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In order to improve the current standard of analysis-ready Synthetic Aperture Radar (SAR) backscatter data, we introduce a machine learning-based approach to estimate the slope of the backscatter–incidence angle relationship from several backscatter statistics. The method requires information from radiometric terrain-corrected gamma nought time series and overcomes the constraints of a limited orbital coverage, as exemplified with the Sentinel-1 constellation. The derived slope estimates contain valuable information on scattering characteristics of different land cover types, allowing for the correction of strong forward-scattering effects over water bodies and wetlands, as well as moderate surface scattering effects over bare soil and sparsely vegetated areas. Comparison of the estimated and computed slope values in areas with adequate orbital coverage shows good overall agreement, with an average RMSE value of 0.1 dB/° and an MAE of 0.05 dB/°. The discrepancy between RMSE and MAE indicates the presence of outliers in the computed slope, which are attributed to speckle and backscatter fluctuations over time. In contrast, the estimated slope excels with a smooth spatial appearance. After correcting backscatter values by normalising them to a certain reference incidence angle, orbital artefacts are significantly reduced. This becomes evident with differences up to 5 dB when aggregating the normalised backscatter measurements over certain time periods to create spatially seamless radar backscatter composites. Without being impacted by systematic differences in the illumination and physical properties of the terrain, these composites constitute a valuable foundation for land cover and land use mapping, as well as bio-geophysical parameter retrieval.

Surface soil moisture (SSM) has proven to be an important variable for the yield prediction of main crops like maize and wheat, but its value for spring barley, the third most cultivated crop in Europe, has not yet been evaluated. This study assesses how much of spring barley yield variability can be explained by the commonly used model and satellite-based global SSM products ERA5 SWVL1 and H SAF. A Feed Forward Neural Network, SSM time series, and reference yield data are used to predict spring barley yield at NUTS level for Austria, Czechia, and Germany. A random train-test split is used to assess the explained variability and a cross-validation at the NUTS level for the spatial evaluation. The results indicate the following: (1) ERA5 SWVL1 achieved an R2 of 0.37, H SAF an R2 of 0.33; (2) Both products achieved the lowest RMSE and MAE in Czechia, high RMSE and MAE values are observed in Eastern Germany. (3) ERA5 SWVL1 performed better in areas with low sensitivity for microwaves like the Alpine region, but both products achieved similar results in 80% of the NUTS regions. These findings contribute to better utilization of SSM and more accurate yield predictions for spring barley and similar crops.

We present a new perspective on Earth’s land surface, providing a normalised microwave backscatter map from spaceborne Synthetic Aperture Radar (SAR) observations. The Sentinel-1 Global Backscatter Model (S1GBM) describes Earth for the period 2016–17 by the mean C-band radar cross section in VV- and VH-polarisation at a 10 m sampling. We processed 0.5 million Sentinel-1 scenes totalling 1.1 PB and performed semi-automatic quality curation and backscatter harmonisation related to orbit geometry effects. The overall mosaic quality excels (the few) existing datasets, with minimised imprinting from orbit discontinuities and successful angle normalisation in large parts of the world. Regions covered by only one or two Sentinel-1 orbits remain challenging, owing to insufficient angular variation and not yet perfect sub-swath thermal noise correction. Supporting the design and verification of upcoming radar sensors, the obtained S1GBM data potentially also serve land cover classification and determination of vegetation and soil states. Here, we demonstrate, as an example of its potential use, the mapping of permanent water bodies and evaluate against the Global Surface Water benchmark. Measurement(s) C-band radar backscatter • extent of permanent water bodies Technology Type(s) Synthetic Aperture Radar (SAR) • Synthetic Aperture Radar (SAR) normalised mosaic Factor Type(s) radar backscatter Sample Characteristic - Environment land Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16432983

To ensure future food security, improved agricultural management approaches are required. For many of those applications, precise knowledge of the distribution of crop types is essential. Various machine and deep learning models have been used for automated crop classification using microwave remote sensing time series. However, the application of these approaches on a large spatial and temporal scale is barely investigated. In this study, the performance of two frequently used algorithms, Long Short-Term Memory (LSTM) networks and Random Forest (RF), for crop classification based on Sentinel-1 time series and meteorological data on a large spatial and temporal scale is assessed. For data from Austria, the Netherlands, and France and the years 2015–2019, scenarios with different spatial and temporal scales were defined. To quantify the complexity of these scenarios, the Fisher Discriminant measurement F1 (FDR1) was used. The results demonstrate that both classifiers achieve similar results for simple classification tasks with low FDR1 values. With increasing FDR1 values, however, LSTM networks outperform RF. This suggests that the ability of LSTM networks to learn long-term dependencies and identify the relation between radar time series and meteorological data becomes increasingly important for more complex applications. Thus, the study underlines the importance of deep learning models, including LSTM networks, for large-scale applications.

