Soil moisture content (SMC) acquisition is vital for crop stress diagnosis and precision irrigation. However, UAV remote sensing-based SMC monitoring usually suffers from low accuracy and spatio-temporal applicability. To address these issues, this study integrated crop physiological spectral response features into an ensemble learning model. A two-year field experiment (2022–2023) was conducted. First, fractional-order differentiation (FOD) and continuous wavelet transform (CWT) were used to enhance the responsiveness of summer maize canopy spectra to SMC and leaf physiological parameters (LPPs), including leaf area index (LAI), leaf chlorophyll content (LCC) and leaf water content (LWC). Afterwards, variable importance in projection (VIP) was adopted to characterize the spectral response properties of each parameter. Finally, a stacked ensemble learning model (SELM) based on Bayesian optimization (BO) was used to construct a SMC monitoring model, and the feasibility of fusing LPP spectral response features for SMC monitoring was evaluated. The results indicated that: (1) Changes in SMC had significant effect on LCC and LAI of summer maize, which in T4 (with field water-holding capacity of 80 %-95 %) were 14.07 % and 34.41 % higher than that in T1 (40 %-50 %), respectively. (2) Spectral transformation could significantly enhance the correlation between SMC and LPPs with canopy spectra (the average increase of R reached 0.23). (3) Consideration of the crop physiology spectral response could improve the SMC monitoring accuracy, the LCC-FOD-BO-SELM had excellent monitoring performance (R²= 0.78; RMSE = 0.019). The monitoring model fusing LPPs spectral response features had the highest accuracy (R²= 0.81; RMSE = 0.016). (4) BO could significantly reduce the overfitting problem of the monitoring model, with the maximum difference between the R of the training and test set after BO being only 0.01), thus improving the model’s generalizability. This new approach to SMC monitoring using UAV hyperspectral data provide scientific support for precision agriculture and irrigation.

中文翻译：

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利用融合作物生理光谱响应的集成学习模型提高夏玉米土壤水分无人机高光谱监测精度


土壤水分含量 (SMC) 采集对于作物胁迫诊断和精准灌溉至关重要。然而，基于无人机遥感的SMC监测通常存在精度和时空适用性较低的问题。为了解决这些问题，本研究将作物生理光谱响应特征集成到集成学习模型中。进行了为期两年的现场实验（2022-2023）。首先，采用分数阶微分（FOD）和连续小波变换（CWT）来增强夏玉米冠层光谱对SMC和叶片生理参数（LPP）的响应，包括叶面积指数（LAI）、叶片叶绿素含量（LCC） ）和叶片含水量（LWC）。随后，采用投影变量重要性（VIP）来表征每个参数的光谱响应特性。最后，采用基于贝叶斯优化（BO）的堆叠集成学习模型（SELM）构建SMC监测模型，并评估融合LPP光谱响应特征用于SMC监测的可行性。结果表明：（1）SMC变化对夏玉米LCC和LAI影响显着，T4（田间持水量80%-95%）比T1分别提高14.07%和34.41%分别为（40%-50%）。 (2)光谱变换可以显着增强SMC和LPP与冠层光谱的相关性(R平均增加达到0.23)。 (3)考虑作物生理光谱响应可以提高SMC监测精度，LCC-FOD-BO-SELM具有优异的监测性能(R²= 0.78；RMSE = 0.019)。融合LPP光谱响应特征的监测模型具有最高的精度（R²= 0.81；RMSE = 0.016）。 (4) BO可以显着减少监测模型的过拟合问题，BO后训练集和测试集的R最大差异仅为0.01)，从而提高了模型的泛化性。这种使用无人机高光谱数据进行 SMC 监测的新方法为精准农业和灌溉提供了科学支持。