Information Mining in Remote Sensing Image Archives

The theoretical backscattering model from Fung et al. (1992) was used to simulate the reflection of an electromagnetic wave from a synthetic aperture radar (SAR) on a bare soil surface depending on the soil properties. These data were applied to a neural network (NN) as a training set to retrieve the soil moisture from the backscattering coefficient. Several network configurations were tested and three are discussed. In the next step the trained networks were fed with remotely sensed data from the SAR on board of ERS-2 (European Remote Sensing Satellite) and data from the airborne 'Experimental SAR' (ESAR) built by the Institute of Radiofrequencies of the 'German Aerospace Center' (DLR). The root mean square error (RMSE) between soil moisture calculated from the network and measured in situ during the overflights was below 5% volumetric soil moisture.

Sufficient remote sensing observation data during crop main growing season is of great importance in improving the accuracy of data assimilation of crop model. The optical remote sensing data are susceptible to cloud and rain, so the amount of clear optical data is very limited in cloudy weather or rainy day. Synthetic Aperture Radar (SAR) is not dependent on cloud cover or light conditions, it can penetrate through clouds and have all-weather capabilities. This allows for a more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. So, the aim of this article is to improve the accuracy for winter wheat yield estimation by joint assimilation of SAR and optical satellite images into crop model. In this study, SAR images are acquired by C-band SAR sensor boarded on Sentinel-1 satellites, and optical images are obtained from Sentinel-2 satellites. Remote sensing data and ground data are all collected during the main growth and development stages of winter wheat. Both normalized difference vegetation index (NDVI) derived from Sentinel-2 images and backscattering coefficients and polarimetric indicators computed from Sentinel-1 images are used in water cloud model to derive soil moisture (SM) time series images. To improve the prediction of crop yields at filed scale, we incorporate remotely sensed soil moisture into the WOrld FOod STudies (WOFOST) model using Ensemble Kalman filter (EnKF) algorithm. In general, the results show that data assimilation schemes of remotely sensed soil moisture slightly improved the correlation of observed and simulated yields (R2 = 0.30; RMSE =782 kg ha−1) compared to the situation without data assimilation (R2 = 0.14; RMSE = 1398 kg ha−1). Results of this study indicate that the potential for assimilation SAR and optical remote sensing data to improve field yield estimates is relatively low, limitations are due to insufficient no-cloud optical remotely sensed data and root zone soil moisture information. Consequently, the results of this study demonstrate the potential and usefulness of assimilating both SAR and optical remote sensing data into crop model for crop monitoring and yield estimation. Moreover, this also provides the reference for crop yield estimation with data assimilation in other agricultural landscapes.

This paper investigates the potential of accounting for temporal contextual information in order to improve the accuracy of land-cover classification in summer with Synthetic Aperture Radar (SAR) data. Bi-temporal multi-sensor datasets collected in the Nonsan area of Korea were used to illustrate this approach. Multi-sensor data, including Japanese Earth Resources Satellite (JERS)-1 Optical Sensor (OPS) data acquired in April, and three different SAR sensor datasets from European Resource Satellite (ERS)-2, JERS-1, and Radarsat-1 obtained in the following July, were used for supervised classification in July. By comparing the classification result in April with a training set in July, transition probabilities between land-cover classes in the April–July period were empirically estimated and regarded as the temporal contextual information. A tau model is applied as a main integration methodology to combine multiple SAR data and the temporal contextual information. From the evaluation of the classification results in terms of accuracy statistics, using multiple SAR sensor data showed an increase of about 29% in overall accuracy compared with the case of single SAR sensor data. The incorporation of temporal contextual information into scattering information greatly contributed to a significant improvement of about 25% in overall accuracy over multiple SAR sensor integration only, and showed the best discrimination capability.

