Brög T, Don A, Scholten T, Erasmi S (2026) Reducing bias in cropland soil organic carbon and clay predictions using Sentinel-2 composites and data balancing. Remote Sens Environ 333:115109, DOI:10.1016/j.rse.2025.115109

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Steinhoff-Knopp B, Neuenfeldt S, Erasmi S, Saggau P (2025) (R)USLE C factor datasets for Germany [Datenpublikation]. 1 ZIP archive. Genève: Zenodo, DOI:10.5281/zenodo.13951344

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2023) [Datenpublikation]. 1 ZIP archive; Version 201. Genève: Zenodo, DOI:10.5281/zenodo.15055561

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2023) [Datenpublikation]. 1 ZIP archive; Version 202. Genève: Zenodo, DOI:10.5281/zenodo.15309479

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 201. Genève: Zenodo, DOI:10.5281/zenodo.16949898

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 201. Genève: Zenodo, DOI:10.5281/zenodo.17122420

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 202. Genève: Zenodo, DOI:10.5281/zenodo.17122646

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 302. Genève: Zenodo, DOI:10.5281/zenodo.17197830

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 301. Genève: Zenodo, DOI:10.5281/zenodo.17197761

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 302. Genève: Zenodo, DOI:10.5281/zenodo.17190438

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 301. Genève: Zenodo, DOI:10.5281/zenodo.17181387

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 302. Genève: Zenodo, DOI:10.5281/zenodo.17182245

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2025) [Datenpublikation]. 1 ZIP archive; Version 301. Genève: Zenodo, DOI:10.5281/zenodo.17181503

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2025) [Datenpublikation]. 1 ZIP archive; Version 302. Genève: Zenodo, DOI:10.5281/zenodo.17182293

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2025) [Datenpublikation]. 1 ZIP archive; Version 302/2. Genève: Zenodo, DOI:10.5281/zenodo.17197871

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2025) [Datenpublikation]. 1 ZIP archive; Version 301. Genève: Zenodo, DOI:10.5281/zenodo.17197766

Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (vector) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2023) [Datenpublikation]. 1 ZIP archive; Version 201. Genève: Zenodo, DOI:10.5281/zenodo.17135735

Brög T, Don A, Scholten T, Erasmi S (2025) Bare soil composite for Germany (10 m) based on Sentinel-2 data from 2015 to 2024 [Datenpublikation]. Genève: Zenodo, DOI:10.5281/zenodo.15402687

Lobert F, Schwieder M, Hostert P, Gocht A, Erasmi S (2025) Characterizing spatio-temporal patterns of winter cropland cover in Germany based on Landsat and Sentinel-2 time series. Int J Appl Earth Observ Geoinf 142:104728, DOI:10.1016/j.jag.2025.104728

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Erasmi S, Schwieder M, Gocht A, Muro J, Blickensdörfer L, Brög T, Lobert F, Tetteh GO, Fuß R, Don A (2025) Fernerkundung für eine Verbesserung der Klimaberichterstattung. In: RessortForschtKlima (ed) Ergebnisse aus drei Jahren Forschung für mehr Klimaschutz in Landwirtschaft, Wald und Ernährung : Abschlussbericht der RessortForschtKlima-Projekte. Göttingen: OpenAgrar, pp 84-87, DOI:10.3220/253-2025-220

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Schwieder M, Lobert F, Tetteh GO, Erasmi S (2025) Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat time series for the year 2023 [Datenpublikation]. 1 ZIP archive; Version 1. Genève: Zenodo, DOI:10.5281/zenodo.16941138

Schwieder M, Lobert F, Tetteh GO, Erasmi S (2025) Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat time series for the year 2024 [Datenpublikation]. 1 ZIP archive; Version 1. Genève: Zenodo, DOI:10.5281/zenodo.16942505

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 Sens Environ 328:114870, DOI:10.1016/j.rse.2025.114870

