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The efficiency of soil apparent electrical conductivity (ECa) as proximal data in the modeling process was assessed.
In the first instance, the proximal data is numerically clustered using a fuzzy k-means (FKM) clustering algorithm, to identify contiguous classes.
Proximal data were then weighted and averaged within the aerial sensor field of view and subsequently tested for significance with historical aerial data using Pearson correlation tests.
Secondly, a linear mixed model (LMM) is fitted in which the proximal data are fixed effects but the residuals are treated as a combination of a spatially correlated random effect and an independent and identically distributed error.
In conclusion, the use of synthesized proximal data from PXRF and VisNIR was shown to be a solid, stable predictor of soil TC and TN with widespread agronomic and environmental science applications.
Red and green lines indicate fitting curves for case 1 (not using proximal data) and case 2 (using proximal data), respectively.
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For the second 16-week period, proximal baseline data (ie, data immediately preceding that period) were used, rather than actual study baseline.
Proximal sensed data was collected using an EM38 in the horizontal (EM38h) and vertical (EM38v) mode of operation.
The study investigated proximal sensor data fusion for topsoil clay mapping on a 22 hectare agricultural field in southwest Sweden.
Combining penetrometer ECa with proximal ECa data improved modeling of conductivity depth relationships in terms of model selection, model parameterization, and model calibration.
We aimed to analyze the exact anthropometric proximal tibial data of Japanese patients undergoing total knee arthroplasty and correlate the measurements with the dimensions of current total knee arthroplasty systems.
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