the incident angle of radar waves. For light/moderate rain (at low rain rates), the radar backscattering intensity increases with increasing rain rate. For heavy rain (at high rain rates), the radar backscattering intensity decreases with increasing rain rate. The maximum radar backscattering intensity occurs at a rain rate that depends on the radar wave frequency and the incident angle of radar waves. The present model is used to explain the radar signatures of a rainfall event simultaneously observed by C-band ENVISAT (European satellite) Advanced Synthetic Aperture Radar (ASAR) and ground-based weather radar in the Northwest Pacific. The relationship between the radar return intensity extracted from the C-band ASAR image and the rain rate obtained from ground-based weather radar is in agreement with the model’s calculation. Also, the air–sea interface in rain fields and its effects on the attenuation of radar backscattering are experimentally studied in the laboratory.

Optimal estimation of interferometric phase for measuring surface deformation

ABSTRACTIn recent times, time-series interferometric synthetic aperture radar (InSAR) methods are developed to retrieve the deformation signal in non-urban areas from distributed scatterers (DS). Phase triangulation algorithm (PTA), an important step in these methods for filtering decorrelation noise from DS, aims at optimal estimation of the filtered wrapped interferometric phase values using InSAR data stack. The uniqueness of this research work lies in the incorporation of one such PTA only to provide an optimal set of wrapped interferometric phase values before phase unwrapping in the open source StaMPS processing environment. The proposed methodology, when adapted to measure surface deformation in Tehri reservoir rim region, Uttarakhand, India using Environmental Satellite (Envisat) C-band advanced synthetic aperture radar images, works efficiently and has enhanced the spatial coverage of measurement pixels compared to standalone PS-InSAR processing. It is also revealed from the one-dimension-line of sight velocity map that resulted velocity estimates are congruent with standalone PS-InSAR processing.

High-spatial-resolution mapping of precipitable water vapour using SAR interferograms, GPS observations and ERA-Interim reanalysis

Abstract. A high spatial and temporal resolution of the precipitable water vapour (PWV) in the atmosphere is a key requirement for the short-scale weather forecasting and climate research. The aim of this work is to derive temporally differenced maps of the spatial distribution of PWV by analysing the tropospheric delay "noise" in interferometric synthetic aperture radar (InSAR). Time series maps of differential PWV were obtained by processing a set of ENVISAT ASAR (Advanced Synthetic Aperture Radar) images covering the area of southern California, USA from 6 October 2007 to 29 November 2008. To get a more accurate PWV, the component of hydrostatic delay was calculated and subtracted by using ERA-Interim reanalysis products. In addition, the ERA-Interim was used to compute the conversion factors required to convert the zenith wet delay to water vapour. The InSAR-derived differential PWV maps were calibrated by means of the GPS PWV measurements over the study area. We validated our results against the measurements of PWV derived from the Medium Resolution Imaging Spectrometer (MERIS) which was located together with the ASAR sensor on board the ENVISAT satellite. Our comparative results show strong spatial correlations between the two data sets. The difference maps have Gaussian distributions with mean values close to zero and standard deviations below 2 mm. The advantage of the InSAR technique is that it provides water vapour distribution with a spatial resolution as fine as 20 m and an accuracy of ∼ 2 mm. Such high-spatial-resolution maps of PWV could lead to much greater accuracy in meteorological understanding and quantitative precipitation forecasts. With the launch of Sentinel-1A and Sentinel-1B satellites, every few days (6 days) new SAR images can be acquired with a wide swath up to 250 km, enabling a unique operational service for InSAR-based water vapour maps with unprecedented spatial and temporal resolution.

Improved statistics of sub-mesoscale eddies in the Baltic Sea retrieved from SAR imagery

ABSTRACTIn the present article the spatio-temporal distribution of sub-mesoscale eddies seen in Envisat Advanced Synthetic Aperture Radar (ASAR) imagery of the Baltic Sea is discussed. A total of 1250 ASAR images acquired between 2009 and 2011 form the basis of our studies and show imprints of almost 7000 sub-mesoscale eddies. Since the visibility of vortical structures in synthetic aperture radar (SAR) imagery significantly depends on the near-surface wind speed, wind data from a numerical model of the Baltic Sea were additionally used to obtain improved eddy statistics. Seasonally averaged fields of near-surface wind speed, surface currents, sea surface temperature (SST), and SST gradient were also analysed in order to reveal the role of these hydrophysical parameters in the observed spatial and temporal variation of sub-mesoscale eddies.

