Temporal stability of soil moisture and radar backscatter observed by the advanced Synthetic Aperture Radar (ASAR)

The high spatio-temporal variability of soil moisture is the result of atmospheric forcing and redistribution processes related to terrain, soil, and vegetation characteristics. Despite this high variability, many field studies have shown that in the temporal domain soil moisture measured at specific locations is correlated to the mean soil moisture content over an area. Since the measurements taken by Synthetic Aperture Radar (SAR) instruments are very sensitive to soil moisture it is hypothesized that the temporally stable soil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located in the Duero basin, Spain. It is found that a time-invariant linear relationship is well suited for relating local scale (pixel) and regional scale (50 km) backscatter. The observed linear model coefficients can be estimated by considering the scattering properties of the terrain and vegetation and the soil moisture scaling properties. For both linear model coefficients, the relative error between observed and modelled values is less than 5 % and the coefficient of determination (R-2) is 86 %. The results are of relevance for interpreting and downscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT) and passive (SMOS, AMSR-E) instruments

First-Year and Multiyear Sea Ice Incidence Angle Normalization of Dual-Polarized Sentinel-1 SAR Images in the Beaufort Sea

Automatic and visual sea ice classification of SAR imagery is impeded by the incidence angle dependence of backscatter intensities. Knowledge of the angular dependence of different ice types is therefore necessary to account for this effect. While consistent estimates exist for HH polarization for different ice types, they are lacking HV polarization data, especially for multiyear sea ice. Here we investigate the incidence angle dependence of smooth and rough/deformed first-year and multiyear ice of different ages for wintertime dual-polarization Sentinel-1 C-band SAR imagery in the Beaufort Sea. Assuming a linear relationship, this dependence is determined using the difference in incidence angle and backscatter intensities from ascending and descending images of the same area. At cross-polarization rough/deformed first-year sea ice shows the strongest angular dependence with -text{0.11} dB/1{circ } followed by multiyear sea ice with -text{0.07} dB/text{1}{circ }, and old multiyear ice (older than three years) with -text{0.04} dB/text{1}{circ }. The noise floor is found to have a strong impact on smooth first-year ice and estimated slopes are therefore not fully reliable. At co-polarization, we obtained slope values of -0.24, -0.20, -text{0.15}, and -text{0.10} dB/text{1}{circ } for smooth first-year, rough/deformed first-year, multiyear, and old multiyear sea ice, respectively. Furthermore, we show that imperfect noise correction of the first subswath influences the obtained slopes for multiyear sea ice. We demonstrate that incidence angle normalization should not only be applied to co-polarization but should also be considered for cross-polarization images to minimize intra ice type variation in backscatter intensity throughout the entire image swath

Détection des cycles de gel/dégel de la couche active du sol en toundra arctique à partir d’imageries radar à synthèse d’ouverture (RSO) multicapteur en bande C

