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Detection of Phenology-Defined Data Acquisition Time Frames For Crop Type Mapping

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Abstract

Agricultural monitoring and assessment based on satellite data increasingly gains importance due to the growing number of available satellite sensors with high geometric and temporal resolution. Such tasks often require multiple images acquired on specific dates that among others account for inter-annual phenological variations to provide accurate results. This contribution presents an approach that links peaks of spectral separability profiles to crop phenological phases. The phases are spatially interpolated using a phenological model and ground observations. The profiles show the respective temporal development of the F-measure which is used as indicator for class-wise separability. It originates from binary classifications of vegetation indices computed for each set of a satellite data archive covering multiple years. Acquisition dates, which repeatedly show a separability maximum define phenological indicator phases. Potential alternative phases can be also defined. Experiments based on multi-temporal RapidEye satellite imagery were performed for three crops at two German test sites under different environmental conditions. The results showed that the phases yellow ripeness, heading and flowering can function as indicator phases for high spectral separability of winter barley, winter wheat and winter rapeseed. We could identify at least two identical, stable indicator phases per crop type for both test sites, which suggests the transferability and robustness of the presented approach.

Zusammenfassung

Detektion von phänologisch definierten Datenaufnahmezeiträumen für die Klassifikation von Feldfrüchten Auf Satellitendaten basierendes landwirtschaftliches Monitoring gewinnt durch die wachsende Anzahl verfügbarer Sensoren mit hoher zeitlicher und geometrischer Auflösung zunehmend an Bedeutung. Für solche Anwendungen werden oftmals Satellitendaten von verschiedenen Aufnahmetagen benötigt, deren Auswahl inter-annuelle phänologische Variationen berücksichtigen muss, um exakte Ergebnisse zu liefern. Dieser Beitrag präsentiert einen Ansatz, um Maxima von spektralen Trennbarkeitsprofilen mit phänologischen Phasen von Feldfrüchten zu verbinden. Diese Phasen werden unter Nutzung eines phänologischen Modelles und Beobachtungsdaten räumlich interpoliert. Die Trennbarkeitsprofile zeigen den zeitlichen Verlauf des F-Maß, das als Indikator für klassenspezifische Trennbarkeit genutzt wird. Dieses stammt von binär klassifizierten Vegetationsindizes, die für jeden Datensatz einer mehrjährigen, multi-temporalen Zeitserie von Satellitenbilddatensätzen berechnet wurden. Zeitpunkte, während denen wiederholt das Trennbarkeitsmaximum beobachtet werden konnte, weisen die Indikatorphasen aus. Potentielle Alternativphasen können ebenso bestimmt werden. Die Untersuchungen wurden für drei Fruchtarten in zwei Untersuchungsgebieten in Deutschland unter verschiedenen Umweltbedingungen auf Basis von RapidEye-Satellitendaten durchgeführt. Die Ergebnisse zeigen, dass die Phasen Gelbreife, Ährenschieben und Blüte als Indikatoren für hohe spektrale Trennbarkeit von Wintergerste, Winterweizen und Winterraps dienen können. Für jede untersuchte Fruchtart konnten wenigstens zwei, für beide Untersuchungsgebiete übereinstimmende, stabile Indikatorphasen ausgewiesen werden, was die Übertragbarkeit und Robustheit des gezeigten Verfahrens belegt.

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Notes

  1. ftp://ftp-cdc.dwd.de/pub/CDC/observations_germany.

  2. http://phase.geo.uni-halle.de/phase-wms-dienste.

  3. http://www.geoserver.org.

  4. http://www.drupal.org.

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Acknowledgements

This study was supported by the German Ministry of Economics and Energy (BMWi) and the German Aerospace Center (DLR) under grant 50EE1263. The authors want to thank Dr. Daniel Doktor and Xingmei Xu (Helmholtz Centre for Environmental Research—UFZ) for the atmospheric correction of the RapidEye data sets for the Harz study site and Dr. Patrick Knöfel (Julius Maximilians University Würzburg) for organizing the preprocessing of the RapidEye data sets of Demmin. The authors want also express their gratitude to Dr. Erik Borg (German Aerospace Center—DLR) for the preparation and provision of the land use information of the Demmin site.

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  1. Institute for Geosciences and Geography, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 4, 06120, Halle (Saale), Germany

    Henning Gerstmann, Cornelia Gläßer & Detlef Thürkow

  2. Institute for Strategies and Technology Assessment, Julius Kühn-Institute, Federal Research Centre for Cultivated Plants, Stahnsdorfer Damm 81, 14532, Kleinmachnow, Germany

    Markus Möller

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  1. Henning Gerstmann
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Correspondence to Henning Gerstmann.

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Gerstmann, H., Gläßer, C., Thürkow, D. et al. Detection of Phenology-Defined Data Acquisition Time Frames For Crop Type Mapping. PFG 86, 15–27 (2018). https://doi.org/10.1007/s41064-018-0043-6

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