## 2 Materials and Methods  
The project was conducted in the central oil palm region of Colombia, where Palmar de la Vizcaina Experimental Station (CEPV) is located (centered on the WGS84 geographic coordinates of: 6\({}^{\circ}\)59\({}^{\prime}\)13\({}^{\prime\prime}\) N, 73\({}^{\circ}\)40\({}^{\prime}\)62\({}^{\prime\prime}\) W). This station is managed by the Centro de Investigacion en Palma de aceite--Cenipalma, located in the municipality of Barrancabermeja, Santander, Colombia (Figure 1). Currently, oil palm crops in Colombia are distributed throughout four palm zones, where the central palm zone is the largest, corresponding to 48% of the total area planted in the country [48]. The study area is a rural zone with a predominance of forest cover, oil palm crops, pastures, low vegetation, bare soils, and water bodies. It is characterized as a tropical zone with an average altitude above sea level of 90 m, a mean annual temperature of 27 \({}^{\circ}\)C, mean annual precipitation of 1600 mm, relative humidity above 80%, and shallow slopes less than 10%. Figure 2 presents the implemented methodology. The cloud computing platform used in this research was GEE, which contains a the collection of archival images from the Sentinel satellite constellation, both optical and SAR. Regarding the SAR image used, a multitemporal speckle reduction methodology was proposed by [30] and implemented in GEE by [49]. The \"salt and pepper\" speckle effect is present in all SAR images and is caused by the interaction of radar waves with different types of coverages [50; 51; 52]. The multi-temporal speckle reduction methodology uses several images with acquisition dates as close as possible to perform a noise reduction process caused by the \"salt and pepper\"  
Figure 1: Location of study area, central palm zone, Palmar de la Vizcaina Experimental Station, Santander, Colombia. speckle effect in all SAR images. Edge noise is reduced by eliminating low-intensity noise and invalid data within the image. Subsequently, the decibel (dB) values of each image within the established period are averaged. The size of the images within the study area for both the Sentinel-2 optical image and the SAR image is 1192 columns by 611 rows, for a total area of 71.83 km\({}^{2}\). Once the Sentinel-2 optical image and the Sentinel-1 SAR image collection were established, the bands associated with the optical and near-infrared spectrum from B2 to B9 and shortwave infrared B11 and B12 were selected. The vertical emission and horizontal reception (\"VH\") polarization band were selected for the SAR case. This band was chosen considering the interaction of radar waves with different types of geometries present in the study area [13]. These waves are highly influenced by crop canopies, especially in oil palm crops, where the radar signal is more sensitive in the cross-polarization bands [53]. The operating wavelength of the Sentinel-1 C-band SAR sensor at 5.55 cm [5] in the \"VH\" polarization presents less reflection when it interacts with the oil palm leaves compared with the reflection of the interaction with the leaves of the trees in the forest, thus allowing more significant differences between these two coverages, as shown by [54]. A normalization process was carried out on the decibel values of the backscatter of the SAR image, assigning a minimum value of 0 and a maximum value of 1 to be comparable with the reflectance values of the Sentinel-2 optical image [55]. Next, image fusion methods were applied: Brovey (BR) [24], high-frequency modulation (HFM) [25], Gram-Schmidt (GS) [26], and principal components (PC) [27]. Each of these fusion methods was evaluated with five commonly used statistical indicators: the  
Figure 2: Overall workflow used in this study describing the pre-processing, image fusion process, and land cover classification assessment.