Tetteh GO, Schwieder M, Pham V-D, Blickensdörfer L, Gocht A, Neuenfeldt S, van der Linden S, Erasmi S (2024) Agrarflächennutzung aus dem All kartiert: Daten zur Quantifizierung von Klimaschutzmaßnahmen. In: Köchy M (ed) Agrarforschung zum Klimawandel: Konferenz der Deutschen Agrarforschungsallianz, 11.-14.03.2024, Potsdam, unter der Schirmherrschaft des Bundesministeriums für Ernährung und Landwirtschaft; Programm und Beiträge, Stand: 7. Mai 2024. Braunschweig: DAFA, p 61, DOI:10.3220/DAFA1713767287000

https://literatur.thuenen.de/digbib_extern/dn068079.pdf

Schwieder M, Tetteh GO, Blickensdörfer L, Gocht A, Erasmi S (2024) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) ; Version 201 [Datenpublikation] [online]. 5 TIFF files, 1 PDF file, 2 CLR files. Genève: Zenodo, zu finden in <https://zenodo.org/records/10617623> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10617623

Schwieder M, Tetteh GO, Blickensdörfer L, Gocht A, Erasmi S (2024) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) ; Version 202 [Datenpublikation] [online]. 5 TIFF files, 1 PDF file, 2 CLR files. Genève: Zenodo, zu finden in <https://zenodo.org/records/10640528> [zitiert am 03.12.2024], DOI:10.5281/zenodo.10640528

Schwieder M, Tetteh GO, Blickensdörfer L, Gocht A, Erasmi S (2024) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2022) ; Version v201 [Datenpublikation] [online]. 6 TIFF files, 1 PDF file, 2 CLR files. Genève: Zenodo, zu finden in <https://zenodo.org/records/10628809> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10628809

Schwieder M, Tetteh GO, Blickensdörfer L, Gocht A, Erasmi S (2024) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2022) ; Version v202 [Datenpublikation] [online]. 1 TIFF file, 1 PDF file, 2 CLR files. Genève: Zenodo, zu finden in <https://zenodo.org/records/10645427> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10645427

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2024) Agricultural land use (vector) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) [Datenpublikation] [online]. 2 PDF files, 5 FGB files, 1 SLD file. Genève: Zenodo, zu finden in <https://zenodo.org/records/10619783> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10619783

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2024) Agricultural land use (vector) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2022) [Datenpublikation] [online]. 1 FGB file, 1 PDF file, 1 SLD file. Genève: Zenodo, zu finden in <https://zenodo.org/records/10621629> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10621629

Schwieder M, Lobert F, Tetteh GO, Erasmi S (2024) Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat time series for the year 2022 [Datenpublikation] [online]. 1 TIFF file. Genève: Zenodo, zu finden in <https://zenodo.org/records/10610283> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10610283

Schwieder M, Lobert F, Tetteh GO, Erasmi S (2024) Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat time series for the years 2017 - 2021 [Datenpublikation] [online]. 5 TIFF files. Genève: Zenodo, zu finden in <https://zenodo.org/records/10609590> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10609590

Pham V-D, Tetteh GO, Thiel F, Erasmi S, Schwieder M, Frantz D, van der Linden S (2024) Temporally transferable crop mapping with temporal encoding and deep learning augmentations. Int J Appl Earth Observ Geoinf 129:103867, DOI:10.1016/j.jag.2024.103867

https://literatur.thuenen.de/digbib_extern/dn068235.pdf

Frank C, Hertzog LR, Klimek S, Schwieder M, Tetteh GO, Böhner HGS, Röder N, Levers C, Katzenberger J, Kreft H, Kamp J (2024) Woody semi-natural habitats modulate the effects of field size and functional crop diversity on farmland birds. J Appl Ecol 61(5):987-999, DOI:10.1111/1365-2664.14604

https://literatur.thuenen.de/digbib_extern/dn067799.pdf

Schwieder M, Tetteh GO, Blickensdörfer L, Gocht A, Erasmi S (2023) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) [Datenpublikation] [online]. 5 TIFF-Dateien, 2 Textdateien. Braunschweig: Thünen-Atlas, zu finden in <https://www.openagrar.de/receive/openagrar_mods_00087489> [zitiert am 10.07.2023], DOI:10.3220/DATA20230707103051-0

Schwieder M, Tetteh GO, Blickensdörfer L, Gocht A, Erasmi S (2023) Agricultural land use (vector) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) [Datenpublikation] [online]. 5 Geopackages, 2 Textdateien. Braunschweig: Thünen-Atlas, zu finden in <https://www.openagrar.de/receive/openagrar_mods_00087490> [zitiert am 10.07.2023], DOI:10.3220/DATA20230707103117-0

Tetteh GO, Schwieder M, Erasmi S, Conrad C, Gocht A (2023) Comparison of an optimised multiresolution segmentation approach with deep neural networks for delineating agricultural fields from Sentinel-2 images. J Photogramm Remote Sensing Geoinf Sci 91(4):295-312, DOI:10.1007/s41064-023-00247-x

https://literatur.thuenen.de/digbib_extern/dn066421.pdf

Osterburg B, Ackermann A, Böhm J, Bösch M, Dauber J, Witte T de, Elsasser P, Erasmi S, Gocht A, Hansen H, Heidecke C, Klimek S, Krämer C, Kuhnert H, Moldovan A, Nieberg H, Pahmeyer C, Plaas E, Rock J, Röder N, Söder M, Tetteh GO, Tiemeyer B, Tietz A, Wegmann J, Zinnbauer M (2023) Flächennutzung und Flächennutzungsansprüche in Deutschland. Braunschweig: Johann Heinrich von Thünen-Institut, 98 p, Thünen Working Paper 224, DOI:10.3220/WP1697436258000

https://literatur.thuenen.de/digbib_extern/dn067046.pdf

Tetteh GO, Gocht A, Erasmi S, Schwieder M, Conrad C (2021) Evaluation of sentinel-1 and sentinel-2 feature sets for delineating agricultural fields in heterogeneous landscapes. IEEE Access 9:116702-116719, DOI:10.1109/ACCESS.2021.3105903

https://literatur.thuenen.de/digbib_extern/dn063902.pdf

Tetteh GO, Gocht A, Conrad C (2020) Optimal parameters for delineating agricultural parcels from satellite images based on supervised Bayesian optimization. Comput Electron Agric 178:105696, DOI:10.1016/j.compag.2020.105696

https://literatur.thuenen.de/digbib_extern/dn062674.pdf

Tetteh GO, Gocht A, Schwieder M, Erasmi S, Conrad C (2020) Unsupervised parameterization for optimal segmentation of agricultural parcels from satellite images in different agricultural landscapes. Remote Sensing 12(18):3096, DOI:10.3390/rs12183096

https://literatur.thuenen.de/digbib_extern/dn062673.pdf

Tetteh GO (2019) Establishment of a time-sensitive crop database of Germany based on multi-temporal Sentinel-1 and Sentinel-2 Data. In: Living Planet Symposium, Milan (Italy), May 13-17 2019.

Neuenfeldt S, Rieger J, Heckelei T, Gocht A, Ciaian P, Tetteh GO (2018) A multiplicative competitive interaction model to explain structural change along farm specialisation, size and exit/entry using Norwegian farm census data [online]. IAAE, 20 p, zu finden in <http://ageconsearch.umn.edu/record/277090/files/886.pdf> [zitiert am 16.10.2018]