Article Dans Une Revue Remote Sensing of Environment Année : 2026

Mowing detection intercomparison exercise (MODCiX) – Evaluation of grassland mowing detection algorithms across Europe

1 Thünen Institute of Farm Economics
2 HU Berlin - Humboldt-Universität zu Berlin = Humboldt University of Berlin = Université Humboldt de Berlin
3 Swiss Federal Institute for Forest, Snow and Landscape Research WSL
4 JMU - Julius-Maximilians-Universität Würzburg = University of Würzburg [Würsburg, Germany]
5 DLR-DFD - German Remote Sensing Data Center
6 UNITO - Università degli studi di Torino = University of Turin
7 CAU - Christian-Albrechts-Universität zu Kiel = Christian-Albrechts University of Kiel = Université Christian-Albrechts de Kiel
8 NOA - National Observatory of Athens
9 CRA-W - Centre wallon de Recherches Agronomiques [Belgique] = Walloon Agricultural Research Centre [Belgium]
10 LaSTIG - Laboratoire en Sciences et Technologies de l'Information Géographique
11 IGN - Institut National de l'Information Géographique et Forestière [IGN]
12 CESBIO - Centre d'études spatiales de la biosphère
13 LASTIG - Laboratoire en Sciences et Technologies de l'Information Géographique
14 JRC - European Commission - Joint Research Centre [Ispra]
15 SLU - Swedish University of Agricultural Sciences = Sveriges lantbruksuniversitet
16 BOKU - Universität für Bodenkultur Wien = University of Natural Resources and Life Sciences [Vienne, Autriche]
17 Agricultural Research and Education Center Raumberg-Gumpenstein
18 LUP – Luftbild Umwelt Planung GmbH
19 TUB - Technical University of Berlin / Technische Universität Berlin
20 NINA - Norwegian Institute for Nature Research
21 Institute for Earth Observation [Bolzano]
22 UCLouvain - Université Catholique de Louvain = Catholic University of Louvain
23 EFTAS Fernerkundung Technologietransfer GmbH
24 TUM - Technische Universität Munchen = Technical University Munich = Université Technique de Munich
25 Georg-August-University of Goettingen = Georg-August-Universität Göttingen
26 FSU - Friedrich-Schiller-Universität Jena = Friedrich Schiller University Jena = Université de Iéna [Jena, Germany]
27 Johann Heinrich von Thünen-Institut = Thünen Institute
Aleksandar Dujakovic
  • Fonction : Auteur

Résumé

Grasslands deliver a variety of ecosystem services, such as the provision of biomass, carbon sequestration or water retention and with that play a key role for climate change mitigation and preservation of biological diversity. The intensity of grassland management directly impacts these ecosystem services and functions. However, spatial information on grassland use intensity is scarce. Remote sensing time series from optical and/or SAR sensors help to overcome this data scarcity, as they enable to derive dates and frequency of mowing events as a proxy of grassland use intensity. A growing number of published algorithms relate abrupt changes in remote sensing time series to grassland management activities either by defining threshold-based rules or by making use of machine/deep learning techniques. So far, the different algorithms have not been compared and, due to a lack of suitable reference data, have usually not been tested for spatial and temporal transferability. We present the results of a comparison exercise based on an unprecedented set of independent reference data, containing information on more than 5000 grassland mowing dates that were compiled from eight European countries over a five-year period. We analyzed the performance of ten mowing detection algorithms across different geographic regions, years, mowing intensity, levels of reference data quality, and method and satellite sensor domains. The overall results show that when using all available reference data, F1 scores ranged from 0.55 to 0.74, and from 0.56 to 0.71 when only the highest quality reference data were considered. This decision, however, reduced the number of reference events to around 1500 with a regional bias to Austria, Germany, and Switzerland. We found that algorithm performance varies across space and time and that overall, the highest accuracies were achieved by machine learning based algorithms, although not substantially outperforming rule-based algorithms. The results did not confirm a consistently positive influence of the combined use of optical and SAR data in the prediction of mowing events, but we observed variations in algorithm performance, towards the lower and higher ends of grassland use intensity. Despite testing a variety of algorithms from different method and sensor domains, we observed a general upper limit of model performance and could not identify one single algorithm that performed best in all cases. The results of this comparison exercise can guide practitioners to choose approaches and input data that are most suitable for their specific use-case. The comparison exercise also highlights the importance of consistent, representative, and reliable reference data. We therefore recommend maintaining and extending this baseline dataset for the evaluation of upcoming algorithms, Earth Observation missions, and derived products for comprehensive monitoring of grassland use intensity.

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DOI

Cite 10.5281/zenodo.18834294 Jeu de données Schwieder, M., Lobert, F., Weber, D., Reinermann, S., Asam, S., Sarvia, F., De Petris, S., Borgogno-Mondino, E., Muhuri, A., Oppelt, N., Atzberger, C., Tsardanidis, I., Kontoes, C., Godechal, F., Lucau-Danila, C., Planchon, V., Garioud, A., Huet, C., Valero, S., … Erasmi, S. (2026). Mowing Detection Intercomparison Exercise (MODCiX) – Evaluation of Grassland Mowing Detection Algorithms across Europe [Data set]. Zenodo. https://doi.org/10.5281/ZENODO.18834294

Dates et versions

hal-05624869 , version 1 (18-05-2026)

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Marcel Schwieder, Felix Lobert, Dominique Weber, Sophie Reinermann, Sarah Asam, et al.. Mowing detection intercomparison exercise (MODCiX) – Evaluation of grassland mowing detection algorithms across Europe. Remote Sensing of Environment, 2026, 342, pp.115466. ⟨10.1016/j.rse.2026.115466⟩. ⟨hal-05624869⟩
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