Soil moisture is a key parameter for the circulation of water and energy exchange between surface and the atmosphere, playing an important role in hydrology, agriculture, and meteorology. Traditional methods for monitoring soil moisture suffer from spatial discontinuity, time-consuming processes, and high costs. Remote sensing technology enables the non-destructive and efficient retrieval of land information, allowing rapid soil moisture monitoring to schedule crop irrigation and evaluate the irrigation efficiency. Satellite data with different resolutions provide different observation scales. Evaluating the accuracy of estimating soil moisture based on open and free satellite data, as well as exploring the comprehensiveness and adaptability of different satellites for soil moisture temporal and spatial observations, are important research contents of current soil moisture monitoring. The study utilized three types of satellite data, namely GF-1, Landsat-8, and GF-4, with respective temporal and spatial resolutions of 16 m (every 4 days), 30 m (every 16 days), and 50 m (daily). The gray relational analysis (GRA) was employed to identify vegetation indices that selected sensitivity to soil moisture at varying depths (3 cm, 10 cm, and 20 cm). Then, this study employed random forest (RF), Extra Tree (ETr), and linear regression (LR) algorithms to estimate soil moisture at different depths with optical satellite data sources. The results showed that the accuracy of soil moisture estimation was different at different growth stages. The model accuracy exhibited an upward trend during the middle and late growth stages, coinciding with higher vegetation coverage; however, it demonstrated a decline in accuracy during the early and late growth stages due to either the absence or limited presence of vegetation. Among the three satellite images, the vegetation indices derived from GF-1 exhibited were more sensitive to vegetation characteristics and demonstrated superior soil moisture estimation accuracy (with R² ranging 0.129–0.928, RMSE ranging 0.017–0.078), followed by Landsat-8 (with R² ranging 0.117–0.862, RMSE ranging 0.017–0.088). The soil moisture estimation accuracy of GF-4 was the worst (with R² ranging 0.070–0.921, RMSE ranging 0.020–0.140). Thus, GF-1 is suitable for vegetated areas. In addition, the ETr model outperformed the other models in both accuracy and stability (ETr model: R² ranging from 0.117 to 0.928, RMSE ranging from 0.021 to 0.091; RF model: R² ranging from 0.225 to 0.926, RMSE ranging from 0.019 to 0.085; LR model: R² ranging from 0.048 to 0.733, RMSE ranging from 0.030 to 0.144). Utilizing GF-1 is recommended to construct the ETr model for assessing soil moisture variations in the farming land of northern China. Therefore, in cases where there are limited ground sample data, it is advisable to utilize high-spatiotemporal-resolution remote sensing data, along with machine learning algorithms such as ETr and RF, which are suitable for small samples, for soil moisture estimation.

In order to discover the resistance characteristics of overland flow with vegetation cover, indoor drainage experiments with six slopes, seven flow discharges, and five degrees of vegetation cover are systematically studied based on the basic theories of hydrodynamics and hydraulics. The experimental results show that the overland flow index of nonsubmerged vegetation gradually increased from 0.333 to 0.845 as the degree of vegetation cover increased. Moreover, the relationship between the flow resistance, water depth, and Reynolds number did not present a monotonic increasing or decreasing tendency, but depended on the vegetation cover. When the degree of vegetation cover was lower than the critical value, the flow resistance coefficient decreased with increasing water depth and Reynolds number, whereas the opposite tendency was found when the degree of vegetation cover was greater than the critical value. The resistance coefficient gradually increased with increasing vegetation cover when the flow discharge was constant. Moreover, the increasing rate became steeper as flow discharge increased. The prediction formulae of average velocity and the resistance coefficient are proposed, and predicted results agree with corresponding experimental data. This conclusion is of scientific significance both in understanding the mechanisms of flow retardation and sand fixation by vegetation as well as in guiding the implementation of ecological restoration projects.

