Abstract
The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.
Highlights
Sensors in agricultural fields collect large amounts of spatial data needed for site-specific management; this may come with a considerable quantity of defective data that need to be excluded to provide quality to maps (Spekken et al, 2013; Lyle et al, 2014)
The results showed an improvement in accuracy of spatial variability within the data sets
We investigated whether the filter could help decrease the sampling error and improve the characterization of spatial variability in high sampling spatial data
Summary
Sensors in agricultural fields collect large amounts of spatial data needed for site-specific management; this may come with a considerable quantity of defective data that need to be excluded to provide quality to maps (Spekken et al, 2013; Lyle et al, 2014). Outliers are spatially referenced objects whose non-spatial attribute values are significantly different from the corresponding values in their respective spatial neighborhoods (Shekhar et al, 2003). They can be observed in local regions that demand specific analysis, making them difficult to exclude (Singh and Lalitha, 2017). Authors have applied sequences of filters to remove defective data errors (Ping and Dobermann, 2005; Simbahan et al, 2004; Menegatti and Molin, 2004; Arslan and Colvin, 2002). Spatial outliers or local defective observations were deleted by using the Local Moran index of spatial autocorrelation and the Moran plot
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