Soil background effects on UAS and proximal remote sensing-derived vegetation indices

Authors: RAMAN, RAHUL - Texas A&M University; NEELY, HALY - Washington State University; RAJAN, NITHYA - Texas A&M University; SIEGFRIED, JEFFREY - Kansas State University; IBRAHIM, AMIR - Texas A&M University; Adams, Curtis; HARDIN, ROBERT - Texas A&M University

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/3/2025
Publication Date: 1/22/2026
Citation: Raman, R., Neely, H., Rajan, N., Siegfried, J., Ibrahim, A., Adams, C.B., Hardin, R. 2026. Soil background effects on UAS and proximal remote sensing-derived vegetation indices. Agronomy Journal. 118(1). Article e70281. https://doi.org/10.1002/agj2.70281.
DOI: https://doi.org/10.1002/agj2.70281

Interpretive Summary: Unmanned aerial systems (UAS) and proximal remote sensing systems have gained widespread attention from plant breeders, agronomists, private companies, and others for their potential applications in agriculture. We know that when crop vegetation cover is low, the presence of exposed soil and its moisture status can influence reflectance and results derived from sensing systems, but the effects are not well characterized. To address this need, we conducted an experiment at College Station, TX in 2020 and 2021 using cotton as the study crop. Factors such as shadows, crop residue, soil moisture, and uneven canopy growth influenced the scene reflectance, resulting in deviation from the general theory. When the reflectance data derived from UAS measurements was transformed into vegetation indices, variations in soil background minimally impacted the results when vegetation cover was above about 30%. Among all the vegetation indices tested, the perpendicular vegetation index (PVI) was least influenced by canopy cover or soil background variations. Overall, the study suggests that UAS can be used for large-scale and multi-location research, with soil background variability having an significant effect under only the lowest levels of vegetation cover.

Technical Abstract: Exposed soil, due to low vegetation cover or in open canopy crops, influences scene reflectance derived from remotely sensed data. An experiment was conducted in College Station, TX, to investigate the potential of six unmanned aerial systems (UASs)-derived and proximally sensed vegetation indices (VIs) in suppressing soil background brightness of four treatments in 2020 and 2021. The treatments were dry soil, dry soil with winter wheat (Triticum aestivum L.) crop residue, wet soil (WS), andwet soilwithwinterwheat crop residue (CRWS) in 2020. In 2021,WS and CRWS were replaced with dry sand and dry compost (DC). The VIs were calculated from remotely sensed data of treatment plots. Cotton (Gossypium hirsutum L.) canopy cover (%) on different dates of UAS flight was extracted using unsupervised classification. Factors such as shadows, crop residue, soil moisture, and uneven canopy growth influenced the scene reflectance. The shadow on the soil decreased the soil background reflectance to <10%. Soil background variations minimally impacted the UAS-derived VIs. Soil wetness resulted in higher normalized difference vegetation index (NDVI) than dry treatment plots at an estimated mean canopy cover > 30% in 2020. Similarly, higher NDVI was observed for DC treatment plots at an estimated mean canopy cover of <35% in 2021. The perpendicular vegetation index was least influenced by canopy cover or soil background variations. The study suggests that UAS can be used for large-scale research without being affected by soil variability when vegetation cover is above 30%.