Soil moisture content is a crucial indicator for understanding the water requirements of crops. The effective monitoring of soil moisture content can provide support for irrigation decision-making and agricultural water management. Traditional ground-based measurement methods are time-consuming and labor-intensive, and point-scale monitoring cannot effectively represent the heterogeneity of soil moisture in the field. Unmanned aerial vehicle (UAV) remote sensing technology offers an efficient and convenient way to monitor soil moisture content in large fields, but airborne multispectral data are prone to spectral saturation effects, which can further affect the accuracy of monitoring soil moisture content. Therefore, we aim to construct effective drought indices for the accurate characterization of soil moisture content in winter wheat fields by utilizing unmanned aerial vehicles (UAVs) equipped with LiDAR, thermal infrared, and multispectral sensors. Initially, we estimated wheat plant height using airborne LiDAR sensors and improved traditional spectral indices in a structured manner based on crop height. Subsequently, we constructed the normalized land surface temperature–structured normalized difference vegetation index (NLST-SNDVI) space by combining the SNDVI with land surface temperature and calculated the improved Temperature–Vegetation Drought Index (iTVDI). The results are summarized as follows: (1) the structured spectral indices exhibit better resistance to spectral saturation, making the NLST-SNDVI space closer to expectations than the NLST-NDVI space, with higher fitting accuracy for wet and dry edges; (2) the iTVDI calculated based on the NLST-SNDVI space can effectively characterize soil moisture content, showing a significant correlation with measured surface soil moisture content; (3) the global Moran’s I calculated based on iTVDI deviations ranges between 0.18 and 0.30, all reaching significant levels, indicating that iTVDI has good spatial applicability. In conclusion, this study proved the effectiveness of the drought index based on a structured vegetation index, and the results can provide support for crop moisture monitoring and irrigation decision-making in the field.

The morphology of wheat leaves is a key indicator of crop stand quality and photosynthetic capacity, with sowing date being a critical factor influencing leaf morphology. To investigate the effects of sowing time on wheat growth, development, and leaf phenotypes, this study utilized image analysis technology to systematically extract key phenotypic traits of winter wheat leaves, including effective leaf area, leaf color, and leaf shape. The results demonstrated that delayed sowing significantly affected the morphology and color characteristics of winter wheat leaves. Specifically, leaf length and width exhibited a quadratic decreasing trend, resulting in an average reduction in leaf area of over 59%. Additionally, the greenness index (EXG) decreased by 25.84%, while the red pigment index (EXR) increased by 21.69%. Significant differences in leaf color changes were observed among the varieties. This study provides reliable data for determining the optimal sowing period for winter wheat and offers valuable guidance for optimizing field management strategies to enhance crop yield and quality.