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Synthetic Aperture Radar (SAR) satellite images are used to monitor Arctic sea ice, with systematic data records dating back to 1991. We propose a semi-supervised classification method that separates open water from sea ice and can utilise ERS-1/2, Envisat ASAR, RADARSAT-2 and Sentinel-1 SAR images. The classification combines automatic segmentation with a manual segment selection stage. The segmentation algorithm requires only the backscatter intensities and incidence angle values as input, therefore can be used to establish a consistent decadal sea ice record. In this study we investigate the sea ice conditions in two Svalbard fjords, Kongsfjorden and Rijpfjorden. Both fjords have a seasonal ice cover, though Rijpfjorden has a longer sea ice season. The satellite image dataset has weekly to daily records from 2002 until now, and less frequent records between 1991 and 2002. Time overlap between different sensors is investigated to ensure consistency in the reported sea ice cover. The classification results have been compared to high-resolution SAR data as well as in-situ measurements and sea ice maps from Ny-Ålesund. For both fjords the length of the sea ice season has shortened since 2002 and for Kongsfjorden the maximum sea ice coverage is significantly lower after 2006.

The microwave radiometers (MWRs) on board the European Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2) and Envisat provide a continuous time series of brightness temperature observations between 1991 and 2012. Here we report on a new total column water vapour (TCWV) and wet tropospheric correction (WTC) dataset that builds on this time series. We use a one-dimensional variational approach to derive TCWV from MWR observations and ERA-Interim background information. A particular focus of this study lies on the intercalibration of the three different instruments, which is performed using constraints on liquid water path (LWP) and TCWV. Comparing our MWR-derived time series of TCWV against TCWV derived from Global Navigation Satellite System (GNSS) we find that the MWR-derived TCWV time series is stable over time. However, observations potentially affected by precipitation show a degraded performance compared to precipitation-free observations in terms of the accuracy of retrieved TCWV. An analysis of WTC shows further that the retrieved WTC is superior to purely ERA-Interim-derived WTC for all satellites and for the entire time series. Even compared to the European Space Agency's (ESA) operational WTC retrievals, which incorporate in addition to MWR additional observational data, the here-described dataset shows improvements in particular for the mid-latitudes and for the two earlier satellites, ERS-1 and ERS-2. The dataset is publicly available under doi:10.5676/DWD_EMIR/V001 (Bennartz et al., 2016).

We performed an extensive analysis of C-band SAR datasets provided by the European Space Agency (ESA) satellites ERS-1/2, Envisat ASAR, and Sentinel-1 in the period 1992–2020 aiming at reconstructing the multi-decadal spatial and temporal evolution of the surface displacements at the Brienz/Brinzauls landslide complex, located in canton Graubünden (Switzerland). To this end, we analyzed about 1000 SAR images by applying differential interferometry (InSAR), multitemporal stacking, and persistent scatterer interferometry (PSI) approaches. Moreover, we jointly considered digital image correlation (DIC) on high-resolution multi-temporal digital terrain models (DTM) generated from airborne surveys and InSAR results to compute 3-D surface deformation fields. The extensive network of GNSS stations across the Brienz landslide complex allowed us to extensively validate the deformation results obtained in our remote sensing analyses. Here, we illustrate the limitations occurring when relying on InSAR and/or PSI measurements for the analysis and interpretation of complex landslide scenarios, especially in cases of relevant spatial and temporal heterogeneities of the deformation field. The joint use of InSAR and DIC can deliver a better picture of the evolution of the deformation field, however, not for all displacement components. Since InSAR, PSI and DIC measurements are nowadays routinely used in the framework of local investigations, as well as in regional, national, and/or continental monitoring programs, our results are of major importance for users aiming at a comprehensive understanding of these datasets in landslide scenarios.

