A New Approach for Estimating Soil Salinity Using A Low-Cost Soil Sensor In Situ: A Case Study in Saline Regions of China’s East Coast

High-throughput 16S rRNA gene-based amplicon sequencing reveals the functional divergence of halophilic bacterial communities in the Suaeda salsa root compartments on the eastern coast of China

The rhizosphere environment of plants, which harbors halophilic bacterial communities, faces significant challenges in coping with environmental stressors, particularly saline soil properties. This study utilizes a high-throughput 16S rRNA gene-based amplicon sequencing to investigate the variations in bacterial community dynamics in rhizosphere soil (RH), root surface soil (RS), root endophytic bacteria (PE) compartments of Suaeda salsa roots, and adjoining soils (CK) across six locations along the eastern coast of China: Nantong (NT), Yancheng (YC), Dalian (DL), Tianjin (TJ), Dongying (DY), and Qingdao (QD), all characterized by chloride-type saline soil. Variations in the physicochemical properties of the RH compartment were also evaluated. The results revealed significant changes in pH, electrical conductivity, total salt content, and ion concentrations in RH samples from different locations. Notably, the NT location exhibited the highest alkalinity and nitrogen availability. The pH variations were linked to HCO3- accumulation in S. salsa roots, while salinity stress influenced soil pH through H+ discharge. Despite salinity stress, enzymatic activities such as catalase and urease were higher in soils from various locations. The diversity and richness of bacterial communities were higher in specific locations, with Proteobacteria dominating PE samples from the DL location. Additionally, Vibrio and Marinobacter were prevalent in RH samples. Significant correlations were found between soil pH, salinity, nutrient content, and the abundance and diversity of bacterial taxa in RH samples. Bioinformatics analysis revealed the prevalence of halophilic bacteria, such as Bacillus, Halomonas, and Streptomyces, with diverse metabolic functions, including amino acid and carbohydrate metabolisms. Essential genes, such as auxin response factor (ARF) and GTPase-encoding genes, were abundant in RH samples, suggesting adaptive strategies for harsh environments. Likewise, proline/betaine transport protein genes were enriched, indicating potential bioremediation mechanisms against high salt stress. These findings provide insight into the metabolic adaptations facilitating resilience in saline ecosystems and contribute to understanding the complex interplay between soil conditions, bacterial communities, and plant adaptation.

Monitoring soil salinization and its spatiotemporal variation at different depths across the Yellow River Delta based on remote sensing data with multi-parameter optimization

Soil salinization is recognized as a key issue negatively affecting agricultural productivity and wetland ecology. It is necessary to develop effective methods for monitoring the spatiotemporal distribution of soil salinity at a regional scale. In this study, we proposed an optimized remote sensing-based model for detecting soil salinity in different depths across the Yellow River Delta (YRD), China. A multi-dimensional model was built for mapping soil salinity, in which five types of predictive factors derived from Landsat satellite images were exacted and tested, 94 in-situ measured soil salinity samples with depths of 30-40 cm and 90-100 cm were collected to establish and validate the predicting model result. By comparing multiple linear regression (MLR) and partial least squares regression (PLSR) models with considering the correlation between predictive factors and soil salinity, we established the optimized prediction model which integrated the multi-parameter (including SWIR1, SI9, MSAVI, Albedo, and SDI) optimization approach to detect soil salinization in the YRD from 2003 to 2018. The results indicated that the estimates of soil salinity by the optimized prediction model were in good agreement with the measured soil salinity. The accuracy of the PLSR model performed better than that of the MLR model, with the R² of 0.642, RMSE of 0.283, and MAE of 0.213 at 30-40 cm depth, and with the R² of 0.450, RMSE of 0.276, and MAE of 0.220 at 90-100 cm depth. From 2003 to 2018, the soil salinity showed a distinct spatial heterogeneity. The soil salinization level of the coastal shoreline was higher; in contrast, lower soil salinization level occurred in the central YRD. In the last 15 years, the soil salinity at depth of 30-40 cm experienced a decreased trend of fluctuating, while the soil salinity at depth of 90-100 cm showed fluctuating increasing trend.