Hydro-topographic contribution to in-field crop yield variation using high-resolution surface and GPR-derived subsurface DEMs

Authors: CHANG, JISUNG - Oak Ridge Institute For Science And Education (ORISE); Anderson, Martha; Gao, Feng; Russ, Andrew; ZHAO, H - Oak Ridge Institute For Science And Education (ORISE); Cirone, Richard; Pachepsky, Yakov; JOHNSON, D - National Agricultural Statistical Service (NASS, USDA)

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/31/2025
Publication Date: 9/3/2025
Citation: Chang, J., Anderson, M.C., Gao, F.N., Russ, A.L., Zhao, H., Cirone, R.J., Pachepsky, Y.A., Johnson, D. 2025. Hydro-topographic contribution to in-field crop yield variation using high-resolution surface and GPR-derived subsurface DEMs. Remote Sensing. 17(17). Article 17173061. https://doi.org/10.3390/rs17173061.
DOI: https://doi.org/10.3390/rs17173061

Interpretive Summary: Crop yield can vary significantly over a field due to a number of different factors including surface topography, which can channel water across the field – causing ponding in some areas, and dryness in others. Variations in soil type and depth can also influence yield. Understanding the importance of these factors in determining yield variability can help to guide management decisions within fields and to better predict yield response to seasonal weather patterns. This study evaluates the influence of surface topography and soil depth along with relevant weather data on crop yields mapped over experimental fields at the USDA-ARS Beltsville Agricultural Research Center. We found that the combined topographic and soil depth data were able to explain about 73% of the variability in yield across the fields in a given year. These findings have the potential to significantly improve the tools used by farmers for precision agriculture activities, such as variable seeding, fertilizer application, and irrigation rates.

Technical Abstract: Understanding spatial variability of crop yields across fields is critical for developing precision agricultural strategies that optimize productivity while reducing environmental negative impacts. This variability often arises from a complex interplay of topographic features, soil characteristics, and hydrological condition. This study investigates the influence of hydro-topographic factors on corn and soybean yield variability from 2016 to 2023 at the well-managed experimental sites in Beltsville, Maryland. High-resolution surface digital elevation model (DEM) and subsurface DEM derived from ground-penetrating radar (GPR) were used to quantify topographic factors (elevation, slope, and aspect) and hydrological factors (surface flow accumulation, depth from the surface to the subsurface-restricting layer, and distance from each crop pixel to the nearest subsurface flow pathway). Topographic variables alone explained yield variation with a relative root mean square error (RRMSE) of 23.7% (r² = 0.38). Adding hydro variables reduced the error to 15.3% (r² = 0.73), and further combining with remote sensing data improved explanatory power to of an RRMSE 10.0% (r² = 0.87). Notably, even without subsurface data, incorporating surface-derived flow accumulation reduced the RRMSE to 18.4% (r² = 0.62), which is especially important for large-scale cropland applications where subsurface data are often unavailable. Annual spatial yield variation maps were generated using hydro-topographic variables enabled the identification of long-term persistent yield regions (LTPs), which served as stable references to reduce spatial anomalies and enhance model robustness. In addition, by combining remote sensing data with interannual meteorological variables, prediction models were evaluated with and without hydro-topographic inputs. The inclusion of hydro-topographic variables improved spatial characterization and enhanced prediction accuracy, reducing error by an average of 4.5% across multiple model combinations. These findings highlight the critical role of hydro-topography in explaining spatial yield variation for corn and soybean and support the development of precise, site-specific management strategies to enhance productivity and resource efficiency.