The CHaracterising ExOPlanet Satellite (CHEOPS) is a partnership between the European Space Agency and Switzerland with important contributions by 10 additional ESA member States. It is the first S-class mission in the ESA Science Programme. CHEOPS has been flying on a Sun-synchronous low Earth orbit since December 2019, collecting millions of short-exposure images in the visible domain to study exoplanet properties. A small yet increasing fraction of CHEOPS images show linear trails caused by resident space objects crossing the instrument field of view. CHEOPS’ orbit is indeed particularly favourable to serendipitously detect objects in its vicinity as the spacecraft rarely enters the Earth’s shadow, sits at an altitude of 700 km, and observes with moderate phase angles relative to the Sun. This observing configuration is quite powerful, and it is complementary to optical observations from the ground. To characterize the population of satellites and orbital debris observed by CHEOPS, all and every science images acquired over the past 3 years have been scanned with a Hough transform algorithm to identify the characteristic linear features that these objects cause on the images. Thousands of trails have been detected. This statistically significant sample shows interesting trends and features such as an increased occurrence rate over the past years as well as the fingerprint of the Starlink constellation. The cross-matching of individual trails with catalogued objects is underway as we aim to measure their distance at the time of observation and deduce the apparent magnitude of the detected objects. As space agencies and private companies are developing new space-based surveillance and tracking activities to catalogue and characterize the distribution of small debris, the CHEOPS experience is timely and relevant. With the first CHEOPS mission extension currently running until the end of 2026, and a possible second extension until the end of 2029, the longer time coverage will make our dataset even more valuable to the community, especially for characterizing objects with recurrent crossings.

Sea ice volume's significant interannual variability requires long-term series of observations to identify trends in its evolution. Despite improvements in sea ice thickness estimations from altimetry during the past few years thanks to CryoSat-2 and ICESat-2, former ESA radar altimetry missions such as the Environmental Satellite (Envisat) and especially the European Remote-Sensing Satellite (ERS-1 and ERS-2) have remained under-exploited so far. Although solutions have already been proposed to ensure continuity of measurements between CryoSat-2 and Envisat, there is no time series integrating ERS. The purpose of this study is to extend the Arctic radar freeboard time series back to 1995. The difficulty in handling ERS measurements comes from a technical issue known as the pulse blurring effect, altering the radar echoes over sea ice and the resulting surface height estimates. Here we present and apply a correction for this pulse blurring effect. To ensure consistency of the CryoSat-2, Envisat and ERS-2 time series, a multiparameter neural-network-based method to calibrate Envisat against CryoSat-2 and ERS-2 against Envisat is presented. The calibration is trained on the discrepancies observed between the altimeter measurements during the mission-overlap periods and a set of parameters characterizing the sea ice state. Monthly radar freeboards are provided with uncertainty estimations based on a Monte Carlo approach to propagate the uncertainties all along the processing chain, including the neural network. Comparisons of corrected radar freeboards during overlap periods reveal good agreement between the missions, with a mean bias of 0.30 cm and a standard deviation of 9.7 cm for Envisat and CryoSat-2 and a 0.20 cm bias and a standard deviation of 3.8 cm for ERS-2 and Envisat. The monthly corrected radar freeboards obtained from Envisat and ERS-2 are then validated by comparison with several independent datasets such as airborne, mooring, direct-measurement and other altimeter products. Except for two datasets, comparisons lead to correlations ranging from 0.41 to 0.94 for Envisat and from 0.60 to 0.74 for ERS-2. The study finally provides radar freeboard estimation for winters from 1995 to 2021 (from the ERS-2 mission to CryoSat-2).

Colored dissolved organic matter (CDOM) in marine environments impacts primary production due to its absorption effect on the photosynthetically active radiation. In coastal seas, CDOM originates from terrestrial sources predominantly and causes spatial and temporal changing patterns of light absorption which should be considered in marine biogeochemical models. We propose a model approach in which Earth Observation (EO) products are used to define boundary conditions of CDOM concentrations in an ecosystem model of the Baltic Sea. CDOM concentrations in riverine water derived from EO products serve as forcing for the ecosystem model. For this reason, we introduced an explicit CDOM state variable in the model.We show that the light absorption by CDOM in the model can be improved considerably in comparison to approaches where CDOM is estimated from salinity. The model performance increases especially with respect to spatial CDOM patterns due to the consideration of single river properties. A prerequisite is high-quality CDOM data with sufficiently high spatial resolution which can be provided by the new generation of ESA satellite sensor systems (Sentinel 2 MSI and Sentinel 3 OLCI). Such data are essential, especially when local differences in riverine CDOM concentrations exist.

