In arid northwest China, water scarcity is the primary constraint on agricultural sustainability. Accurate prediction of soil moisture under vegetation is essential for optimizing water use and enabling precision irrigation. Furthermore, water and nitrogen management are often studied in isolation, and their spatiotemporal synergy in regulating soil moisture remains unclear, which hinders the development of optimized coupled strategies. To address this, this study integrated UAV hyperspectral (450–950 nm), multispectral remote sensing, and ground sensor networks to systematically conduct field experiments covering three irrigation levels: full irrigation (W1) at 100% of maintaining soil moisture content; mild deficit irrigation (W2), with soil moisture content set at three-quarters of W1; and severe deficit irrigation (W3), with soil moisture content set at half of W1 and three nitrogen application rates (N1: 350, N2: 250, and N3: 150 kg/ha) in a field experiment. Through sensitive band extraction and spectral index optimization, triple-band indices (RES: Reflectance Extraction Index, MSR: Moisture Sensitive Ratio Index, two novel triple-band spectral indices developed based on Kubelka–Munk and Hapke models) were innovatively developed to enhance signals and suppress noise. Random Forest algorithms were employed to construct soil moisture inversion models for different soil layers. Rigorous comparative analysis comprehensively evaluated performance differences between hyperspectral and multispectral technologies in the indirect retrieval of soil moisture based on crop physiological response and detecting soil moisture at varying depths (10–100 cm). The results indicate that the 450–760 nm visible band represents the optimal spectral region for soil moisture detection. The two indices (MSR and RES) constructed within this range demonstrated prediction correlations 18–32% higher than traditional indices. Hyperspectral technology exhibited comprehensive advantages, particularly in monitoring deep soil layers (>80 cm) (R2 = 0.49 vs. 0.18 for multispectral). The spatiotemporal dynamics of soil moisture are primarily governed by irrigation intensity, while nitrogen fertilizers indirectly influence water redistribution through physiological processes such as root architecture regulation, rather than directly altering soil water-holding capacity. This study demonstrates the efficacy of a UAV-based hyperspectral system for precision soil moisture monitoring in vegetated farmland, and it provides a critical scientific basis for optimizing water–nitrogen management and enhancing water use efficiency in arid agriculture.
Sun et al. (Sat,) studied this question.