## 1 Introduction
For instance, a synergy between satellite imagery and finer resolution imagery captured by other platforms that can precisely observe an object can expand the observation coverage on a global scale to break the resolution limitation in satellite imagery [18]. This approach has been widely applied in forestry studies for measuring plant populations by using airborne-based light detection and ranging (LiDAR) and satellite imagery together [14; 19]. Data accessed from LiDAR can assess the individual structure of plants, such as height and canopy area, then incorporated with satellite imagery data (e.g., spectral reflectance) to measure plant populations in a broader coverage using regression analysis [19]. A similar approach should be applied in agricultural studies for accessing crop growth status, which is plant density; however, to the best of our knowledge, this kind of approach has not yet been applied for soybean production. The utilization of LiDAR was also used to quantify small grain crop plant density with great accuracy, as proposed by [PERSON] et al. [20]; however, it has a significant shortcoming, since the sensor must be mounted on a combine harvester, causing limited area observation. Moreover, the cost of LiDAR sensors is not affordable for most farmers. As a substitute to LiDAR, UAV imagery can record photogrammetry scenes, presenting geometric agricultural traits on a detailed scale. Unmanned aerial vehicles have been widely used in scientific research for several years with the benefits of flexible time and flight operation and cloud-free data [21]. Unmanned aerial vehicles are commonly equipped with high-resolution camera sensors that are capable of providing high spatial resolution, depending on the platform's height. Photogrammetry can also provide geometrical traits of vegetation similar to LiDAR. The use of UAVs in the study of delineating or detecting a single crop plant also became a trend in the last decade with the rise of machine learning techniques. Object detection algorithms have become a popular approach to identifying an individual crop canopy from photogrammetry images. The combination of UAV imagery and machine learning has been applied to accurately estimate the numbers of plants of various crops, including soybean [22], rice [23], wheat [24], maize [25], rapeseed [26], and cotton [27], and is even used to detect weed seedlings in cultivation fields [28]. The proposed methods in the literature have utilized machine learning techniques that provide high accuracy output for detecting individual plants in UAV imagery; thus, the quantification of field plant density can be accurately measured. However, since previous studies only utilized a single remote sensing instrument--namely, UAV--with the flight altitude limited to a maximum height of 20 m to keep the high spatial resolution imagery, the area coverage became limited to a few hectares in a single flight. Considering that farmers have broad fields, monitoring plant density with UAV will be a challenging task. As noted above, satellite imagery is directly incapable of identifying individual plants because of the limited resolution; however, optical sensors of satellite imagery can provide surface reflectance values and vegetation indices, reflecting the plant biomass and leaf area index [29]. Plant biomass per unit area is a product of individual plant biomass and plant density. Therefore, surface reflectance values and vegetation indices are hypothesized to be indirect predictors for estimating plant density by referring to the values at the same phenological stage as long as the individual plant biomass is almost constant. Thus, accurately measured plant population from UAV imagery by the object detection algorithm are expected to be a response variable for plant density estimation model, which treats reflectance data and vegetation indices derived from satellite imagery as predictors in a wider area coverage of observations.