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Optimizing Data Consistency in UAV Multispectral Imaging for Radiometric Correction and Sensor Conversion Models.

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Title: Optimizing Data Consistency in UAV Multispectral Imaging for Radiometric Correction and Sensor Conversion Models.
Authors: Yang, Weiguang1,2,3 (AUTHOR), Fu, Huaiyuan2,3,4 (AUTHOR), Xu, Weicheng3,5 (AUTHOR), Wu, Jinhao1,2,3,4 (AUTHOR), Liu, Shiyuan2,3,4,5 (AUTHOR), Li, Xi1,2,3,4 (AUTHOR), Tan, Jiangtao2,3,4 (AUTHOR), Lan, Yubin1,2,3 (AUTHOR), Zhang, Lei2,3,4 (AUTHOR) zhanglei@scau.edu.cn
Source: Remote Sensing. Jun2025, Vol. 17 Issue 12, p2001. 21p.
Subjects: Agricultural remote sensing, Data conversion, Random forest algorithms, Precision farming, Decision trees, Multispectral imaging
Abstract: Recent advancements in precision agriculture have been significantly bolstered by the Uncrewed Aerial Vehicles (UAVs) equipped with multispectral sensors. These systems are pivotal in transforming sensor-recorded Digital Number (DN) values into universal reflectance, crucial for ensuring data consistency irrespective of collection time, region, and illumination. This study, conducted across three regions in China using Sequoia and Phantom 4 Multispectral cameras, focused on examining the effects of radiometric correction on data consistency and accuracy, and developing a conversion model for data from these two sensors. Our findings revealed that radiometric correction substantially enhances data consistency in vegetated areas for both sensors, though its impact on non-vegetated areas is limited. Recalibrating reflectance for calibration plates significantly improved the consistency of band values and the accuracy of vegetation index calculations for both cameras. Decision tree and random forest models emerged as more effective for data conversion between the sensors, achieving R2 values up to 0.91. Additionally, the P4M generally outperformed the Sequoia in accuracy, particularly with standard reflectance calibration. These insights emphasize the critical role of radiometric correction in UAV remote sensing for precision agriculture, underscoring the complexities of sensor data consistency and the potential for generalization of models across multi-sensor platforms. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Optimizing Data Consistency in UAV Multispectral Imaging for Radiometric Correction and Sensor Conversion Models.
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  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Weiguang%22">Yang, Weiguang</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fu%2C+Huaiyuan%22">Fu, Huaiyuan</searchLink><relatesTo>2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Weicheng%22">Xu, Weicheng</searchLink><relatesTo>3,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Jinhao%22">Wu, Jinhao</searchLink><relatesTo>1,2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Shiyuan%22">Liu, Shiyuan</searchLink><relatesTo>2,3,4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Xi%22">Li, Xi</searchLink><relatesTo>1,2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tan%2C+Jiangtao%22">Tan, Jiangtao</searchLink><relatesTo>2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lan%2C+Yubin%22">Lan, Yubin</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Lei%22">Zhang, Lei</searchLink><relatesTo>2,3,4</relatesTo> (AUTHOR)<i> zhanglei@scau.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2025, Vol. 17 Issue 12, p2001. 21p.
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  Data: <searchLink fieldCode="DE" term="%22Agricultural+remote+sensing%22">Agricultural remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Data+conversion%22">Data conversion</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Precision+farming%22">Precision farming</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br /><searchLink fieldCode="DE" term="%22Multispectral+imaging%22">Multispectral imaging</searchLink>
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  Data: Recent advancements in precision agriculture have been significantly bolstered by the Uncrewed Aerial Vehicles (UAVs) equipped with multispectral sensors. These systems are pivotal in transforming sensor-recorded Digital Number (DN) values into universal reflectance, crucial for ensuring data consistency irrespective of collection time, region, and illumination. This study, conducted across three regions in China using Sequoia and Phantom 4 Multispectral cameras, focused on examining the effects of radiometric correction on data consistency and accuracy, and developing a conversion model for data from these two sensors. Our findings revealed that radiometric correction substantially enhances data consistency in vegetated areas for both sensors, though its impact on non-vegetated areas is limited. Recalibrating reflectance for calibration plates significantly improved the consistency of band values and the accuracy of vegetation index calculations for both cameras. Decision tree and random forest models emerged as more effective for data conversion between the sensors, achieving R2 values up to 0.91. Additionally, the P4M generally outperformed the Sequoia in accuracy, particularly with standard reflectance calibration. These insights emphasize the critical role of radiometric correction in UAV remote sensing for precision agriculture, underscoring the complexities of sensor data consistency and the potential for generalization of models across multi-sensor platforms. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs17122001
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        Text: English
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        PageCount: 21
        StartPage: 2001
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      – SubjectFull: Agricultural remote sensing
        Type: general
      – SubjectFull: Data conversion
        Type: general
      – SubjectFull: Random forest algorithms
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      – SubjectFull: Precision farming
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      – SubjectFull: Decision trees
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      – SubjectFull: Multispectral imaging
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      – TitleFull: Optimizing Data Consistency in UAV Multispectral Imaging for Radiometric Correction and Sensor Conversion Models.
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              M: 06
              Text: Jun2025
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              Y: 2025
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