Flight altitude is a critical parameter influencing both the spatial resolution and operational efficiency of UAV multispectral imaging; however, its quantitative effects on crop monitoring accuracy remain insufficiently characterized. This study investigated maize in the Yuci District, Jinzhong, China, using multispectral imagery and ground measurements of soil moisture, SPAD, leaf water content (LWC), leaf area index (LAI), plant height (PH), and aboveground biomass (AGB) collected at eight altitudes (65–200 m). Correlation analysis and three modeling approaches were applied: stepwise linear regression (SLR), random forest (RF), and back-propagation neural network (BPNN). Accuracy decreased with altitude. At 65–100 m, the correlations were strongest: LAI–NDVI/GNDVI ranged from 0.818 to 0.938, and SPAD–NDVI/GNDVI exceeded 0.816. At 80–100 m, RMSE values for LAI, SPAD, and LWC were 0.05, 10.37, and 0.67, with RE below 15%. At 200 m, the lowest R² dropped to 0.23, with errors rising sharply. RF and BPNN outperformed SLR, with BPNN yielding the highest accuracy for LAI and AGB. Overall, 65–100 m is optimal for precision monitoring, 120–160 m balances accuracy and efficiency, and 180–200 m suits large-scale reconnaissance. These findings provide methodological guidance for UAV flight parameter optimization in precision agriculture.

Accurate profiling of deep (20–300 cm) soil moisture is crucial for precision irrigation but remains technically challenging and costly at operational scales. We systematically benchmark eight regression algorithms—including linear regression, Lasso, Ridge, elastic net, support vector regression, multi-layer perceptron (MLP), random forest (RF), and gradient boosting trees (GBDT)—that use easily accessible inputs of 0–20 cm surface soil moisture (SSM) and ten meteorological variables to non-invasively infer soil moisture at fourteen 20 cm layers. Data from a typical agricultural site in Wenxi, Shanxi (2020–2022), were divided into training and testing datasets based on temporal order (2020–2021 for training, 2022 for testing) and standardized prior to modeling. Across depths, non-linear ensemble models significantly outperform linear baselines. Ridge Regression achieves the highest accuracy at 0–20 cm, SVR performs best at 20–40 cm, and MLP yields consistently optimal performance across deep layers from 60 cm to 300 cm (R² = 0.895–0.978, KGE = 0.826–0.985). Although ensemble models like RF and GBDT exhibit strong fitting ability, their generalization performance under temporal validation is relatively limited. Model interpretability combining SHAP, PDP, and ALE shows that surface soil moisture is the dominant predictor across all depths, with a clear attenuation trend and a critical transition zone between 160 and 200 cm. Precipitation and humidity primarily drive shallow to mid-layers (20–140 cm), whereas temperature variables gain relative importance in deeper profiles (200–300 cm). ALE analysis eliminates feature correlation biases while maintaining high predictive accuracy, confirming surface-to-deep information transmission mechanisms. We propose a depth-adaptive modeling strategy by assigning the best-performing model at each soil layer, enabling practical non-invasive deep soil moisture prediction for precision irrigation and water resource management.

This paper presents a proposed LSTM-based vertical-gradient prediction combined with three-dimensional kriging that enables reconstruction of greenhouse 3D temperature fields under sparse-sensor deployments while capturing temporal dynamics and spatial correlations. In northern China, winter solar greenhouses rely on standardized structures and passive climate-control strategies, which often lead to non-uniform thermal conditions that complicate precise regulation. To address this challenge, 24 sensors were deployed, and their time-series data were used to train a long short-term memory (LSTM) model for vertical temperature-gradient prediction. The predicted values at multiple heights were fused with in situ observations, and three-dimensional ordinary kriging (3D-OK) was applied to reconstruct the spatiotemporal temperature field. Compared with conventional 2D monitoring and computationally intensive CFD, the proposed approach balances accuracy, efficiency, and deployability. LSTM–Kriging validation showed Trend + Residual Kriging had the lowest RMSE (0.45558 °C) and bias (−0.03148 °C) (p < 0.01), outperforming Trend-only RMSE (3.59 °C) and Kriging-only RMSE (0.48 °C); the 3D model effectively distinguished sunny and rainy dynamics. This cost-effective framework balances accuracy, efficiency, and deployability, overcoming limitations of 2D monitoring and CFD. It provides critical support for adaptive greenhouse climate regulation and digital-twin development, directly advancing precision management and yield stability in CEA.

This study, conducted in the Lifang Dryland Experimental Area in Jinzhong, Shanxi Province, China, aimed to develop a method to accurately predict maize yield by combining UAV multispectral data with meteorological information. A DJI Mavic 3M UAV was used to capture four-band imagery (red, green, red-edge, and near-infrared), from which 16 vegetation indices were calculated, along with daily meteorological data. Among eight machine learning algorithms tested, ensemble models, Random Forest and Gradient Boosting Trees performed best, with R² values of 0.8696 and 0.8163, respectively. SHAP analysis identified MSR and RVI as the most important features. The prediction accuracy varied across growth stages, with the jointing stage showing the highest performance (R² = 0.7161), followed by the flowering stage (R² = 0.6588). The yield exhibited a strip-like spatial distribution, ranging from 6450 to 9600 kg·ha⁻¹, influenced by field management, soil characteristics, and microtopography. K-means clustering revealed high-yield areas in the central-northern region and low-yield areas in the south, supported by a global Moran’s I index of 0.4290, indicating moderate positive spatial autocorrelation. This study demonstrates that integrating UAV multispectral data, meteorological information, and machine learning can achieve accurate yield prediction (with a relative RMSE of about 2.8%) and provides a quantitative analytical framework for spatial management in drought-prone areas.

Efficient water management is critical for sustainable dryland agriculture, especially under increasing water scarcity and climate variability. Shanxi Province, a typical dryland region in northern China characterized by pronounced climatic variability and limited soil water availability, faces severe challenges due to uneven precipitation and restricted water resources. This study aimed to evaluate the spatiotemporal dynamics of soil water resources and their coupling with crop water demand under different hydrological year types. Using daily meteorological data from 27 stations (1963–2023), we identified dry, normal, and wet years through frequency analysis. Soil water resources were assessed under rainfed conditions, and water deficits of major crops—including millet, soybean, sorghum, winter wheat, maize, and potato—were quantified during key reproductive stages. Results showed a statistically significant declining trend in seasonal precipitation during both summer and winter cropping periods (p < 0.05), which corresponds with the observed intensification of crop water stress over recent decades. Notably, more than 86% of daily rainfall events were less than 5 mm, indicating low effective rainfall. Soil water availability closely followed precipitation distribution, with higher values in the south and west. Crop-specific analysis revealed that winter wheat and sorghum had the largest water deficits in dry years, necessitating timely supplemental irrigation. Even in wet years, water regulation strategies were required to improve water use efficiency and mitigate future drought risks. This study provides a practical framework for soil water–crop demand assessment and supports precision irrigation planning in dryland farming. The findings contribute to improving agricultural water use efficiency in semi-arid regions and offer valuable insights for adapting to climate-induced water challenges.