The Sentinel-1 Synthetic Aperture Radar (SAR) satellites allow global monitoring of the Earth’s land surface with unprecedented spatio-temporal coverage. Yet, implementing large-scale monitoring capabilities is a challenging task given the large volume of data from Sentinel-1 and the complex algorithms needed to convert the SAR intensity data into higher-level geophysical data products. While on-demand processing solutions have been proposed to cope with the petabyte-scale data volumes, in practice many applications require preprocessed datacubes that permit fast access to multi-year time series and image stacks. To serve near-real-time as well as offline land monitoring applications, we have created a Sentinel-1 backscatter datacube for all continents (except Antarctica) that is constantly being updated and maintained to ensure consistency and completeness of the data record over time. In this technical note, we present the technical specifications of the datacube, means of access and analysis capabilities, and its use in scientific and operational applications.

Due to the remote location and the extreme climate, monitoring stations in Arctic rivers such as Lena in Siberia have been decreasing through time. Every year, after a long harsh winter, the accumulated snow on the Lena watershed melts, leading to the major annual spring flood event causing heavy transport of sediments, organic carbon, and trace metals, both into as well as within the delta. This study aims to analyze the hydrodynamic processes of the spring flood taking place every year in the Lena Delta. Thus, a combination of remote sensing techniques and hydrodynamic modeling methodologies is used to overcome limitations caused by missing ground-truth data. As a test site for this feasibility study, the outlet of the Lena River to its delta was selected. Lena Delta is an extensive wetland spanning from northeast Siberia into the Arctic Ocean. Spaceborne Synthetic Aperture Radar (SAR) data of the TerraSAR-X/TanDEM-X satellite mission served as input for the hydrodynamic modeling software HEC-RAS. The model resulted in inundation areas, flood depths, and flow velocities. The model accuracy assessed by comparing the multi-temporal modeled inundation areas with the satellite-derived inundation areas ranged between 65 and 95%, with kappa coefficients ranging between 0.78 and 0.97, showing moderate to almost perfect levels of agreement between the two inundation boundaries. Modeling results of high flow discharges show a better agreement with the satellite-derived inundation areas compared to that of lower flow discharges. Overall, the remote-sensing-based hydrodynamic modeling succeeded in indicating the increase and decrease in the inundation areas, flood depths, and flow velocities during the annual flood events.

We present the generation and validation of an updated version of the TanDEM-X digital elevation model (DEM) of Antarctica: the TanDEM-X PolarDEM 90 m of Antarctica. Improvements compared to the global TanDEM-X DEM version comprise filling gaps with newer bistatic synthetic aperture radar (SAR) acquisitions of the TerraSAR-X and TanDEM-X satellites, interpolation of smaller voids, smoothing of noisy areas, and replacement of frozen or open sea areas with geoid undulations. For the latter, a new semi-automatic editing approach allowed for the delineation of the coastline from DEM and amplitude data. Finally, the DEM was transformed into the cartographic Antarctic Polar Stereographic projection with a homogeneous metric spacing in northing and easting of 90 m. As X-band SAR penetrates the snow and ice pack by several meters, a new concept for absolute height adjustment was set up that relies on areas with stable penetration conditions and on ICESat (Ice, Cloud, and land Elevation Satellite) elevations. After DEM generation and editing, a sophisticated height error characterization of the whole Antarctic continent with ICESat data was carried out, and a validation over blue ice achieved a mean vertical height error of just −0.3 m ± 2.5 m standard deviation. The filled and edited Antarctic TanDEM-X PolarDEM 90 m is outstanding due to its accuracy, homogeneity, and coverage completeness. It is freely available for scientific purposes and provides a high-resolution data set as basis for polar research, such as ice velocity, mass balance estimation, or orthorectification.

