Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat-8 imagery

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dc.contributor.author
Croft, Holly
Arabian, Joyce
Chen, Jing M.
Shang, Jiali
Liu, Jiangui
dc.date.accessioned
2024-07-16T19:26:23Z
dc.date.available
2024-07-16T19:26:23Z
dc.date.issued
2019-11-19
dc.description.abstract - en
Spatial information on crop nutrient status is central for monitoring vegetation health, plant productivity and managing nutrient optimization programs in agricultural systems. This study maps the spatial variability of leaf chlorophyll content within fields with differing quantities of nitrogen fertilizer application, using multispectral Landsat-8 OLI data (30 m). Leaf chlorophyll content and leaf area index measurements were collected at 15 wheat (Triticum aestivum) sites and 13 corn (Zea mays) sites approximately every 10 days during the growing season between May and September 2013 near Stratford, Ontario. Of the 28 sites, 9 sites were within controlled areas of zero nitrogen fertilizer application. Hyperspectral leaf reflectance measurements were also sampled using an Analytical Spectral Devices FieldSpecPro spectroradiometer (400–2500 nm). A two-step inversion process was developed to estimate leaf chlorophyll content from Landsat-8 satellite data at the sub-field scale, using linked canopy and leaf radiative transfer models. Firstly, at the leaf-level, leaf chlorophyll content was modelled using the PROSPECT model, using both hyperspectral and simulated mulitspectral Landsat-8 bands from the same leaf sample. Hyperspectral and multispectral validation results were both strong (R2 = 0.79, RMSE = 13.62 μg/cm2 and R2 = 0.81, RMSE = 9.45 μg/cm2, respectively). Secondly, leaf chlorophyll content was estimated from Landsat-8 satellite imagery for 7 dates within the growing season, using PROSPECT linked to the 4-Scale canopy model. The Landsat-8 derived estimates of leaf chlorophyll content demonstrated a strong relationship with measured leaf chlorophyll values (R2 = 0.64, RMSE = 16.18 μg/cm2), and compared favourably to correlations between leaf chlorophyll and the best performing tested spectral vegetation index (Green Normalised Difference Vegetation Index, GNDVI; R2 = 0.59). This research provides an operational basis for modelling within-field variations in leaf chlorophyll content as an indicator of plant nitrogen stress, using a physically-based modelling approach, and opens up the possibility of exploiting a wealth of multispectral satellite data and UAV-mounted multispectral imaging systems.
dc.identifier.citation
Croft, H., Arabian, J., Chen, J. M., Shang, J., & Liu, J. (2019). Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using landsat-8 imagery. Precision Agriculture, 21(4), 856–880. https://doi.org/10.1007/s11119-019-09698-y
dc.identifier.doi
https://doi.org/10.1007/s11119-019-09698-y
dc.identifier.issn
1573-1618
dc.identifier.uri
https://open-science.canada.ca/handle/123456789/2698
dc.language.iso
en
dc.publisher
Springer Nature
dc.rights - en
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.rights - fr
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.rights.openaccesslevel - en
Gold
dc.rights.openaccesslevel - fr
Or
dc.rights.uri - en
https://creativecommons.org/licenses/by/4.0/
dc.rights.uri - fr
https://creativecommons.org/licenses/by/4.0/deed.fr
dc.subject - en
Agriculture
dc.subject - fr
Agriculture
dc.subject.en - en
Agriculture
dc.subject.fr - fr
Agriculture
dc.title - en
Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat-8 imagery
dc.type - en
Article
dc.type - fr
Article
local.article.journalissue
4
local.article.journaltitle
Precision Agriculture
local.article.journalvolume
21
local.pagination
856-880
local.peerreview - en
Yes
local.peerreview - fr
Oui
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