Remote sensing has been used as an important tool in precision agriculture. With the development of unmanned aerial vehicle (UAV) technology, collection of high-resolution site-specific field data becomes promising. Field topography affects spatial variation in soil organic carbon, nitrogen and water content, which ultimately affect crop performance. To improve crop production and reduce inputs to the field, it is critical to collect site-specific information in a real-time manner and at a large scale. The goal of this study was to evaluate the feasibility of a remote sensing system based on a UAV and imaging sensors to quantify the influence of topographic variables on apparent soil electrical conductivity (ECa) and plant performance. The experiment was conducted in 6.2 ha area within a 20-ha research soybean field with varying topography. Geo-referenced ECa data were collected before planting soybean in 2017. Geo-referenced crop yield was measured using a yield monitor system in 2016 and 2017. A RGB camera and a multispectral camera were used to take images on the field at four critical times during soybean growth in 2017. The UAV system was flying at an altitude of 100 m or 50 m above ground level with an image overlap > 70%. The image data were processed to generate geo-referenced orthophotos and a digital surface model (DSM) which was used to develop a digital elevation model (DEM). Results showed that image-based elevation represented 95% of the variability in elevation as measured by a GPS system. Results show that the relationships of field topography, i.e. elevation and slope, to soil ECa and crop were significant. Field regions with the lowest elevation had significantly lower yield and the lowest normalized difference vegetation index (NDVI) values, indicating a negative effect on crop development. Meanwhile, field slope also showed significant relationships to crop development, with significantly lower NDVI and crop yield in regions having the highest slope. The study showed that it was possible to use UAV-based remote sensing for monitoring crop growth and yield differences due to topographic variation.