Quantifying the Effects of UAV Flight Altitude on the Multispectral Monitoring Accuracy of Soil Moisture and Maize Phenotypic Parameters
2025
Yaoyu Li | Shangyuan Guo | Shujie Jia | Yuqiao Yan | Haojie Jia | Wuping Zhang
Flight altitude is a critical parameter influencing both the spatial resolution and operational efficiency of UAV multispectral imaging: however, its quantitative effects on crop monitoring accuracy remain insufficiently characterized. This study investigated maize in the Yuci District, Jinzhong, China, using multispectral imagery and ground measurements of soil moisture, SPAD, leaf water content (LWC), leaf area index (LAI), plant height (PH), and aboveground biomass (AGB) collected at eight altitudes (65&ndash:200 m). Correlation analysis and three modeling approaches were applied: stepwise linear regression (SLR), random forest (RF), and back-propagation neural network (BPNN). Accuracy decreased with altitude. At 65&ndash:100 m, the correlations were strongest: LAI&ndash:NDVI/GNDVI ranged from 0.818 to 0.938, and SPAD&ndash:NDVI/GNDVI exceeded 0.816. At 80&ndash:100 m, RMSE values for LAI, SPAD, and LWC were 0.05, 10.37, and 0.67, with RE below 15%. At 200 m, the lowest R2 dropped to 0.23, with errors rising sharply. RF and BPNN outperformed SLR, with BPNN yielding the highest accuracy for LAI and AGB. Overall, 65&ndash:100 m is optimal for precision monitoring, 120&ndash:160 m balances accuracy and efficiency, and 180&ndash:200 m suits large-scale reconnaissance. These findings provide methodological guidance for UAV flight parameter optimization in precision agriculture.
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