IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v266y2022ics0378377422001287.html
   My bibliography  Save this article

Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes

Author

Listed:
  • Yousfi, Salima
  • Marín, José
  • Parra, Lorena
  • Lloret, Jaime
  • Mauri, Pedro V.

Abstract

Turfgrass phenotyping is a potential tool in different grass program breeding. The traditional methods for turfgrass drought phenotyping in field are time-consuming and labor-intensive. However, remote sensing techniques emerge as effective, rapid and easy approaches to optimize turfgrass selection under water stress. Remote sensing approaches are considerate as important strategies to select species of turfgrass tolerable to drought allowing green space sustainability and environment protection in regions with water limitation. Here we evaluated differences between six mixtures of C3-C4 turfgrass grown under two water regimes (limited and high irrigation). The performance of turf species was achieved using the green area (GA) vegetation index calculated from RGB (red green, blue) images obtained by ground camera and drone imagery, the normalized difference vegetation index (NDVI), the plant canopy temperature (CT) and soil moisture content (SM). Both vegetation (GA and NDVI) and water status (CT and SM) indices presented a significant difference in turfgrass growth under the two water regimes. Differences among turfgrass species were detected under limited and high irrigation using the vegetation indices. Both NDVI and GA allowed clear separation between drought-tolerant and susceptible turfgrass, as well as the identification of the mixtures with a rapid green regeneration after a period of limited irrigation. Moreover, the canopy temperature also discriminated between turfgrass species but only under limited irrigation, while soil moisture values did not differentiate between species. Furthermore, the regression and conceptual model using remote sensing parameters revealed the most adequate criteria to detect turfgrass variability under each growing condition. This study also highlights the usefulness of green area vegetation index derived from drone imagery. GA obtained by drone images in this study explained turfgrass variability better than that derived from ground RGB images or the NDVI.

Suggested Citation

  • Yousfi, Salima & Marín, José & Parra, Lorena & Lloret, Jaime & Mauri, Pedro V., 2022. "Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes," Agricultural Water Management, Elsevier, vol. 266(C).
    • Handle: RePEc:eee:agiwat:v:266:y:2022:i:c:s0378377422001287
    DOI: 10.1016/j.agwat.2022.107581
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377422001287
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2022.107581?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yousfi, Salima & Kellas, Nassim & Saidi, Lila & Benlakehal, Zahra & Chaou, Lydia & Siad, Djamila & Herda, Farid & Karrou, Mohamed & Vergara, Omar & Gracia, Adrian & Araus, José Luis & Serret, Maria Do, 2016. "Comparative performance of remote sensing methods in assessing wheat performance under Mediterranean conditions," Agricultural Water Management, Elsevier, vol. 164(P1), pages 137-147.
    2. José Marín & Salima Yousfi & Pedro V. Mauri & Lorena Parra & Jaime Lloret & Alberto Masaguer, 2020. "RGB Vegetation Indices, NDVI, and Biomass as Indicators to Evaluate C 3 and C 4 Turfgrass under Different Water Conditions," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. J. Dhakshayani & B. Surendiran, 2023. "M2F-Net: A Deep Learning-Based Multimodal Classification with High-Throughput Phenotyping for Identification of Overabundance of Fertilizers," Agriculture, MDPI, vol. 13(6), pages 1-19, June.
    2. Phumelelani Mbuqwa & Hezekiel Bheki Magagula & Ahmed Mukalazi Kalumba & Gbenga Abayomi Afuye, 2024. "Interdecadal Variations in Agricultural Drought Monitoring Using Land Surface Temperature and Vegetation Indices: A Case of the Amahlathi Local Municipality in South Africa," Sustainability, MDPI, vol. 16(18), pages 1-21, September.

    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. Elshikha, Diaa Eldin M. & Hunsaker, Douglas J. & Waller, Peter M. & Thorp, Kelly R. & Dierig, David & Wang, Guangyao & Cruz, Von Mark V. & Katterman, Matthew E. & Bronson, Kevin F. & Wall, Gerard W. &, 2022. "Estimation of direct-seeded guayule cover, crop coefficient, and yield using UAS-based multispectral and RGB data," Agricultural Water Management, Elsevier, vol. 265(C).
    2. Wang, Chu & Zhu, Kai & Bai, YanYan & Li, ChenYan & Li, Maona & Sun, Yan, 2024. "Response of stomatal conductance to plant water stress in buffalograss seed production: Observation with UAV thermal infrared imagery," Agricultural Water Management, Elsevier, vol. 292(C).
    3. Roberto Mancinelli & Mohamed Allam & Verdiana Petroselli & Mariam Atait & Merima Jasarevic & Alessia Catalani & Sara Marinari & Emanuele Radicetti & Aftab Jamal & Zainul Abideen & Gabriele Chilosi, 2023. "Durum Wheat Production as Affected by Soil Tillage and Fertilization Management in a Mediterranean Environment," Agriculture, MDPI, vol. 13(2), pages 1-15, February.
    4. Rezzouk, Fatima Zahra & Gracia-Romero, Adrian & Kefauver, Shawn C. & Nieto-Taladriz, Maria Teresa & Serret, Maria Dolores & Araus, José Luis, 2022. "Durum wheat ideotypes in Mediterranean environments differing in water and temperature conditions," Agricultural Water Management, Elsevier, vol. 259(C).
    5. Mohamed A. Darwish & Ahmed F. Elkot & Ahmed M. S. Elfanah & Adel I. Selim & Mohamed M. M. Yassin & Elsayed A. Abomarzoka & Maher A. El-Maghraby & Nazih Y. Rebouh & Abdelraouf M. Ali, 2023. "Evaluation of Wheat Genotypes under Water Regimes Using Hyperspectral Reflectance and Agro-Physiological Parameters via Genotype by Yield*Trait Approaches in Sakha Station, Delta, Egypt," Agriculture, MDPI, vol. 13(7), pages 1-20, June.

    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:eee:agiwat:v:266:y:2022:i:c:s0378377422001287. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    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.