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Processing of remote sensing information to retrieve leaf area index in barley: a comparison of methods

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F22%3A00559877" target="_blank" >RIV/86652079:_____/22:00559877 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/article/10.1007/s11119-022-09893-4" target="_blank" >https://link.springer.com/article/10.1007/s11119-022-09893-4</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s11119-022-09893-4" target="_blank" >10.1007/s11119-022-09893-4</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Processing of remote sensing information to retrieve leaf area index in barley: a comparison of methods

  • Original language description

    Leaf area index (LAI) is a key variable in understanding and modeling crop-environment interactions. With the advent of increasingly higher spatial resolution satellites and sensors mounted on remotely piloted aircrafts (RPAs), the use of remote sensing in precision agriculture is becoming more common. Since also the availability of methods to retrieve LAI from image data have also drastically expanded, it is necessary to test simultaneously as many methods as possible to understand the advantages and disadvantages of each approach. Ground-based LAI data from three years of barley experiments were related to remote sensing information using vegetation indices (VI), machine learning (ML) and radiative transfer models (RTM), to assess the relative accuracy and efficacy of these methods. The optimized soil adjusted vegetation index and a modified version of the Weighted Difference Vegetation Index performed slightly better than any other retrieval method. However, all methods yielded coefficients of determination of around 0.7 to 0.9. The best performing machine learning algorithms achieved higher accuracies when four Sentinel-2 bands instead of 12 were used. Also, the good performance of VIs and the satisfactory performance of the 4-band RTM, strongly support the synergistic use of satellites and RPAs in precision agriculture. One of the methods used, Sen2-Agri, an open source ML-RTM-based operational system, was also able to accurately retrieve LAI, although it is restricted to Sentinel-2 and Landsat data. This study shows the benefits of testing simultaneously a broad range of retrieval methods to monitor crops for precision agriculture.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    40101 - Agriculture

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Precision Agriculture

  • ISSN

    1385-2256

  • e-ISSN

    1573-1618

  • Volume of the periodical

    23

  • Issue of the periodical within the volume

    4

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    23

  • Pages from-to

    1449-1472

  • UT code for WoS article

    000767905400001

  • EID of the result in the Scopus database

    2-s2.0-85126116428