IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i7p990-d1421694.html
   My bibliography  Save this article

Early Crop Identification Study Based on Sentinel-1/2 Images with Feature Optimization Strategy

Author

Listed:
  • Jiansong Luo

    (College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010018, China)

  • Min Xie

    (College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010018, China)

  • Qiang Wu

    (College of Agronomy, Henan Agricultural University, Zhengzhou 450046, China)

  • Jun Luo

    (College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010018, China)

  • Qi Gao

    (College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010018, China)

  • Xuezhi Shao

    (College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010018, China)

  • Yongping Zhang

    (College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010018, China)

Abstract

The timely and accurate mapping of crop types is crucial for agricultural insurance, futures, and assessments of food security risks. However, crop mapping is currently focused on the post-harvest period, and less attention has been paid to early crop mapping. In this study, the feasibility of using Sentinel-1 (S1) and Sentinel-2 (S2) data for the earliest identifiable time (EIT) for major crops (sunflower, maize, spring wheat, and melon) was explored in the Hetao Irrigation District (HID) of China, based on the Google Earth Engine (GEE) platform. An early crop identification strategy based on the Random Forest (RF) model for HID was proposed, and the performance of the model transfer was evaluated. First, the median synthesis, linear shift interpolation, and the Savitzky–Golay (SG) filter methods were used to reconstruct the time series of S1 and S2. Subsequently, the sensitivity of different input features, time intervals, and data integration to different early crop identifications was evaluated based on the RF model. Finally, the model with optimal parameters was evaluated in terms of its transfer capacity and used for the early mapping of crops in the HID area. The results showed that the features extracted from S2 images synthesized at 10-day intervals performed well in obtaining crop EITs. Sunflower, maize, spring wheat, and melon could be identified 90, 90, 70, and 40 days earlier than the harvest date. The identification accuracy, measured by the F1-score, could reach 0.97, 0.95, 0.98, and 0.90, respectively. The performance of the model transfer is good, with the F1-score decreasing from 0 to 0.04 and no change in EIT for different crops. It was also found that the EIT of crops obtained using S1 data alone was 50–90 days later than that obtained using S2 data alone. Additionally, when S1 and S2 were used jointly, S1 data contributed little to early crop identification. This study highlights the potential of early crop mapping using satellite data, which provides a feasible solution for the early identification of crops in the HID area and valuable information for food security assurance in the region.

Suggested Citation

  • Jiansong Luo & Min Xie & Qiang Wu & Jun Luo & Qi Gao & Xuezhi Shao & Yongping Zhang, 2024. "Early Crop Identification Study Based on Sentinel-1/2 Images with Feature Optimization Strategy," Agriculture, MDPI, vol. 14(7), pages 1-28, June.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:7:p:990-:d:1421694
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/7/990/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/7/990/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Linhao Zhang & Haiping Tang & Peijun Shi & Wei Jia & Luwei Dai, 2023. "Geographically and Ontologically Oriented Scoping of a Dry Valley and Its Spatial Characteristics Analysis: The Case of the Three Parallel Rivers Region," Land, MDPI, vol. 12(6), pages 1-17, June.
    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.

      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:14:y:2024:i:7:p:990-:d:1421694. 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.