TY - JOUR
T1 - Using targeted sampling to process multivariate soil sensing data
AU - Adamchuk, Viacheslav I.
AU - Viscarra Rossel, Raphael A.
AU - Marx, David B.
AU - Samal, Ashok K.
N1 - Funding Information:
This publication is a contribution of the University of Nebraska Agricultural Research Division , supported in part by funds provided through the Hatch Act. Sample data were provided by Veris Technologies, Inc. (Salina, Kansas, USA). Additional support was granted through the Channing B. and Katherine W. Baker Fund #3424 of the University of Nebraska-Lincoln . Dr. Viscarra Rossel acknowledges CSIRO Land and Water, the Sustainable Agriculture Flagship (SAF) and the Australian Collaborative Land Evaluation Program (ACLEP) for supporting the research on proximal soil sensing.
PY - 2011/6/15
Y1 - 2011/6/15
N2 - Most soil properties sensed on-the-go (e.g., electrical conductivity, capacitance, optical reflectance, mechanical resistance and soluble ion activity) are not directly related to the agronomic parameters used to make management decisions. Nonetheless, these sensors provide an opportunity to obtain fine-resolution data about the spatial variability of soil in agricultural fields, rapidly and at a relatively low cost. To process this information, a limited number of targeted samples must be collected and undergo conventional laboratory testing for site-specific calibration of the sensor data. Selecting sampling locations based on multiple sensor data layers is an important process and, in practice, is conducted in a very subjective manner. This paper discusses an analytical methodology to assess the quality of targeted sampling strategies for on-the-go soil sensor data calibration prior to site-specific soil treatments, and demonstrates the potential for the automated selection of sampling sites. The methodology uses an arbitrary objective function that maximizes the spread among sensor output, local homogeneity (spatial uniformity around each location), and physical coverage across an entire field. Soil pH and electrical conductivity maps of a 23-ha agricultural field were used to illustrate the applicability of this method. From those considered, a Latin hypercube sampling (LHS) procedure with homogeneity and field coverage constraints provided the highest probability of maximum objective function outcomes, when individual criteria were normalized by the median of a large number of random prescription sets.
AB - Most soil properties sensed on-the-go (e.g., electrical conductivity, capacitance, optical reflectance, mechanical resistance and soluble ion activity) are not directly related to the agronomic parameters used to make management decisions. Nonetheless, these sensors provide an opportunity to obtain fine-resolution data about the spatial variability of soil in agricultural fields, rapidly and at a relatively low cost. To process this information, a limited number of targeted samples must be collected and undergo conventional laboratory testing for site-specific calibration of the sensor data. Selecting sampling locations based on multiple sensor data layers is an important process and, in practice, is conducted in a very subjective manner. This paper discusses an analytical methodology to assess the quality of targeted sampling strategies for on-the-go soil sensor data calibration prior to site-specific soil treatments, and demonstrates the potential for the automated selection of sampling sites. The methodology uses an arbitrary objective function that maximizes the spread among sensor output, local homogeneity (spatial uniformity around each location), and physical coverage across an entire field. Soil pH and electrical conductivity maps of a 23-ha agricultural field were used to illustrate the applicability of this method. From those considered, a Latin hypercube sampling (LHS) procedure with homogeneity and field coverage constraints provided the highest probability of maximum objective function outcomes, when individual criteria were normalized by the median of a large number of random prescription sets.
KW - Electrical conductivity
KW - On-the-go soil sensing
KW - Soil pH
KW - Targeted sampling
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U2 - 10.1016/j.geoderma.2011.04.004
DO - 10.1016/j.geoderma.2011.04.004
M3 - Article
AN - SCOPUS:79955974982
SN - 0016-7061
VL - 163
SP - 63
EP - 73
JO - Geoderma
JF - Geoderma
IS - 1-2
ER -