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Soil salinization is one of the main factors contributing to land degradation, affecting ecological equilibrium, environmental health, and the sustainable development of agriculture. Due to the spatial and temporal heterogeneity of soil properties and environmental conditions in a large-scale region, the monitoring accuracy of soil salinization can be challenging. This study investigated whether the classification of diverse crop types on a time series can improve the prediction accuracy of regional soil salinity levels. Specifically, we evaluated the changes in soil salt content (SSC) under diverse vegetation cover over time in the Hetao Irrigation District (HID) using multi-phase Sentinel-2 imagery and ground-truth data collected from June to September 2021 and 2022. Focused on sunflower and maize fields, this study analyzed the impact of classifying these two crop types and examining four distinct time series on the accuracy of SSC estimation. Five indices were selected as characteristic parameters from a pool of 17 vegetation indices (VIs) and 13 soil salinity indices (SIs) derived from satellite images. Moreover, three machine learning algorithms were used to establish SSC estimation models. The findings underscored the efficacy of classifying crop types and considering different time series in enhancing the response sensitivity of spectral indices to SSC and improving modeling accuracy. Among the spectral indices, VIs made more contributions to the SSC estimation model than SIs, achieving the highest coefficient of determination (R2) of 0.71. The artificial neural networks algorithm outperformed the other two machine learning algorithms in terms of accuracy and stability, yielding an optimal R2 of 0.72 and a Root Mean Square Error (RMSE) of 0.15%. This study proposed a modeling and mapping approach that considers crop types and various time series, offering valuable insights for accurately assessing soil salinization, guiding strategies for its prevention and remediation.

Soil salinization seriously affects the sustainable development of agricultural production; thus, the timely, efficient, and accurate estimation of soil salt content (SSC) has important research significance. In this study, the feasibility of soil salt content retrieval using machine learning models was explored based on a UAV (unmanned aerial vehicle) multi-spectral remote sensing platform. First, two variable screening methods (Pearson correlation analysis and Grey relational analysis) are used to screen the characteristic importance of 20 commonly used spectral indices. Then, the sensitive spectral variables were divided into a vegetation index group, a salt index group, and a combination variable group, which represent the model. To estimate SSC information for soil depths of 0–20 cm and 20–40 cm, three machine learning regression models were constructed: Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network (BPNN). Finally, the salt distribution map for a 0–20 cm soil depth was drawn based on the best estimation model. The results of experiments show that GRA is better than PCA in improving the accuracy of the estimation model, and the combination variable group containing soil moisture information performs best. The three machine learning models have achieved good prediction effects to some extent. The accuracy and stability of the model are considered comprehensively, the prediction effect of 0–20 cm is higher than that of 20–40 cm, and the validation set coefficient of determination (R2), Root-Mean-Square-Error (RMSE), and Mean Absolute Error (MAE) of the best inversion model are 0.775, 0.055, and 0.038, and the soil salt spatial map based on the optimal estimation model can reflect the salinization distribution in the study area. Therefore, this study shows that a UAV multi-spectral remote sensing platform combined with machine learning models can better monitor farmland soil salt content.

Soil salinization is a severe soil degradation issue in arid and semiarid regions. The distribution of soil salinization can prove useful in mitigating soil degradation. Remote sensing monitoring technology is available for obtaining the distribution of soil salinization rapidly and nondestructively. In this study, experimental data were collected from seven study areas of the Hetao Irrigation District from July to August in 2021 and 2022. The soil salt content (SSC) was considered at various soil depths, and the crop type and time series were considered as environmental factors. We analyzed the effects of various environmental factors on the sensitivity response of unmanned aerial vehicle (UAV)-derived spectral index variables to the SSC and assessed the accuracy of SSC estimations. The five indices with the highest correlation with the SSC under various environmental factors were the input parameters used in modeling based on three machine learning algorithms. The best model was subsequently used to derive prediction distribution maps of the SSC. The results revealed that the crop type and time series did not affect the relationship strength between the SSC and spectral indices, and that the classification of the crop type and time series can considerably enhance the accuracy of SSC estimation. The mask treatment of the soil pixels can improve the correlation between some spectral indices and the SSC. The accuracies of the ANN and RFR models were higher than SVR accuracy (optimal R2 = 0.52–0.79), and the generalization ability of ANN was superior to that of RFR. In this study, considering environmental factors, a UAV remote sensing estimation and mapping method was proposed. The results of this study provide a reference for the high-precision prediction of soil salinization during the vegetation cover period.

