# Analysis of Sentinel-1 Radiometric Stability and Quality for Land Surface Applications
## 3 Data Analysis
### S-1A Radiometric Stability  
To assess the radiometric stability of the S-1A data, \(\sigma^{\circ}\)-value temporal variations of each invariant target were analyzed in VH and VV polarizations (Figure 2). This analysis was performed separately for radar signals at medium (32\({}^{\circ}\)-33\({}^{\circ}\)) and high incidence angles (40\({}^{\circ}\)-45\({}^{\circ}\)) (Figure 2). To explain potential temporal variations in \(\sigma^{\circ}\)-values, the effects of rainfall were considered. For the racetrack, temporal variations in \(\sigma^{\circ}\) for VH polarization (roughly \(-\)22 and \(-\)24 dB at 33\({}^{\circ}\) and 43\({}^{\circ}\), respectively) are not presented in Figure 2, as they are less than or similar to the Noise Equal Sigma Zero (NESZ) value for S-1A (\(-\)22 dB) [8]. For all of the targets, it is possible to identify three nearly stable sub-time series of \(\sigma^{\circ}\) (Figure 2). The end of the first sub-time series (the beginning of the second: 19 March 2015) and the end of the second sub-time series (the beginning of the third: 25 November 2015) closely correspond to dates that enhance the calibration of S-1A products [3; 4]. However, for the racetrack and Mende forest, certain \(\sigma^{\circ}\)-values within a given sub-time series present lower or higher signal levels relative to other \(\sigma^{\circ}\)-values of the same sub-time series (\(\sigma^{\circ}\) points circled in Figure 2). For the racetrack, a \(\sigma^{\circ}\) level (in VV at 34\({}^{\circ}\) and 42\({}^{\circ}\)) that was lower by roughly 3.7 dB was found due to rainfall events occurring at the same time as S-1A acquisitions. The presence of water on the racetrack reduces backscatter phenomena and spurs signal specular reflection, thus generating weaker \(\sigma^{\circ}\)-values. Moreover, for the Mende forest, rainfall events occurring during acquisition increase \(\sigma^{\circ}\)-values (roughly 1 dB in both VH and VV at 40\({}^{\circ}\)). Rain droplets on leaves also increase \(\sigma^{\circ}\)-values. Figure 1: Locations of the three invariant targets. (**a**) Racetrack; (**b**) Mende forest; (**c**) location of the racetrack and the Mende forest targets in France map; (**d**) Gadon forest; (**e**) location of the Gabon forest target in Gabon map. To quantify the S-1A signal stability for both VH and VV polarizations, mean (M) and standard deviation (SD) statistical indexes were computed for each stable sub-time series of \(\sigma^{\circ}\)-values independent of Ascending and Descending mode images and at medium and high incidence angles (Table 1). \(\sigma^{\circ}\)-values that were perturbed due to rainfall (circled in Figure 2) were eliminated from each sub-time series. For each sub-time series, the results show stability levels better than 0.48 dB (SD \(\leqslant\) 0.48 dB) for both Ascending and Descending modes regardless of the invariant target used (Table 1). As an example, for the Gabon forest, the stability of \(\sigma^{\circ}\)-values in the second and third sub-time series is better than 0.30 dB (Table 1). [PERSON] _et al._[9] assessed the stability of ENVISAT data using transponders instruments and found that the stability of ENVISAT signals is roughly 0.55 dB. Moreover, [PERSON] _et al._[10] found that the stability of RADARSAT signals returned from rain forests is better than 0.60 dB. Moreover, the effects of IPF upgrades on \(\sigma^{\circ}\)-values were assessed by calculating the difference between mean \(\sigma^{\circ}\)-values of sub-time series for each target (Table 1). For all invariant targets, correcting the EAP phase increases mean \(\sigma^{\circ}\)-values of the second sub-series by roughly 1 dB (between 0.8 and 1.1 depending on the target) relative to that of the first sub-time series (Table 1). Moreover, correcting for gain imbalances decreases mean \(\sigma^{\circ}\)-values of the third sub-series by roughly 0.9 dB (between 0.8 and 1.1 depending on the target) relative to that of the second sub-time series (Table 1).