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Brög T, Don A, Scholten T, Erasmi S (2025) High-resolution maps (10 m) of cropland soil organic carbon and clay in Germany based on Sentinel-2 data [Datenpublikation]. 3 PNG files, 6 TIFF files. Genève: Zenodo, DOI:10.5281/zenodo.15403341

Levers C, Schwieder M, Dieker P, Erasmi S, Rodríguez RA, Bayr U, Calvo Obando AJ, Fjellstad W, Furubayashi S, Heliölä J, Herzog F, Hyvönen T, Ievina L, Lakovskis P, Meier E, Ojanen H, Pitkänen T, Tomas WM (2025) Implementing an OECD Farmland Habitat Biodiversity Indicator : lessons and guidelines from eight pilot studies. 49 p

Brög T, Don A, Scholten T, Erasmi S (2025) Reducing bias in cropland soil organic carbon and clay predictions using Sentinel-2 composites and data balancing [Preprint]. EarthArXiv, 43 p, DOI:10.31223/X50M91

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Muro J, Blickensdörfer L, Köber A, Schwieder M, Don A, Erasmi S (2025) Spatially explicit distribution of hedgerows across German agricultural landscapes [Datenpublikation]. 1 TIFF file; Version 1. Genève: Zenodo, DOI:10.5281/zenodo.15654506

Muro J, Blickensdörfer L, Köber A, Schwieder M, Don A, Erasmi S (2025) Spatially explicit distribution of hedgerows across German agricultural landscapes [Datenpublikation]. 1 TIFF file; Version 2. Genève: Zenodo, DOI:10.5281/zenodo.15782863

Steinhoff-Knopp B, Neuenfeldt S, Erasmi S, Saggau P (2025) Spatiotemporal detailed crop cover and management factor maps as agri-environmental indicators for soil erosion in Germany. Int Soil Water Conserv Res 13(4):933-944, DOI:10.1016/j.iswcr.2025.06.002

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Montoya-Sanchez V, Schweiger AK, Schlund M, Paterno GB, Erasmi S, Kreft H, Hölscher D, Brambach F, Irawan B, Sundawati L, Zemp DC (2025) Spectral characterization of plant diversity in a biodiversity-enriched oil palm plantation. Remote Sensing Ecol Conserv: Online First, Sep 2025, DOI:10.1002/rse2.70034

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Lobert F, Schwieder M, Alsleben J, Brög T, Kowalski K, Okujeni A, Hostert P, Erasmi S (2025) Unveiling year-round cropland cover by soil-specific spectral unmixing of Landsat and Sentinel-2 time series. Remote Sens Environ 318:114594, DOI:10.1016/j.rse.2024.114594

https://literatur.thuenen.de/digbib_extern/dn069411.pdf

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

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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

Wenzel A, Westphal C, Ballauff J, Berkelmann D, Brambach F, Buchori D, Camaretta N, Corre MD, Daniel R, Darras K, Erasmi S, Formaglio G, Hölscher D, Al-Amin Iddris N, Irawan B, Knohl A, Kotowska MM, Krashevska V, Kreft H, Mulyani Y, et al (2024) Balancing economic and ecological functions in smallholder and industrial oil palm plantations. PNAS 121(17):e2307220121, DOI:10.1073/pnas.2307220121

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Brög T, Don A, Wiesmeier M, Scholten T, Erasmi S (2024) Dataset and code for "Spatiotemporal monitoring of cropland soil organic carbon changes from space" [Datenpublikation] [online]. Genève: Zenodo, zu finden in <https://zenodo.org/records/14191435> [zitiert am 04.12.2024], DOI:10.5281/zenodo.14191435

Paterno GB, Brambach F, Guerrero-Ramírez N, Zemp DC, Cantillo AF, Camarretta N, Moura CCM, Gailing O, Ballauff J, Polle A, Schlund M, Erasmi S, Iddris NA, Khokthong W, Sundawati L, Irawan B, Hölscher D, Kreft H (2024) Diverse and larger tree islands promote native tree diversity in oil palm landscapes. Science 386(6723):795-802, DOI:10.1126/science.ado1629

