Global climate change and the spread of COVID-19 have caused widespread concerns about food security. The development of smart agriculture could contribute to food security; moreover, the targeted and accurate management of crop nitrogen is a topic of concern in the field of smart agriculture. Unmanned aerial vehicle (UAV) spectroscopy has demonstrated versatility in the rapid and non-destructive estimation of nitrogen in summer maize. Previous studies focused on the entire growth season or early stages of summer maize; however, systematic studies on the diagnosis of nitrogen that consider the entire life cycle are few. This study aimed to: (1) construct a practical diagnostic model of the nitrogen life cycle of summer maize based on ground hyperspectral data and UAV multispectral sensor data and (2) evaluate this model and express a change in the trend of nitrogen nutrient status at a spatiotemporal scale. Here, a comprehensive data set consisting of a time series of crop biomass, nitrogen concentration, hyperspectral reflectance, and UAV multispectral reflectance from field experiments conducted during the growing seasons of 2017–2019 with summer maize cultivars grown under five different nitrogen fertilization levels in Beijing, China, were considered. The results demonstrated that the entire life cycle of summer maize was divided into four stages, viz., V6 (mean leaf area index (LAI) = 0.67), V10 (mean LAI = 1.94), V12 (mean LAI = 3.61), and VT-R6 (mean LAI = 3.94), respectively; moreover, the multi-index synergy model demonstrated high accuracy and good stability. The best spectral indexes of these four stages were GBNDVI, TCARI, NRI, and MSAVI2, respectively. The thresholds of the spectral index of nitrogen sufficiency in the V6, V10, V12, VT, R1, R2, and R3–R6 stages were 0.83–0.44, −0.22 to −5.23, 0.42–0.35, 0.69–0.87, 0.60–0.75, 0.49–0.61, and 0.42–0.53, respectively. The simulated nitrogen concentration at the various growth stages of summer maize was consistent with the actual spatial distribution.

Accurate monitoring of the crop water status is of great significance for agricultural water management. To address the limitations of traditional spectral models that neglect the synergistic effects of environmental factors, this study aimed to improve the prediction ability of winter wheat water status by integrating multi-source data and machine learning algorithms. The results demonstrated significant improvements in prediction accuracy when environmental factors were integrated with hyperspectral data. During the jointing, heading, and filling stages, the prediction accuracy of the winter wheat plant water content model based on canopy hyperspectral fusion environmental factors (temperature and soil water content) was significantly higher than that based on the canopy spectral data model. The model performance (R²) increased from 0.74, 0.59, and 0.70 to 0.82, 0.69, and 0.76, respectively. The SVM-based full-growth-stage fusion model exhibited superior performance (R² = 0.85, RMSE = 5.10%, RE = 7.79%), achieving accuracy improvements of 3.53%, 23.19%, and 11.84% compared to three key growth-period models. This study confirms that integrating canopy hyperspectral data with environmental factors systematically enhances the generalization capability and accuracy of winter wheat water content prediction, providing a reliable technical solution for precision irrigation and innovative agricultural development in the future.