## 1 Introduction  
In soybean (_Glycine max_ (L.) Merr.) production, ensuring a high plant density is one of the main ways to maximize crop yield [1, 2, 3]. Plant density is a product of seedling rates and stands establishment ratios. However, soybean establishment is highly variable depending on climate and soil conditions [4, 5]. Furthermore, there is an optimal plant density for maximizing yield according to the environment [6] and genotype [7], because an excessively high soybean population affects plant structure, mainly by reducing the number of pods per plant. Thus, the quantification of plant density is an essential step to identifying the optimal plant population, row spacing, and seed density during sowing, which could contribute to developing better crop management practices. Given the recent diffusion of precision agricultural technologies, such as variable-rate seeding, not only field-specific but also within-field variability of plant density should be quantified. Evaluating plant density can be done by manually counting the soybean stands in the field by employing various methods, such as systematic design counting, which can assess a wide range of soybean plant densities [8]. However, this kind of method is labor-intensive and time-consuming. Although the optimal seeding rates have been tested in field-scale trials [9; 10], information on final plant density is mostly unavailable. Detailed spatial information on plant density, which cannot be assessed by manual measurement, is needed to provide better prescriptions across fields. Thus, it is essential to develop a high-throughput measurement method for plant density. The application of remote sensing technology has been widely addressed in agricultural fields using various platforms, such as satellite, aircraft, and unmanned aerial vehicles (UAVs), to achieve better efficiency, cost-effectiveness, and high-throughput measurement, rather than direct monitoring on the field [11]. The determination of suitable remote sensing instruments for measuring a specific variable is crucial to obtaining the appropriate method and data set [12], since each platform has different specifications, namely, the spatial and temporal resolution, flight altitude, acquisition schedule, spectral ranges, operational cost, and others [13]. The measurement of plant density requires explicit information on the individual plant canopies in high accuracy of the quantification and geographic location in a detailed scale. The remote sensing scene can be used to access individual plant stands with a canopy delineation process to obtain the location, size, shape, and number of plant canopies [14]. In larger vegetation canopies, such as forest vegetation, satellite remote sensing has been widely used to delineate vegetation canopy at the tree-crown scale [15; 16]. However, commonly used optical satellite imagery, such as Landsat (30 m resolution), Sentinel-2 (10 m resolution), and PlanetScope (3 m resolution), has an insufficient spatial resolution to detect small canopy, namely, crop plants [15]. Crop plant density that represents the geometrical agronomic trait [11; 17] is more challenging to be measured using satellite imagery, since the majority of satellite imagery does not provide a detailed spatial resolution that can show textures or geometrical features of crop plants; thus, it prevents directly accessing the number of plants inside the pixels. Crop plant density usually has a dense population in a one-meter square area that is hard to capture using either satellite- or aircraft-based remote sensing. The limitation of the spatial resolution of satellite-based remote sensing instruments prevents high-throughput analysis for small grain crops. Each remote-sensing instrument and platform has different characteristics such as spatiotemporal resolution, coverage, and spectral specifications. Thus, a multiscale remote sensing and image analysis has been applied to compensate for each other's shortcomings.