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

Saggau P, Brög T, Gocht A, Erasmi S, Steinhoff-Knopp B (2024) HotSpots der Bodenerosionsgefährdung durch Wasser in Deutschland. 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 58, DOI:10.3220/DAFA1713767287000

https://literatur.thuenen.de/digbib_extern/dn068077.pdf

Erasmi S, Muro J, Brög T, Blickensdörfer L, Fuß R, Gocht A, Don A, Schwieder M (2024) KlimaFern - Fernerkundung für eine Verbesserung der Klimaberichterstattung. 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 60, DOI:10.3220/DAFA1713767287000

https://literatur.thuenen.de/digbib_extern/dn068078.pdf

May PB, Schlund M, Armston J, Kotowska MM, Brambach F, Wenzel A, Erasmi S (2024) Mapping aboveground biomass in Indonesian lowland forests using GEDI and hierarchical models. Remote Sens Environ 313:114384, DOI:10.1016/j.rse.2024.114384

Klein K, Ogan S, Tönshoff C, Böhner HGS, Dauber J, Erasmi S, Gocht A, Hellwig N, Klimek S, Krüger L, Lakemann L, Levers C, Lindermann L, Richter A, Röder N, Schwieder M, Sickel W, Sommerlandt FMJ, Stahl J, Tebbe CC, et al (2024) MonViA Indikatorenbericht 2024 : Bundesweites Monitoring der biologischen Vielfalt in Agrarlandschaften. Bonn: BLE, 199 p

https://literatur.thuenen.de/digbib_extern/dn069418.pdf

Follath T, Mickisch D, Hemmerling J, Erasmi S, Schwieder M, Demir B (2024) Multi-modal vision transformers for crop mapping from satellite image time series. In: IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium : Athens, Greece, 17-12 July 2024. IEEE, DOI:10.1109/IGARSS53475.2024.10641794

Brög T, Don A, Wiesmeier M, Scholten T, Erasmi S (2024) Spatiotemporal monitoring of cropland soil organic carbon changes from space. Global Change Biol 30(12):e17608, DOI:10.1111/gcb.17608

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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

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, DOI:10.1016/j.geoderma.2024.116850

https://literatur.thuenen.de/digbib_extern/dn067810.pdf

Lobert F, Löw J, Schwieder M, Gocht A, Schlund M, Hostert P, Erasmi S (2023) A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level. Remote Sens Environ 298:113800, DOI:10.1016/j.rse.2023.113800

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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

Erasmi S, Ackermann A, Bolte A, Dunger K, Elsasser P, Fuß R, Gocht A, Hoedt F, Klimek S, Neumeier S, Osterburg B, Röder N, Strer M, Weingarten P, Isermeyer F (2023) Bundesweite Landnutzungsdaten am Thünen-Institut : Sachstand und Perspektiven. Braunschweig: Johann Heinrich von Thünen-Institut, 41 p, Thünen Working Paper 213, DOI:10.3220/WP1683702994000

https://literatur.thuenen.de/digbib_extern/dn066325.pdf

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

Holtgrave A-K, Lobert F, Erasmi S, Röder N, Kleinschmit B (2023) Grassland mowing event detection using combined optical, SAR, and weather time series. Remote Sens Environ 295:113680, DOI:10.1016/j.rse.2023.113680

https://literatur.thuenen.de/digbib_extern/dn066527.pdf

Schlund M, Erasmi S, Knohl A (2023) High-resolution true-color orthophotos with 10 cm resolution 2022 [Datenpublikation] [online]. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/S6JS8E> [zitiert am 18.10.2023], DOI:10.25625/S6JS8E

