Premium
Validation of Sensor‐Directed Spatial Simulated Annealing Soil Sampling Strategy
Author(s) -
Scudiero Elia,
Lesch Scott M.,
Corwin Dennis L.
Publication year - 2016
Publication title -
journal of environmental quality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 171
eISSN - 1537-2537
pISSN - 0047-2425
DOI - 10.2134/jeq2015.09.0458
Subject(s) - environmental science , sampling (signal processing) , spatial variability , soil science , sampling design , simulated annealing , remote sensing , soil water , digital soil mapping , soil map , hydrology (agriculture) , computer science , geology , mathematics , statistics , algorithm , geotechnical engineering , population , demography , filter (signal processing) , sociology , computer vision
Soil spatial variability has a profound influence on most agronomic and environmental processes at field and landscape scales, including site‐specific management, vadose zone hydrology and transport, and soil quality. Mobile sensors are a practical means of mapping spatial variability because their measurements serve as a proxy for many soil properties, provided a sensor–soil calibration is conducted. A viable means of calibrating sensor measurements over soil properties is through linear regression modeling of sensor and target property data. In the present study, two sensor‐directed, model‐based, sampling scheme delineation methods were compared to validate recent applications of soil apparent electrical conductivity (EC a )‐directed spatial simulated annealing against the more established EC a –directed response surface sampling design (RSSD) approach. A 6.8‐ha study area near San Jacinto, CA, was surveyed for EC a , and 30 soil sampling locations per sampling strategy were selected. Spatial simulated annealing and RSSD were compared for sensor calibration to a target soil property (i.e., salinity) and for evenness of spatial coverage of the study area, which is beneficial for mapping nontarget soil properties (i.e., those not correlated with EC a ). The results indicate that the linear modeling EC a –salinity calibrations obtained from the two sampling schemes provided salinity maps characterized by similar errors. The maps of nontarget soil properties show similar errors across sampling strategies. The Spatial Simulated Annealing methodology is, therefore, validated, and its use in agronomic and environmental soil science applications is justified. Core Ideas Sensor‐directed sampling is valuable for mapping soil properties using few samples. Apparent electrical conductivity is a good proxy for soil salinity. Spatial simulated annealing and response surface design sampling are compared. EC a ‐directed SSA is a valuable sampling approach for soil science applications.