Soil moisture (SM) monitoring in farmland at a regional scale is crucial for precision irrigation management and ensuring food security. However, existing methods for SM estimation encounter significant challenges related to accuracy, generalizability, and automation. This study proposes an integrated data fusion method to systematically assess the potential of three automated machine learning (AutoML) frameworks—tree-based pipeline optimization tool (TPOT), AutoGluon, and H2O AutoML—in retrieving SM. To evaluate the impact of input variables on estimation accuracy, six input scenarios were designed: multispectral data (MS), thermal infrared data (TIR), MS combined with TIR, MS with auxiliary data, TIR with auxiliary data, and a comprehensive combination of MS, TIR, and auxiliary data. The research was conducted in a winter wheat cultivation area within the People’s Victory Canal Irrigation Area, focusing on the 0–40 cm soil layer. The results revealed that the scenario incorporating all data types (MS + TIR + auxiliary) achieved the highest retrieval accuracy. Under this scenario, all three AutoML frameworks demonstrated optimal performance. AutoGluon demonstrated superior performance in most scenarios, particularly excelling in the MS + TIR + auxiliary data scenario. It achieved the highest retrieval accuracy with a Pearson correlation coefficient (R) value of 0.822, root mean square error (RMSE) of 0.038 cm³/cm³, and relative root mean square error (RRMSE) of 16.46%. This study underscores the critical role of input data types and fusion strategies in enhancing SM estimation accuracy and highlights the significant advantages of AutoML frameworks for regional-scale SM retrieval. The findings offer a robust technical foundation and theoretical guidance for advancing precision irrigation management and efficient SM monitoring.

This research provides a comprehensive analysis of the spatiotemporal evolution of the regional cropping structure and its influencing factors. Using Landsat satellite images, field surveys, and yearbook data, we developed a planting structure extraction model employing the classification regression tree algorithm to obtain data on the major crop cultivation and structural characteristics of Alar reclamation from 1990 to 2023. A dynamic model and transfer matrix were used to analyze temporal changes, and a centroid migration model was used to study spatial changes in the cropping structure. Nonparametric mutation tests and through-traffic coefficient analysis were utilized to quantify the main driving factors influencing the cropping structure. During the period of 1990–2023, the cotton area in the Alar reclamation region expanded by 722.08 km², while the jujube exhibited an initial increase followed by a decrease in the same period. The primary reasons are linked to the cost of purchase, agricultural mechanization, and crop compatibility. In the Alar reclamation area, cotton, chili, and jujube are the primary cultivated crops. Cotton is mainly grown on the southern side of the Tarim River, while chili cultivation is concentrated on the northern bank of the river. Over the years, there has been a noticeable spatial complementarity in the distribution and density of rice and cotton crops in this region. In the Alar reclamation, the main factors influencing the change in cultivated land area are cotton price, agricultural machinery gross power, and population. Consequently, implementing measures such as providing planting subsidies and other policy incentives to enhance planting income can effectively stimulate farmers’ willingness to engage in planting activities.

Accurate estimation of crop evapotranspiration (ET) is essential for achieving efficient agricultural water use in the North China Plain. Although machine learning techniques have demonstrated considerable potential for ET simulation, a systematic evaluation of model-architecture suitability and hyperparameter optimization strategies specifically for summer soybean ET estimation in this region is still lacking. To address this gap, we systematically compared several machine learning architectures and their hyperparameter optimization schemes to develop a high-accuracy daily ET model for summer soybean in the North China Plain. Synchronous observations from a large-scale weighing lysimeter and an automatic weather station were first used to characterize the day-to-day dynamics of soybean ET and to identify the key driving variables. Four algorithms—support vector regression (SVR), Random Forest (RF), extreme gradient boosting (XGBoost), and a stacking ensemble—were then trained for ET simulation, while Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Randomized Grid Search (RGS) were employed for hyperparameter tuning. Results show that solar radiation (R_S), maximum air temperature (T_max), and leaf area index (LAI) are the dominant drivers of ET. The Stacking-PSO-F3 combination, forced with Rs, T_max, LAI, maximum relative humidity (RH_max), and minimum relative humidity (RH_min), achieved the highest accuracy, yielding R² values of 0.948 on the test set and 0.900 in interannual validation, thereby demonstrating excellent precision, stability, and generalizability. The proposed model provides a robust technical tool for precision irrigation and regional water resource optimization.