Polarimetric analysis of radar backscatter from ground-based scatterometers and wheat biomass monitoring with advanced synthetic aperture radar images

This article presents an analysis of the scattering measurements for an entire wheat growth cycle by ground-based scatterometers at a frequency of 5.3 GHz. Since wheat ears are related to wheat growth and yield, the radar backscatter of wheat was analyzed at two different periods, i.e., with and without wheat ears. Simultaneously, parameters such as wheat and soil characteristics as well as volume scattering and soil scattering were analyzed for the two periods during the entire growth cycle. Wheat ears have been demonstrated to have a great influence on radar backscatter; therefore, a modified version of water-cloud model used for retrieving biomass should consider the effect of wheat ears. This work presents two retrieval models based on the water-cloud model and adopts the advanced integral equation model to simulate the soil backscatter before the heading stage and the backscatter from the layer under wheat ears after the heading stage. The research results showed that the biomass retrieved from the advanced synthetic aperture radar (ASAR) images to agree well with the data measured <italic<in situ</italic< after setting the modified water-cloud model for the growth stages with ears. Furthermore, it was concluded that wheat ears should form an essential component of theoretical modeling as they influence the final yield.

Anatomy of Subsidence in Tianjin from Time Series InSAR

Groundwater is a major source of fresh water in Tianjin Municipality, China. The average rate of groundwater extraction in this area for the last 20 years fluctuates between 0.6 and 0.8 billion cubic meters per year. As a result, significant subsidence has been observed in Tianjin. In this study, C-band Envisat (Environmental Satellite) ASAR (Advanced Synthetic Aperture Radar) images and L-band ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar) data were employed to recover the Earth’s surface evolution during the period between 2007 and 2009 using InSAR time series techniques. Similar subsidence patterns can be observed in the overlapping area of the ASAR and PALSAR mean velocity maps with a maximum radar line of sight rate of ~170 mm·year−1. The west subsidence is modeled for ground water volume change using Mogi source array. Geological control by major faults on the east subsidence is analyzed. Storage coefficient of the east subsidence is estimated by InSAR displacements and temporal pattern of water level changes. InSAR has proven a useful tool for subsidence monitoring and displacement interpretation associated with underground water usage.

Interseismic Deformation of the Altyn Tagh Fault Determined by Interferometric Synthetic Aperture Radar (InSAR) Measurements

The Altyn Tagh Fault (ATF) is one of the major left-lateral strike-slip faults in the northeastern area of the Tibetan Plateau. In this study, the interseismic deformation across the ATF at 85°E was measured using 216 interferograms from 33 ENVISAT advanced synthetic aperture radar images on a descending track acquired from 2003 to 2010, and 66 interferograms from 15 advanced synthetic aperture radar images on an ascending track acquired from 2005 to 2010. To retrieve the pattern of interseismic strain accumulation, a global atmospheric model (ERA-Interim) provided by the European Center for Medium Range Weather Forecast and a global network orbital correction approach were applied to remove atmospheric effects and the long-wavelength orbital errors in the interferograms. Then, the interferometric synthetic aperture radar (InSAR) time series with atmospheric estimation model was used to obtain a deformation rate map for the ATF. Based on the InSAR velocity map, the regional strain rates field was calculated for the first time using the multi-scale wavelet method. The strain accumulation is strongly focused on the ATF with the maximum strain rate of 12.4 × 10−8/year. We also show that high-resolution 2-D strain rates field can be calculated from InSAR alone, even without GPS data. Using a simple half-space elastic screw dislocation model, the slip-rate and locking depth were estimated with both ascending and descending surface velocity measurements. The joint inversion results are consistent with a left-lateral slip rate of 8.0 ± 0.7 mm/year on the ATF and a locking depth of 14.5 ± 3 km, which is in agreement with previous results from GPS surveys and ERS InSAR results. Our results support the dynamic models of Asian deformation requiring low fault slip rate.

