# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
## 3 Results
### Influence of LAI and Soil Moisture on \(\sigma^{\circ}\) Calculated from Sentinel-1 Data. There is a relationship between leaf area index (LAI) and wet biomass (Bw) (both measured in-situ) independent of vegetation habitat types. Figure 7 presents the best fit which was a power model at a 95% confidence level. The data also include measurements taken from the previous studies. A large increase in R\({}^{2}\) was achieved when particular habitat types (Figure 2) were included in the analysis separately. Table 6 presents results of statistical analyses performed for each of the classified non-forest wetland habitats. The highest R\({}^{2}\) has been found for sedges, the lowest for reeds, very often water logged. LAI can be estimated by applying optical or microwave satellite data [31; 43; 44]. In the present study the analysis between LAI and the backscattering coefficient calculated from Sentinel-1A data has been investigated by means of regression models. Vegetation covering the soil plays a certain role on the registered microwave signal. In the present study, it has been examined how vegetation (represented by LAI) affects the S1 microwave signal. At fixed LAI values, soil moisture is in simple linear correlation with \(\sigma^{\circ}\) VH, but otherwise the dependence of LAI on backscatter has a polynomial character. The linear multiply regression model of \(\sigma^{\circ}\) VH includes these two components as the independent variables. The resulted regression equation was as follows:  
\[\sigma^{\circ}\,\mathrm{VH}\,=\,-22.45\,+\,0.05\times\mathrm{SM}\,+\,0.55 \times\mathrm{LAI}-0.086\times\mathrm{LAI}^{2} \tag{2}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Habitat** & **R\({}^{2}\)** & **St. Error of Est.** & **Number of Data** & **\(p\)-Value** & **Equation** \\ \hline Reeds & 0.66 & 0.32 & 116 & 0.0000 & LAI = 0.077 Bw\({}^{0.533}\) \\ Sedge-mos & 0.67 & 0.29 & 149 & 0.0000 & LAI = 0.041 Bw\({}^{0.646}\) \\ Sedges & 0.70 & 0.36 & 112 & 0.0000 & LAI = 0.023 Bw\({}^{0.722}\) \\ Grass-herbs & 0.67 & 0.37 & 214 & 0.0000 & LAI = 0.016 Bw\({}^{0.787}\) \\ Grass & 0.69 & 0.34 & 207 & 0.0000 & LAI = 0.022 Bw\({}^{0.749}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the relationships at the 95% confidence level between measured in-situ LAI and Bw. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Platform Sensor** & **Habitat** & **R\({}^{2}\)** & \
\begin{tabular}{c} **Standard Error** \\ **of Est.** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Data** \\ \end{tabular} & \(p\)**-Value** & **Equation** \\ \hline Sentinel-1 IW VV & all & 0.63 & 15.28 & 58 & 0.0000 & SM = 235.666 + 14.980 \(\times\)\(\sigma^{\circ}\) \\ \hline Sentinel-1 IW VH & all & 0.72 & 14.32 & 62 & 0.0000 & SM = 347.273 + 15.413 \(\times\)\(\sigma^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the relationships at the 95% confidence level between measured SM and calculated SM from Sentinel-1 VV and VH data. Figure 7: Relationship between LAI and Bw (wet biomass) for all considered habitats. where R\({}^{2}\) = 0.75, \(p\)! 0.05. Figure 8 presents the scatterplot of this model. The root mean square error (RMSE) and the mean bias error (MBE) were used as the model performance indicators for the training and validation datasets. The RMSE was equal to 0.89 dB and MBE was equal to 0.002 dB for the training dataset (49 points). RMSE was equal to 1.8 dB and MBE was equal to \(-\)0.37 dB for the test containing 63 observations. The partial correlations for the particular components have been examined. For the model presented by Equation (2) the R was equal to 0.85 for SM, and for LAI--R was equal to 0.22 (expression of LAI in Equation (2) was treated as one component). It means that the influence of SM for the explanation of the variance of \(\sigma^{\circ}\) VH, after establishing the influence of LAI, is several times stronger than the influence of LAI when SM is constant. Figure 9 illustrates this case in 3D space.