Angle Effects in UAV Quantitative Remote Sensing: Research Progress, Challenges and Trends

In recent years, unmanned aerial vehicle (UAV) quantitative remote sensing technology has demonstrated significant advantages in fields such as agricultural monitoring and ecological environment assessment. However, achieving the goal of quantification still faces major challenges due to the angle effect. This effect, caused by the bidirectional reflectance distribution function (BRDF) of surface targets, leads to significant spectral response variations at different observation angles, thereby affecting the inversion accuracy of physicochemical parameters, internal components, and three-dimensional structures of ground objects. This study systematically reviewed 48 relevant publications from 2000 to the present, retrieved from the Web of Science Core Collection through keyword combinations and screening criteria. The analysis revealed a significant increase in both the number of publications and citation frequency after 2017, with research spanning multiple disciplines such as remote sensing, agriculture, and environmental science. The paper comprehensively summarizes research progress on the angle effect in UAV quantitative remote sensing. Firstly, its underlying causes based on BRDF mechanisms and radiative transfer theory are explained. Secondly, multi-angle data acquisition techniques, processing methods, and their applications across various research fields are analyzed, considering the characteristics of UAV platforms and sensors. Finally, in view of the current challenges, such as insufficient fusion of multi-source data and poor model adaptability, it is proposed that in the future, methods such as deep learning algorithms and multi-platform collaborative observation need to be combined to promote theoretical innovation and engineering application in the research of the angle effect in UAV quantitative remote sensing. This paper provides a theoretical reference for improving the inversion accuracy of surface parameters and the development of UAV remote sensing technology.

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Publication Details

Journal
Drones
Published
2025-09-23
DOI
https://doi.org/10.3390/drones9100665
Citations
1
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.80
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article

Angle Effects in UAV Quantitative Remote Sensing: Research Progress, Challenges and Trends

Dongqin You, Hongtao Cao, Dabin Ji, Jian Wu et al.
1 citations
Drones
Remote Sensing and LiDAR Applications
0.80
article

Angle Effects in UAV Quantitative Remote Sensing: Research Progress, Challenges and Trends

Dongqin You, Hongtao Cao, Dabin Ji, Jian Wu, Yanmei Wang, Yuquan Guo, Hu Zhang, Menghao Zhang, Weikang Zhang
article en
1 citations

Abstract

In recent years, unmanned aerial vehicle (UAV) quantitative remote sensing technology has demonstrated significant advantages in fields such as agricultural monitoring and ecological environment assessment. However, achieving the goal of quantification still faces major challenges due to the angle effect. This effect, caused by the bidirectional reflectance distribution function (BRDF) of surface targets, leads to significant spectral response variations at different observation angles, thereby affecting the inversion accuracy of physicochemical parameters, internal components, and three-dimensional structures of ground objects. This study systematically reviewed 48 relevant publications from 2000 to the present, retrieved from the Web of Science Core Collection through keyword combinations and screening criteria. The analysis revealed a significant increase in both the number of publications and citation frequency after 2017, with research spanning multiple disciplines such as remote sensing, agriculture, and environmental science. The paper comprehensively summarizes research progress on the angle effect in UAV quantitative remote sensing. Firstly, its underlying causes based on BRDF mechanisms and radiative transfer theory are explained. Secondly, multi-angle data acquisition techniques, processing methods, and their applications across various research fields are analyzed, considering the characteristics of UAV platforms and sensors. Finally, in view of the current challenges, such as insufficient fusion of multi-source data and poor model adaptability, it is proposed that in the future, methods such as deep learning algorithms and multi-platform collaborative observation need to be combined to promote theoretical innovation and engineering application in the research of the angle effect in UAV quantitative remote sensing. This paper provides a theoretical reference for improving the inversion accuracy of surface parameters and the development of UAV remote sensing technology.

DronesVol. 9(10)
Tianjin Normal University (CN), Chinese Academy of Sciences (CN), Aerospace Information Research Institute (CN)
Openalex Percentile: Top 25%
Remote Sensing and LiDAR Applications
0.80
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