Accurate estimation of root-zone soil moisture (SM) is of great significance for accurate irrigation management. This study was purposed to identify planted-by-planted mapping of root-zone SM on three critical fruit growth periods based on UAV multispectral images using three machine learning (ML) algorithms in a kiwifruit orchard in Shaanxi, China. Several spectral variables were selected based on variable importance (VIP) rankings, including reflectance Ri at wavelengths 560, 668, 740, and 842 nm. Results indicated that the VIP method effectively reduced 42 vegetation indexes (VIs) to less than 7 with an evaluation accuracy of root-zone SM models. Compared with deep root-zone SM models (SM40 and SM60), shallow root-zone SM models (SM10, SM20, and SM30) have better performance (R² from 0.65 to 0.82, RRMSE from 0.02 to 0.03, MAE from 0.20 to 0.54) in the three fruit growth stages. Among three ML algorithms, random forest models were recommended for simulating kiwi root-zone SM during the critical fruit growth period. Overall, the proposed planted-by-planted root-zone SM estimation approach can be considered a great tool to upgrade the toolbox of the growers in site-specific field management for the high spatiotemporal resolution of SM maps.

Given the complex separation mechanisms of the particulate mixture in a hydrocyclone and the uncertain effects of particle size and shape on separation, this study explored the influence of the maximum projected area of particles on the separation effect as well as single and mixed separations based on CFD–DEM (Computational Fluid Dynamics and Discrete Element Method) coupling and experimental test methods. The results showed that spherical particles flowed out more easily from the downstream as their sizes increased. Furthermore, with the enlargement of maximum projected area, the running space of the particles with the same volume got closer to the upward flow and particles tended to be separated from the upstream. The axial velocity of the combined separation of 60 µm particles and 120 µm particles increased by 25.74% compared with that of single separation of 60 µm particles near the transition section from a cylinder to a cone. The concentration of 60 µm particles near the running space of 120 µm particles increased by 20.73% and those separated from the downstream increased by 4.1%. This study showed the influence of particle size and maximum projected area on the separation effect and the separation mechanism of mixed sand particles in a hydrocyclone, thereby providing a theoretical basis for later studies on the effect of particle size and shape on sedimentation under the cyclone action in a hydrocyclone.

Complete and accurate global solar radiation (Rs) data at a specific region are crucial for regional climate assessment and crop growth modeling. The objective of this paper was to evaluate the capability of 12 solar radiation models based on meteorological data obtained from 21 meteorological stations in China. The results showed that the estimated and measured dailyRshad statistically significant correlations (P<0.01) for all the 12 models in 7 subzones of China. The Bahel model showed the best performance for dailyRsestimation among the sunshine-based models, with averageR2of 0.910, average RMSE of 2.306 MJ m⁻² d⁻¹, average RRMSE of 17.3%, average MAE of 1.724 MJ m⁻² d⁻¹, and average NS of 0.895, respectively. The Bristow-Campbell (BC) model showed the best performance among the temperature-based models, with averageR2of 0.710, average RMSE of 3.952 MJ m⁻² d⁻¹, average RRMSE of 29.5%, average MAE of 2.958 MJ m⁻² d⁻¹, and average NS of 0.696, respectively. On monthly scale, Ögelman model showed the best performance among the sunshine-based models, with average RE of 5.66%. The BC model showed the best performance among the temperature-based models, with average RE of 8.26%. Generally, the sunshine-based models were more accurate than the temperature-based models. Overall, the Bahel model is recommended to estimate dailyRs, Ögelman model is recommended to estimate monthly average dailyRsin China when the sunshine duration is available, and the BC model is recommended to estimate both dailyRsand monthly average dailyRswhen only temperature data are available.

Faced with severe global shortage of water and soil resources, studies on the integrated effect of water and nitrogen on tomato cultivation are urgently needed for sustainable agriculture. Two successive greenhouse experiments with three irrigation regimes (1, 2/3, 1/3 full irrigation) and four nitrogen levels (1, 2/3, 1/3, 0 nitrogen) were conducted; plant growth, fruit yield and quality were surveyed; and comprehensive quality and net profit were evaluated. The results show that water and nitrogen deficit decreased plant growth, evapotranspiration and yield while increasing production efficiency and fruit comprehensive quality. An antagonism effect from water and nitrogen application was found in tomato yield, organic acid, solids acid ratio, vitamin C and lycopene, whereas synergistic impact was observed in total soluble solids content. Water deficit had more significant effect on tomato yield and fruit quality parameters compared with that of nitrogen deficiency. Synthesizing the perspectives of yield, quality, resource productivity, market price index and profits, 1/3 full irrigation and 2/3 full nitrogen was the best strategy and could be recommended to farmers as an effective guidance for tomato production.