Humboldt-Universität zu Berlin
Freie Universität Berlin
Starting date: 1st November 2017
What is a T-Profile?
Looking outside of one's own (academic) box is part of the program of the Geo.X Young Academy: Since innovative solutions to complex problems require several disciplines, it is beneficial for problem solvers to speak different "languages" to ease understanding and allow for varying points of view. In order to attain better insight not only into your professional experience and academic knowledge, but also your interfacing expertise, interdisciplinary skills and projects. The vertical bar must be filled with relevant information about your academic studies. In the horizontal bar, you can map competencies and activities that transcend or do not relate directly to your field of study.
Project: Near-Real Time Derivation of Land Surface Phenology using Sentinel Data: the FORCE-NRT approach
Space-based Earth observation is a prerequisite for sustainable land management. Nowadays, ESA provides data at unprecedented spatial, spectral and temporal resolution, acquiring several TB every single day - which increasingly need to be processed in near-real time (NRT), e.g. using the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE).
With Sentinel-2, it is for the first time possible to derive land surface phenology (LSP - metrics describing seasonal cycles of land surfaces) for a single season at high spatial resolution. However, deriving LSP did not keep pace with the trend towards NRT information as it is currently only estimated retrospectively.
The goal of this project is to develop a software component to derive LSP in NRT by updating models and metrics whenever new acquisitions become available. Candidate dates will be identified whenever a remarkable event (e.g. inflection point) is found in the time series. The following acquisitions will be used to build confidence through time. As cloud-free observations cannot be guaranteed, a more experimental part of my research will be to integrate optical (Sentinel-2) and radar (Sentinel-1) data streams. The potential and limits of estimating LSP from heterogeneous data sources in different regions of the world will be assessed.