SPATIAL ANALYSIS OF NDVI READINGS WITH DIFFERENT SAMPLING DENSITIES

Advanced remote sensing technologies provide researchers an innovative way to collect spatial data in precision agriculture. Sensor information and spatial analysis together allow for a detailed understanding of the spatial complexity of a field and its crop. The objective of the study was to describe field variability in the normalized difference vegetation index (NDVI) and characterize the spatial structure of NDVI data by geostatistical variogram analysis. Data sets at three different sampling densities were investigated and compared to characterize NDVI variation within the specified study area. Variograms were computed by Matheron's method of moments (MoM) estimator and fitted by theoretical models. The fitted spherical model was determined to be the best model for the data analysis in the study. The range of spatial dependence of the NDVI data was 40 m for a sampling area of 4 m 3 m. Knowing the amount of remotely sensed data needed to characterize the spatial variation of the field with NDVI allows us to save sampling costs and prescribe site-specific nitrogen and other agrichemical applications. 2011 American Society of Agricultural and Biological Engineers.

SPATIAL ANALYSIS OF NDVI READINGS WITH DIFFERENT SAMPLING DENSITIES Conference Paper