A study of radar backscattering from water surface in response to rainfall

AbstractIn this paper, radar backscattering from a water surface in response to rainfall was studied. The paper consists of two parts. First, the spatial characteristics of raindrops in rain fields were analyzed based on published data and the response of a water surface to rainfall was experimentally studied in the laboratory. Rain‐generated surface features including stalks, crowns, ring waves, and secondary drops were measured. It was found that stalks and crowns are dominant in terms of their height and energy. Second, the radar signatures of a rainfall event simultaneously observed by C band ENVISAT (European satellite), ASAR (Advanced Synthetic Aperture Radar), and ground‐based weather radar in the Northwest Pacific were investigated. The relationship between the radar return intensity extracted from the C band ASAR image and the reflectivity factor (rain rate) obtained from ground‐based weather radar was analyzed. For light/moderate rain (with low reflectivity factors), the radar backscattering intensity increases as the reflectivity factor increases. For heavy rain (with high reflectivity factors), the radar backscattering intensity decreases as the reflectivity factor increases. The maximum radar backscattering intensity occurs at a reflectivity factor of 45 dBZ (with rain rate of 24 mm/h). It was found that the spaceborne radar backscattering intensity strongly correlates with the average distance between the stalks on the water surface in the rain field in a nonlinear manner. The physics of the radar signatures of the rain event are explored.

Spatial analysis of surface deformation distribution detected by persistent scatterer interferometry in Lanzhou Region, China

Persistent scatterer synthetic aperture radar interferometry (PS-InSAR) is a remote sensing method that can be used to detect surface deformation, which is an indicator of potential geohazards. By capturing such deformations over time, it is possible to obtain valuable information regarding geohazards such as landslides. This study focused on the use of PS-InSAR to investigate the distribution and causes of surface deformation in the Lanzhou region of Gansu Province in China. Between 2003 and 2010, 41 advanced synthetic aperture radar images were captured by the Envisat satellite and analyzed using PS-InSAR, and the correlation between the observed surface deformation and topographic, geologic, and anthropogenic factors was derived based on a geographic information system platform. It was found that the largest number and highest density of surface deformations occurred at elevations of 1486–1686 m. It was also established that slope ranges of 25°–30° and 35°–40° are threshold values at which surface deformation changes abruptly, and that slopes with north and northwest aspects are most prone to surface deformation. The lithologies most susceptible to surface deformation are clay, sandy soil, and loess. The normalized difference vegetation index indicated that surface deformation occurred most often in areas with sparse vegetation. Anthropogenic activities, e.g., construction and wastewater discharge, could be inferred as causal mechanisms of surface deformation. Comparison of the distributions of geohazards and surface deformation showed considerable consistency, which proves surface deformation can induce geohazards. These results could help governments improve urban planning and geohazard mitigation.

Multi-sensor monitoring of Ulva prolifera blooms in the Yellow Sea using different methods

The massive Ulva (U.) prolifera bloom in the Yellow Sea was first observed and reported in summer of 2008. After that, the green tide event occurred every year and influenced coastal areas of Jiangsu and Shandong provinces of China. Satellite remote sensing plays an important role in monitoring the floating macroalgae. In this paper, U. prolifera patches are detected from quasisynchronous satellite images with different spatial resolution, i.e., Aqua MODIS (Moderate Resolution Imaging Spectroradiometer), HJ-1A/B (China Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting), CCD (Charge-Coupled Device), Landsat 8 OLI (Operational Land Imager), and ENVISAT (Environmental Satellite) ASAR (Advanced Synthetic Aperture Radar) images. Two comparative experiments are performed to explore the U. prolifera monitoring abilities by different data using detection methods such as NDVI (Normalized Difference Vegetation Index) with different thresholds. Results demonstrate that spatial resolution is an important factor affecting the extracted area of the floating macroalgae. Due to the complexity of Case II sea water characteristics in the Yellow Sea, a fixed threshold NDVI method is not suitable for U. prolifera monitoring. A method with adaptive ability in time and space, e.g., the threshold selection method proposed by Otsu (1979), is needed here to obtain accurate information on the floating macroalgae.

Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR

This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10 degrees N) from hyper-temporal observations of Envisat Advanced Synthetic Aperture Radar (ASAR) backscattered intensity using the BIOMASAR algorithm. Approximately 70,000 ASAR images at a pixel size of 0.01 degrees were used to estimate GV representative for the year 2010. The spatial distribution of the GSV across four ecological zones (polar, boreal, temperate subtropical) was well captured by the ASAR-based estimates. The uncertainty of the retrieved GSV was smallest in boreal and temperate forest ( 30 m^3/ha) and fragmented forest landscapes. For the major forested countries within the study region, the relative RMSE betwen ASAR-derived GSV averages at provincial level and corresponding values from National Forest Inventory was between 12% and 45% (average: 29%).