A ground segment is essentially a “system of systems”, and as such possesses a high degree of complexity. This paper outlines what a typical ESA ground segment will look like in the near future and at how it is planned to evolve this to improve maintainability, increase reliability and decrease development costs. Also examined are ways of improving the utilisation of the hardware and increasing system longevity. I. Introduction HERE are a number of developments currently being carried out at the European Space Operations Centre (ESOC) which will have significant implications for the development, deployment, operation and maintenance of its ground segments. One of the main components of this is the ESA Ground Operation System (EGOS), an ambitious program to standardize the infrastructure used throughout its ground segments in an effort to improve the reliability, cost effectiveness and interoperability. Initially it is concentrating on 3 areas of the ground segment, the station baseband system, the mission control systems and the simulators. Implementation of the initial phases of this has recently begun and is based on the CORBA Component Model (CCM). Another significant element is this EGOS User Desktop, this is being developed with the goal of proving a standard desktop that can be used across a wide range of systems (ideally all) and provide a common “look and feel” across the ground segment. In parallel to this a number of other systems are being developed addressing needs in the areas of automation, service provision, allocation of ground network resources and interoperability. Further activities are also underway defining the requirements for future data distribution systems, database systems, the station computer and also at harmonizing interfaces to elements of the ground segment not currently covered by EGOS, in particular with the Flight Dynamics systems. Underlying all these activities is an approach that is based on, where possible, the adoption of internationally recognised standards, the standardisation of interfaces and designing the new systems with reuse in mind. Longevity of the systems is also an important issue, and investigations are ongoing into the use of virtualisation to improve the lifetime of the software systems and also to reduce the amount of physical hardware required. The possible implications for future ESOC ground segments of the above developments are significant. Developments of future ground segments should be simplified since many of the aspects of the various component systems will be standardised, plus development of any components or systems required should be more straightforward as these will be built on the standard infrastructure provided by EGOS. In addition the physical deployment of systems becomes more flexible and this, combined with the use of virtualisation technology, will enable a more resource efficient deployment of systems. By using virtualisation the amount of physical hardware required will be reduced and there will be a considerable increase in the ability to distribute resources as and when required, e.g. for critical mission phases. With this in mind possible future deployment scenarios are discussed. On the operational side the development of the automation aspects offer the potential to significantly offload the routine workload of the spacecraft controller, offering the possibility of reduced staffing levels by permitting one operator to monitor the operations on more than one spacecraft. More flexible and efficient allocation of ground station resources will give the potential for increased science return from the missions supported.

Remote sensing of Antarctic sea ice is required to characterize properties of the vast sea ice cover to understand its long-term increase in contrast to the decrease of Arctic sea ice. For this objective, the OIB/TanDEM-X Coordinated Science Campaign (OTASC) was successfully conducted in 2017 to obtain contemporaneous and collocated remote sensing data from NASA's Operation IceBridge (OIB) and the German Aerospace Center (DLR) TanDEM-X Synthetic Aperture Radar (SAR) system at X band together with Sentinel-1 and RADARSAT-2 SARs at C band in conjunction with WorldView satellite spectral sensors, surface measurements, and field observations. The Weddell Sea and the Ross Sea were two primary regions while SAR data were also collected over six other regions in the Southern Ocean. Satellite SAR data included both polarimetric and interferometric capabilities to infer snow and sea ice information in three dimensions (3D), while OIB/P-3 aircraft data include snow radar together with altimeter data for snow and sea ice observations in 3D over the Weddell Sea. Across the Ross Sea, IcePOD and AntNZ/York-University flights were carried out together with satellite SAR data acquisitions.

Synthetic Aperture Radar (SAR) is a remote sensing technique that allows the generation of microwave images of the Earth surface, independently of weather condition and of sun illumination. SAR sensors can be either mounted on spacecrafts (spaceborne systems) or placed on airplanes (airborne systems). In this thesis the study of problems related to the use of airborne SAR is addressed. The thesis is organized in five chapters and Chapter 1 presents the basic rationale of SAR technique. The assumption that the sensor flight path is a straight line is adopted in the first chapter, which is preliminary to the subsequent analysis, and allows introducing the formalism adopted in Chapters 2-5, where the assumption of straight flight path, not realistic for airborne systems, is relaxed, and the original results of this thesis are presented. Airborne SAR acquisition model is deeply analyzed, and realization of an efficient airborne raw data simulator, which represents a useful tool for mission planning, SAR system design and inversion algorithm testing, is presented (Chapter 2). The analysis of the airborne acquisition model allows also highlighting problems related to airborne SAR focusing. In particular, a quantitative analysis of the accuracy achievable by airborne SAR images is performed; extension to the repeat pass airborne interferometry is also included (Chapter 3). Such an analysis allows, on one side, quantifying the airborne repeat pass interferogram phase accuracy in terms of the expected motion measurement system accuracy (Chapter 3); on the other side, designing a new efficient airborne SAR processor (Chapter 4). Theoretical results presented in Chapter 3 are, finally, validated by an airborne differential interferometry experiment carried out by using real data (Chapter 5).

