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  - Reinermann, Sophie
A1  - Gessner, Ursula
A1  - Asam, Sarah
A1  - Kuenzer, Claudia
A1  - Dech, Stefan
T1  - The Effect of Droughts on Vegetation Condition in Germany: An Analysis Based on Two Decades of Satellite Earth Observation Time Series and Crop Yield Statistics
JF  - Remote Sensing
N2  - Central Europe experienced several droughts in the recent past, such as in the year 2018, which was characterized by extremely low rainfall rates and high temperatures, resulting in substantial agricultural yield losses. Time series of satellite earth observation data enable the characterization of past drought events over large temporal and spatial scales. Within this study, Moderate Resolution Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) 250 m time series were investigated for the vegetation periods of 2000 to 2018. The spatial and temporal development of vegetation in 2018 was compared to other dry and hot years in Europe, like the drought year 2003. Temporal and spatial inter- and intra-annual patterns of EVI anomalies were analyzed for all of Germany and for its cropland, forest, and grassland areas individually. While vegetation development in spring 2018 was above average, the summer months of 2018 showed negative anomalies in a similar magnitude as in 2003, which was particularly apparent within grassland and cropland areas in Germany. In contrast, the year 2003 showed negative anomalies during the entire growing season. The spatial pattern of vegetation status in 2018 showed high regional variation, with north-eastern Germany mainly affected in June, north-western parts in July, and western Germany in August. The temporal pattern of satellite-derived EVI deviances within the study period 2000-2018 were in good agreement with crop yield statistics for Germany. The study shows that the EVI deviation of the summer months of 2018 were among the most extreme in the study period compared to other years. The spatial pattern and temporal development of vegetation condition between the drought years differ.
KW  - drought
KW  - time series
KW  - heat wave
KW  - agriculture
KW  - climate extremes
KW  - climate change
KW  - crop statistics
KW  - MODIS
KW  - Germany
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-225165
VL  - 11
IS  - 15
ER  -