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Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series

Author

Listed:
  • Babak Ghassemi

    (Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU), Peter-Jordan-Straße 82, 1190 Vienna, Austria)

  • Markus Immitzer

    (Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU), Peter-Jordan-Straße 82, 1190 Vienna, Austria)

  • Clement Atzberger

    (Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU), Peter-Jordan-Straße 82, 1190 Vienna, Austria)

  • Francesco Vuolo

    (Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU), Peter-Jordan-Straße 82, 1190 Vienna, Austria)

Abstract

This investigation evaluates the potential of combining Copernicus Sentinel-1 (S1) and Sentinel-2 (S2) satellite data in producing a detailed Land Use and Land Cover (LULC) map with 19 crop type classes and 2 broader categories containing Woodland/Shrubland and Grassland over 28 Member States of Europe (EU-28). The Eurostat Land Use and Coverage Area Frame Survey (LUCAS) 2018 dataset is employed as ground truth for model training and validation. Monthly and yearly optical features from S2 spectral reflectance and spectral indices, alongside decadal (10-days) composites from an S1 microwave sensor, are extracted for the EU-28 territory for 2018 using Google Earth Engine (GEE). Five different feature sets using a mixture of indicators were created as input training data. A Random Forest (RF) machine learning algorithm was applied to classify these feature sets, and the generated classification models were compared using an identical validation dataset. Results show that S1 and S2 yearly features together are able to provide a full coverage map less dependent on cloud effects and having appropriate overall accuracy (OA). Based on this feature set, the 21 classes could be classified with an OA of 78.3% using the independent validation data set. The OA increases to 82.7% by grouping 21 classes into 8 broader categories. The comparison with similar studies using individual S1 and S2 data indicates that combining S1 and S2 time series can attain slightly better results while enhancing spatial coverage.

Suggested Citation

  • Babak Ghassemi & Markus Immitzer & Clement Atzberger & Francesco Vuolo, 2022. "Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series," Land, MDPI, vol. 11(9), pages 1-15, August.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:9:p:1397-:d:897861
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    References listed on IDEAS

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    1. Odile Close & Beaumont Benjamin & Sophie Petit & Xavier Fripiat & Eric Hallot, 2018. "Use of Sentinel-2 and LUCAS Database for the Inventory of Land Use, Land Use Change, and Forestry in Wallonia, Belgium," Land, MDPI, vol. 7(4), pages 1-16, December.
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    Cited by:

    1. Longo-Minnolo, Giuseppe & Consoli, Simona & Vanella, Daniela & Ramírez-Cuesta, Juan Miguel & Greimeister-Pfeil, Isabella & Neuwirth, Martin & Vuolo, Francesco, 2022. "A stand-alone remote sensing approach based on the use of the optical trapezoid model for detecting the irrigated areas," Agricultural Water Management, Elsevier, vol. 274(C).

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