TY  - JOUR
A1  - Dech, Stefan
A1  - Holzwarth, Stefanie
A1  - Asam, Sarah
A1  - Andresen, Thorsten
A1  - Bachmann, Martin
A1  - Boettcher, Martin
A1  - Dietz, Andreas
A1  - Eisfelder, Christina
A1  - Frey, Corinne
A1  - Gesell, Gerhard
A1  - Gessner, Ursula
A1  - Hirner, Andreas
A1  - Hofmann, Matthias
A1  - Kirches, Grit
A1  - Klein, Doris
A1  - Klein, Igor
A1  - Kraus, Tanja
A1  - Krause, Detmar
A1  - Plank, Simon
A1  - Popp, Thomas
A1  - Reinermann, Sophie
A1  - Reiners, Philipp
A1  - Roessler, Sebastian
A1  - Ruppert, Thomas
A1  - Scherbachenko, Alexander
A1  - Vignesh, Ranjitha
A1  - Wolfmueller, Meinhard
A1  - Zwenzner, Hendrik
A1  - Kuenzer, Claudia
T1  - Potential and challenges of harmonizing 40 years of AVHRR data: the TIMELINE experience
JF  - Remote Sensing
N2  - 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.
KW  - AVHRR
KW  - Earth Observation
KW  - harmonization
KW  - time series analysis
KW  - climate related trends
KW  - automatic processing
KW  - Europe
KW  - TIMELINE
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-246134
SN  - 2072-4292
VL  - 13
IS  - 18
ER  - 
TY  - JOUR
A1  - Philipp, Marius
A1  - Dietz, Andreas
A1  - Buchelt, Sebastian
A1  - Kuenzer, Claudia
T1  - Trends in satellite earth observation for permafrost related analyses — A review
JF  - Remote Sensing
N2  - Climate change and associated Arctic amplification cause a degradation of permafrost which in turn has major implications for the environment. The potential turnover of frozen ground from a carbon sink to a carbon source, eroding coastlines, landslides, amplified surface deformation and endangerment of human infrastructure are some of the consequences connected with thawing permafrost. Satellite remote sensing is hereby a powerful tool to identify and monitor these features and processes on a spatially explicit, cheap, operational, long-term basis and up to circum-Arctic scale. By filtering after a selection of relevant keywords, a total of 325 articles from 30 international journals published during the last two decades were analyzed based on study location, spatio-
temporal resolution of applied remote sensing data, platform, sensor combination and studied environmental focus for a comprehensive overview of past achievements, current efforts, together with future challenges and opportunities. The temporal development of publication frequency, utilized platforms/sensors and the addressed environmental topic is thereby highlighted. The total
number of publications more than doubled since 2015. Distinct geographical study hot spots were revealed, while at the same time large portions of the continuous permafrost zone are still only sparsely covered by satellite remote sensing investigations. Moreover, studies related to Arctic greenhouse gas emissions in the context of permafrost degradation appear heavily underrepresented.
New tools (e.g., Google Earth Engine (GEE)), methodologies (e.g., deep learning or data fusion etc.)and satellite data (e.g., the Methane Remote Sensing LiDAR Mission (Merlin) and the Sentinel-fleet)will thereby enable future studies to further investigate the distribution of permafrost, its thermal state and its implications on the environment such as thermokarst features and greenhouse gas emission rates on increasingly larger spatial and temporal scales.
KW  - satellite remote sensing
KW  - permafrost
KW  - degradation
KW  - thaw
KW  - thermokarst
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-234198
VL  - 13
IS  - 6
ER  -