Accurately obtaining the multi-year spatial distribution information of crops combined with the corresponding agricultural production data is of great significance to the optimal management of agricultural production in the future. However, there are still some problems, such as low generality of crop type mapping models and susceptibility to cloud pollution in large-area crop mapping. Here, the models were constructed by using multi-phase images at the key periods to improve model generality. Multi-phase images in key periods masked each other to obtain large-area cloud-free images, which were combined with the general models to map large areas. The key periods were determined by calculating the global separation index (GSI) of the main crops (wheat, maize, sunflower, and squash) in different growth stages in the Hetao Irrigation District (HID) in China. The multi-phase images in the key period were used to make the data set and were then combined with a variety of deep learning algorithms (U-Net, U-Net++, Deeplabv3+, and SegFormer) to construct general models. The selection of the key periods, the acquisition of regional cloud-free images, and the construction of the general crop mapping models were all based on 2021 data. Relevant models and methods were respectively applied to crop mapping of the HID from 2017 to 2020 to study the generality of mapping methods. The results show that the images obtained by combining multi-phase images in the key period effectively avoided the influence of clouds and aerosols in large areas. Compared with the other three algorithms, U-Net had better mapping results. The F1-score, mean intersection-over-union, and overall accuracy were 78.13%, 75.39% and 96.28%, respectively. The crop mapping model was applied to images in 2020, and its average overall accuracy was more than 88.28%. When we applied the model to map crops (county food crops, cash crops, and cultivated land area) from 2017 to 2019, the regression analysis between the mapping areas obtained by the model and the ground measurements was made. The R2 was 0.856, and the RMSE was 17,221 ha, which reached the application accuracy, indicating that the mapping method has certain universality for mapping in different years.

The flow and dispersion characteristics of the fire extinguishing agent in the pipings and the concentration distribution in the nacelle are essential for optimizing the aircraft fire extinguishing system. In the present work, we developed a three-dimensional CFD model to simulate the transport and dispersion of the agent in piping and nacelle. The results show that the length and structure of the pipings near the nozzles affect the concentration, pressure, flow rate, and flow distribution of the extinguishing agent. The smaller the bend of the pipings near the nozzles and the angle of connection with the main piping, the less time it takes for the agent to reach the nozzles and the more mass flow rate of the agent is injected, which is more conducive to extinguishing fire rapidly. External ventilation and the blockage of the nacelle’s ribs and other components impact the concentration distribution of the fire extinguishing agent in the nacelle. The agent is mainly concentrated in the middle and rear areas of the engine nacelle. Agent concentration tests were carried out in the simulated engine nacelle. The experimental result is similar to the simulation result, which verifies the feasibility of the simulation method. The simulation method can be used to increase the concentration of fire extinguishing agent to meet the safety requirements by changing the outside ventilation and increasing the filling amount of fire extinguishing agent, so as to achieve the optimization of the fire extinguishing system.

The filling conditions and pipeline characteristics of the aircraft fire extinguishing system determine the pressure of the fire extinguishing cylinder outlet, the discharge quality of the extinguishing agent, and the flow distribution during the discharge process. The simulation model of the fire extinguishing system pipeline of an aircraft was established by Amesim. The influence of filling conditions and pipeline characteristics was studied. It was found that the mass curves of the fire extinguishing agent were similar under filling pressures of 4, 5, and 6 MPa with a filling amount of 5.55 kg. The lower the initial temperature is, the pressure at the outlet of the cylinder decreases, but the emptying time is similar to 1.22 s. The lower the roughness is, the faster the discharge is. Under the ideal smooth pipe (ε = 0 mm), the emptying time of the fire extinguishing cylinder is 0.72 s. When the diameter of the short branch pipe is 10 mm, and the diameter of the long branch pipe is 14 mm, the discharge quality of the two pipes is close. The larger the diameter of the main pipe, the higher the discharge rate. The research results have a certain guiding significance for the pipeline design of certain aircraft.

The rapid and accurate identification of sunflower lodging is important for the assessment of damage to sunflower crops. To develop a fast and accurate method of extraction of information on sunflower lodging, this study improves the inputs to SegNet and U-Net to render them suitable for multi-band image processing. Random forest and two improved deep learning methods are combined with RGB, RGB + NIR, RGB + red-edge, and RGB + NIR + red-edge bands of multi-spectral images captured by a UAV (unmanned aerial vehicle) to construct 12 models to extract information on sunflower lodging. These models are then combined with the method used to ignore edge-related information to predict sunflower lodging. The results of experiments show that the deep learning methods were superior to the random forest method in terms of the obtained lodging information and accuracy. The predictive accuracy of the model constructed by using a combination of SegNet and RGB + NIR had the highest overall accuracy of 88.23%. Adding NIR to RGB improved the accuracy of extraction of the lodging information whereas adding red-edge reduced it. An overlay analysis of the results for the lodging area shows that the extraction error was mainly caused by the failure of the model to recognize lodging in mixed areas and low-coverage areas. The predictive accuracy of information on sunflower lodging when edge-related information was ignored was about 2% higher than that obtained by using the direct splicing method.