Soil salinization is prominent environmental issue in arid and semi-arid regions, such as Xinjiang in Northwest China. Salinization severely restricts economic and agricultural development and would lead to ecosystem degradation. Finding a method of rapidly and accurately determining soil salinity (SS) is one of the main challenges in salinity evaluation, saline soil development, and utilization. In situ visible and near infrared (Vis-NIR) spectroscopy has proven to be a promising technique for detecting soil properties since it can realize real-time, rapid detection of SS. However, it still remains challenging whether Vis-NIR in situ spectroscopy can invert SS with high accuracy due to the interference of environmental factors (e.g., light, water vapor, solar altitude angle, etc.) on the spectral in the field. To fill this knowledge gap, we collected Vis-NIR in situ spectral and lab-measured SS data from 135 surface soil samples in the Kongterik Pasture Nature Reserve (KPNR) in the desert oasis ecotone of southern Xinjiang, China. We used genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithms to select the feature bands of SS. Subsequently, we combined extreme learning machines (ELM), back-propagation neural networks (BPNN), and convolutional neural networks (CNN) to build inversion models of SS. The results showed that different feature bands selection methods could improve the Vis-NIR in situ spectral prediction model accuracy. Either SS inversion models were built using full-band spectral data or feature-band spectral data. Compared with the full-band (401–2400 nm) spectral modeling, the validation set R2 of ELM, BPNN, and CNN models built selected feature bands selected by PSO, GA, and SA, respectively, were improved by more than 0.06. The accuracy of predicting SS varied widely among modeling methods. The accuracy of CNN model was obviously higher than that of BPNN and ELM models. The optimal hybrid model for predicting SS constructed in this study is SA-CNN model (R2 = 0.79, RMSE = 9.41 g kg−1, RPD = 1.81, RPIQ = 2.37). This study showed that the spectral feature bands selection methods can reduce the influence of environmental factors on in situ spectroscopy and significantly enhance the inversion accuracy of SS. The present study provided that estimating SS using in situ Vis-NIR spectral is feasible.
Dendrite, a typical geological pattern, is developed along the bedding plane in limestone and dolomite strata in Jinzigou area, Zhoukoudian, Beijing, China, with a self-similar dendritic morphology. In order to reveal the dynamic mechanism of its formation, the diffusion-limited aggregation method, a model of fractal growth, was selected to simulate the microscopic dynamic mechanism of its formation. Take an L × L square lattice, with L = 200 lattices, 20,000 particles, and L = 250 lattices, 30,000 particles, for simulation. Obtain the simulation results of the dendritic pattern and the intermediate results with 8057 and 10,827 random particles released in the simulation process with 20,000 particles. The results show that the pattern is a typical fractal pattern formed in the process of fractal growth, with a fractal dimension of about 1.6. Its morphology is characterized by fractal geometry, and the dynamic mechanism of its formation is the fractal growth phenomenon generated by self-organizing criticality. The growth process is a nonlinear and non-equilibrium evolutionary process, which is dynamic and far from equilibrium. It is consistent with the fractal dimension of Zhoukoudian dendrite (1.52–1.78). Diffusion-limited aggregation (DLA) is a typical growth process in fractal growth. It leads to the growth of randomly branching structures that closely resemble various important systems in the earth sciences, providing a theoretical basis for revealing the nature of complex geological processes.
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