@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{UereyenBachoferKuenzer2022,
  author    = {Uereyen, Soner and Bachofer, Felix and Kuenzer, Claudia},
  title     = {A framework for multivariate analysis of land surface dynamics and driving variables — a case study for Indo-Gangetic river basins},
  series = {Remote Sensing},
  volume    = {14},
  journal   = {Remote Sensing},
  number    = {1},
  issn      = {2072-4292},
  doi       = {10.3390/rs14010197},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-255295},
  year      = {2022},
  abstract  = {The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI.},
  language  = {en}
}