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  - Mayr, Stefan
A1  - Kuenzer, Claudia
A1  - Gessner, Ursula
A1  - Klein, Igor
A1  - Rutzinger, Martin
T1  - Validation of earth observation time-series: a review for large-area and temporally dense land surface products
JF  - Remote Sensing
N2  - Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided.
KW  - accuracy
KW  - error estimation
KW  - global
KW  - intercomparison
KW  - remote sensing
KW  - uncertainty
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193202
SN  - 2072-4292
VL  - 11
IS  - 22
ER  -