From consumer to RTK-enabled UAVs: A comparative assessment of vineyard mapping accuracy and spectral indices

Abstract

Abstract Background This study evaluates the performance of two UAV (Unmanned Aerial Vehicle) platforms for vineyard monitoring, focusing on geometric accuracy, effective resolution, and vegetation index consistency. Methods Two UAV (Unmanned Aerial Vehicle) platforms were compared over a vineyard using multiple flights with a consumer-grade drone and a single RTK (Real-Time Kinematic) enabled flight at similar altitude over a single vineyard block under comparable acquisition conditions, followed by photogrammetric reconstruction, DSM (Digital Surface Model) co-registration, effective resolution analysis, and RGB (Red-Green-Blue) based vegetation index comparison. Results This study demonstrates that the integration of RTK (Real-Time Kinematic) technology in the Mavic 3E improves the absolute accuracy of DSMs (Digital Surface Models), eliminating systematic vertical offsets of ~ 35 m observed in the Mavic 2E products. After applying a robust Z-shift (vertical) correction, DSMs (Digital Surface Models) from both platforms became directly comparable, with RMSE (Root Mean Square Error) values reduced to ~ 1.3 m while NMAD (Normalized Median Absolute Deviation) remained stable. RTK (Real-Time Kinematic) positioning in the Mavic 3E ensured reliable absolute georeferencing, whereas DSMs (Digital Surface Models) derived from the Mavic 2 Enterprise Zoom remained internally consistent after post-processing, though with lower absolute positional accuracy. Effective resolution analysis further showed that the Mavic 3E imagery preserves higher spatial detail than the Mavic 2E, underscoring the importance of sensor optics and stability for vineyard monitoring. Comparisons of vegetation indices revealed that normalized indices such as NGRDI (Normalized Green Red Difference Index) and VARI (Visible Atmospherically Resistant Index) provide consistent results across platforms, while ExG (Excess Green Index) exhibited strong biases and wide limits of agreement, reflecting its sensitivity to radiometric differences. Conclusions For cross-platform or multi-temporal monitoring, NGRDI (Normalized Green Red Difference Index) and VARI (Visible Atmospherically Resistant Index) proved to be more robust, whereas ExG (Excess Green Index) should only be applied after radiometric harmonization. This study provides a replicable workflow for UAV (Unmanned Aerial Vehicle) based vineyard monitoring that integrates geometric alignment, DSM (Digital Surface Model) correction, effective resolution assessment, and index comparison, offering practical recommendations for researchers and practitioners aiming to ensure reliable and comparable UAV (Unmanned Aerial Vehicle) derived vineyard metrics.

Plant phenotyping relevance

UAV画像・写真測量・DSM補正・植生指数を比較検証し、ブドウ園の植物状態を再現可能に測定するワークフローを中心に扱っているため、植物フェノタイピング手法研究に該当する。

Code and data availability

The paper's UAV vineyard datasets (orthomosaics, DSMs, vegetation index products) and analysis workflow are not publicly deposited; the Data Availability statement says they are available from the corresponding author upon reasonable request. No public repository, code URL, or dataset identifier appears in the supplied

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