Hauck M, Klinge M, Erasmi S, Dulamsuren C (2023) No signs of long-term greening trend in Western Mongolian Grasslands. Ecosystems 26(5):1125-1143, DOI:10.1007/s10021-023-00819-3

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Schlund M, Erasmi S, Knohl A (2023) Rasters for ALS metrics at 1000m² resolution 2022 [Datenpublikation] [online]. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/J93NG5> [zitiert am 18.10.2023], DOI:10.25625/J93NG5

Schlund M, Erasmi S, Knohl A (2023) Rasters for ALS metrics at 10m resolution 2022 [Datenpublikation] [online]. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/39VQPW> [zitiert am 18.10.2023], DOI:10.25625/39VQPW

Schlund M, Erasmi S, Knohl A (2023) Rasters for ALS metrics at 50m resolution 2022 [Datenpublikation] [online]. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/KBPPPL> [zitiert am 18.10.2023], DOI:10.25625/KBPPPL

Wenzel A, Westphal C, Ballauff J, Berkelmann D, Brambach F, Buchori D, Camaretta N, Corre MD, Darras K, Erasmi S, Formaglio G, Hölscher D, Al-Amin Iddris N, Irawan B, Knohl A, Kotowska MM, Krashevska V, Kreft H, Mulyani Y, Mußhoff O, et al (2023) Trade-offs and synergies of economic and ecological functions across oil palm systems. In: Brückner D, Kietzmann K (eds) Book of abstracts : 52nd Annual Meeting of the Ecological Society of Germany, Austria and Switzerland ; Leipzig - 12-16 September 2023. Berlin: Gesellschaft für Ökologie, p 148

Schlund M, Wenzel A, Camarretta N, Stiegler C, Erasmi S (2023) Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM-X supported by GEDI data. Methods Ecol Evol 14(7):1639-1656, DOI:10.1111/2041-210X.13933

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Schlund M, Erasmi S, Knohl A (2022) Airborne acquisition extent 2020 [Datenpublikation] [online]. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/EKRJI2> [zitiert am 16.11.2023], DOI:10.25625/EKRJI2

Camarretta N, Knohl A, Erasmi S, Schlund M (2022) Core plots hyperspectral vegetation indices [Datenpublikation] [online]. 1 TAB-File, 43.2 KB. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/BXLGQJ> [zitiert am 16.11.2023], DOI:10.25625/BXLGQJ

Schlund M, Erasmi S, Knohl A (2022) High-resolution true-color orthophotos with 5 cm resolution [Datenpublikation] [online]. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/1RB4CC> [zitiert am 16.11.2023], DOI:10.25625/1RB4CC

Schlund M, Erasmi S, Knohl A (2022) Land use maps 2020 based on LiDAR [Datenpublikation] [online]. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/LOPQ2K> [zitiert am 16.11.2023], DOI:10.25625/LOPQ2K

Camarretta N, Knohl A, Erasmi S, Schlund M (2022) Landscape Assessment (LA) hyperspectral vegetation indices [Datenpublikation] [online]. 1 TAB-File, 114.1 KB. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/1LI57Z> [zitiert am 16.11.2023], DOI:10.25625/1LI57Z

Schlund M, Erasmi S, Knohl A (2022) Landscape Assessment plots maps (with RGB, canopy height, NDVI and 3d point cloud) [Datenpublikation] [online]. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/MDWJR3> [zitiert am 16.11.2023], DOI:10.25625/MDWJR3

Schwieder M, Wesemeyer M, Frantz D, Pfoch K, Erasmi S, Pickert J, Nendel C, Hostert P (2022) Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sens Environ 269:112795, DOI:10.1016/j.rse.2021.112795

https://literatur.thuenen.de/digbib_extern/dn064247.pdf

Blickensdörfer L, Schwieder M, Pflugmacher D, Nendel C, Erasmi S, Hostert P (2022) Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sens Environ 269:112831, DOI:10.1016/j.rse.2021.112831

https://literatur.thuenen.de/digbib_extern/dn064297.pdf

Lobert F, Röder N, Gocht A, Schwieder M, Erasmi S (2022) Mowing detection from combined Sentinel-1, Sentinel-2, and Landsat 8 time series on fallow cropland with transfer learning. Publikationen der DGPF eV 30:117-126

