Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation

Article

Banerjee, Bikram Pratap, Spangenberg, German and Kant, Surya. 2020. "Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation." Remote Sensing. 12 (19). https://doi.org/10.3390/rs12193164
Article Title

Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation

ERA Journal ID201448
Article CategoryArticle
AuthorsBanerjee, Bikram Pratap, Spangenberg, German and Kant, Surya
Journal TitleRemote Sensing
Journal Citation12 (19)
Article Number3164
Number of Pages22
Year2020
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2072-4292
Digital Object Identifier (DOI)https://doi.org/10.3390/rs12193164
Web Address (URL)https://www.mdpi.com/2072-4292/12/19/3164
AbstractEfficient, precise and timely measurement of plant traits is important in the assessment of a breeding population. Estimating crop biomass in breeding trials using high-throughput technologies is difficult, as reproductive and senescence stages do not relate to reflectance spectra, and multiple growth stages occur concurrently in diverse genotypes. Additionally, vegetation indices (VIs) saturate at high canopy coverage, and vertical growth profiles are difficult to capture using VIs. A novel approach was implemented involving a fusion of complementary spectral and structural information, to calculate intermediate metrics such as crop height model (CHM), crop coverage (CC) and crop volume (CV), which were finally used to calculate dry (DW) and fresh (FW) weight of above-ground biomass in wheat. The intermediate metrics, CHM (R2 = 0.81, SEE = 4.19 cm) and CC (OA = 99.2%, Κ = 0.98) were found to be accurate against equivalent ground truth measurements. The metrics CV and CV×VIs were used to develop an effective and accurate linear regression model relationship with DW (R2 = 0.96 and SEE = 69.2 g/m2) and FW (R2 = 0.89 and SEE = 333.54 g/m2). The implemented approach outperformed commonly used VIs for estimation of biomass at all growth stages in wheat. The achieved results strongly support the applicability of the proposed approach for high-throughput phenotyping of germplasm in wheat and other crop species.
Keywordshigh-throughput phenotyping; wheat; plant breeding; crop coverage; crop volume
ANZSRC Field of Research 2020300406. Crop and pasture improvement (incl. selection and breeding)
300206. Agricultural spatial analysis and modelling
401304. Photogrammetry and remote sensing
Byline AffiliationsAgriculture Victoria
La Trobe University
University of Melbourne
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