Accurate soil moisture estimation is fundamental for optimizing irrigation strategies, enhancing crop yields, and managing water resources efficiently. This study harnesses time-series RGB-thermal imagery to assess soil moisture throughout various growth stages of corn, emphasizing depth-specific soil moisture estimation and time-series analysis of canopy information such as canopy structure and canopy spectral across growth stages. By integrating a comprehensive dataset that covers the full spectrum of the growing season from early to late stages. we evaluated soil moisture at multiple depths including 10, 20, 30, and 40 cm. Sophisticated regression models such as Gradient Boosting Machines (GBM), Least Absolute Shrinkage and Selection Operator (Lasso), and Support Vector Machines (SVM) were employed to analyze the effects of spectral indices, land surface temperature (LST), and structural canopy variables on soil moisture estimation accuracy. Our results reveal that thermal variables, particularly LST, exhibit significant correlations with soil moisture at shallower depths, especially in non-irrigated plots where moisture variability tends to be greater. The GBM model performed exceptionally well, achieving a coefficient of determination (R²) of 0.79 and a root mean square error (RMSE) of 1.86 % at a depth of 10 cm, showcasing its precision in moisture prediction. At a depth of 30 cm, the GBM model still demonstrated robust performance with an R² of 0.69 and an RMSE of 3.38 %, adapting effectively to different canopy densities and soil conditions. As canopy density increased, the effectiveness of LST in predicting soil moisture decreased, underscoring the dynamic interaction between plant growth stages and moisture estimation accuracy.

中文翻译：

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使用冠层信息模型对植被玉米田进行深度特定土壤水分估计：RGB 热无人机数据和机器学习的融合


准确的土壤湿度估算是优化灌溉策略、提高作物产量和有效管理水资源的基础。本研究利用时间序列 RGB 热图像来评估玉米各个生长阶段的土壤水分，强调特定深度的土壤水分估计和冠层信息的时间序列分析，例如整个生长阶段的冠层结构和冠层光谱。通过集成一个全面的数据集，该数据集涵盖了从早期到晚期生长季节的所有范围。我们评估了 10 、 20 、 30 和 40 cm 等多个深度的土壤水分。采用梯度提升机 （GBM）、最小绝对收缩和选择运算符 （Lasso） 和支持向量机 （SVM） 等复杂的回归模型来分析光谱指数、地表温度 （LST） 和结构冠层变量对土壤水分估算精度的影响。我们的结果表明，热变量，特别是 LST，与较浅深度的土壤水分表现出显著的相关性，尤其是在水分变化较大的非灌溉地块中。GBM 模型表现非常出色，在 10 cm 深度实现了 0.79 的决定系数 （R²） 和 1.86% 的均方根误差 （RMSE），展示了其在水分预测方面的精确性。在 30 厘米的深度，GBM 模型仍然表现出稳健的性能，R² 为 0.69，RMSE 为 3.38%，有效适应不同的冠层密度和土壤条件。随着冠层密度的增加，LST 预测土壤水分的有效性降低，突显了植物生长阶段与水分估算准确性之间的动态相互作用。