As an important halophyte in the Yellow River Delta, the Amaranthaceae C3 Suaeda salsa (L.) Pall. has attracted much attention for the “red carpet” landscape, and could be simply divided into red and green phenotypes according to the betacyanin content in the fleshy leaves. However, S. salsa has not been sequenced yet, which limited people’s understanding at the molecular level. We constructed a high-quality chromosome-level reference genome by combining high-throughput sequencing, PacBio Single Molecule Real-Time (SMRT) sequencing, and Hi-C sequencing techniques with a genome size of 445 MB and contigs N50 of 2.94 Mb. Through the annotation of the reference genome, a total of 288.23 Mb of the repeating elements (64.76% of the total genome size) and 23,965 protein-coding genes were identified. Comparative genomics indicated that S. salsa undergone a WGD event about 146.15 million years ago (mya), and the estimated divergence time between S. salsa and S. aralocaspica was about 16.9 mya. A total of four betacyanins including betanidin, celosianin II, amaranthin and 6’-O-malonyl-celosianin II were identified and purified in both phenotypes, while two significantly up-regulated betacyanins (celosianin II and amaranthin) may be the main reason for the red color in red phenotype. In addition, we also performed transcriptomics and metabolomics in both phenotypes to explore the molecular mechanisms of pigment synthesis, and a series of structural genes and transcription factors concerned with betacyanin production were selected in S. salsa.

Mapping the SOC distributions in coastal wetlands plays an important role in assessing ecosystem services, predicting the greenhouse effects and investigating global carbon cycle. Few research has explored the relationships of SOC and environmental variables with seasonal changes, and the effects of multi-temporal environmental variables on Digital Soil Mapping (DSM). The results showed that the relationships between SOC and environmental variables in different months varied significantly in coastal wetlands of the Yellow River Delta (YRD). In general, the environmental variables in wet season showed stronger correlations and higher importance scores with SOC compared with those in dry season. In addition, SOC prediction models based on multi-temporal data in wet season and mono-temporal data in April had stronger prediction performance compared with those based on multi-temporal data in dry season. As a result, data fusion of multi-temporal data did not necessarily contribute to the model performance enhancement. Relative homogenous soil-landscape attributes and spectral characteristics in coastal wetlands of the YRD in dry season could not accurately explain the strong spatial variation of SOC in this area, and it might be the major reason that caused the stronger model performance of soil prediction models based on wet season than those based on dry season. Therefore, the accurate spatial prediction of soil properties requires the characterization of the seasonal dynamics of soil-landscape relationships. In general, the findings of this research demonstrated that the selection of the environmental variables in the establishment of DSM model should consider the seasonal effects of environmental variables.