Miller, Jarrod O.Mondal, PinkiSarupria, Manan2024-04-252024-04-252024-02-01Miller, Jarrod O., Pinki Mondal, and Manan Sarupria. “Sensor-Based Measurements of NDVI in Small Grain and Corn Fields by Tractor, Drone, and Satellite Platforms.” Crop and Environment 3, no. 1 (March 2024): 33–42. https://doi.org/10.1016/j.crope.2023.11.001.2773-126Xhttps://udspace.udel.edu/handle/19716/34308This article was originally published in Crop and Environment. The version of record is available at: https://doi.org/10.1016/j.crope.2023.11.001. © 2023 The Author(s). Published by Elsevier Ltd on behalf of Huazhong Agricultural University.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.en-USAttribution-NonCommercial-NoDerivatives 4.0 InternationalcorndroneNDVInitrogensatellitesmall grainszero hungerresponsible consumption and productionArticle