Soil salinization threatens sustainable agriculture, necessitating innovative restoration strategies. Suaeda salsa (L.) Pall., a halophyte with exceptional salt tolerance and vivid pigmentation, serves as an ideal model for salinity adaptation. This study integrates physiological and transcriptomic analyses to reveal how high salinity (400 mmolL(-)1 NaCl) upregulates 4,5-DOPA dioxygenase after 30 days of salt stress, promoting betacyanin accumulation to mitigate oxidative damage. Compared to the control, betacyanin content in the 200 mmolL(-)1 and 400 mmolL(-)1 NaCl groups increased to 1.975-fold and 3.675-fold, respectively, while chlorophyll a content decreased by 45.78% and 69.88%, chlorophyll b by 11.45% and 28.24%, and total chlorophyll by 30.28% and 53.06%. This trade-off in pigment allocation highlights the plant's adaptive strategy under salinity stress. The photosynthetic characteristics of S. salsa confirm that its photoprotective mechanisms are significantly enhanced under 400 mmolL(-)1 NaCl. At the molecular level, betacyanin biosynthesis alleviates oxidative stress, while transcriptional regulation of photosystem I (PSI) and photosystem II (PSII) genes-such as PsbY, PsaO, PsbM, and PsbW-partially restores photosynthetic activity. Stabilization of the electron transport chain by upregulated genes like PetA and PetH further enhances photosynthetic resilience. These findings highlight the synergy between betacyanin production and photosynthetic regulation in enhancing salinity resilience, providing insights for soil restoration and salt-tolerant crop breeding.

The cadmium (Cd) in saline-alkali soil poses a serious threat to the ecological environment and human health. Suaeda salsa, as a hyperaccumulator plant, can remediate Cd in saline-alkali soil, but the efficiency of phytoremediation is low. To improve the remediation effect of Cd pollution in saline-alkali soil, this study for the first time uses the synergy of hydrogel and Suaeda salsa for the remediation of Cd in saline-alkali soil. Hydrogel possesses excellent mechanical properties and outstanding adsorption properties. In addition, the hydrogel increases the content of some nutrient elements in the soil and improves the physicochemical properties of the soil. The water retention capacity of the hydrogel and the improvement of the physicochemical properties of the soil further promote the growth of Suaeda salsa. Meanwhile, both the hydrogel and Suaeda salsa have a positive impact on microorganisms. Our experiment provides a brand-new way for the remediation of Cd pollution in saline-alkali soil and is of great significance for soil health and ecological protection.

In recent years, China has gradually begun restoring native salt marsh vegetation such as Suaeda salsa (S. salsa) in coastal wetlands that were damaged by the long-term invasion of Spartina alterniflora. Chlorophyll content (C-ab), an important indicator of vegetation health, necessitates extensive and long-term monitoring using Sentinel-2. However, due to the influence of betacyanin (Beta), S. salsa exhibits different phenotypes (red and green) under various stress conditions, making remote sensing mechanism studies of this unique vegetation more challenging. In particular, satellite multispectral images are significantly affected by soil background in mixed pixels, making it imperative to mitigate this influence. This study explores the applicability of a recently proposed spectral separation of soil and vegetation (3SV) in Sentinel-2 multispectral and S. salsa vegetation from a remote sensing mechanism perspective, and further improves it. Additionally, a comparative analysis was conducted on the effectiveness of combining 3SV with several mainstream chlorophyll-sensitive indices. The advantages of machine learning algorithms were leveraged to develop a high-precision hybrid semi-empirical model for estimating C-ab in different S. salsa phenotypes. The research findings indicate that: (1) The 3SV algorithm, adjusted with slope compensation and B2 and B4 bands, is applicable to green S. salsa scenarios. For red S. salsa scenarios, further adjustment using B2 and B3 bands and coverage fraction is required. (2) The MTCI, MRENDVI, MND, and MNDRE indices combined best with the modified 3SV, significantly reducing the RMSE of the semi-empirical models, especially under wet soil conditions with soil fraction f(soil) < 0.5. (3) The highest accuracy (RMSE = 3.83 mu g/cm(2)) for C-ab estimation models for different S. salsa phenotypes was achieved by combining the modified 3SV soil-removed algorithm and the four indices with particle swarm optimization random forest regression (PSO-RFR).