Variation in Bacterial Community Structure in Rhizosphere and Bulk Soils of Different Halophytes in the Yellow River Delta

Soil microorganisms play the important role in driving biogeochemical cycles. However, it is still unclear on soil microbial community characteristics and microbial driving mechanism in rhizosphere and bulk soils of different halophyte species. In this study, we analyzed bacterial communities in the rhizosphere and bulk soils of three typical halophytes in the Yellow River Delta, i.e., Phragmites communis, Suaeda salsa, and Aeluropus sinensis, by high-throughput sequencing. The contents of total carbon, total nitrogen, and available phosphorus in rhizosphere soils of the three halophytes were significantly higher than those in bulk soils, which suggested a nutrient enrichment effect of the rhizosphere. Rhizosphere soil bacterial α-diversity of P. communis was higher than that in bulk soil, whereas bacterial α-diversity in rhizosphere soil of S. salsa and A. sinensis was lower than those in bulk soil. The dominant bacterial phyla were Proteobacteria, Actinobacteria, Chloroflexi, and Bacteroidetes, which accounted for 31, 20.5, 16.3, and 10.3%, respectively. LDA effect size (LEfSe) analysis showed that the bacterial species with significant differences in expression abundance was obviously different in the rhizosphere and bulk soil of three halophytes. The principal component analysis (PCoA) showed that bacterial community composition was greatly different between rhizosphere and bulk soils of P. communis and S. salsa, while no difference in A. sinensis. Changed bacterial community composition was mainly ascribed to salinity in rhizosphere and bulk soils. Additionally, salinity was positively correlated with Bacteroidetes and negatively correlated with Actinobacteria and Acidobacteria. Our study clarified the variation in bacterial community structure between rhizosphere and bulk soils with soil physicochemical properties, which proved a biological reference to indicate the characteristics of saline and alkaline land.

Precise Monitoring of Soil Salinity in China’s Yellow River Delta Using UAV-Borne Multispectral Imagery and a Soil Salinity Retrieval Index

Monitoring salinity information of salinized soil efficiently and precisely using the unmanned aerial vehicle (UAV) is critical for the rational use and sustainable development of arable land resources. The sensitive parameter and a precise retrieval method of soil salinity, however, remain unknown. This study strived to explore the sensitive parameter and construct an optimal method for retrieving soil salinity. The UAV-borne multispectral image in China’s Yellow River Delta was acquired to extract band reflectance, compute vegetation indexes and soil salinity indexes. Soil samples collected from 120 different study sites were used for laboratory salt content measurements. Grey correlation analysis and Pearson correlation coefficient methods were employed to screen sensitive band reflectance and indexes. A new soil salinity retrieval index (SSRI) was then proposed based on the screened sensitive reflectance. The Partial Least Squares Regression (PLSR), Multivariable Linear Regression (MLR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF) methods were employed to construct retrieval models based on the sensitive indexes. The results found that green, red, and near-infrared (NIR) bands were sensitive to soil salinity, which can be used to build SSRI. The SSRI-based RF method was the optimal method for accurately retrieving the soil salinity. Its modeling determination coefficient (R2) and Root Mean Square Error (RMSE) were 0.724 and 1.764, respectively; and the validation R2, RMSE, and Residual Predictive Deviation (RPD) were 0.745, 1.879, and 2.211.