This article scientifically studies the direct impact of climate problems on the time transition of reference crop evapotranspiration in the Xinjiang Autonomous Region of China from 1957 to 2017, which is conducive to formulating irrigation scheduling and adaptive capacity countermeasures. The objective of this study is to investigate the impacts of climate change on ET_o for the cotton growing seasons. The meteorological data were collected from 48 meteorological stations in the region and analyzed using the Mann–Kendall test and linear trend. The results show the following points: (1) the ET_o decreases from low to high elevations, and with the increase in northern latitude. (2) The annual mean ET_o and average values of ET_o during the growing seasons for cotton exhibited two abrupt changes in the period 1957–2017, with the first abrupt change in 1995 to 1999 and the second abrupt change in 2006 to 2011. (3) The ET_o in Xinjiang of China demonstrates a decreasing trend during 1957–1996; a significant decreasing trend during 1997–2008; and a significant increasing trend during 2009–2017.

Incomplete spatio-temporal (ST) data from sensor networks in precision agriculture often limits environmental modeling and decision-making accuracy. To address this, we propose the Spatio-Temporal Conditional Diffusion Framework (ST-CDF), a generative approach for high-fidelity data reconstruction. The framework’s core is a deep denoising network that integrates a Graph Attention Network (GAT) to explicitly model non-Euclidean spatial correlations, a Differential Attention Transformer to capture abrupt temporal dynamics, and an Inverse Discrete Wavelet Transform (IDWT) module to preserve multi-scale signal details. The generative process is constrained by a physics-informed training objective, which injects known physical laws (i.e., the Penman–Monteith equation for reference evapotranspiration, $E{T}_0$) as an inductive bias, ensuring the imputed data maintains physical consistency. For privacy-preserving deployment on resource-constrained IoT devices, we extend the framework with a Federated Cluster-Guided Distillation (Fed-CGD) strategy. We conducted extensive experiments against established methods on two real-world agricultural datasets. ST-CDF demonstrated improved imputation accuracy across evaluated metrics. Its efficacy was most pronounced in the physically-demanding $E{T}_0$ calculation task, where data imputed by ST-CDF at an 80% missing rate achieved a Root Mean Square Error (RMSE) of 0.3485 and a Coefficient of Determination ($R^2$) of 0.7558, outperforming the baseline models. Furthermore, we explore ST-CDF as an explainable (XAI) framework for active agricultural decision support, demonstrating its utility in performing counterfactual simulations of “what-if” interventions, such as irrigation. The findings highlight ST-CDF as an effective, physically-grounded, and interpretable tool for data-driven scientific computation and precision agriculture.

The objective of this research was to improve the accuracy and representativeness of experimental plot studies by determining the optimum plot area and replication number for winter wheat with border irrigation. Considering the spatial distribution of soil water content, the border effect in relation to crop growth, and the lateral seepage of soil water, we sought to study and optimize the area and specifications of irrigation experiment plots with different levels and replicates. The results show that the experimental irrigation plot consisted of two parts—the core area and the guard area. The most suitable area for the experiment plot core area, with a single level and without replicates, was 60–80 m². The core experimental area can be arranged with two replicates per 40 m², with differences in soil moisture content between the treatments reaching more than 15% at the two experiment levels. Each plot comprised two replicates, or if they were 20 m², then they contained three replicates; when the soil moisture contents differed between 10% and 15%, the area of each replicate plot was 80 m², comprising two replicates, or 30 m² with three replicates. When the difference in soil moisture content between the treatments exceeded 15% with the three experimental levels, the area of each plot was 30 m² and they contained two replicates, or 20 m² containing three replicates; at differences of 10% to 15%, each replicate plot was 50 m² containing two replicates, or 30 m² with three replicates. The experimental plots were rectangular, with irrigation furrows dug lengthwise; therefore, the plots had aspect ratios between 7:1 and 5:1. The width of the buffer area was over 60 cm. The effect of the border on plant height and LAI for winter wheat primarily emerged with one to three rows (20–60 cm) at the jointing stage, while the effect on grain yield and biomass in winter wheat mainly emerged with one to two rows (20–40 cm). The conclusions of this research will inform the development of surface irrigation methods for silt loam in northern Henan, as a reference for optimizing experiment plots employing border irrigation with different soil textures.