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).

With the growth of the population and the development of modern industry and the economy, the problem of heavy metal pollution in cultivated soil has become increasingly prominent. Moreover, heavy metal poses a serious threat to plant growth due to its characteristics of difficult degradation, high mobility, easy enrichment, and potential toxicity and has become a social topic. Melatonin is a new type of plant hormone widely present in animals, plants, fungi, and bacteria, and its biological role has begun investigated in the last dozen years. Facing heavy metal stress, melatonin can play a pleiotropic role in the physiological processes of plants, such as stress resistance and growth regulation, mitigate the damage caused by stress on plants, and provide a new research idea for alleviating heavy metal stress in plants. From the aspects of the plant phenotype, physiology, element absorption, and molecular structure, this paper, therefore, mainly reviews the effects of melatonin on plants subjected to heavy metal stress and the mechanism of melatonin alleviating heavy metal stress and then puts forward future research directions. This information may be of great significance to the normal growth of crops under heavy metal stress and will provide an important theoretical basis for the genetic improvement of crop resistance in the future.

Transparent soil (TS) presents immense potential for root phenotyping due to its ability to facilitate high-resolution imaging. However, challenges related to transparency, mechanical properties, and cost hinder its development. Herein, we introduce super-transparent soil (s-TS) prepared via the droplet method using low acyl gellan gum and hydroxyethyl cellulose crosslinked with magnesium ions. The refractive index of the hydroxyethyl cellulose solution (1.345) closely aligns with that of water (1.333) and the low acyl gellan gum solution (1.340), thereby significantly enhancing the transmittance of hydrogel-based transparent soil. Optimal transmittance (98.45%) is achieved with polymer concentrations ranging from 0.8 to 1.6 wt.% and ion concentrations between 0.01 and 0.09 molL-1. After 60 days of plant cultivation, s-TS maintains a transmittance exceeding 89.5%, enabling the detailed visualization of root growth dynamics. Furthermore, s-TS exhibits remarkable mechanical properties, withstanding a maximum compressive stress of 477 kPa and supporting a maximum load-bearing depth of 186 cm. This innovative approach holds promising implications for advanced root phenotyping studies, fostering the investigation of root heterogeneity and the development of selective expression under controlled conditions.