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Radiometric correction of multispectral uas images : Evaluating the accuracy of the parrot sequoia camera and sunshine sensor

Olsson, Per Ola LU ; Vivekar, Ashish LU ; Adler, Karl ; Garcia Millan, Virginia E. LU ; Koc, Alexander ; Alamrani, Marwan and Eklundh, Lars LU orcid (2021) In Remote Sensing 13(4).
Abstract

Unmanned aerial systems (UAS) carrying commercially sold multispectral sensors equipped with a sunshine sensor, such as Parrot Sequoia, enable mapping of vegetation at high spatial resolution with a large degree of flexibility in planning data collection. It is, however, a challenge to perform radiometric correction of the images to create reflectance maps (orthomosaics with surface reflectance) and to compute vegetation indices with sufficient accuracy to enable comparisons between data collected at different times and locations. Studies have compared different radiometric correction methods applied to the Sequoia camera, but there is no consensus about a standard method that provides consistent results for all spectral bands and for... (More)

Unmanned aerial systems (UAS) carrying commercially sold multispectral sensors equipped with a sunshine sensor, such as Parrot Sequoia, enable mapping of vegetation at high spatial resolution with a large degree of flexibility in planning data collection. It is, however, a challenge to perform radiometric correction of the images to create reflectance maps (orthomosaics with surface reflectance) and to compute vegetation indices with sufficient accuracy to enable comparisons between data collected at different times and locations. Studies have compared different radiometric correction methods applied to the Sequoia camera, but there is no consensus about a standard method that provides consistent results for all spectral bands and for different flight conditions. In this study, we perform experiments to assess the accuracy of the Parrot Sequoia camera and sunshine sensor to get an indication if the quality of the data collected is sufficient to create accurate reflectance maps. In addition, we study if there is an influence of the atmosphere on the images and suggest a workflow to collect and process images to create a reflectance map. The main findings are that the sensitivity of the camera is influenced by camera temperature and that the atmosphere influences the images. Hence, we suggest letting the camera warm up before image collection and capturing images of reflectance calibration panels at an elevation close to the maximum flying height to compensate for influence from the atmosphere. The results also show that there is a strong influence of the orientation of the sunshine sensor. This introduces noise and limits the use of the raw sunshine sensor data to compensate for differences in light conditions. To handle this noise, we fit smoothing functions to the sunshine sensor data before we perform irradiance normalization of the images. The developed workflow is evaluated against data from a handheld spectroradiometer, giving the highest correlation (R2 = 0.99) for the normalized difference vegetation index (NDVI). For the individual wavelength bands, R2 was 0.80–0.97 for the red-edge, near-infrared, and red bands.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Multispectral camera, Radiometric correction, Unmanned aerial systems
in
Remote Sensing
volume
13
issue
4
article number
577
pages
26 pages
publisher
MDPI AG
external identifiers
  • scopus:85100745544
ISSN
2072-4292
DOI
10.3390/rs13040577
language
English
LU publication?
yes
id
419bef93-9cf7-49fc-87e4-a03d4e10d2ef
date added to LUP
2021-03-01 08:22:59
date last changed
2023-02-21 11:26:34
@article{419bef93-9cf7-49fc-87e4-a03d4e10d2ef,
  abstract     = {{<p>Unmanned aerial systems (UAS) carrying commercially sold multispectral sensors equipped with a sunshine sensor, such as Parrot Sequoia, enable mapping of vegetation at high spatial resolution with a large degree of flexibility in planning data collection. It is, however, a challenge to perform radiometric correction of the images to create reflectance maps (orthomosaics with surface reflectance) and to compute vegetation indices with sufficient accuracy to enable comparisons between data collected at different times and locations. Studies have compared different radiometric correction methods applied to the Sequoia camera, but there is no consensus about a standard method that provides consistent results for all spectral bands and for different flight conditions. In this study, we perform experiments to assess the accuracy of the Parrot Sequoia camera and sunshine sensor to get an indication if the quality of the data collected is sufficient to create accurate reflectance maps. In addition, we study if there is an influence of the atmosphere on the images and suggest a workflow to collect and process images to create a reflectance map. The main findings are that the sensitivity of the camera is influenced by camera temperature and that the atmosphere influences the images. Hence, we suggest letting the camera warm up before image collection and capturing images of reflectance calibration panels at an elevation close to the maximum flying height to compensate for influence from the atmosphere. The results also show that there is a strong influence of the orientation of the sunshine sensor. This introduces noise and limits the use of the raw sunshine sensor data to compensate for differences in light conditions. To handle this noise, we fit smoothing functions to the sunshine sensor data before we perform irradiance normalization of the images. The developed workflow is evaluated against data from a handheld spectroradiometer, giving the highest correlation (R<sup>2</sup> = 0.99) for the normalized difference vegetation index (NDVI). For the individual wavelength bands, R<sup>2</sup> was 0.80–0.97 for the red-edge, near-infrared, and red bands.</p>}},
  author       = {{Olsson, Per Ola and Vivekar, Ashish and Adler, Karl and Garcia Millan, Virginia E. and Koc, Alexander and Alamrani, Marwan and Eklundh, Lars}},
  issn         = {{2072-4292}},
  keywords     = {{Multispectral camera; Radiometric correction; Unmanned aerial systems}},
  language     = {{eng}},
  month        = {{02}},
  number       = {{4}},
  publisher    = {{MDPI AG}},
  series       = {{Remote Sensing}},
  title        = {{Radiometric correction of multispectral uas images : Evaluating the accuracy of the parrot sequoia camera and sunshine sensor}},
  url          = {{http://dx.doi.org/10.3390/rs13040577}},
  doi          = {{10.3390/rs13040577}},
  volume       = {{13}},
  year         = {{2021}},
}