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Precision monitoring of leaf area index and chlorophyll content of major field crops in Northern Europe using UAV remote sensing and radiative transfer modeling

Thapa, Shangharsha LU orcid ; Bouras, EI Houssaine ; Olsson, Per-Ola LU ; Alexandersson, Erik ; Roitsch, Thomas ; Albertsson, Johannes ; Hörteborn, Axel and Eklundh, Lars LU orcid (2026) In Precision Agriculture
Abstract
Purpose
Long-term monitoring of crop biophysical and biochemical traits remains challenging in high-latitude regions due to short growing seasons, frequent cloud cover, and highly variable weather. In this context, unmanned aerial vehicles (UAVs) offer flexible, high-resolution observations, but their added value relative to low-cost proximal sensors and their effectiveness for radiative transfer model (RTM) inversion across diverse crop canopies remain insufficiently quantified. This study evaluated the potential of a two-band proximal spectral reflectance sensor (SRS) and a five-band multispectral UAV sensor for retrieving leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) using PROSAIL... (More)
Purpose
Long-term monitoring of crop biophysical and biochemical traits remains challenging in high-latitude regions due to short growing seasons, frequent cloud cover, and highly variable weather. In this context, unmanned aerial vehicles (UAVs) offer flexible, high-resolution observations, but their added value relative to low-cost proximal sensors and their effectiveness for radiative transfer model (RTM) inversion across diverse crop canopies remain insufficiently quantified. This study evaluated the potential of a two-band proximal spectral reflectance sensor (SRS) and a five-band multispectral UAV sensor for retrieving leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) using PROSAIL inversion across major crops in Northern Europe over two growing seasons (2023–2024).

Methods and Results
Two inversion approaches – look-up table (LUT) and artificial neural network (ANN) were applied to PROSAIL simulations. UAV–PROSAIL–ANN outperformed LUT-based inversion and SRS observations, achieving the highest accuracy for LAI (R2 = 0.81–0.95; RMSE = 0.27–0.77 m2/m2), followed by CCC (R2 = 0.58–0.94; RMSE < 60 μg/cm2), while LCC remained less accurately estimated (R2 = 0.26–0.78; RMSE < 16 μg/cm2). Across sensors and methods, retrieval accuracy decreased in the order of LAI, CCC, and LCC, reflecting the stronger spectral control of canopy structure compared to biochemical traits.

Conclusions
The UAV–PROSAIL–ANN framework effectively captured spatial and temporal variability in crop traits, producing canopy-scale maps consistent with field observations. These results demonstrate the robustness and scalability of hybrid PROSAIL–ANN inversion for high-latitude crop monitoring, while highlighting current limitations in biochemical trait retrieval using multispectral data. (Less)
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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
UAV remote sensing, Radiative transfer modeling, PROSAIL inversion, Leaf area index, Chlorophyll content, Precision agriculture
in
Precision Agriculture
publisher
Springer
external identifiers
  • scopus:105047492578
ISSN
1385-2256
DOI
10.1007/s11119-026-10403-z
language
English
LU publication?
yes
id
f40ee5cd-e7a4-41b7-9178-41399ae813ca
date added to LUP
2026-10-01 11:50:15
date last changed
2026-10-01 15:29:41
@article{f40ee5cd-e7a4-41b7-9178-41399ae813ca,
  abstract     = {{Purpose<br/>Long-term monitoring of crop biophysical and biochemical traits remains challenging in high-latitude regions due to short growing seasons, frequent cloud cover, and highly variable weather. In this context, unmanned aerial vehicles (UAVs) offer flexible, high-resolution observations, but their added value relative to low-cost proximal sensors and their effectiveness for radiative transfer model (RTM) inversion across diverse crop canopies remain insufficiently quantified. This study evaluated the potential of a two-band proximal spectral reflectance sensor (SRS) and a five-band multispectral UAV sensor for retrieving leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) using PROSAIL inversion across major crops in Northern Europe over two growing seasons (2023–2024).<br/><br/>Methods and Results<br/>Two inversion approaches – look-up table (LUT) and artificial neural network (ANN) were applied to PROSAIL simulations. UAV–PROSAIL–ANN outperformed LUT-based inversion and SRS observations, achieving the highest accuracy for LAI (R2 = 0.81–0.95; RMSE = 0.27–0.77 m2/m2), followed by CCC (R2 = 0.58–0.94; RMSE &lt; 60 μg/cm2), while LCC remained less accurately estimated (R2 = 0.26–0.78; RMSE &lt; 16 μg/cm2). Across sensors and methods, retrieval accuracy decreased in the order of LAI, CCC, and LCC, reflecting the stronger spectral control of canopy structure compared to biochemical traits.<br/><br/>Conclusions<br/>The UAV–PROSAIL–ANN framework effectively captured spatial and temporal variability in crop traits, producing canopy-scale maps consistent with field observations. These results demonstrate the robustness and scalability of hybrid PROSAIL–ANN inversion for high-latitude crop monitoring, while highlighting current limitations in biochemical trait retrieval using multispectral data.}},
  author       = {{Thapa, Shangharsha and Bouras, EI Houssaine and Olsson, Per-Ola and Alexandersson, Erik and Roitsch, Thomas and Albertsson, Johannes and Hörteborn, Axel and Eklundh, Lars}},
  issn         = {{1385-2256}},
  keywords     = {{UAV remote sensing; Radiative transfer modeling; PROSAIL inversion; Leaf area index; Chlorophyll content; Precision agriculture}},
  language     = {{eng}},
  month        = {{08}},
  publisher    = {{Springer}},
  series       = {{Precision Agriculture}},
  title        = {{Precision monitoring of leaf area index and chlorophyll content of major field crops in Northern Europe using UAV remote sensing and radiative transfer modeling}},
  url          = {{http://dx.doi.org/10.1007/s11119-026-10403-z}},
  doi          = {{10.1007/s11119-026-10403-z}},
  year         = {{2026}},
}