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Potential and challenges of harmonizing 40 years of AVHRR data: the TIMELINE experience

Please always quote using this URN: urn:nbn:de:bvb:20-opus-246134
  • 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 climateEarth 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.show moreshow less

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Author: Stefan Dech, Stefanie Holzwarth, Sarah Asam, Thorsten Andresen, Martin Bachmann, Martin Boettcher, Andreas Dietz, Christina Eisfelder, Corinne Frey, Gerhard Gesell, Ursula Gessner, Andreas Hirner, Matthias Hofmann, Grit Kirches, Doris Klein, Igor Klein, Tanja Kraus, Detmar Krause, Simon Plank, Thomas Popp, Sophie Reinermann, Philipp Reiners, Sebastian Roessler, Thomas Ruppert, Alexander Scherbachenko, Ranjitha Vignesh, Meinhard Wolfmueller, Hendrik Zwenzner, Claudia Kuenzer
URN:urn:nbn:de:bvb:20-opus-246134
Document Type:Journal article
Faculties:Philosophische Fakultät (Histor., philolog., Kultur- und geograph. Wissensch.) / Institut für Geographie und Geologie
Language:English
Parent Title (English):Remote Sensing
ISSN:2072-4292
Year of Completion:2021
Volume:13
Issue:18
Article Number:3618
Source:Remote Sensing (2021) 13:18, 3618. https://doi.org/10.3390/rs13183618
DOI:https://doi.org/10.3390/rs13183618
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 52 Astronomie / 526 Mathematische Geografie
5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Tag:AVHRR; Earth Observation; Europe; TIMELINE; automatic processing; climate related trends; harmonization; time series analysis
Release Date:2023/05/25
Date of first Publication:2021/09/10
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International