Water repellency has a great influence on water infiltration into soil. Currently, there is no modified correlation model that is applicable to the water infiltration of water-repellent soils (WRS). In order to better construct a model suitable for water infiltration in water-repellent soil, our objectives are to validate the effect of a modified Green-Ampt model. We modified the model by assuming that the saturated and unsaturated zones had the same thickness and by combining three formulas of the suction head (S_{f VG}, S_{f BC}, S_{f GP}) and the average saturated hydraulic conductivity. Therefore, we obtained three modified models: the Green-Ampt-VG, Green-Ampt-BC, and Green-Ampt-GP models. Indoor one-dimensional water infiltration experiments were conducted to simulate the cumulative infiltration (CI), the distance of the wetting front (Z_f), and the infiltration rate of a hydrophilic treatment and repellent treatments. The results showed that as the degree of repellency increased, the soil suction head decreased, and the relationship between the value of the soil suction head and the degree of WRS was exponential. In addition, the simulated values of the modified CI formula highly fit the measured values of all treatments in the three models (RMSE: 1.696, 1.812, and 0.694). The modified Green-Ampt-VG model had the best simulation effect on the infiltration rate (RMSE: 0.036) and Z_f (RMSE: 3.976). The results indicated that the suction head values obtained from the parameters of the VG model were closest to the actual values compared the other models. These results can provide a reference for the solution of problems involving the suction head and water infiltration into WRS in the future.

The leaf area index (LAI) is a crucial physiological indicator of crop growth. This paper introduces a new spectral index to overcome angle effects in estimating the LAI of crops. This study quantitatively analyzes the relationship between LAI and multi-angle hyperspectral reflectance from the canopy of winter oilseed rape (Brassica napus L.) at various growth stages, nitrogen application levels and coverage methods. The angular stability of 16 traditional vegetation indices (VIs) for monitoring the LAI was tested under nine view zenith angles (VZAs). These multi-angle VIs were input into machine learning models including support vector machine (SVM), eXtreme gradient boosting (XGBoost), and Random Forest (RF) to determine the optimal monitoring strategy. The results indicated that the back-scattering direction outperformed the vertical and forward-scattering direction in terms of monitoring the LAI. In the solar principal plane (SPP), EVI-1 and REP showed angle stability and high accuracy in monitoring the LAI. Nevertheless, this relationship was influenced by experimental conditions and growth stages. Compared with traditional VIs, the observation perspective insensitivity vegetation index (OPIVI) had the highest correlation with the LAI (r = 0.77–0.85). The linear regression model based on single-angle OPIVI was most accurate at −15° (R² = 0.71). The LAI monitoring achieved using a multi-angle OPIVI-RF model had the higher accuracy, with an R² of 0.77 and with a root mean square error (RMSE) of 0.38 cm²·cm⁻². This study provides valuable insights for selecting VIs that overcome the angle effect in future drone and satellite applications.