This article presents a frame of research for information retrieval from remote sensing (RS) images, based on a new concept developed at the German Aerospace Center, DLR Oberpfaffenhofen in collaboration with the Swiss Federal Institute of Technology, ETH Zurich. The newly developed technology emphasises the user access to the intrinsic information in RS images and on information instead of data dissemination. Thus the users are enabled to enhance the robustness of their decisions and to extend the range of applications.

Oil spill is considered one of the main threats to marine and coastal environments. Efficient monitoring and early identification of oil slicks are vital for the corresponding authorities to react expediently, confine the environmental pollution and avoid further damage. Synthetic aperture radar (SAR) sensors are commonly used for this objective due to their capability for operating efficiently regardless of the weather and illumination conditions. Black spots probably related to oil spills can be clearly captured by SAR sensors, yet their discrimination from look-alikes poses a challenging objective. A variety of different methods have been proposed to automatically detect and classify these dark spots. Most of them employ custom-made datasets posing results as non-comparable. Moreover, in most cases, a single label is assigned to the entire SAR image resulting in a difficulties when manipulating complex scenarios or extracting further information from the depicted content. To overcome these limitations, semantic segmentation with deep convolutional neural networks (DCNNs) is proposed as an efficient approach. Moreover, a publicly available SAR image dataset is introduced, aiming to consist a benchmark for future oil spill detection methods. The presented dataset is employed to review the performance of well-known DCNN segmentation models in the specific task. DeepLabv3+ presented the best performance, in terms of test set accuracy and related inference time. Furthermore, the complex nature of the specific problem, especially due to the challenging task of discriminating oil spills and look-alikes is discussed and illustrated, utilizing the introduced dataset. Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors. Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing.

Observation of the Earth from aerial and spaceborne platforms using synthetic aperture radar (SAR) has provided scientists and researchers with a deeper knowledge of the physical surface information of the planet. With the aim of launching our own SAR sensor into orbit for the purpose of remote sensing, we have a program to test our systems in an airborne platform. Prior to that, we need to first test all related on-board subsystems of SAR sensor on the ground. A semi-automated rail enabled ground-based SAR imaging test-bed was developed and is able to function carrying our L-band SAR systems. Part of the challenge in this project is the scale, as related L-band SAR system is relatively larger than systems of higher bands. The target and scan area covered in the ground measurement is also large, leading to a rail design that need to be extendable yet still mobile. This paper explains our system's radio frequency (RF) subsystem, which generates, transmits and receives the electromagnetic waves and also the rail-based Positioner Subsystem. In addition, the paper also shows the SAR system design specifications, how they are applied into the RF subsystem, and its implementation with the moving platform of the Positioner Subsystem in a field test.

Archaeological prospecting is pivotal to understanding human civilization’s development and past human–environment interactions (PHEIs). Synthetic aperture radar (SAR), a unique active remote sensing (RS) technology capable of penetrating vegetation and dry soil, has become an indispensable tool for large-scale archaeological surveys. This review, encompassing 45 years of progress (1980–2024), systematically chronicles SAR’s evolution—a field we term <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SARchaeology</i>—from pioneering L-band discoveries to sophisticated modern techniques like high-resolution SAR (HiSAR), interferometric SAR (InSAR), polarimetric SAR (PolSAR), and multitemporal SAR analysis (MtSAR). These methods have enabled unprecedented discoveries of hidden landscapes, particularly in areas obscured by dense cover or aridity. However, SARchaeology grapples with inherent limitations we define as the “SAR gap,” driven by technical complexity and, critically, the difficulty in achieving adequate signal-to-noise ratios (SNR) for small low-backscatter archaeological targets. Traditional challenges like speckle noise, cost, and interpretation ambiguity are compounded by this fundamental issue. To bridge this gap, we propose an integrated framework of five strategic frontiers: 1) leveraging innovative long-wavelength SAR technologies (e.g., the P band) for deep penetration; 2) establishing collaborative space–air–ground networks; 3) integrating SAR with complementary RS techniques; 4) implementing artificial intelligence (AI) and machine learning (ML) for noise suppression and weak signal feature extraction; and 5) developing explainable SARchaeology (X-SARchaeology). This framework provides a definitive road map for the systematic exploration of buried civilizations in hyper-arid regions (e.g., the Silk Road and ancient Mesopotamia). By focusing on these solutions, SARchaeology is poised to transcend conventional limitations and illuminate much more of human history before its Golden Jubilee.

