MURAL - Maynooth University Research Archive Library



    Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques


    Askaril, Mohammad Sadegh and McCarthy, Tim and Magee, Aidan and Murphy, Darren J. (2019) Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques. Remote Sensing, 11 (15). pp. 1-23. ISSN 2072-4292

    [img]
    Preview
    Download (9MB) | Preview
    Official URL: https://www.mdpi.com/2072-4292/11/15/1835/pdf


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    Hyperspectral and multispectral imagery have been demonstrated to have a considerable potential for near real-time monitoring and mapping of grass quality indicators. The objective of this study was to evaluate the efficiency of remote sensing techniques for quantification of aboveground grass biomass (BM) and crude protein (CP) in a temperate European climate such as Ireland. The experiment was conducted on 64 plots and 53 paddocks with varying quantities of nitrogen applied. Hyperspectral imagery (HSI) and multispectral imagery (MSI) were analyzed to develop the prediction models. The MSI data used in this study were captured using an unmanned aircraft vehicle (UAV) and the satellite Sentinel-2, while the HSI data were obtained using a handheld hyperspectral camera. The prediction models were developed using partial least squares regression (PLSR) and stepwise multi-linear regression (MLR). Eventually, the spatial distribution of grass biomass over plots and paddocks was mapped to assess the within-field variability of grass quality metrics. An excellent accuracy was achieved for the prediction of BM and CP using HSI (RPD > 2.5 and R 2 > 0.8), and a good accuracy was obtained via MSI-UAV (2 0.7) for the grass quality indicators. The accuracy of the models calculated using MSI-Sentinel-2 was reasonable for BM prediction and insufficient for CP estimation. The red-edge range of the wavelengths showed the maximum impact on the predictability of grass BM, and the NIR range had the greatest influence on the estimation of grass CP. Both the PLSR and MLR techniques were found to be sufficiently robust for spectral modelling of aboveground BM and CP. The PLSR yielded a slightly better model than MLR. This study suggested that remote sensing techniques can be used as a rapid and reliable approach for near real-time quantitative assessment of fresh grass quality under a temperate European climate.

    Item Type: Article
    Additional Information: ©2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
    Keywords: hyperspectral; multispectral; fertilization; grass biomass; crude protein;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Item ID: 10974
    Identification Number: https://doi.org/10.3390/rs11151835
    Depositing User: Tim McCarthy
    Date Deposited: 26 Aug 2019 13:56
    Journal or Publication Title: Remote Sensing
    Publisher: MDPI
    Refereed: Yes
    Funders: Department of Agriculture, Food and the Marine
    URI:

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year