L’augmentation de la température de l’air moyenne annuelle, chiffrée à +2,3 °C pour les régions de l’arctique Canadien entre 1948 et 2016, a des impacts considérables sur le couvert nival arctique et sur la végétation en place. Ces deux paramètres influencent le régime thermique du sol et donc, les cycles de gel/dégel de sa couche active dans l’écosystème arctique. L’importance du suivi de ces cycles réside dans leur influence sur plusieurs paramètres de la cryosphère tels que le cycle hydrologique et du carbone, la saison de croissance de la végétation, l’état du pergélisol sous-jacent ainsi que l’épaisseur de sa couche active. L’utilisation de données ponctuelles ou provenant de capteurs micro-onde passive à basse résolution présente un enjeu pour le suivi spatial et temporel de ces cycles. Le projet vise à développer un algorithme de détection des cycles de gel/dégel du sol en toundra arctique à partir d’imageries RSO multicapteur (i.e., Sentinel-1 et RADARSAT-2) ayant une couverture temporelle quasi journalière en bande C, afin d’évaluer l’impact de la variabilité spatiale et temporelle des paramètres influençant le régime thermique du sol tel que, les écosystèmes terrestres (i.e., écotype) et la présence de neige. L’étude se concentre sur une zone à l’intérieur du bassin versant du lac Greiner à proximité de la ville de Cambridge Bay au Nunavut. La normalisation de l’angle d’incidence a permis de diminuer le bruit dans les séries temporelles ainsi que de rendre possible l’utilisation d'images acquises à l'intérieur de plusieurs orbites d’observation. Cela a aussi permis d’uniformiser les données des deux capteurs pour les combiner en une seule série temporelle. Deux algorithmes de détections ont été utilisés, soit un algorithme de seuil saisonnier (STA) ainsi qu’un algorithme de détection de changement (CPD). La validation s’est faite à partir des données spatialement distribuées de température du sol et de l’air indépendamment sous forme de précision (%) et de délai (#jours) de détection. Les deux algorithmes ont permis d’obtenir une précision de détection de plus de 97% sur les sites de référence. Une spatialisation, pixel par pixel, de la méthode STA a permis la création de cartes de jour de gel/dégel pour le site d’étude. La combinaison des cartes de jour de transition avec la carte d’écotype a permis de modéliser l’impact des caractéristiques des écotypes sur le jour de transition. Les résultats obtenus dans ce projet démontrent clairement le potentiel de l’utilisation des données RSO en bande C pour la détection des cycles de gel/dégel, ce qui constitue un résultat important en raison de la quantité grandissante de données à cette fréquence (e.g., RCM, Sentinel-1A-C-D). La méthode présentée dans ce projet pourrait permettre de créer des cartes de transition pour tout le bassin versant du lac Greiner à partir de données RSO en bande C.Abstract : The observed average annual surface temperature increase of 2.3°C in the Canadian Arctic regions between 1948 and 2016 has significant effects on the Arctic snow cover and on the vegetation in place. Those two parameters influence the thermal regime of the ground and therefore the freeze and thaw (F/T) cycles of the soil active layer in the Arctic tundra ecosystem. The importance of monitoring these cycles lies in their influence on several parameters of the cryosphere such as the hydrological and carbon cycle, the vegetation growing season, the state of the underlying permafrost and the thickness of its active layer. The use of punctual data or low-resolution passive microwave sensors presents a challenge for the spatial and temporal monitoring of these cycles. The project aims to develop an algorithm for soil freeze/thaw cycles detection in arctic tundra from multisensor C-band imagery (i.e., Sentinel-1 and RADARSAT-2) to assess the impact of the spatial and temporal variability of the parameters influencing the thermal regime of the ground, such as the terrestrial ecosystems (i.e., ecotype) and the snow cover. The study focused on a region of the Greiner lake watershed on Victoria Island in Nunavut. An incidence angle normalization was applied to the backscatter time series to remove influence of the acquisition angle on backscatter and to allow for the use of images acquired within several orbits of observation. This also standardized the data from the two sensors to combine them into a single time series. Two detection algorithms were used on the normalized backscatter coefficient data, namely a seasonal threshold algorithm (STA) and a change point detection algorithm (CPD). A spatially distributed network of soil and air temperature were used for validation in the form of accuracy (%) and delay (#days) of detection. Both algorithms achieved a detection accuracy of more than 97% for the entire analysis period on the reference sites. A pixel-by-pixel spatialization of the STA method allowed to create F/T transition maps for the extended study site. The combination of the transition maps with the ecotype data made it possible to model the impact of ecotype characteristics on the day of transition. The results obtained in this project clearly demonstrate the potential of using C-band for the detection of F/T cycles, which is an important aspect due to the increasing number of data at this frequency (e.g., RCM, Sentinel -1A-C-D). The method presented in this project could then make it possible to create transition maps for the entire Greiner Lake watershed from C-band SAR data and thus improve the integration of this parameter in climate models

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification performance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91% for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05% using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes.Peer reviewe

Automated Ice-Water Classification using Dual Polarization SAR Imagery

Mapping ice and open water in ocean bodies is important for numerous purposes including environmental analysis and ship navigation. The Canadian Ice Service (CIS) currently has several expert ice analysts manually generate ice maps on a daily basis. The CIS would like to augment their current process with an automated ice-water discrimination algorithm capable of operating on dual-pol synthetic aperture radar (SAR) images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions that are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAGIC. The algorithm first classifies the HV scene using the glocal method, a hierarchical region-based classification method. The glocal method incorporates spatial context information into the classification model using a modified watershed segmentation and a previously developed MRF classification algorithm called IRGS. Second, a pixel-based support vector machine (SVM) using a nonlinear RBF kernel classification is performed exploiting SAR grey-level co-occurrence matrix (GLCM) texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 61 ground truthed dual-pol RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 95.8% and MAGIC attains an accuracy of 90% or above on 88% of the scenes. The MAGIC system is now under consideration by CIS for operational use

Automated Ice-Water Classification using Dual Polarization SAR Imagery

Mapping ice and open water in ocean bodies is important for numerous purposes including environmental analysis and ship navigation. The Canadian Ice Service (CIS) currently has several expert ice analysts manually generate ice maps on a daily basis. The CIS would like to augment their current process with an automated ice-water discrimination algorithm capable of operating on dual-pol synthetic aperture radar (SAR) images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions that are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAGIC. The algorithm first classifies the HV scene using the glocal method, a hierarchical region-based classification method. The glocal method incorporates spatial context information into the classification model using a modified watershed segmentation and a previously developed MRF classification algorithm called IRGS. Second, a pixel-based support vector machine (SVM) using a nonlinear RBF kernel classification is performed exploiting SAR grey-level co-occurrence matrix (GLCM) texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 61 ground truthed dual-pol RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 95.8% and MAGIC attains an accuracy of 90% or above on 88% of the scenes. The MAGIC system is now under consideration by CIS for operational use

Detection and classification of sea ice from spaceborne multi-frequency synthetic aperture radar imagery and radar altimetry

