## 2 Materials and Methods
### Preprocessing of SAR Images  
Initially, two types of preprocessing on the SAR images were performed: backscatter to obtain the backscatter coefficients (\(\sigma_{VV}^{0}\) and \(\sigma_{VH}^{0}\)) (Figure 3a) and polarimetric decomposition (angle \(\alpha\) and entropy) (Figure 3b). To obtain the _VH_ and _VV_ backscatter variables, the GRD data were processed following the method proposed by [PERSON] [28]: (1) applying orbit files to obtain accurate satellite orbit and velocity vectors, ensuring proper georeferencing of the images; (2) thermal noise removal to eliminate thermal antenna noise affecting the images; (3) edge noise removal to eliminate noise at the edges of the images; and (4) radiometric calibration to normalize the amplitude observed in each band concerning the radar cross-section and obtain the backscattering coefficient (reflectivity per unit area) in \(\beta\)0 (a section necessary for performing radiometric terrain corrections). To reduce speckle noise, the Boxcar, Gamma, Lee, and Refined Lee filters available in the SNAP 10.0.0. software were evaluated using a filtering window of 3 \(\times\) 3 to avoid loss of detail [13]. Terrain correction was performed using the digital elevation model (DEM) with a spatial resolution of 12.5 m from the Pulsar sensor ([[https://search.asf.alaska.edu/](https://search.asf.alaska.edu/)]([https://search.asf.alaska.edu/](https://search.asf.alaska.edu/)), accessed on 20 June 2024), transformed to the UTM\(-\)23S coordinate system, datum WGS4. Finally, the backscatter coefficient was converted to dB using a logarithmic transformation. For the second processing, SLC data were used to perform polarimetric decomposition and generate the Entropy and Alpha variables. With these data, we first performed the split to select a subset and lighten the processing load. Next, we applied the orbit file to improve accuracy, as described in the GRD data. After that, we conducted a deburst to remove the bursts inherent to these images. The covariance matrix C2 was calculated, followed by the Cloude and Potitier polarimetric decomposition for dual-polarization data, generating Entropy (H) and Alpha. Multilooking was also applied to these images to regularize the pixels [29], using a 4 (range) \(\times\) 1 (azimuth) window, resulting in an image with a spatial resolution of 14.05 m [30]. Finally, terrain correction was applied in the same manner as for the GRD data. Figure 2: Temporal profiles of SAR data in _VV_ and _VH_ backscatter coefficient (**a**) and optical data considering EVI (**b**). The red circle represents the selected image dates based on the EVI. This study evaluated the most mentioned filters in the literature, namely Boxcar, Lee, Gamma, and Refined Lee (Table 1). The Boxcar filter calculates the average value of a window centered on each pixel to homogenize the image based on the surrounding pixels [31]. The Lee filter assumes that the intensity of the image at each pixel follows a local Gaussian distribution, which allows for speckle noise reduction without significant loss of detail [32]. The Gamma filter focuses on minimizing texture information loss by using the coefficient of variation (CV) and contrast ratios, with theoretical probability density functions determining the smoothing process [33]. The Refined Lee filter is an enhancement of the Lee filter, capable of preserving textural features, which is important for detecting crops, soils, and other terrain characteristics [34]. Therefore, at the end of the image preprocessing, 16 SAR images were obtained for each development stage: 8 from the backscatter coefficient data (considering the two polarizations \(VH\) and \(VV\), with 4 different filters) and 8 images from the polarimetric decomposition (Alpha and entropy, using the 4 tested filters).