## 1 Introduction  
Research has increasingly focused on maximizing quality and productivity while minimizing the consumption of agricultural inputs and water resources, acknowledging the limitations of natural resources [1]. In this context, predicting the productivity of an agricultural crop enables the planning of the entire production chain, both at the national and field levels, allowing for better control over price speculation; more efficient use of storage, operational, and logistical resources; and improved planning for commercialization [2, 3]. It also provides essential information for agricultural management adjustments and validation of treatments. In this scenario, Precision Agriculture (PA) uses techniques and technologies to maximize crop productivity, reduce costs and input usage, and manage field variability. Among the technologies used in PA, remote sensing stands out, providing information on the interaction of solar electromagnetic radiation with targets on the Earth's surface [4]. These data have been widely used to monitor crop development, investigate potential issues, and predict agricultural productivity [5, 6], with some techniques allowing yield prediction while the crop is still growing [7]. The popularization of knowledge about optical remote sensing has enabled the acquisition of information about agricultural crops by data from spectral bands and vegetation indices. These data are provided by platforms that acquire sensor data from satellites, such as the Landsat and Sentinel-2 series [8]. However, data collection by these optical sensors is limited due to cloud interference and the inability to capture images at night [9]. In Brazil, optical remote sensing is already widely used in agricultural applications. Nevertheless, the planting window for some crops, such as soybean, occurs during rainy and cloudy periods, which reduces the availability of high-quality data throughout the soybean growth development [10; 11]. The Synthetic Aperture Radar (SAR) sensor, on the other hand, operates in the microwave range of electromagnetic wavelengths, where, depending on the wavelength, there is minimal atmospheric attenuation [4], allowing data collection from agricultural areas even under cloudy conditions or at night. The availability of free data and its temporal resolution have sparked interest in studies using SAR sensors since the launch of Sentinel-1 in 2014. Although it operates in the C-band and has penetration limitations in the canopy due to its intensity, it provides complementary information to optical sensors [12]. The SAR signal response is sensitive to plant structure (size, shape, and orientation of leaves, stems, and fruits) as well as the water volume in the vegetation canopy (at the molecular level) [13; 14]. Additionally, as with optical remote sensing, SAR vegetation indices allow for more efficient crop information acquisition. Thus, radar sensing can generate different images that relate in some way to the plant canopy. Moreover, different results can be obtained from distinct SAR image preprocessing techniques. These various SAR preprocessing have been used for agricultural purposes, such as identifying phenological stages [15], crop monitoring [13; 16], crop mapping and classification [13; 17], as well as estimating and predicting crop yields [18]. However, SAR images have inherent noise: speckle noise, caused by the interaction of electromagnetic waves with the Earth's surface and the way the data is reflected to the sensor [19; 20]. Nonetheless, various available filters can mitigate this noise when applied, but there are no studies using different speckle noise filters for yield prediction in soybean crops. In addition to speckle noise, the use of SAR data for agricultural fields faces challenges due to the interference of canopy moisture, planting row orientation, and soil properties. Furthermore, SAR images require complex processing, demanding high computational costs. Optical remote sensing is already widely used to obtain yield prediction information [21; 22].