The sea ice cover in the Arctic is undergoing drastic changes. Since the start of satellite observations by microwave remote sensing in the late 1970\u27s, the maximum summer sea ice extent has been decreasing and thereby causing a generally thinner and younger sea ice cover. Spaceborne radar remote sensing facilitates the determination of sea ice properties in a changing climate with the high spatio-temporal resolution necessary for a better understanding of the ongoing processes as well as safe navigation and operation in ice infested waters.The work presented in this thesis focuses on the one hand on synergies of multi-frequency spaceborne synthetic aperture radar (SAR) imagery for sea ice classification. On the other hand, the fusion of radar altimetry observations with near-coincidental SAR imagery is investigated for its potential to improve 3-dimensional sea ice information retrieval.Investigations of ice/water classification of C- and L-band SAR imagery with a feed-forward neural network demonstrated the capabilities of both frequencies to outline the sea ice edge with good accuracy. Classification results also indicate that a combination of both frequencies can improve the identification of thin ice areas within the ice pack compared to C-band alone. Incidence angle normalisation has proven to increase class separability of different ice types. Analysis of incidence angle dependence between 19-47\ub0 at co- and cross-polarisation from Sentinel-1 C-band images closed a gap in existing slope estimates at cross-polarisation for multiyear sea ice and confirms values obtained in other regions of the Arctic or with different sensors. Furthermore, it demonstrated that insufficient noise correction of the first subswath at cross-polarisation increased the slope estimates by 0.01 dB/1\ub0 for multiyear ice. The incidence angle dependence of the Sentinel-1 noise floor affected smoother first-year sea ice and made the first subswath unusable for reliable incidence angle estimates in those cases.Radar altimetry can complete the 2-dimensional sea ice picture with thickness information. By comparison of SAR imagery with altimeter waveforms from CryoSat-2, it is demonstrated that waveforms respond well to changes of the sea ice surface in the order of a few hundred metres to a few kilometres. Freeboard estimates do however not always correspond to these changes especially when mixtures of different ice types are found within the footprint. Homogeneous ice floes of about 10 km are necessary for robust averaged freeboard estimates. The results demonstrate that multi-frequency and multi-sensor approaches open up for future improvements of sea ice retrievals from radar remote sensing techniques, but access to in-situ data for training and validation will be critical

Compaction of C-band synthetic aperture radar based sea ice information for navigation in the Baltic Sea

In this work operational sea ice synthetic aperture radar (SAR) data products were improved and developed. A SAR instrument is transmitting electromagnetic radiation at certain wavelengths and measures the radiation which is scattered back towards the instrument from the target, in our case sea and sea ice. The measured backscattering is converted to an image describing the target area through complex signal processing. The images, however, differ from optical images, i.e. photographs, and their visual interpretation is not straightforward. The main idea in this work has been to deliver the essential SAR-based sea ice information to end-users (typically on ships) in a compact and user-friendly format. The operational systems at Finnish Institute of Marine Research (FIMR) are currently based on the data received from a Canadian SAR-satellite, Radarsat-1. The operational sea ice classification, developed by the author with colleagues, has been further developed. One problem with the SAR data is typically that the backscattering varies depending on the incidence angle. The incidence angle is the angle in which the transmitted electromagnetic wave meets the target surface and it varies within each SAR image and between different SAR images depending on the measuring geometry. To improve this situation, an incidence angle correction algorithm to normalize the backscattering over the SAR incidence angle range for Baltic Sea ice has been developed as part of this work. The algorithm is based on SAR backscattering statistics over the Baltic Sea. To locate different sea ice areas in SAR images, a SAR segmentation algorithm based on pulse-coupled neural networks has been developed and tested. The parameters have been tuned suitable for the operational data in use at FIMR. The sea ice classification is based on this segmentation and the classification is segment-wise rather than pixel-wise. To improve SAR-based distinguishing between sea ice and open water an open water detection algorithm based on segmentation and local autocorrelation has been developed. Also ice type classification based on higher-order statistics and independent component analysis have been studied to get an improved SAR-based ice type classification. A compression algorithm for compressing sea ice SAR data for visual use has been developed. This algorithm is based on the wavelet decomposition, zero-tree structure and arithmetic coding. Also some properties of the human visual system were utilized. This algorithm was developed to produce smaller compressed SAR images, with a reasonable visual quality. The transmission of the compressed images to ships with low-speed data connections in reasonable time is then possible. One of the navigationally most important sea ice parameters is the ice thickness. SAR-based ice thickness estimation has been developed and evaluated as part of this work. This ice thickness estimation method uses the ice thickness history derived from digitized ice charts, made daily at the Finnish Ice Service, as its input, and updates this chart based on the novel SAR data. The result is an ice thickness chart representing the ice situation at the SAR acquisition time in higher resolution than in the manually made ice thickness charts. For the evaluation of the results a helicopter-borne ice thickness measuring instrument, based on electromagnetic induction and laser altimeter, was used.reviewe