High-spatiotemporal-resolution and accurate soil moisture (SM) data are crucial for investigating climate, hydrology, and agriculture. Existing SM products do not yet meet the demands for high spatiotemporal resolution. The objective is to develop and evaluate a retrieval framework to derive SM estimates with high spatial (100 m) and temporal (&lt;3 days) resolution that can be used on a national scale in China. Therefore, this study integrates multi-source data, including optical remote sensing (RS) data from Sentinel-2 and Landsat-7/8/9, synthetic aperture radar (SAR) data from Sentinel-1, and auxiliary data. Four machine learning and deep learning algorithms are applied, including Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Ensemble Learning (EL). The integrated framework (IF) considers three feature scenarios (SC1: optical RS + auxiliary data, SC2: SAR + auxiliary data, SC3: optical RS + SAR + auxiliary data), encompassing a total of 33 features. The results are as follows: (1) The correlation coefficients (r) between auxiliary data (such as sand fraction, r = −0.48; silt fraction, r = 0.47; and evapotranspiration, r = −0.42), SAR features (such as the backscatter coefficients for VV-pol (σvv0), r = 0.47), and optical RS features (such as Shortwave Infrared Band 2 (SWIR2) reflectance data from Sentinel-2 and Landsat-7/8/9, r = −0.39) with observed SM are significant. This indicates that multi-source data can provide complementary information for SM monitoring. (2) Compared to XGBoost and LSTM, RFR and EL demonstrate superior overall performance and are the preferred models for SM prediction. Their R2 for the training and test sets exceed 0.969 and 0.743, respectively, and their ubRMSE are below 0.022 and 0.063 m3/m3, respectively. (3) The SM prediction accuracy is highest for the scenario of optical + SAR + auxiliary data, followed by SAR + auxiliary data, and finally optical + auxiliary data. (4) With an increasing Normalized Difference Vegetation Index (NDVI) and SM values, the trained models exhibit a general decrease in prediction performance and accuracy. (5) In 2021 and 2022, without considering cloud cover, the IF theoretically achieved an SM revisit time of 1–3 days across 95.01% and 96.53% of China’s area, respectively. However, SC1 was able to achieve a revisit time of 1–3 days over 60.73% of China’s area in 2021 and 69.36% in 2022, while the area covered by SC2 and SC3 at this revisit time accounted for less than 1% of China’s total area. This study validates the effectiveness of combining multi-source RS data with auxiliary data in large-scale SM monitoring and provides new methods for improving SM retrieval accuracy and spatiotemporal coverage.

Over the past decade, synthetic aperture radar (SAR) sensors have demonstrated their ability to provide accurate wind speed and direction measurements in the ocean. Although SAR capability to image the sea surface at a suitable spatial resolution, SAR wind field retrieval in marine coastal areas still remains an open issue. The aim of this work is to assess the potential and limits of the Bayesian approach to the C-band NRCS SAR inversion problem for the retrieval of wind fields in marine coastal areas. A dataset of 139 SAR images gathered by the European ERS-2 and ENVISAT satellites during 2007 was analyzed to estimate the sea surface wind vectors over three selected coastal sites in the Mediterranean Sea, offshore Puglia region (Italy). The sites were instrumented to monitor marine and environmental parameters, including wind intensity and direction for further comparison with SAR wind estimations. Results discussed in this study show that the statistical approach in the retrieval of SAR wind speed can be applied in coastal areas with performances comparable with those obtained off-shore. In contrast, the SAR inversion procedure was not able to retrieve wind direction with the same accuracy.

