High-Resolution Mapping of Winter Cereals in Europe by Time Series Landsat and Sentinel Images for 2016-2020

被引:16
作者
Huang, Xiaojuan [1 ]
Fu, Yangyang [1 ]
Wang, Jingjing [2 ]
Dong, Jie [3 ]
Zheng, Yi [1 ]
Pan, Baihong [1 ]
Skakun, Sergii [4 ]
Yuan, Wenping [1 ]
机构
[1] Sun Yat Sen Univ, Sch Atmospher Sci, Zhuhai 519082, Peoples R China
[2] Xi An Jiao Tong Univ, Dept Environm Sci & Engn, State Key Lab Multiphase Flow Power Engn, Xian 710049, Peoples R China
[3] Zhejiang Univ Water Resources & Elect Power, Coll Geomat & Municipal Engn, Hangzhou 310018, Peoples R China
[4] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
关键词
winter cereals; vegetation index; time-weighted dynamic time warping; Landsat; Sentinel; Google Earth Engine; NATIONAL-SCALE; CROPPING SYSTEMS; RANDOM FOREST; PADDY RICE; NDVI DATA; CROPS; WHEAT; AREA; YIELD; ALGORITHM;
D O I
10.3390/rs14092120
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Winter cereals, including wheat, rye, barley, and triticale, are important food crops, and it is crucial to identify the distribution of winter cereals for monitoring crop growth and predicting yield. The production and plating area of winter cereals in Europe both contribute 12.57% to the total global cereal production and plating area in 2020. However, the distribution maps of winter cereals with high spatial resolution are scarce in Europe. Here, we first used synthetic aperture radar (SAR) data from Sentinel-1 A/B, in the Interferometric Wide (IW) swath mode, to distinguish rapeseed and winter cereals; we then used a time-weighted dynamic time warping (TWDTW) method to discriminate winter cereals from other crops by comparing the similarity of seasonal changes in the Normalized Difference Vegetation Index (NDVI) from Landsat and Sentinel-2 images. We generated winter cereal maps for 2016-2020 that cover 32 European countries with 30 m spatial resolution. Validation using field samples obtained from the Google Earth Engine (GEE) platform show that the producer's and user's accuracies are 91% +/- 7.8% and 89% +/- 10.3%, respectively, averaged over 32 countries in Europe. The winter cereal map agrees well with agricultural census data for planted winter cereal areas at municipal and country levels, with the averaged coefficient of determination R-2 as 0.77 +/- 0.15 for 2016-2019. In addition, our method can identify the distribution of winter cereals two months before harvest, with an overall accuracy of 88.4%, indicating that TWDTW is an effective method for timely crop growth monitoring and identification at the continent level. The winter cereal maps in Europe are available via an open-data repository.
引用
收藏
页数:16
相关论文
共 55 条
[1]  
Andrimont R., REMOTE SENS ENVIRON, V2021, DOI [10.1016/j.rse.2021.112708, DOI 10.1016/J.RSE.2021.112708]
[2]   Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets [J].
Atzberger, Clement ;
Rembold, Felix .
REMOTE SENSING, 2013, 5 (03) :1335-1354
[3]   Strengthening agricultural decisions in countries at risk of food insecurity: The GEOGLAM Crop Monitor for Early Warning [J].
Becker-Reshef, Inbal ;
Justice, Christina ;
Barker, Brian ;
Humber, Michael ;
Rembold, Felix ;
Bonifacio, Rogerio ;
Zappacosta, Mario ;
Budde, Mike ;
Magadzire, Tamuka ;
Shitote, Chris ;
Pound, Jonathan ;
Constantino, Alessandro ;
Nakalembe, Catherine ;
Mwangi, Kenneth ;
Sobue, Shinichi ;
Newby, Terence ;
Whitcraft, Alyssa ;
Jarvis, Ian ;
Verdin, James .
REMOTE SENSING OF ENVIRONMENT, 2020, 237
[4]   Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis [J].
Belgiu, Mariana ;
Csillik, Ovidiu .
REMOTE SENSING OF ENVIRONMENT, 2018, 204 :509-523
[5]  
Bontemps S., 2015, IEEE INT GEOSCI REMO, V4185, P4188
[6]   Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2 [J].
Bontemps, Sophie ;
Arias, Marcela ;
Cara, Cosmin ;
Dedieu, Gerard ;
Guzzonato, Eric ;
Hagolle, Olivier ;
Inglada, Jordi ;
Matton, Nicolas ;
Morin, David ;
Popescu, Ramona ;
Rabaute, Thierry ;
Savinaud, Mickael ;
Sepulcre, Guadalupe ;
Valero, Silvia ;
Ahmad, Ijaz ;
Begue, Agnes ;
Wu, Bingfang ;
de Abelleyra, Diego ;
Diarra, Alhousseine ;
Dupuy, Stephane ;
French, Andrew ;
Akhtar, Ibrar ul Hassan ;
Kussul, Nataliia ;
Lebourgeois, Valentine ;
Le Page, Michel ;
Newby, Terrence ;
Savin, Igor ;
Veron, Santiago R. ;
Koetz, Benjamin ;
Defourny, Pierre .
REMOTE SENSING, 2015, 7 (12) :16062-16090
[7]   Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program [J].
Boryan, Claire ;
Yang, Zhengwei ;
Mueller, Rick ;
Craig, Mike .
GEOCARTO INTERNATIONAL, 2011, 26 (05) :341-358
[8]   Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape [J].
Chipanshi, Aston ;
Zhang, Yinsuo ;
Kouadio, Louis ;
Newlands, Nathaniel ;
Davidson, Andrew ;
Hill, Harvey ;
Warren, Richard ;
Qian, Budong ;
Daneshfar, Bahram ;
Bedard, Frederic ;
Reichert, Gordon .
AGRICULTURAL AND FOREST METEOROLOGY, 2015, 206 :137-150
[9]   Spatio-Temporal Segmentation Applied to Optical Remote Sensing Image Time Series [J].
Costa, Wanderson Santos ;
Garcia Fonseca, Leila Maria ;
Korting, Thales Sehn ;
Bendini, Hugo do Nascimento ;
Modesto de Souza, Ricardo Cartaxo .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2018, 15 (08) :1299-1303
[10]   Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world [J].
Defourny, Pierre ;
Bontemps, Sophie ;
Bellemans, Nicolas ;
Cara, Cosmin ;
Dedieu, Gerard ;
Guzzonato, Eric ;
Hagolle, Olivier ;
Inglada, Jordi ;
Nicola, Laurentiu ;
Rabaute, Thierry ;
Savinaud, Mickael ;
Udroiu, Cosmin ;
Valero, Silvia ;
Begue, Agnes ;
Dejoux, Jean-Francois ;
El Harti, Abderrazak ;
Ezzahar, Jamal ;
Kussul, Nataliia ;
Labbassi, Kamal ;
Lebourgeois, Valentine ;
Miao, Zhang ;
Newby, Terrence ;
Nyamugama, Adolph ;
Salh, Norakhan ;
Shelestov, Andrii ;
Simonneaux, Vincent ;
Traore, Pierre Sibiry ;
Traore, Souleymane S. ;
Koetz, Benjamin .
REMOTE SENSING OF ENVIRONMENT, 2019, 221 :551-568