Accurately assessing root-zone soil moisture is crucial for precision irrigation, as it directly influences crop yield. The Temperature-Vegetation Index (Ts-VI) Feature Space, which combines land surface temperature (Ts) and vegetation index (VI), is widely used to evaluate root-zone soil moisture in vegetated areas. However, its effectiveness in estimating crop yield remains unclear. Therefore, the objectives of this study are: (1) to collect multispectral and thermal infrared remote sensing data from a two-year (2021–2023) field experiment on winter oilseed rape (Brassica napus L.), and to optimize and evaluate the fitting methods of the dry and wet edges of the Ts-VI feature space based on the selected vegetation indices; (2) to analyze the spatiotemporal patterns of the Temperature Vegetation Dryness Index (TVDI) derived from the optimized Ts-VI feature space and estimate root-zone soil moisture (SM) and crop yield; and (3) to precisely invert the SM and yield of winter oilseed rape in the 0–60 cm root-zone using three machine learning algorithms—Support Vector Regression (SVR), Extreme Gradient Boosting Regression (XGBR), and Random Forest Regression (RFR)—based on the optimized TVDI. Results indicate that, among the various fitting methods, the polynomial fitting method shows the best performance. The performance of the root-zone soil moisture prediction models across different growth stages follows the order of budding stage > seedling stage > flowering stage, and with the increase of soil depth, the performance of the model gradually deteriorates.In the yield inversion of winter oilseed rape, TVDI effectively predicts yield, with the coefficient of determination (R2) ranging from 0.430 to 0.480 and RMSE ranging from 213.399 to 267.212 kg ha−1 during the seedling stage, R2 ranging from 0.640 to 0.747 and RMSE ranging from 110.712 to 178.133 kg ha−1 during the budding stage, and R2 ranging from 0.680 to 0.773 and RMSE ranging from 83.815 to 147.301 kg ha−1 during the flowering stage. The flowering stage effectively reflects crop yield trends and allows for accurate yield prediction of winter oilseed rape up to two months in advance. A comparison of the modeling results from XGBR, SVR, and RFR shows that XGBR provides the best fit for both root-zone soil moisture and yield predictions. Compared to linear regression models, the three machine learning models significantly improve accuracy and fit, providing more precise evaluations of root-zone soil moisture and yield. In addition, through the comparison and verification of this method in other regions, it shows that the results also have certain reference value. The combination of the Ts-VI feature space and machine learning algorithms not only enables precise monitoring of root-zone soil moisture conditions but also predicts future crop yield trends, offering valuable insights for water resource management and irrigation decision-making in precision agriculture.

中文翻译：

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融合多源遥感与机器学习技术对冬油菜根区土壤水分和产量预测：基于温度-植被指数特征空间的新视角


准确评估根区土壤水分对于精确灌溉至关重要，因为它直接影响作物产量。温度-植被指数 （Ts-VI） 特征空间结合了地表温度 （Ts） 和植被指数 （VI），广泛用于评估植被区域的根区土壤水分。然而，它在估计作物产量方面的有效性仍不清楚。因此，本研究的目标是：（1） 从为期两年（2021-2023 年）的冬油菜 （Brassica napus L.） 田间试验中收集多光谱和热红外遥感数据，并根据选定的植被指数优化和评价 Ts-VI 特征空间干湿边缘的拟合方法;（2） 分析从优化的 Ts-VI 特征空间得出的温度植燥指数 （TVDI） 的时空模式，并估计根区土壤水分 （SM） 和作物产量;（3） 使用三种机器学习算法——支持向量回归 （SVR）、极端梯度提升回归 （XGBR） 和随机森林回归 （RFR）——在优化的 TVDI 的基础上，精确反转 0-60 cm 根区冬油菜的 SM 和产量。结果表明，在各种拟合方法中，多项式拟合方法表现出最佳性能。根区土壤水分预测模型在不同生长阶段的性能遵循出芽期 > 苗期 > 开花期的顺序，随着土壤深度的增加，模型的性能逐渐变差。在冬油菜产量反转中，TVDI 可以有效地预测产量，决定系数 （R2） 范围为 0.430 至 0.480，RMSE 范围为 213.399 至 267。幼苗期为 212 kg ha-1，萌芽期 R2 为 0.640 至 0.747，RMSE 为 110.712 至 178.133 kg ha-1，开花期 R2 为 0.680 至 0.773，RMSE 为 83.815 至 147.301 kg ha-1。开花阶段有效地反映了作物产量趋势，并允许提前两个月准确预测冬季油菜的产量。XGBR、SVR 和 RFR 建模结果的比较表明，XGBR 为根区土壤水分和产量预测提供了最佳拟合。与线性回归模型相比，这三种机器学习模型显著提高了准确性和拟合度，从而可以更精确地评估根区土壤水分和产量。此外，通过其他地区对该方法的对比验证，表明结果也具有一定的参考价值。Ts-VI 特征空间和机器学习算法的结合不仅可以精确监测根区土壤水分状况，还可以预测未来的作物产量趋势，为精准农业中的水资源管理和灌溉决策提供有价值的见解。