Sensor-based measurements of NDVI in small grain and corn fields by tractor, drone, and satellite platforms

Abstract

The use of sensors for variable rate nitrogen (VRN) applications is transitioning from equipment-based to drone and satellite technologies. However, regional algorithms, initially designed for proximal active sensors, require evaluation for compatibility with remotely sensed reflectance and N-rate predictions. This study observed normalized difference vegetation index (NDVI) data from six small grain and two corn fields over three years. We employed three platforms: tractor-mounted active sensors (T-NDVI), passive multispectral drone (D-NDVI), and satellite (S-NDVI) sensors. Averaged NDVI values were extracted from the as-applied equipment polygons. Correlations between NDVI values from the three platforms were positive and strong, with D-NDVI consistently recording the highest values, particularly in areas with lower plant biomass. This was attributed to D-NDVI's lower soil reflectance and its ability to measure the entire biomass within equipment polygons. For small grains, sensors spaced on equipment booms might not capture accurate biomass in poor-growing and low NDVI regions. Regarding VRN, S-NDVI and D-NDVI occasionally aligned with T-NDVI recommendations but often suggested half the active sensor rate. Final yields showed some correlation with landscape variables, irrespective of N application. This finding suggests the potential use of drone or satellite imagery to provide multiple NDVI maps before application, incorporating expected landscape responses and thereby enhancing VRN effectiveness.

Keywords

Corn / Drone / NDVI / Nitrogen / Satellite / Small grains

Abbreviations

DSM: digital surface model

GDD: growing degree days

GS23: growth stage 23

GS30: growth stage 30

NDRE: normalized difference red edge

NDVI: normalized difference vegetation index

TWI: topographic wetness index

VR: variable rate

VRN: variable rate nitrogen

Availability of data and materials

Data are available upon request by the readers.

Authors' contributions

J.M.: conception of the project, collection of drone and tractor data, analyses and first draft of manuscript; P.M.: consulting on project, editing of draft; and M.S.: collection of satellite imagery, analyses and editing of draft.

Declaration of competing interest

The authors of this manuscript declare no conflict of interest or competing interest.

Acknowledgments

We would like to acknowledge our Maryland farmer cooperators and Shawn Tingle for his assistance in the field. We acknowledge the support provided by the Harry R. Hughes Center for Agro-Ecology. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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