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The prevention and control of saline soil is of great significance to the sustainable development of agriculture in Xinjiang, and comprehensive knowledge of the Salt content of saline soil is a prerequisite for the prevention and control of salinization, so it is necessary to find an accurate and rapid method to estimate salt content information of saline soil. Ground Penetrating Radar (GPR) is widely used in geophysical and environmental assessment as a non-destructive detection technology. In recent years, this technology focuses on the detection of water content. However, the detection of soil salt is relatively weak due to the large dielectric loss in saline-alkali soil, the easily-absorbed electromagnetic wave energy the complex way to process radar signal, and even the different frequency combinations are needed to test, all of which above bring great troubles to the detection of salt. However, the rapid development of dielectric models in salinized soil makes it possible to use this technique to retrieve salt content. In order to better understand the soil salt content in drip irrigation cotton field, a typical drip irrigation cotton field was selected as the research object to be detected with a Canada's Ekko PRO series GPR multi-frequency antenna (250 MHz and 1 000 MHz). Then, the soil dielectric constant was obtained through radar amplitude envelope Average method (AEA). Soil salt content was estimated by adopting the Dobson saline soil dielectric model (Dobson dielectric model of saline soil, Dobson) as the theoretical tools. Meanwhile, the dielectric constant, soil bulk density, water content, and soil clay content were applied as the input variables of the model; Multiple Linear Regression (MLR) method was used to establish soil salinity inversion model; BP neural network (BP) was employed for simulation and prediction. Finally, the accuracy of salt content inversion by MLR model, BP model and Dobson model was evaluated based on the measured salt content. The results showed that: (l) The effective depth of GPR 250 MHz and 1000 MHz frequency antenna AEA method was 0-30 cm. (2) The dielectric constant obtained by 1 000 MHz frequency antenna AEA had a good polynomial relationship with the measured water content, and the fitting effect and accuracy of the measured water content and the inverse water content were good (R², RMSE, MER were 0.96, 1.61%, and 7.25%, respectively). (3) Among the three salt inversion models, the inversion accuracy of Dobson salinized soil dielectric model was significantly higher than that of the other two methods, with R² and RMSE of 0.91 and 0.313 g/kg respectively. Therefore, it is suitable and reliable to use GPR multi-frequency antenna AEA method to estimate soil salt content in cotton fields under drip irrigation. This method provides a new way to retrieve soil salt content and enriches the methods and means of detecting soil salt content. [ABSTRACT FROM AUTHOR]

Accurate and rapid assessment and measurement of soil salt accumulation and spatial distribution changes are essential for preventing land degradation and improving the ecological environment. The soil Apparent Conductivity (ECa) obtained by electromagnetic induction technology can be more effective and faster to obtain soil salinity information, which helps to overcome some challenges in traditional sampling methods and reduce costs. However, the differences in soil properties among different geomorphologic types may lead to the decrease in the accuracy of EM38 in predicting soil salinity. In order to clarify the effect of soil properties on apparent conductivity under different geomorphologic types, three typical landforms (alluvial-proluvial fan edge, alluvial plain and dry delta) in Manas River Basin of Xinjiang were taken as the research objects. The apparent conductivity data of two measurement modes (horizontal mode EMh and vertical mode EMv) were obtained by using EM38 at different heights from the ground, i.e., 30 cm, 50 cm, 70 cm, 90 cm, 110 cm and 130 cm, respectively. Moreover, in each landform, 20 representative sampling points were selected for soil sample collection, with sampling depth of 0-20, 20-40, 40-60, 60-80, 80-100 cm. Soil salt content, soil moisture content, soil clay mass fraction, soil Cation Exchange Capacity (CEC) and soil organic carbon content (SOC) were determined. Firstly, path analysis method was used to analyze the influence degree and contribution rate of salt content, soil clay mass fraction, soil CEC and SOC at different depths on apparent conductivity (ECa) measured at different heights. Next, by selecting non-salinity factors with high contribution rate of ECa as auxiliary variables, a multi-factor collaborative interpretation model of soil salinity was established, and compared with the model established only with ECa as independent variable. Finally, the optimal interpretation model of soil salt content was established and the accuracy of the model was evaluated. The results showed that among the three types of landforms, soil salt content was the most important factor affecting the contribution rate of ECa, and there were significant differences in the factors affecting ECa of each soil layer under different landforms. Water content of the upper soil (0-60 cm) contributed most to ECa, whereas soil CEC content and organic carbon content of the bottom soil (60-100 cm) in the alluvial-proluvial fan edge. A high contribution was made by soil salt content at 0-20 cm layer and 40-80 cm layer, whereas CEC and clay mass fraction for 20-40 cm layer in the alluvial plain. And ECa can be significantly affected by CEC content of the upper layer (0-60 cm) and soil organic carbon content of the lower layer (60-100 cm) in the dry delta. According to the accumulative contribution rate of more than 80%, non-salt factors were selected to establish the synergistic interpretation model of layered soil salt under different landforms. The R2 adj of different soil layers under three types of landforms increased from 0.81-0.86, 0.57-0.87 and 0.27-0.47 to 0.83-0.91, 0.63-0.93 and 0.48-0.70, respectively. The accuracy verification results showed that the R2 of salt prediction models for different soil layers under three types of landforms were 0.61-0.81, 0.48-0.85 and 0.35-0.66, respectively. The research results can provide reliable theoretical basis and technical methods for rapid and accurate monitoring of saline soil. [ABSTRACT FROM AUTHOR]