@article{DechHolzwarthAsametal.2021,
  author    = {Dech, Stefan and Holzwarth, Stefanie and Asam, Sarah and Andresen, Thorsten and Bachmann, Martin and Boettcher, Martin and Dietz, Andreas and Eisfelder, Christina and Frey, Corinne and Gesell, Gerhard and Gessner, Ursula and Hirner, Andreas and Hofmann, Matthias and Kirches, Grit and Klein, Doris and Klein, Igor and Kraus, Tanja and Krause, Detmar and Plank, Simon and Popp, Thomas and Reinermann, Sophie and Reiners, Philipp and Roessler, Sebastian and Ruppert, Thomas and Scherbachenko, Alexander and Vignesh, Ranjitha and Wolfmueller, Meinhard and Zwenzner, Hendrik and Kuenzer, Claudia},
  title     = {Potential and challenges of harmonizing 40 years of AVHRR data: the TIMELINE experience},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {18},
  issn      = {2072-4292},
  doi       = {10.3390/rs13183618},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-246134},
  year      = {2021},
  abstract  = {Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.},
  language  = {en}
}
@article{MayrKleinRutzingeretal.2021,
  author    = {Mayr, Stefan and Klein, Igor and Rutzinger, Martin and Kuenzer, Claudia},
  title     = {Systematic water fraction estimation for a global and daily surface water time-series},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {14},
  issn      = {2072-4292},
  doi       = {10.3390/rs13142675},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-242586},
  year      = {2021},
  abstract  = {Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product's performance regarding mixed water/non-water pixels by an average of 11.6\% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.},
  language  = {en}
}
@article{WagnerDrouetTeschnerWolschkeetal.2021,
  author    = {Wagner-Drouet, Eva and Teschner, Daniel and Wolschke, Christine and Sch{\"a}fer-Eckart, Kerstin and G{\"a}rtner, Johannes and Mielke, Stephan and Schreder, Martin and Kobbe, Guido and Hilgendorf, Inken and Klein, Stefan and Verbeek, Mareike and Ditschkowski, Markus and Koch, Martina and Lindemann, Monika and Schmidt, Traudel and Rascle, Anne and Barabas, Sascha and Deml, Ludwig and Wagner, Ralf and Wolff, Daniel},
  title     = {Comparison of cytomegalovirus-specific immune cell response to proteins versus peptides using an IFN-γ ELISpot assay after hematopoietic stem cell transplantation},
  series = {Diagnostics},
  volume    = {11},
  journal   = {Diagnostics},
  number    = {2},
  issn      = {2075-4418},
  doi       = {10.3390/diagnostics11020312},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-228843},
  year      = {2021},
  abstract  = {Cytomegalovirus (CMV) infection is a major cause of morbidity and mortality following hematopoietic stem cell transplantation (HSCT). Measuring CMV-specific cellular immunity may improve the risk stratification and management of patients. IFN-γ ELISpot assays, based on the stimulation of peripheral blood mononuclear cells with CMV pp65 and IE-1 proteins or peptides, have been validated in clinical settings. However, it remains unclear to which extend the T-cell response to synthetic peptides reflect that mediated by full-length proteins processed by antigen-presenting cells. We compared the stimulating ability of pp65 and IE-1 proteins and corresponding overlapping peptides in 16 HSCT recipients using a standardized IFN-γ ELISpot assay. Paired qualitative test results showed an overall 74.4\% concordance. Discordant results were mainly due to low-response tests, with one exception. One patient with early CMV reactivation and graft-versus-host disease, sustained CMV DNAemia and high CD8\(^+\) counts showed successive negative protein-based ELISpot results but a high and sustained response to IE-1 peptides. Our results suggest that the response to exogenous proteins, which involves their uptake and processing by antigen-presenting cells, more closely reflects the physiological response to CMV infection, while the response to exogenous peptides may lead to artificial in vitro T-cell responses, especially in strongly immunosuppressed patients.},
  language  = {en}
}
@article{MayrKleinRutzingeretal.2021,
  author    = {Mayr, Stefan and Klein, Igor and Rutzinger, Martin and Kuenzer, Claudia},
  title     = {Determining temporal uncertainty of a global inland surface water time series},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {17},
  issn      = {2072-4292},
  doi       = {10.3390/rs13173454},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-245234},
  year      = {2021},
  abstract  = {Earth observation time series are well suited to monitor global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90\%), consequently enhancing the product's quality with respect to its use for modeling and the geoscientific community.},
  language  = {en}
}