Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France

被引:21
|
作者
Xie, Guanyao [1 ]
Niculescu, Simona [1 ,2 ]
机构
[1] IUEM UBO, Lab LETG Brest, Geomer, UMR 6554 CNRS, F-29200 Brest, France
[2] Univ Western Brittany, Dept Geog, 3 Rue Archives, F-29238 Brest, France
关键词
winter crops mapping; winter crops phenology; machine learning; hierarchical classification; object-based classification; pixel-based classification; Google Earth Engine (GEE); Sentinel-1; Sentinel-2; RANDOM FOREST; CLASSIFICATION ACCURACY; VEGETATION; COVER; INDEX; NDVI; SEGMENTATION; SAR; MULTIRESOLUTION; SENSITIVITY;
D O I
10.3390/rs14184437
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Crop supply and management is a global issue, particularly in the context of global climate change and rising urbanization. Accurate mapping and monitoring of specific crop types are crucial for crop studies. In this study, we proposed: (1) a methodology to map two main winter crops (winter wheat and winter barley) in the northern region of Finistere with high-resolution Sentinel-2 data. Different classification approaches (the hierarchical classification and the classical direct extraction), and classification methods (pixel-based classification (PBC) and object-based classification (OBC)) were performed and evaluated. Subsequently, (2) a further study that involved monitoring the phenology of the winter crops was carried out, based on the previous results. The aim is to understand the temporal behavior from sowing to harvesting, identifying three important phenological statuses (germination, heading, and ripening, including harvesting). Due to the high frequency of precipitation in our study area, crop phenology monitoring was performed using Sentinel-1 C-band SAR backscatter time series data using the Google Earth Engine (GEE) platform. The results of the classification showed that the hierarchical classification achieved a better accuracy when it is compared to the direct extraction, with an overall accuracy of 0.932 and a kappa coefficient of 0.888. Moreover, in the hierarchical classification process, OBC reached a better accuracy in cropland mapping, and PBC was proven more suitable for winter crop extraction. Additionally, in the time series backscatter coefficient of winter wheat, the germination and ripening (harvesting) phases can be identified at VV and VH/VV polarizations, and heading can be identified in both VV and VH polarizations. Secondly, we were able to detect the germination phase of winter barley in VV and VH, ripening with both polarizations and VH/VV, and finally, heading in VV and VH polarizations.
引用
收藏
页数:27
相关论文
共 50 条
  • [41] Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images
    Maleki, Saeideh
    Baghdadi, Nicolas
    Bazzi, Hassan
    Dantas, Cassio Fraga
    Ienco, Dino
    Nasrallah, Yasser
    Najem, Sami
    REMOTE SENSING, 2024, 16 (23)
  • [42] National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series
    Frantz, David
    Schug, Franz
    Okujeni, Akpona
    Navacchi, Claudio
    Wagner, Wolfgang
    van der Linden, Sebastian
    Hostert, Patrick
    REMOTE SENSING OF ENVIRONMENT, 2021, 252
  • [43] Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms
    Rao, Preeti
    Zhou, Weiqi
    Bhattarai, Nishan
    Srivastava, Amit K.
    Singh, Balwinder
    Poonia, Shishpal
    Lobell, David B.
    Jain, Meha
    REMOTE SENSING, 2021, 13 (10)
  • [44] Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring
    Beriaux, Emilie
    Jago, Alban
    Lucau-Danila, Cozmin
    Planchon, Viviane
    Defourny, Pierre
    REMOTE SENSING, 2021, 13 (14)
  • [45] Mowing detection using Sentinel-1 and Sentinel-2 time series for large scale grassland monitoring
    De Vroey, Mathilde
    de Vendictis, Laura
    Zavagli, Massimo
    Bontemps, Sophie
    Heymans, Diane
    Radoux, Julien
    Koetz, Benjamin
    Defourny, Pierre
    REMOTE SENSING OF ENVIRONMENT, 2022, 280
  • [46] Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms
    Ma, Guolin
    Ding, Jianli
    Han, Lijng
    Zhang, Zipeng
    Ran, Si
    REGIONAL SUSTAINABILITY, 2021, 2 (02) : 177 - 188
  • [47] Exploiting Time Series of Sentinel-1 and Sentinel-2 Imagery to Detect Meadow Phenology in Mountain Regions
    Stendardi, Laura
    Karlsen, Stein Rune
    Niedrist, Georg
    Gerdol, Renato
    Zebisch, Marc
    Rossi, Mattia
    Notarnicola, Claudia
    REMOTE SENSING, 2019, 11 (05)
  • [48] Deep learning-based building height mapping using Sentinel-1 and Sentinel-2 data
    Cai, Bowen
    Shao, Zhenfeng
    Huang, Xiao
    Zhou, Xuechao
    Fang, Shenghui
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 122
  • [49] Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms
    MA Guolin
    DING Jianli
    HAN Lijing
    ZHANG Zipeng
    RAN Si
    Regional Sustainability, 2021, 2 (02) : 177 - 188
  • [50] CerealNet: A Hybrid Deep Learning Architecture for Cereal Crop Mapping Using Sentinel-2 Time-Series
    Machichi, Mouad Alami
    El Mansouri, Loubna
    Imani, Yasmina
    Bourja, Omar
    Hadria, Rachid
    Lahlou, Ouiam
    Benmansour, Samir
    Zennayi, Yahya
    Bourzeix, Francois
    INFORMATICS-BASEL, 2022, 9 (04):