IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i12p2083-d993529.html
   My bibliography  Save this article

A Convolutional Neural Network Method for Rice Mapping Using Time-Series of Sentinel-1 and Sentinel-2 Imagery

Author

Listed:
  • Mohammad Saadat

    (School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran)

  • Seyd Teymoor Seydi

    (School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran)

  • Mahdi Hasanlou

    (School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran)

  • Saeid Homayouni

    (Center Eau Terre Environnement, Institut National de la Recherche Scientifique, Québec, QC G1K 9A9, Canada)

Abstract

Rice is one of the most essential and strategic food sources globally. Accordingly, policymakers and planners often consider a special place in the agricultural economy and economic development for this essential commodity. Typically, a sample survey is carried out through field observations and farmers’ consultations to estimate annual rice yield. Studies show that these methods lead to many errors and are time-consuming and costly. Satellite remote sensing imagery is widely used in agriculture to provide timely, high-resolution data and analytical capabilities. Earth observations with high spatial and temporal resolution have provided an excellent opportunity for monitoring and mapping crop fields. This study used the time series of dual-pol synthetic aperture radar (SAR) images of Sentinel-1 and multispectral Sentinel-2 images from Sentinel-1 and Sentinel-2 ESA’s Copernicus program to extract rice cultivation areas in Mazandaran province in Iran. A novel multi-channel streams deep feature extraction method was proposed to simultaneously take advantage of SAR and optical imagery. The proposed framework extracts deep features from the time series of NDVI and original SAR images by first and second streams. In contrast, the third stream integrates them into multi-levels (shallow to deep high-level features); it extracts deep features from the channel attention module (CAM), and group dilated convolution. The efficiency of the proposed method was assessed on approximately 129,000 in-situ samples and compared to other state-of-the-art methods. The results showed that combining NDVI time series and SAR data can significantly improve rice-type mapping. Moreover, the proposed methods had high efficiency compared with other methods, with more than 97% overall accuracy. The performance of rice-type mapping based on only time-series SAR images was better than only time-series NDVI datasets. Moreover, the classification performance of the proposed framework in mapping the Shirodi rice type was better than that of the Tarom type.

Suggested Citation

  • Mohammad Saadat & Seyd Teymoor Seydi & Mahdi Hasanlou & Saeid Homayouni, 2022. "A Convolutional Neural Network Method for Rice Mapping Using Time-Series of Sentinel-1 and Sentinel-2 Imagery," Agriculture, MDPI, vol. 12(12), pages 1-19, December.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:2083-:d:993529
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/12/2083/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/12/2083/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Muhammad Nurfaiz Abd. Kharim & Aimrun Wayayok & Ahmad Fikri Abdullah & Ahmad Fikri Abdullah, 2020. "Effect Of Variable Rate Application On Rice Leaves Burn And Chlorosis In System Of Rice Intensification," Malaysian Journal of Sustainable Agriculture (MJSA), Zibeline International Publishing, vol. 4(2), pages 66-70, March.
    2. Munyasya, Alex Ndolo & Koskei, Kiprotich & Zhou, Rui & Liu, Shu-Tong & Indoshi, Sylvia Ngaira & Wang, Wei & Zhang, Xu-Cheng & Cheruiyot, Wesly Kiprotich & Mburu, David Mwehia & Nyende, Aggrey Bernard , 2022. "Integrated on-site & off-site rainwater-harvesting system boosts rainfed maize production for better adaptation to climate change," Agricultural Water Management, Elsevier, vol. 269(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hasan Mirzakhaninafchi & Manjeet Singh & Anoop Kumar Dixit & Apoorv Prakash & Shikha Sharda & Jugminder Kaur & Ali Mirzakhani Nafchi, 2022. "Performance Assessment of a Sensor-Based Variable-Rate Real-Time Fertilizer Applicator for Rice Crop," Sustainability, MDPI, vol. 14(18), pages 1-25, September.
    2. Wang, Wendi & Straffelini, Eugenio & Tarolli, Paolo, 2023. "Steep-slope viticulture: The effectiveness of micro-water storage in improving the resilience to weather extremes," Agricultural Water Management, Elsevier, vol. 286(C).
    3. Muhammad Waseem Rasheed & Jialiang Tang & Abid Sarwar & Suraj Shah & Naeem Saddique & Muhammad Usman Khan & Muhammad Imran Khan & Shah Nawaz & Redmond R. Shamshiri & Marjan Aziz & Muhammad Sultan, 2022. "Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    4. Qian, Long & Meng, Huayue & Chen, Xiaohong & Tang, Rong, 2023. "Evaluating agricultural drought and flood abrupt alternation: A case study of cotton in the middle-and-lower Yangtze River, China," Agricultural Water Management, Elsevier, vol. 283(C).
    5. Maham Navida & Muhammad Nadeem & Tahir Mahmood Qureshi & Rami Adel Pashameah & Faiqa Malik & Aqsa Iqbal & Muhammad Sultan & Hala M. Abo-Dief & Abdullah K. Alanazi, 2022. "The Synergistic Effects of Sonication and Microwave Processing on the Physicochemical Properties and Phytochemicals of Watermelon ( Citrullus lanatus ) Juice," Agriculture, MDPI, vol. 12(9), pages 1-13, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:2083-:d:993529. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.