Accurate estimation of soil moisture content (SMC) is essential for optimizing irrigation schedules and identifying drought-tolerant varieties. The integration of unmanned aerial vehicles (UAVs) with advanced sensors provides a novel method for monitoring SMC with high flexibility, resolution, and performance. This study utilized UAVs to capture RGB, multispectral, and thermal imagery of alfalfa (Medicago sativa L.) at the Linze Grassland Agricultural Experiment Station, Lanzhou University, and to evaluate the potential of fusing multimodal UAV data for SMC estimation in the root zone of densely and uniformly distributed leafy plants, using alfalfa as a case study, within a deep learning framework. Results showed that combining multimodal data—encompassing canopy spectral, structural, thermal, and textural information—significantly improved SMC estimation accuracy. Among the four regression models evaluated—partial least squares (PLSR), support vector machine (SVM), random forest (RF), and deep neural network (DNN)—the DNN model achieved the highest accuracy in overall multimodal data fusion, with a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 4.98%. It demonstrated good predictive performance for both full and deficit irrigation scenarios, with R2 values of 0.74 and 0.75, respectively. The DNN model also provided reliable SMC estimates across the three alfalfa canopy types, with R2 values of 0.72, 0.74, and 0.58, respectively. Moreover, it exhibited superior accuracy under both irrigation regimes and demonstrated strong spatial adaptability, characterized by low spatial dependence and autocorrelation. In conclusion, the DNN model based on UAV-derived multimodal data fusion offers a reliable and robust approach for SMC estimation, providing valuable insights for irrigation management at farmland-scale.

中文翻译：

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基于无人机的多模态遥感和深度学习增强紫花苜蓿根区土壤水分估算


准确估计土壤含水量 （SMC） 对于优化灌溉计划和确定耐旱品种至关重要。无人机 （UAV） 与高级传感器的集成提供了一种具有高灵活性、分辨率和性能的 SMC 监测新方法。本研究在兰州大学临泽草原农业实验站利用无人机捕获紫花苜蓿 （Medicago sativa L.） 的 RGB、多光谱和热图像，并以紫花苜蓿为案例研究，在深度学习框架内评估融合多模态无人机数据在密集均匀分布的叶植物根区进行 SMC 估计的潜力。结果表明，结合多模态数据（包括冠层光谱、结构、热和纹理信息）显著提高了 SMC 估计的准确性。在评估的四种回归模型（偏最小二乘法 （PLSR）、支持向量机 （SVM）、随机森林 （RF） 和深度神经网络 （DNN））中，DNN 模型在整体多模态数据融合中取得了最高的准确性，决定系数 （R2） 为 0.72，均方根误差 （RMSE） 为 4.98%。它对完全灌溉和亏欠灌溉方案均表现出良好的预测性能，R2 值分别为 0.74 和 0.75。DNN 模型还提供了三种苜蓿冠层类型的可靠 SMC 估计值，R2 值分别为 0.72、0.74 和 0.58。此外，它在两种灌溉条件下都表现出优异的准确性，并表现出很强的空间适应性，其特点是空间依赖性和自相关性低。 总之，基于无人机衍生的多模态数据融合的 DNN 模型为 SMC 估计提供了一种可靠而稳健的方法，为农田规模的灌溉管理提供了有价值的见解。