Spatial relation between NDVI and grain yield: Impact of spatial resolution and measurement device

With the increasing availability of sensors to gather information on the soil and crop status in farm management, the question of optimum spatial resolution of measurements and their spatial aggregation arises. The aim of this study was to examine spatial resolution and data aggregation of two different data sets obtained under different climatic conditions and geographic regions, i.e., in Luettewitz, Germany, and Lexington, Kentucky, USA. Vehicle-based remote sensing of Normalized Difference Vegetation Index (NDVI), Red Edge Inflection Point (REIP), and Pendulum Angle were used to provide reliable and spatially representative measurements. Automatically monitored and georeferenced combine harvester yield measurements were collected as well. Data aggregation between 10 and 40 m decreased the variance of measurements and increased the nugget variance with decreasing resolution. Valuable information was lost if data were aggregated over an area with 40 m radius and measurements were spatially uncorrelated if locations were more than 80 m apart from each other at the Luettewitz site. On the other hand, aggregating remote sensing data to levels less than 5 m did not increase information at the Kentucky site, nor are domains smaller than 5 m presently manageable. Vehicle-based sensor information strongly supported spatial estimation of crop yield using state-space models.