A Mechanistic Approach for Modeling Soil Development Using Remotely Sensed Data Collected from Invaded Coasts

The invasion of the exotic species Spartina alterniflora (S. alterniflora) has profoundly influenced coastal soil development in China. Accurate assessment and monitoring of invasion-driven development in coastal soils requires the development of reliable methods to support the sustainable governance of coastal ecosystems. A space-for-time substitution method and a stratified random sampling strategy were utilized in this study to obtain soil data from 15 sites at three depth intervals (0–30, 30–60 and 60–100 cm) to obtain a total of 45 soil samples. We developed a mechanistic approach to model soil development using Sentinel-1 data. Here, soil development was represented by a comprehensive soil index, the soil quality index (SQI), which was calculated from key physical and chemical soil properties. In the structural equation model (SEM), soil, vegetation and remote-sensing data were initially assumed to be related to each other based on prior knowledge and were constructed from their corresponding observed variables. The results of the correlation analysis showed that there was a significant correlation between the invasion processes and SQI values, especially in the topsoil of the upper 30 cm. The final SEM model showed that the invasion process had great direct and positive effects on SQI in the upper 60 cm depth soil: however, vegetation (represented by a vegetation index) had a negative influence on SQI in the topmost layer. We found that Sentinel-1 data explained the large variation in the interacting ecosystem of the invasion, vegetation, and soils, with R2 values ranging between 0.45 and 0.96. The results of model performance evaluation demonstrated the efficacy of the proposed model in predicting SQI, with a ratio of performance to deviation (RPD) of 1.44 in the upper 60 cm. Our findings highlight the potential of Sentinel-1 data in monitoring the pace of soil development in constructed S. alterniflora marshes.