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Title: Spatial analysis of NDVI readings with difference sampling density

item ZHANG, HUIHUI - Texas A&M University
item Lan, Yubin
item LACEY, RON - Texas A&M University
item Hoffmann, Wesley
item Westbrook, John

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/4/2011
Publication Date: 2/16/2011
Citation: Zhang, H., Lan, Y., Lacey, R., Hoffmann, W.C., Westbrook, J.K. 2011. Spatial analysis of NDVI readings with difference sampling density. Transactions of the ASABE. 54:349-354.

Interpretive Summary: Advanced remote sensing technologies possess a capability to instantaneously acquire vegetative reflectance data but new analytical methods are needed to characterize the spatial variability of crop conditions within the field. A ground-based hyperspectral instrument (measuring light energy in 128 wavelength intervals) monitored the reflectance of light by soybean plants during the growing season from which to calculate vegetative indices. Spatial analysis identified a 40-m spatial dependence of the vegetative index data when sampled over a 12-square-meter area. Remote sensing technologies and spatial analysis techniques evaluated here will enhance the use of ground-based reflectance measurements for precision crop management.

Technical Abstract: Advanced remote sensing technologies provide research an innovative way of collecting spatial data for use in precision agriculture. Sensor information and spatial analysis together allow for a complete 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 Vegetative 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 4X3 meters squared. The spherical model offers good guidance for site-specific management of a 1-ha soybean, such as nitrogen application and irrigation.