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Soil salinization is a significant global ecological issue that leads to soil degradation and is recognized as one of the primary factors hindering the sustainable development of irrigated farmlands and deserts. The integration of remote sensing (RS) and machine learning algorithms is increasingly employed to deliver cost-effective, time-efficient, spatially resolved, accurately mapped, and uncertainty-quantified soil salinity information. We reviewed articles published between January 2016 and December 2023 on remote sensing-based soil salinity prediction and synthesized the latest research advancements in terms of innovation points, data, methodologies, variable importance, global soil salinity trends, current challenges, and potential future research directions. Our observations indicate that the innovations in this field focus on detection depth, iterations of data conversion methods, and the application of newly developed sensors. Statistical analysis reveals that Landsat is the most frequently utilized sensor in these studies. Furthermore, the application of deep learning algorithms remains underexplored. The ranking of soil salinity prediction accuracy across the various study areas is as follows: lake wetland (R2 = 0.81) > oasis (R2 = 0.76) > coastal zone (R2 = 0.74) > farmland (R2 = 0.71). We also examined the relationship between metadata and prediction accuracy: (1) Validation accuracy, sample size, number of variables, and mean sample salinity exhibited some correlation with modeling accuracy, while sampling depth, variable type, sampling time, and maximum salinity did not influence modeling accuracy. (2) Across a broad range of scales, large sample sizes may lead to error accumulation, which is associated with the geographic diversity of the study area. (3) The inclusion of additional environmental variables does not necessarily enhance modeling accuracy. (4) Modeling accuracy improves when the mean salinity of the study area exceeds 30 dS/m. Topography, vegetation, and temperature are relatively significant environmental covariates. Over the past 30 years, the global area affected by soil salinity has been increasing. To further enhance prediction accuracy, we provide several suggestions for the challenges and directions for future research. While remote sensing is not the sole solution, it provides unique advantages for soil salinity-related studies at both regional and global scales.

The excellent non-equilibrium characteristic of the nanosecond pulsed dielectric barrier discharge (NPDBD) plasma can overcome thermodynamically barriers of reactions in the dry reforming of methane (DRM), so that the NPDBD plasma coupled with catalyst provides an attractive alternative to the traditional catalytic method of the DRM. In this work, the one-dimensional fluid model, including 68 species and 276 reactions, is built up to numerically investigate the atmospheric-pressure CH4/CO2 plasma driven by the nanosecond pulsed power supply. Discharge current densities, discharge gap voltages, dissipated power densities, spatial averaged particle densities and spatial distributions of the high-density species, and generating reaction pathways of the significant species in CH4, CO2, and CH4/CO2 NPDBD plasmas at atmospheric pressure are systematically illustrated and discussed. The simulation results should be valuable for optimizations of both existing and emerging DRM approaches using the NPDBD plasma, the plasma-assisted catalyst, and other novel plasma-based fuel reforming technologies.

Ar/CF4 discharge plasma etching is a promising approach to achieving a high etch rate and anisotropic etching. A 1D fluid model is established to numerically study the effects of CF4 content on particle densities and reaction pathways in Ar/CF4 pulsed dielectric barrier discharge plasma at atmospheric pressure. The simulation results indicate that, with increasing CF4 content, Ar+ densities decrease but the densities of other positive ions increase, and the electron densities do not undergo a dramatic change but the densities present an approximately linear increase. The densities of CF3 and F reach their maximums when the CF4 content is 70%. In Ar, the electron impacts with Ar are the essential reaction pathways to generate Ar+, Arm, and Ar*. In CF4, the electron impacts with CF4 are the main paths to produce , , CF+, F+, , CF2, F2, , F−, CF3, and F. Besides,   →    +  F and   +  F  →  CF3  +  F− produce part of and F−. Decomposition, recombination, and charge exchange reactions all contribute to the generation of CF3 and F. In the Ar/CF4 mixture, Ar  +    →  CF3  +  Ar+ and CF4  +  Ar+  →    +  F  +  Ar produce a great deal of Ar+, , CF3, and F. The contribution ratios of the reactions CF4  +  e  →  CF3  +  F+  +  2e and F−  +  CF+  →  F  +  CF continuously increase with increasing CF4 content. [ABSTRACT FROM AUTHOR]