From parcel to continental scale-A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations

被引:124
作者
d'Andrimont, Raphael [1 ]
Verhegghen, Astrid [1 ]
Lemoine, Guido [1 ]
Kempeneers, Pieter [1 ]
Meroni, Michele [1 ]
Velde, Marijn van der [1 ]
机构
[1] European Commiss, Joint Res Ctr JRC, Ispra, Italy
关键词
Copernicus; Monitoring; Sentinel-1; Sentinel-2; LUCAS; Crop modeling; Crop type; Crop yield forecasting; Climate change; Crop production; Classification; Validation; Time series; Parcel; LAND-COVER; TIME-SERIES; RANDOM FOREST; ESTIMATING AREA; SAR; AGRICULTURE; ACCURACY; PRODUCT; SYSTEM;
D O I
10.1016/j.rse.2021.112708
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Detailed parcel-level crop type mapping for the whole European Union (EU) is necessary for the evaluation of agricultural policies. The Copernicus program, and Sentinel-1 (S1) in particular, offers the opportunity to monitor agricultural land at a continental scale and in a timely manner. However, so far the potential of S1 has not been explored at such a scale. Capitalizing on the unique LUCAS 2018 Copernicus in-situ survey, we present the first continental crop type map at 10-m spatial resolution for the EU based on S1A and S1B Synthetic Aperture Radar observations for the year 2018. Random Forest classification algorithms are tuned to detect 19 different crop types. We assess the accuracy of this EU crop map with three approaches. First, the accuracy is assessed with independent LUCAS core in-situ observations over the continent. Second, an accuracy assessment is done specifically for main crop types from farmers declarations from 6 EU member countries or regions totaling >3 M parcels and 8.21 Mha. Finally, the crop areas derived by classification are compared to the subnational (NUTS 2) area statistics reported by Eurostat. The overall accuracy for the map is reported as 80.3% when grouping main crop classes and 76% when considering all 19 crop type classes separately. Highest accuracies are obtained for rape and turnip rape with user and produced accuracies higher than 96%. The correlation between the remotely sensed estimated and Eurostat reported crop area ranges from 0.93 (potatoes) to 0.99 (rape and turnip rape). Finally, we discuss how the framework presented here can underpin the operational delivery of inseason high-resolution based crop mapping.
引用
收藏
页数:19
相关论文
共 72 条
  • [31] First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe
    Immitzer, Markus
    Vuolo, Francesco
    Atzberger, Clement
    [J]. REMOTE SENSING, 2016, 8 (03)
  • [32] Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series
    Inglada, Jordi
    Vincent, Arthur
    Arias, Marcela
    Tardy, Benjamin
    Morin, David
    Rodes, Isabel
    [J]. REMOTE SENSING, 2017, 9 (01)
  • [33] A look inside the Pl@ntNet experience
    Joly, Alexis
    Bonnet, Pierre
    Goeau, Herve
    Barbe, Julien
    Selmi, Souheil
    Champ, Julien
    Dufour-Kowalski, Samuel
    Affouard, Antoine
    Carre, Jennifer
    Molino, Jean-Francois
    Boujemaa, Nozha
    Barthelemy, Daniel
    [J]. MULTIMEDIA SYSTEMS, 2016, 22 (06) : 751 - 766
  • [34] Crop-type mapping from a sequence of Sentinel 1 images
    Kenduiywo, Benson Kipkemboi
    Bargiel, Damian
    Soergel, Uwe
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2018, 39 (19) : 6383 - 6404
  • [35] Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery
    Kussul, Nataliia
    Mykola, Lavreniuk
    Shelestov, Andrii
    Skakun, Sergii
    [J]. EUROPEAN JOURNAL OF REMOTE SENSING, 2018, 51 (01) : 627 - 636
  • [36] Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
    Kussul, Nataliia
    Lavreniuk, Mykola
    Skakun, Sergii
    Shelestov, Andrii
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2017, 14 (05) : 778 - 782
  • [37] Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App
    Laso Bayas, Juan Carlos
    See, Linda
    Bartl, Hedwig
    Sturn, Tobias
    Karner, Mathias
    Fraisl, Dilek
    Moorthy, Inian
    Busch, Michaela
    van der Velde, Marijn
    Fritz, Steffen
    [J]. LAND, 2020, 9 (11) : 1 - 18
  • [38] Lemoine G., 2017, HDB REMOTE SENSING A, DOI [10.13140/RG.2.2.13259.69920., DOI 10.13140/RG.2.2.13259.69920]
  • [39] Mack B, 2017, REMOTE SENS LETT, V8, P244, DOI [10.1080/2150704X.2016.1249299, 10.1080/2150704x.2016.1249299]
  • [40] Integrating cloud-based workflows in continental-scale cropland extent classification
    Massey, Richard
    Sankey, Temuulen T.
    Yadav, Kamini
    Congalton, Russell G.
    Tilton, James C.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2018, 219 : 162 - 179