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Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor

Olsson, Per-Ola and Vivekar, Ashish and Adler, Karl and Garcia Millan, Virginia E. and Koc, Alexander and Alamrani, Marwan and Eklundh, Lars (2021). Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor. Remote Sensing. 13 , 577
[Research article]

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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 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-2 = 0.99) for the normalized difference vegetation index (NDVI). For the individual wavelength bands, R-2 was 0.80-0.97 for the red-edge, near-infrared, and red bands.

Authors/Creators:Olsson, Per-Ola and Vivekar, Ashish and Adler, Karl and Garcia Millan, Virginia E. and Koc, Alexander and Alamrani, Marwan and Eklundh, Lars
Title:Radiometric Correction of Multispectral UAS Images: Evaluating the Accuracy of the Parrot Sequoia Camera and Sunshine Sensor
Series Name/Journal:Remote Sensing
Year of publishing :2021
Volume:13
Article number:577
Number of Pages:26
Publisher:MDPI
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
Keywords:unmanned aerial systems, multispectral camera, radiometric correction
URN:NBN:urn:nbn:se:slu:epsilon-p-111306
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-111306
Additional ID:
Type of IDID
DOI10.3390/rs13040577
Web of Science (WoS)000624418700001
ID Code:23108
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
LTV - Fakulteten för landskapsarkitektur, trädgårds- och växtproduktionsvetenskap
Department:(NL, NJ) > Dept. of Soil and Environment
(S) > Dept. of Soil and Environment

(LTJ, LTV) > Department of Plant Breeding (from 130101)
Deposited By: SLUpub Connector
Deposited On:08 Apr 2021 08:03
Metadata Last Modified:08 Apr 2021 08:11

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