Over the past three decades, there has been a 4.5-fold increase in the loss of ice from the Greenland and Antarctic Ice Sheets, resulting in an enhanced contribution to global sea level rise. Accurately tracking these changes in ice mass requires comprehensive, long-term measurements, which are only feasible from space. Satellite radar altimetry provides the longest near-continuous record of ice sheet surface elevation and volume change, dating back to the launch of ERS-1 in 1991, and maintained through the successive ERS-2, Envisat, CryoSat-2, and Sentinel-3 missions. To reliably constrain multi-decadal trends in ice sheet imbalance, and to place current observations within a longer-term context, requires continued efforts to optimise the processing of data acquired by the older historical missions and to evaluate the accuracy of these measurements. Here, we present new ERS-1, ERS-2, and Envisat altimeter datasets, comprising measurements of ice sheet elevation spanning two decades. This new observational record has been derived using consistent and improved retrieval methods, including enhancements to key Level-2 processing steps such as waveform retracking and echo relocation. Through comparison with independent airborne datasets, we undertake a comprehensive assessment of the accuracy of these measurements and demonstrate the improvements delivered relative to previously available products. With this updated processing, we find that all missions achieve sub-metre (<0.85 m) median elevation biases and dispersion of elevation differences relative to coincident airborne data. These new along-track datasets will be of benefit to a broad range of applications, including the quantification of ice sheet mass imbalance, investigations of the processes driving contemporary ice loss, and the constraint of numerical ice sheet models.

High-altitude lakes are sensitive indicators of climate change, reflecting the hydrological impacts of global warming in alpine regions. This study investigates the long-term dynamics of the water level and surface area of Lake Karakul on the eastern Pamir Plateau from 1991 to 2020 using integrated satellite altimetry data from ERS-1/2, ICESat, and CryoSat-2. A multi-source fusion approach was applied to generate a continuous time series, overcoming the temporal limitations of individual missions. The results show a significant upward trend in both water level and area, with an average lake level rise of 8 cm per year and a surface area increase of approximately 13.2 km2 per decade. The two variables exhibit a strong positive correlation (r = 0.84), and the Mann–Kendall test confirms the significance of the trends at the 95% confidence level. The satellite-derived water levels show high reliability, with an RMSE of 0.15 m when compared to reference data. These changes are primarily attributed to increased glacial meltwater inflow, driven by regional warming and accelerated glacier retreat, with glacier area shrinking by over 10% from 1978 to 2001 in the eastern Pamir. This study highlights the value of integrating multi-sensor satellite data for monitoring inland waters and provides critical insights into the climatic drivers of hydrological change in high-altitude endorheic basins.

Several practical applications like disaster detection, remote surveillance, object recognition using remote sensing satellite images, object monitoring and tracking using radar images etc. essentially require real-time image segmentation. In these applications computational complexity of the algorithm play a vital role along with accuracy. In this work image segmentation is dealt as a clustering problem and a bio-inspired algorithm based on the behavior of ‘Roller Dung Beetles (RDB)’ is proposed to determine effective solutions. The beauty of this proposed RDB Clustering architecture is its lower computational complexity O(N). The software implementation of the proposed algorithm is carried out in MATLAB environment and a hardware architecture is developed in Verilog HDL using Modelsim, Xilinx ISE for FPGA environment. The architecture has a comparison free sorting module, two data storage modules and a parallel threshold comparator unit, all of which use fewer mathematical operations. The performance of the proposed architecture is validated on many synthetic and standard benchmark color images. Further application of the proposed architecture is carried out for real-time segmentation of 8 NASA LANDSAT / ESA satellite images. Performance comparison has been carried out with other existing architectures based on Artificial Immune System (AIS), Genetic, K-means clustering, CNN etc. Simulation results reveal that the proposed RDBC architecture is 42% faster than the K-Means implementation with a clock frequency of 230.52MHz with an increased PSNR of 6.825% and SSIM of 15.55%. Statistical analysis and silhouette index also confirms the superiority of new clustering architecture over existing implementations.Compared to CNN the RDBC architecture is economical both in terms of lower chip-area and power consumption.

The aim of described project is to study the possibilities and constrains of using ERS-1, ERS-2 and Envisat radar data to monitor the temporal growth of the urban areas. The analysis was done on the example of Krakow city (Poland). In order to perform this task the fifty two archival ERS-1, ERS-2 and Envisat SAR images acquired between years 1992 and 2010 were selected. The images were divided into five two-year time intervals. Each interval contain data stack of eight or nine SAR images. For each data stack the analysis of coherent scatterers was done. Several methods of coherent scatterer's identification were tested and only the best scatterers were chosen for further analysis. The changes in the number of coherent scatterers within studied period of time derived information about growth of urban areas. Additionally, the study of maps of coherent scatterers allowed to identify regions of most and least intensive urban growth.