The vast majority of peripheral nerve sheath tumors derive from the Schwann cell lineage and comprise diverse histological entities ranging from benign schwannomas and neurofibromas to high-grade malignant peripheral nerve sheath tumors (MPNST), each with several variants. There is increasing evidence for methylation profiling being able to delineate biologically relevant tumor groups even within the same cellular lineage. Therefore, we used DNA methylation arrays for methylome- and chromosomal profile-based characterization of 171 peripheral nerve sheath tumors. We analyzed 28 conventional high-grade MPNST, three malignant Triton tumors, six low-grade MPNST, four epithelioid MPNST, 33 neurofibromas (15 dermal, 8 intraneural, 10 plexiform), six atypical neurofibromas, 43 schwannomas (including 5 NF2 and 5 schwannomatosis associated cases), 11 cellular schwannomas, 10 melanotic schwannomas, 7 neurofibroma/schwannoma hybrid tumors, 10 nerve sheath myxomas and 10 ganglioneuromas. Schwannomas formed different epigenomic subgroups including a vestibular schwannoma subgroup. Cellular schwannomas were not distinct from conventional schwannomas. Nerve sheath myxomas and neurofibroma/schwannoma hybrid tumors were most similar to schwannomas. Dermal, intraneural and plexiform neurofibromas as well as ganglioneuromas all showed distinct methylation profiles. Atypical neurofibromas and low-grade MPNST were indistinguishable with a common methylation profile and frequent losses of CDKN2A . Epigenomic analysis finds two groups of conventional high-grade MPNST sharing a frequent loss of neurofibromin. The larger of the two groups shows an additional loss of trimethylation of histone H3 at lysine 27 (H3K27me3). The smaller one retains H3K27me3 and is found in spinal locations. Sporadic MPNST with retained neurofibromin expression did not form an epigenetic group and most cases could be reclassified as cellular schwannomas or soft tissue sarcomas. Widespread immunohistochemical loss of H3K27me3 was exclusively seen in MPNST of the main methylation cluster, which defines it as an additional useful marker for the differentiation of cellular schwannoma and MPNST.

Recently, we described a machine learning approach for classification of central nervous system tumors based on the analysis of genome-wide DNA methylation patterns [6]. Here, we report on DNA methylation-based central nervous system (CNS) tumor diagnostics conducted in our institution between the years 2015 and 2018. In this period, more than 1000 tumors from the neurosurgical departments in Heidelberg and Mannheim and more than 1000 tumors referred from external institutions were subjected to DNA methylation analysis for diagnostic purposes. We describe our current approach to the integrated diagnosis of CNS tumors with a focus on constellations with conflicts between morphological and molecular genetic findings. We further describe the benefit of integrating DNA copy-number alterations into diagnostic considerations and provide a catalog of copy-number changes for individual DNA methylation classes. We also point to several pitfalls accompanying the diagnostic implementation of DNA methylation profiling and give practical suggestions for recurring diagnostic scenarios.

BackgroundPrimary cytoreductive surgery followed by chemotherapy has been considered standard management for patients with advanced ovarian cancer over decades. An alternative approach of interval debulking surgery following neoadjuvant chemotherapy was subsequently reported by two randomized phase III trials (EORTC‐GCG, CHORUS), which were criticized owing to important limitations, especially regarding the rate of complete resection.Primary ObjectiveTo clarify the optimal timing of surgical therapy in advanced ovarian cancer.Study HypothesisPrimary cytoreductive surgery is superior to interval cytoreductive surgery following neoadjuvant chemotherapy for overall survival in patients with advanced ovarian cancer.Trial DesignTRUST is an international open, randomized, controlled multi-center trial investigating overall survival after primary cytoreductive surgery versus neoadjuvant chemotherapy and subsequent interval cytoreductive surgery in patients with FIGO stage IIIB–IVB ovarian, tubal, and peritoneal carcinoma. To guarantee adequate surgical quality, participating centers need to fulfill specific quality assurance criteria (eg, ≥50% complete resection rate in upfront surgery for FIGO IIIB–IVB patients, ≥36 debulking-surgeries/year) and agree to independent audits by TRUST quality committee delegates. Patients in the primary cytoreductive surgery arm undergo surgery followed by 6 cycles of platinum-based chemotherapy, whereas patients in the interval cytoreductive surgery arm undergo 3 cycles of neoadjuvant chemotherapy after histologic confirmation of the disease, followed by interval cytoreductive surgery and subsequently, 3 cycles of platinum-based chemotherapy. The intention of surgery for both groups is complete tumor resection according to guideline recommendations.Major Inclusion/Exclusion CriteriaMajor inclusion criteria are suspected or histologically confirmed, newly diagnosed invasive epithelial ovarian cancer, fallopian tube carcinoma, or primary peritoneal carcinoma FIGO stage IIIB–IVB (IV only if resectable metastasis). Major exclusion criteria are non-epithelial ovarian malignancies and borderline tumors; prior chemotherapy for ovarian cancer; or abdominal/pelvic radiotherapy.Primary EndpointOverall survival.Sample Size772 patients.Estimated Dates for Completing Accrual and Presenting ResultsAccrual completion approximately mid-2019, results are expected after 5 years' follow-up in 2024.Trial Registration NCT02828618.