Camarretta N, Knohl A, Erasmi S, Schlund M (2022) Normalized Difference Vegetation Indices (NDVIs) at 1 m resolution [Datenpublikation] [online]. 4 TIFF-Files. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/AIDFG2> [zitiert am 16.11.2023], DOI:10.25625/AIDFG2

Schlund M, Erasmi S, Knohl A (2022) Rasters for ALS metrics at 1000m² resolution [Datenpublikation] [online]. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/TJNWUI> [zitiert am 18.10.2023], DOI:10.25625/TJNWUI

Camarretta N, Knohl A, Erasmi S, Schlund M (2022) Rasters for ALS metrics at 10m resolution [Datenpublikation] [online]. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/HWTBW5> [zitiert am 16.11.2023], DOI:10.25625/HWTBW5

Camarretta N, Knohl A, Erasmi S, Schlund M (2022) Rasters for ALS metrics at 50m resolution [Datenpublikation] [online]. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/6JQDNA> [zitiert am 16.11.2023], DOI:10.25625/6JQDNA

Schlund M, Erasmi S (2022) Rasters of vegetation canopy height estimated with spaceborne TanDEM-X data [Datenpublikation] [online]. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/QJZ9XI> [zitiert am 16.11.2023], DOI:10.25625/QJZ9XI

Nketia KA, Asabere SB, Ramcharan A, Herbold S, Erasmi S, Sauer D (2022) Spatio-temporal mapping of soil water storage in a semi-arid landscape of northern Ghana - A multi-tasked ensemble machine-learning approach. Geoderma 410:115691, DOI:10.1016/j.geoderma.2021.115691

Camarretta N, Knohl A, Erasmi S, Schlund M, Seidel D, Ehbrecht M (2021) ALS metrics for core plots [Datenpublikation] [online]. 1 TAB-File, 25.9 KB. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/AIKOI9> [zitiert am 16.11.2023], DOI:10.25625/AIKOI9

Camarretta N, Knohl A, Erasmi S, Schlund M, Seidel D, Ehbrecht M (2021) ALS metrics for Landscape Assessment (LA) plots within LiDAR boundaries [Datenpublikation] [online]. 1 TAB-File, 69.1 KB. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/NWX23T> [zitiert am 16.11.2023], DOI:10.25625/NWX23T

Camarretta N, Ehbrecht M, Seidel D, Wenzel A, Zudhi M, Merk MS, Schlund M, Erasmi S, Knohl A (2021) ALS-derived metrics used in the manuscript "Using Airborne Laser Scanning to characterize land-use systems in a tropical landscape based on vegetation structural metrics" [Datenpublikation] [online]. 1 TAB-File, 39.5 KB. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/TH5KEX> [zitiert am 16.11.2023], DOI:10.25625/TH5KEX

Lobert F, Holtgrave A-K, Schwieder M, Pause M, Gocht A, Vogt J, Erasmi S (2021) Detection of mowing events from combined Sentinel-1, Sentinel-2, and Landsat 8 time series with machine learning. Grassl Sci Europe 26:123-125

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

Klinge M, Dulamsuren C, Schneider F, Erasmi S, Bayarsaikhan U, Sauer D, Hauck M (2021) Geoecological parameters indicate discrepancies between potential and actual forest area in the forest-steppe of Central Mongolia. For Ecosyst 8:55, DOI:10.1186/s40663-021-00333-9

https://literatur.thuenen.de/digbib_extern/dn063892.pdf

Schwieder M, Wesemeyer M, Frantz D, Pfoch K, Erasmi S, Pickert J, Nendel C, Hostert P (2021) Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat 8 time series for the years 2017 - 2020 [Datenpublikation] [online]. Genève: Zenodo, zu finden in <https://zenodo.org/records/5571613> [zitiert am 03.12.2024], DOI:10.5281/zenodo.5571613

