IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-3-030-84144-7_8.html
   My bibliography  Save this book chapter

Trends in Satellite Sensors and Image Time Series Processing Methods for Crop Phenology Monitoring

In: Information and Communication Technologies for Agriculture—Theme I: Sensors

Author

Listed:
  • Luca Pipia

    (Parc de Montjüic
    Universitat de València. C/Catedrático José Beltrán)

  • Santiago Belda

    (Universitat de València. C/Catedrático José Beltrán)

  • Belen Franch

    (Universitat de València. C/Catedrático José Beltrán)

  • Jochem Verrelst

    (Universitat de València. C/Catedrático José Beltrán)

Abstract

National and International space agencies are determined to keep their fingers on the pulse of crop monitoring through Earth Observation (EO) satellites, which is typically tackled with optical imagery. In this regard, there has long been a trade-off between repetition time and spatial resolution. Another limitation of optical remotely sensed data is their typical discontinuity in time, caused by cloud cover or adverse atmospheric effects. Enduring clouds over agricultural fields can mask key stages of crop growth, leading to uncertainties in crop monitoring practices such as yield predictions. Gap-filling methods can provide a key solution for accurate crop phenology characterization. This chapter first provides a historical overview of EO missions dedicated to crop monitoring. Then, it addresses the rapidly evolving fields of gap-filling and land surface phenology (LSP) metrics calculation using a new in-house developed toolbox, DATimeS. These techniques have been put into practice for homogeneous and heterogeneous demonstration landscapes over the United States. Time series of Difference Vegetation Index (DVI) were processed from two EO data sources: high spatial resolution Sentinel-2 and, low spatial resolution MODIS data. LSP metrics such as start and end of season were calculated after gap filling processing at 1km resolution. Over the homogeneous area both S2 and MODIS are well able to capture the phenology trends of the dominant crop and LSP metrics were successfully mapped. Conversely, the MODIS dataset presented more difficulties than S2 to capture the phenology trend of winter wheat over heterogeneous landscape.

Suggested Citation

  • Luca Pipia & Santiago Belda & Belen Franch & Jochem Verrelst, 2022. "Trends in Satellite Sensors and Image Time Series Processing Methods for Crop Phenology Monitoring," Springer Optimization and Its Applications, in: Dionysis D. Bochtis & Maria Lampridi & George P. Petropoulos & Yiannis Ampatzidis & Panos Pardalos (ed.), Information and Communication Technologies for Agriculture—Theme I: Sensors, pages 199-231, Springer.
  • Handle: RePEc:spr:spochp:978-3-030-84144-7_8
    DOI: 10.1007/978-3-030-84144-7_8
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:spochp:978-3-030-84144-7_8. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.