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Title: Spatial analysis of hyperspectral vegetation index

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: ASABE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 6/21/2009
Publication Date: 6/21/2009
Citation: Zhang, H., Lan, Y., Lacey, R., Hoffmann, W.C., Westbrook, J.K. 2009. Spatial analysis of hyperspectral vegetation index. ASABE Annual International Meeting. Paper No. 09-5982.

Interpretive Summary: While advanced remote sensing technologies are capabile of instantaneously acquiring vegetative reflectance data, new analytical methods are needed to characterize the spatial variability of crop conditions within fields. A ground-based hyperspectral instrument (measuring light energy in 128 wavelength intervals) measured 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 researchers an innovative way of collecting spatial data for use in precision agriculture. Sensor information and spatial analysis together allow for a completely 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 sampling area 4x3 m2. It offers a good guidance for site-specific management of soybeans, such as nitrogen application and irrigation.