Camarretta N, Knohl A, Erasmi S, Schlund M (2021) Landscape Assessment (LA) centre plot coordinates [Datenpublikation] [online]. Göttingen: GROdata, zu finden in <https://data.goettingen-research-online.de/dataset.xhtml?persistentId=doi:10.25625/SSN6RO> [zitiert am 16.11.2023], DOI:10.25625/SSN6RO

Erasmi S, Klinge M, Dulamsuren C, Schneider F, Hauck M (2021) Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe. Environ Monit Assessm 193:200, DOI:10.1007/s10661-021-08996-1

https://literatur.thuenen.de/digbib_extern/dn063505.pdf

Lobert F, Holtgrave A-K, Schwieder M, Pause M, Vogt J, Gocht A, Erasmi S (2021) Mowing event detection in permanent grasslands: Systematic evaluation of input features from Sentinel-1, Sentinel-2, and Landsat 8 time series. Remote Sens Environ 267:112751, DOI:10.1016/j.rse.2021.112751

https://literatur.thuenen.de/digbib_extern/dn064075.pdf

Schlund M, Lobert F, Erasmi S (2021) Potential of Sentinel-1 time series data for the estimation of season length in winter wheat phenology. In: Institute of Electrical and Electronics Engineers (ed) IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium : proceedings ; 12-16 July 2021, Virtual Symposium, Brussels, Belgium. IEEE, pp 5917-5920, DOI:10.1109/IGARSS47720.2021.9554454

Schlund M, Kotowska MM, Brambach F, Hein J, Wessel B, Camarretta N, Silalahi M, Surati Jaya IN, Erasmi S, Leuschner C, Kreft H (2021) Spaceborne height models reveal above ground biomass changes in tropical landscapes. Forest Ecol Manag 497:119497, DOI:10.1016/j.foreco.2021.119497

https://literatur.thuenen.de/digbib_extern/dn063766.pdf

Camarretta N, Ehbrecht M, Seidel D, Wenzel A, Zuhdi M, Merk MS, Schlund M, Erasmi S, Knohl A (2021) Using airborne laser scanning to characterize land-use systems in a tropical landscape based on vegetation structural metrics. Remote Sensing 13:4794, DOI:10.3390/rs13234794

https://literatur.thuenen.de/digbib_extern/dn064242.pdf

Holtgrave A-K, Röder N, Ackermann A, Erasmi S, Kleinschmit B (2020) Comparing Sentinel-1 and -2 data and indices for agricultural land use monitoring. Remote Sensing 12:2919, DOI:10.3390/rs12182919

https://literatur.thuenen.de/digbib_extern/dn062663.pdf

Schlund M, Erasmi S (2020) Sentinel-1 time series data for monitoring the phenology of winter wheat. Remote Sens Environ 246:111814, DOI:10.1016/j.rse.2020.111814

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

Erasmi S, Semmler M, Schall P, Schlund M (2019) Sensitivity of bistatic TanDEM-X data to stand structural parameters in temperate forests. Remote Sensing 11(24):2966, DOI:10.3390/rs11242966

https://literatur.thuenen.de/digbib_extern/dn061759.pdf

Schneider J, Jungkunst HF, Wolf U, Schreiber P, Gazovic M, Miglovets M, Mikhaylov O, Grunwald D, Erasmi S, Wilmking M, Kutzbach L (2016) Russian boreal peatlands dominate the natural European methane budget. Environ Res Lett 11(1):14004, DOI:10.1088/1748-9326/11/1/014004

https://literatur.thuenen.de/digbib_extern/dn061770.pdf