elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Normalizing Sentinel-1 orbits for combined time series applications in forested areas

Zehner, Markus and Schellenberg, Konstantin and Dubois, Clémence and Hese, Sören and Brenning, Alexander and Thiel, Christian and Baade, Jussi and Schmullius, C. (2022) Normalizing Sentinel-1 orbits for combined time series applications in forested areas. ESA Living Planet Symposium, 23.05.-27.05.2022, Bonn, Deutschland.

Full text not available from this repository.

Abstract

Forests are one of the largest above ground CO2 storage landcovers and therefore, as essential climate variables, an important asset to quantify and monitor. The current availability of more than 7 years of data from the Copernicus program is shifting analysis methods from single time steps to multitemporal and time-series studies with an unprecedented spatial and temporal resolution. In particular, the SAR sensor onboard of Sentinel-1 (S1) A and B enables the estimation of phenologically active phases within days and weeks [1], measure of seasonality of different forest types [2, 3, 5] or fallen trees [4] at regular interval through a weather and daylight independency. A high repetition rate is especially important in the detection of change points (beginning/end of growing season, or abrupt and permanent changes in land cover through, for example, logging). The possible resolution of changes in the observed area depends on the temporal sampling rate. S1 offers the possibility to increase the temporal sampling rate by using information from the twin satellites, reducing the repeat rate to 6 days over Europe. Additionally, overlapping orbits can be employed to increase data availability while including different viewing directions, resulting in one image roughly every 1.5 days. As recent examples of S1 time series studies, Soudani et al. [1] and Frison et al. [5] combined ascending and descending orbits for increased temporal sampling, relying on the fact that the sensor incidence angles of both orbits are similar throughout the study area. However, the combination of sensors and orbits as described above introduces systemic shifts in the data, if done without bias correction. We would like to highlight three mechanisms that result in such shifts.

Item URL in elib:https://elib.dlr.de/186745/
Document Type:Conference or Workshop Item (Poster)
Title:Normalizing Sentinel-1 orbits for combined time series applications in forested areas
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Zehner, MarkusMarkus.Zehner (at) uni-jena.deUNSPECIFIED
Schellenberg, Konstantinkonstantin.schellenberg (at) uni-jena.deUNSPECIFIED
Dubois, Clémenceclemence.dubois (at) uni-jena.deUNSPECIFIED
Hese, Sörensoeren.hese (at) uni-jena.deUNSPECIFIED
Brenning, Alexanderalexander.brenning (at) uni-jena.deUNSPECIFIED
Thiel, ChristianChristian.Thiel (at) dlr.deUNSPECIFIED
Baade, JussiFriedrich-Schiller-Universität JenaUNSPECIFIED
Schmullius, C.c.schmullius (at) uni-jena.deUNSPECIFIED
Date:2022
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Sentinel-1, DTM, Processing
Event Title:ESA Living Planet Symposium
Event Location:Bonn, Deutschland
Event Type:international Conference
Event Dates:23.05.-27.05.2022
Organizer:ESA
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Exploration
DLR - Research area:Raumfahrt
DLR - Program:R EW - Space Exploration
DLR - Research theme (Project):R - QS-Project_04 Big-Data-Plattform
Location: Jena
Institutes and Institutions:Institute of Data Science > Data Acquisition and Mobilisation
Deposited By: Thiel, Christian
Deposited On:14 Jun 2022 09:22
Last Modified:14 Jun 2022 09:22

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.