Using time-series Sentinel-1 data for soil prediction on invaded coastal wetlands.

Coastal soils are particularly sensitive to nonnative species invasion. In this context, spatially explicit soil information is essential for improving the knowledge of the role of soil in changing environments, supporting coastal sustainable management. Synthetic-aperture radar (SAR) data provides an attractive opportunity to monitor soil because the acquisition of images is independent of weather and daylight. However, SAR has not been commonly used for soil prediction. In this study, we firstly investigated the temporal variation of vegetation canopy and the soil-vegetation relationship using Sentinel-1 data in an invaded coastal wetland. And then we built 3D models to predict soil properties at multiple depths. A total of 16 Sentinel-1 images were acquired in a growing season. A series of soil physicochemical properties were examined including soil bulk density, texture, organic/inorganic carbon, pH, salinity, total nitrogen, and C/N ratio, relating to three depth layers in the top 1-m depth. Our results showed that time-series Sentinel-1 data can capture temporal characteristics of vegetation, and VH/VV was more sensitive to the vegetation growth than VH and VV. The soil-vegetation relationship captured by time-series SAR data was beneficial to predict soil properties, especially for soil chemical properties. The models provided permissible prediction accuracy, with an average RPD of 0.99. We concluded that the prior understanding of the temporal variation of SAR data is essential for developing practical soil prediction strategy. Our results highlight that SAR has the potential to predict a diverse set of soil properties in coastal wetlands with dense vegetation cover.