The Living Planet Fellowship "ISLAND2VAP"
Integrating Sentinel-2 and Landsat-8 data to systematically generat value-added products at high resolution.

Quality and quantity of current high-resolution optical Earth observation data is unprecedented and provides an opportunity to advance remote sensing-based land systems analyses. However, cloud coverage and a lack of gridded higher-level products still hampers the widespread usability of such data. This research project addressed these shortcomings by developing toolsets to combine data streams from ESA Sentinel-2 and USGS/NASA Landsat-8. It for the first time allowed for the systematic (i.e. weekly, monthly, seasonal-) generation of composited reflectance and downstream value-added products (e.g. percent cover estimates, annual phenology metrics). These novel integrated time-series were for the first time synergistically exploited to address land-use science questions related to agricultural land use and land use intensity that can only be answered when using dense time-series based on wall-to-wall analyses at 10-30m spatial resolution. While methods were developed to be capable of working with most ecosystems, a specific focus is on improving agricultural mapping and analyses. We specifically focused on crop type mapping and grassland use intensity.

Principal Investigators
Hostert, Patrick Prof. Dr. (Details) (Geomatics)

Duration of Project
Start date: 03/2015
End date: 03/2017

Research Areas
Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography

Research Areas
Big Data

Griffiths, P., Nendel, C., & Hostert, P. (2018). National-scale crop- and land-cover map of Germany (2016) based on imagery acquired by Sentinel-2A MSI and Landsat-8 OLI. Retrieved from:
Griffiths, P., Nendel, C., & Hostert, P. (2019). Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sensing of Environment, 220, 135-151. doi:
Griffiths, P., Nendel, C., Pickert, J., & Hostert, P. (2019). Towards national-scale characterization of grassland use intensity from integrated Sentinel-2 and Landsat time series. Remote Sensing of Environment. doi:

Last updated on 2021-04-01 at 17:45