Leaf CA measurement should take into account angle variation during measurement time. Leaf wettability of common deciduous forest plants is characterized by wetting contact angles ranging from 60° to 140° with a significant variation between species of the same family.

Leaf surface wetness has numerous physiological and ecological consequences, and the morphological structures on the leaf surface can affect its extent and duration, contributing to interception rates in the scale of the whole ecosystem. Wetland plants have developed morphological adaptations to high water level allowing them to avoid water excess. Droplet contact angle and surface free energy are measurable parameters which relate to how the plant influences water usage and redistribution. We analysed patterns of contact angle and the surface free energy of the adaxial and abaxial surface of 10 wetland plant species and related them to the optimal habitat conditions and functional traits of the plants. Despite the consistent environment of these plants, we found them to vary greatly in terms of leaf surface wettability and surface free energy, with contact angles ranging from 75 to 169° and surface free energy, from 1.32 to 30.38 mJ/m². Canopy height and leaf longevity were significantly correlated to leaf wettability, whilst SLA (Specific Leaf Area) and leaf shape were not related to hydrophobicity. Investigating adaptations of wetland plants to their environment showed that including wettability and surface free energy in combination with other plant traits improves our understanding of water plant-soil-water interactions in wetland habitats.

Surface soil moisture (SSM) is one of the factors affecting plant growth. Methods involving direct soil moisture measurement in the field or requiring laboratory tests are commonly used. These methods, however, are laborious and time-consuming and often give only point-by-point results. In contrast, SSM can vary across a field due to uneven precipitation, soil variability, etc. An alternative is using satellite data, for example, optical data from Sentinel-2 (S2). The main objective of this study was to assess the accuracy of SSM determination based on S2 data versus standard measurement techniques in three different agricultural areas (with irrigation and drainage systems). In the field, we measured SSM manually using non-destructive techniques. Based on S2 data, we estimated SSM using the optical trapezoid model (OPTRAM) and calculated eighteen vegetation indices. Using the OPTRAM model gave a high SSM estimating accuracy (R² = 0.67, RMSE = 0.06). The use of soil porosity in the OPTRAM model significantly improved the results. Among the vegetation indices, at the NDVI ≤ 0.2, the highest value of R² was obtained for the STR to OPTRAM index, while at the NDVI > 0.2, the shadow index had the highest R² comparable with OPTRAM.

The proper monitoring of soil moisture content is important to understand water-related processes in peatland ecosystems. Time domain reflectometry (TDR) is a popular method used for soil moisture content measurements, the applicability of which is still challenging in field studies due to requirements regarding the calibration curve which converts the dielectric constant into the soil moisture content. The main objective of this study was to develop a general calibration equation for the TDR method based on simultaneous field measurements of the dielectric constant and gravimetric water content in the surface layers of degraded peatlands. Data were collected during field campaigns conducted temporarily between the years 2006 and 2016 at the drained peatland Kuwasy located in the north-east area of Poland. Based on the data analysis, a two-slopes linear calibration equation was developed as a general broken-line model (GBLM). A site-specific calibration model (SSM-D) for the TDR method was obtained in the form of a two-slopes equation describing the relationship between the soil moisture content and the dielectric constant and introducing the bioindices as covariates relating to plant species biodiversity and the state of the habitats. The root mean squared error for the GBLM and SSM-D models were equal, respectively, at 0.04 and 0.035 cm³ cm⁻³.