Abstract. Floods are extreme hydrological events that can reshape the landscape, transform entire ecosystems and alter the relationship between living organisms and the surrounding environment. Every year, fluvial and coastal floods claim thousands of human lives and cause enormous direct damages and inestimable indirect losses, particularly in less developed and more vulnerable regions. Monitoring the spatiotemporal evolution of floods is fundamental to reducing their devastating consequences. Observing floods from space can make the difference: from this distant vantage point it is possible to monitor vast areas consistently, and, by leveraging multiple sensors on different satellites, it is possible to acquire a comprehensive overview on the evolution of floods at a large scale. Synthetic aperture radar (SAR) sensors, in particular, have proven extremely effective for flood monitoring, as they can operate day and night and in all weather conditions, with a highly discriminatory power. On the other hand, SAR sensors are unable to reliably detect water in some cases, the most critical being urban areas. Furthermore, flood water depth – which is a fundamental variable for emergency response and impact calculations – cannot be estimated remotely. In order to address such limitations, this study proposes a framework for estimating flood water depths and enhancing flood delineations, based on readily available topographical data. The methodology is specifically designed to accommodate, as additional inputs, masks delineating water bodies and/or no-data areas. In particular, the method relies on simple morphological arguments to expand flooded areas into no-data regions and to estimate water depths based on the terrain elevation of the boundaries between flooded and non-flooded areas. The underlying algorithm – named FLEXTH – is provided as Python code and is designed to run in an unsupervised mode in a reasonable time over areas of several hundred thousand square kilometers. This new tool aims to quantify and ultimately to reduce the impacts of floods, especially when used in synergy with the recently released Global Flood Monitoring product of the Copernicus Emergency Management Service.

The paper deals the presence of a natural source of oil, which is sometimes detected in the southeastern part of the Black Sea as part of the monitoring of oil pollution of the Black Sea using the EMSA (European Maritime Safety Agency) satellite service. With the collaboration of EMSA CLEANSEANET System and MRCC (Maritime Rescue Coordination Center) of Maritime Agency of Georgia MRCC is regularly receiving data to region possible spills pollution. The EMSA satellite service offers extensive surveillance of European waters for oil spills by using radar images acquired by Synthetic Aperture Radar (SAR) sensors on polar orbiting satellites. SAR sensors have the capability to detect oil slicks on the sea surface in darkness as well as daylight hours and to see through clouds. The analysis CleanSeaNet Alert Report possible spills, it's safe to say that we are observing a natural expression of oil on the sea surface, which is formed by the migration of oil hydrocarbons in the sedimentary complex of the southeastern part of the Black Sea, seaward of the city of Poti (Georgia). Source identified based on the analysis of multi-temporal radar satellite imagery.

This paper considers the problem of high-resolution remote sensing (RS) of the environment formalized in the terms of a nonlinear ill-posed inverse problem of estimation of the power spatial spectrum pattern (SSP) of the wavefield scattered from an extended remotely sensed scene via processing the discrete measurements of a finite number of independent realizations of the observed degraded data signals [single realization of the trajectory signal in the case of synthetic aperture radar (SAR)]. We address a new descriptive experiment design regularization (DEDR) approach to treat the SSP reconstruction problem in the uncertain RS environment that unifies the paradigms of maximum likelihood nonparametric spectral estimation, descriptive experiment design, and worst case statistical performance optimization-based regularization. Pursuing such an approach, we establish a family of the DEDR-related SSP estimators that encompass a manifold of algorithms ranging from the traditional matched filter to the modified robust adaptive spatial filtering and minimum variance beamforming methods. The theoretical study is resumed with the development of a fixed-point iterative DEDR technique that incorporates the regularizing projections onto convex solution sets into the SSP reconstruction procedures to enforce the robustness and convergence. For the imaging SAR application, the proposed DEDR approach is aimed at performing, in a single optimized processing, adaptive SAR focusing, speckle reduction and RS scene image enhancement, and accounts for the possible presence of uncertain trajectory deviations.