Soil Sampling Framework Using Remote Sensing for Fertility Mapping

The patent applies local quality by transitioning from uniform grid sampling to variable-interval sampling where sample density and location are adapted to local soil property variations. Remote sensing data identifies areas with different levels of soil variability, and sampling intensity is adjusted accordingly - higher density in heterogeneous zones, lower density in homogeneous zones, thereby optimizing both coverage and measurement precision.

The patent changes the sampling parameter from fixed grid intervals to variable intervals based on remote sensing-derived soil property predictions. The sampling design dynamically adjusts sample locations and densities according to predicted soil heterogeneity, transforming the static grid approach into a adaptive sampling strategy that responds to actual soil variation patterns.

The patent uses remote sensing data as a proxy or copy of actual soil properties to predict soil fertility patterns across the field. Instead of relying solely on complex spatial interpolation of limited soil samples, the system creates a digital replica of soil variation using satellite or aerial imagery that captures vegetation responses to underlying soil conditions, simplifying the modeling process while maintaining spatial resolution.

The patent replaces the mechanical sampling and complex spatial statistics approach with a remote sensing-based system. Instead of using kriging and spatial autocorrelation models that require extensive computational resources and minimum sample thresholds, the system uses spectral signatures from remote sensors to directly predict soil properties, substituting physical sampling mechanics with optical/electromagnetic sensing.