@phdthesis{Dietz2013,
  author    = {Dietz, Andreas},
  title     = {Central Asian Snow Cover Characteristics between 1986 and 2012 derived from Time Series of Medium Resolution Remote Sensing Data},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-101221},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2013},
  abstract  = {The eminent importance of snow cover for climatic, hydrologic, anthropogenic, and economic reasons has been widely discussed in scientific literature. Up to 50\% of the Northern Hemisphere is covered by snow at least temporarily, turning snow to the most prevalent land cover types at all. Depending on regular precipitation and temperatures below freezing point it is obvious that a changing climate effects snow cover characteristics fundamentally. Such changes can have severe impacts on local, national, and even global scale. The region of Central Asia is not an exception from this general rule, but are the consequences accompanying past, present, and possible future changes in snow cover parameters of particular importance. Being characterized by continental climate with hot and dry summers most precipitation accumulates during winter and spring months in the form of snow. The population in this 4,000,000 km² vast area is strongly depending on irrigation to facilitate agriculture. Additionally, electricity is often generated by hydroelectric power stations. A large proportion of the employed water originates from snow melt during spring months, implying that changes in snow cover characteristics will automatically affect both the total amount of obtainable water and the time when this water becomes available. The presented thesis explores the question how the spatial extent of snow covered surface has evolved since the year 1986. This investigation is based on the processing of medium resolution remote sensing data originating from daily MODIS and AVHRR sensors, thus forming a unique approach of snow cover analysis in terms of temporal and spatial resolution. Not only duration but also onset and melt of snow coverage are tracked over time, analyzing for systematic changes within this 26 years lasting time span. AVHRR data are processed from raw Level 1B orbit data to Level 3 thematic snow cover products. Both, AVHRR and MODIS snow maps undergo a further post-processing, producing daily full-area mosaics while completely eliminating inherent cloud cover. Snow cover parameters are derived based on these daily and cloud-free time series, allowing for a detailed analysis of current status and changes. The results confirm the predictions made by coarse resolution predictions from climate models: Central Asian snow cover is changing, posing new challenges for the ecosystem and future water supply. The changes, however, are not aimed at only one direction. Regions with decreasing snow cover exist as well as those where the duration of snow cover increases. A shift towards earlier snow cover start and melt can be observed, posing a serious challenge to water management authorities due to a changed runoff regime.},
  subject      = {Zentralasien},
  language  = {en}
}
@article{DietzConradKuenzeretal.2014,
  author    = {Dietz, Andreas J. and Conrad, Christopher and Kuenzer, Claudia and Gesell, Gerhard and Dech, Stefan},
  title     = {Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data},
  series = {Remote Sensing},
  volume    = {6},
  journal   = {Remote Sensing},
  number    = {12},
  issn      = {2072-4292},
  doi       = {10.3390/rs61212752},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-114470},
  pages     = {12752-12775},
  year      = {2014},
  abstract  = {Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of similar to 4 Mio km(2). The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500-1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation.},
  language  = {en}
}
@article{ReinersAsamFreyetal.2021,
  author    = {Reiners, Philipp and Asam, Sarah and Frey, Corinne and Holzwarth, Stefanie and Bachmann, Martin and Sobrino, Jose and G{\"o}ttsche, Frank-M. and Bendix, J{\"o}rg and Kuenzer, Claudia},
  title     = {Validation of AVHRR Land Surface Temperature with MODIS and in situ LST — a TIMELINE thematic processor},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {17},
  issn      = {2072-4292},
  doi       = {10.3390/rs13173473},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-246051},
  year      = {2021},
  abstract  = {Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed.},
  language  = {en}
}
@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}
}