Remote sensing for improved climate reporting (KlimaFern)
Objective
Comprehensive coverage of agricultural use with satellite data to improve the data basis for climate reporting for the LULUCF sector (land use, land use change, forestry)
Methodology
- Development of instruments for the area-wide collection and evaluation of activity data based on temporally and spatially high-resolution satellite data of different systems (optical and radar)
- Generating a data basis for monitoring of measures in the LULUCF sub-sector "agricultural land use" (a) for humus conservation in arable land, (b) for conservation of permanent grassland as well as (c) of soil carbon content in arable land
- Calculation of indicators from the satellite data, which are used not only for climate change monitoring, but also to characterise other ecosystem services provided by agriculture
Climate effect under consideration
Improved estimation of the scope and development of activity data (including GHG mitigation measures) in agricultural land use
Contact persons
Thünen Institute, Institute of Farm Economics
Link to project page: KlimaFern
Publications
Lobert, F, Schwieder, M, Alsleben, J, Broeg, T, Kowalski, K, Okunjeni, A, Hostert, P, Erasmi, S (2025): Unveiling Year-Round Cropland Cover by Soil-Specific Spectral Unmixing of Landsat and Sentinel-2 Time Series. Remote Sensing of Environment, 318, 114594. https://doi.org/10.1016/j.rse.2024.114594
Muro J, Blickensdörfer L, Don A, Köber A, Asam S, Schwieder M, Erasmi S (2025): Hedgerow mapping with high resolution satellite imagery to support policy initiatives at national level. Remote Sensing of Environment, 328, 114870. https://doi.org/10.1016/j.rse.2025.114870
Brög, T, Don, A, Gocht, A, Scholten, T, Taghizadeh-Mehrjardi, R, & Erasmi, S (2024): Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland. Geoderma, 444, 116850. https://doi.org/10.1016/j.geoderma.2024.116850
Brög, T, Don, A, Wiesmeier, M, Scholten, T, Erasmi, S (2024): Spatiotemporal monitoring of cropland soil organic carbon changes from space. Global Change Biology. https://doi.org/10.1111/gcb.17608
Lobert, F, Schwieder, M, Alsleben, J, Broeg, T, Kowalski, K, Okunjeni, A, Hostert, P, Erasmi, S (2025): Unveiling Year-Round Cropland Cover by Soil-Specific Spectral Unmixing of Landsat and Sentinel-2 Time Series. Remote Sensing of Environment, 318, 114594. https://doi.org/10.1016/j.rse.2024.114594
Pham, V-D, Tetteh, G, Thiel, F, Erasmi, S, Schwieder, M, Frantz, D, van der Linden, S (2024): Temporally transferable crop map-ping with temporal encoding and deep learning augmenta-tions. International Journal of Applied Earth Observation and Geoinformation 129, 103867. https://doi.org/10.1016/j.jag.2024.103867
Brög, T, Don, A, Gocht, A, Scholten, T, Taghizadeh-Mehrjardi, R, Erasmi, S (2023): Using Local Ensemble Models and Landsat Bare Soil Composites for Large-Scale Soil Organic Carbon Maps. Preprint, available at SSRN: http://dx.doi.org/10.2139/ssrn.4594434
