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ARS Home Pacific West Area Riverside, California Agricultural Water Efficiency and Salinity Research Unit Docs Model





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ESAP Modelheadline bar The full version of the program, and examples and manual,can be downloaded from the software download area.**Year: 2006Version: 2.35 (Windows XP Edition)**Abstract:
The ESAP software package currently contains five programs:
ESAP-RSSDDesigned to generate optimal soil sampling designs from bulk soil electrical conductivity survey information
ESAP-CalibrateDesigned to estimate both stochastic (regression model) and deterministic (soil theory based) calibration equations; i.e., the equations which you will ultimately use to predict the spatial values of one or more soil variables from your EM survey data.
ESAP-SaltMapperUsed to produce high quality 1-D or 2-D graphical output of your EM survey data and/or predicted soil variables. This software can also be used to map out the locations of tile lines in saline fields, using EM survey data.
ESAP-SigDPASignal Data pre-processing software for managing raw Conductivity/GPS data file.
ESAP-DPPC CalculatorUsed to convert insertion four-probe conductivity data into soil salinity estimates.All five programs have been designed to work together in a seamless and efficient manner, and each program employs a simple, easy to learn graphical user interface.
Program Descriptions:
Each program in this ESAP software suite contains a number of features designed to help perform the various components of the soil salinity assessment process, described below:
1. ESAP-RSSD (Response Surface Sampling Design software)
Used to generate optimal sampling designs for stochastic calibration models based on conductivity survey data. The following capabilities listed below have been incorporated into the RSSD program:
Ability to process grid or transect data (i.e., can be used to process EM-38, EM-31, Verris 3100, or Mobile 4-Electrode types of signal data)
Ability to handle arbitrarily large survey sizes (up to 30,000 sites per field)
Allow for interactive validation of signal data
Ability to handle either 1 or 2 signal readings per survey site
Can be used to generate calibration sample size of 6, 12, or 20 sites per field, or allow user to enter and record a custom sampling design
Can adjust sampling design based on signal variability (i.e., transition analysis)2. ESAP-Calibrate (Conductivity to Salinity Calibration software)
Used to convert raw conductivity data to soil salinity data via either stochastic calibration or deterministic techniques (i.e., direct MLR estimation and/or the Dual Pathway Parallel Conductance equation, a.k.a. the Rhoades conductivity model).Additional capabilities currently include the ability to:
Use stochastic calibration models to predict levels of secondary soil properties (provided secondary sample data has been acquired)
Use deterministic DPPC model to estimate theoretical strength of correlations between raw conductivity and ECe, SP, volumetric H2o, and/or other secondary sample data which may have been acquired during the sampling process
Produce 1D profile data graphs and perform bivariate profile data correlation analysis
Fully automate the stochastic calibration (regression modeling) process3. ESAP-SaltMapper (1-D Transect and 2-D raster mapping software)
Used to generate 1-D transect and 2-D raster maps of raw conductivity, estimated soil salinity, and/or estimated secondary soil physical properties (i.e., designed accept input files from either ESAP-RSSD or ESAP-Calibrate).
4. ESAP-SigDPA
The ESAP-SigDPA program is a signal data preprocessing program designed to operate on raw Conductivity/GPS data files (i.e., Trimble output ASCII sensor files, Sandia XYZ files, etc.). You can use this program to organize and clean up your raw data files, remove outlier observations, and/or to correctly assign row numbers to transect rows.
5. ESAP-DPPC Calculator
This simple-to-use Calculator program can be used to convert insertion 4-probe conductivity readings into soil salinity estimates, given knowledge of the corresponding soil temperature, texture, and water content levels. It is based on the methodology develped by Rhoades (1992), and extended by Lesch & Corwin (2003).References:Corwin, D.L., and S.M. Lesch. 2005. Apparent soil electrical conductivity measurements in agriculture. Comput. Electron. Agric., 46:11-44.
Corwin, D.L., and S.M. Lesch. 2005. Characterizing soil spatial variability with apparent soil electrical conductivity. I. Survey protocols. Comput. Electron. Agric., 46:103-134.
Lesch, S.M., 2005. Sensor-directed response surface sampling designs for characterizing spatial variation in soil properties. Comput. Electron. Agric., 46:153-180.
Lesch, S.M., Corwin, D.L., 2003. Predicting EM/soil property correlation estimates via the Dual Pathway Parallel Conductance model. Agron. J. 95, 365-379.
Rhoades, J.D., F. Chanduvi, and S.M. Lesch. 1999. Soil salinity assessment: Methods and interpretation of electrical conductivity measurements. FAO Irrigation and Drainage Paper #57, Food and Agriculture Organization of the United Nations, Rome, Italy.
Rhoades, J.D. 1992. Instrumental field methods of salinity appraisal, pp. 231-48, in Advances in measurement of soil physical properties: Bring theory into practice. G.C. Topp, W.D. Reynolds, and R.E. Green (Eds.). SSSA Special Publ. no.30. ASA, CSSA, and SSA, Madison, WI.
Lesch, S.M., J.D. Rhoades, and D.L. Corwin. 2000. The ESAP Version 2.01r user manual and tutorial guide. Research Report #146. George E. Brown Jr., Salinity Laboratory, Riverside, CA, 153pp.
Lesch, S.M., D.J. Strauss and J.D. Rhoades. 1995b. Spatial prediction of soil salinity using electromagnetic induction techniques: 2. An efficient spatial sampling algorithm suitable for multiple linear regression model identification and estimation. Water Resour. Res. 31:3387-398.
Lesch, S.M., D.J. Strauss and J.D. Rhoades. 1995a. Spatial prediction of soil salinity using electromagnetic induction techniques: 1. Statistical prediction models: A comparison of multiple linear regression and cokriging. Water Resour. Res. 31:373-386.


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