Arrowhead conference Scott M. Lesch 1 & Dennis L. Corwin 2 1. Principal Statistician, Dept. of Environmental Science, UCR 2. Research Soil Scientist, USDA-ARS.

Slides:



Advertisements
Similar presentations
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Advertisements

Selected results of FoodSat research … Food: what’s where and how much is there? 2 Topics: Exploring a New Approach to Prepare Small-Scale Land Use Maps.
Gregg Carlson, David Clay, Doug Malo, Sharon Clay, and Cheryl Reese.
Electrical conductivity, EC A quick method to measure the salinity of water. EC is approximately one-tenth of the total dissolved cation, or anion concentration.
Matt Galloway. 2 Volumetric water content sensors measure volumetric water content, right?
11 Simple Linear Regression and Correlation CHAPTER OUTLINE
Probabilistic & Statistical Techniques Eng. Tamer Eshtawi First Semester Eng. Tamer Eshtawi First Semester
Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 13 Nonlinear and Multiple Regression.
NOAA-CREST GPR Study Eric Harmsen, Associate Researcher Agricultural and Biosystems Engineering.
Walker River Basin Project Water PlantSoil Interactions Interactions.
Modeling of Soil Nutrients – An Introduction to Logical Spreadsheeting Russell Yost Department of Tropical Plant and Soil Science, University of Hawai`i.
Chapter 17 Overview of Multivariate Analysis Methods
Crop Yield Modeling through Spatial Simulation Model.
BA 555 Practical Business Analysis
Chapter 12 - Forecasting Forecasting is important in the business decision-making process in which a current choice or decision has future implications:
GIS Models and Modeling Chapter 14. Introduction A model is a simplified representation of a phenomenon or system A model is a simplified representation.
A Macroscale Glacier Model to Evaluate Climate Change Impacts in the Columbia River Basin Joseph Hamman, Bart Nijssen, Dennis P. Lettenmaier, Bibi Naz,
CHAPTER 3 Describing Relationships
Energy-efficient Self-adapting Online Linear Forecasting for Wireless Sensor Network Applications Jai-Jin Lim and Kang G. Shin Real-Time Computing Laboratory,
U.S. Department of the Interior U.S. Geological Survey Development of Inferential Sensors for Real-time Quality Control of Water- level Data for the EDEN.
Week 14 Chapter 16 – Partial Correlation and Multiple Regression and Correlation.
Making Electrical Conductivity Meaningful Gaylon Campbell Decagon Devices, Inc. Pullman, WA.
Multi-parameter Water-Quality Probes (YSI/Hydrolab) Lecture 5.
1 14 Design of Experiments with Several Factors 14-1 Introduction 14-2 Factorial Experiments 14-3 Two-Factor Factorial Experiments Statistical analysis.
Cost Analysis of Using Soil Electrical Conductivity Information for Precision Management in Cotton Production J.A. Larson R.K. Roberts B.C. English C.
GIS UPDATE!
FRUIT GROWERS LABORATORY, INC. Darrell H. Nelson Horticulturalist.
Internet Map Server Help This presentation briefly describes the Internet map server viewer and model interface and how to work them.
Roger Miller, Arkansas Department of Environmental Quality Barry Jackson, USGS Arkansas Water Science Center ARKANSAS EXCHANGE NETWORK FOR GROUNDWATER-QUALITY.
 1  Outline  stages and topics in simulation  generation of random variates.
Eric GrahamNathan Yau Staff Ecologist, CENSGraduate Student, Department of Statistics Use CasesSensorBase Coupled Human-Observational Systems Technology.
Impacts of temporal resolution and timing of streambed temperature measurements on heat tracing of vertical flux Paper No. H11D-1228 INTRODUCTION 1D heat.
Soil Electrical Conductivity
Precision Farming Using Veris Technologies for Texture Mapping
Generic Approaches to Model Validation Presented at Growth Model User’s Group August 10, 2005 David K. Walters.
Soil Physics schedule: Overview on hydraulic characteristics
What is Soil Electrical Conductivity?
Chapter 1 Introduction to Statistics. Statistical Methods Were developed to serve a purpose Were developed to serve a purpose The purpose for each statistical.
The Semivariogram in Remote Sensing: An Introduction P. J. Curran, Remote Sensing of Environment 24: (1988). Presented by Dahl Winters Geog 577,
Sundermeyer MAR 550 Spring Laboratory in Oceanography: Data and Methods MAR550, Spring 2013 Miles A. Sundermeyer Observations vs. Models.
1 11 Simple Linear Regression and Correlation 11-1 Empirical Models 11-2 Simple Linear Regression 11-3 Properties of the Least Squares Estimators 11-4.
Agronomy Training November 9, Ryan Maiden, Ag Tech Rep.
DRAINMOD APPLICATION ABE 527 Computer Models in Environmental and Natural Resources.
The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 3 Describing Relationships 3.2 Least-Squares.
MEASUREMENT OF SOIL ELECTRICAL PROPERTIES FOR THE CLASSIFICATION OF MANAGEMENT VARIANTS T. TÓTH 1, A. RISTOLAINEN 2,V. NAGY 3,D. KOVÁCS 1,CS. FARKAS 1.
ELECTRICAL RESISTIVITY SOUNDING TO STUDY WATER CONTENT DISTRIBUTION IN HETEROGENEOUS SOILS 1 University of Maryland, College Park MD; 2 BA/ANRI/EMSL, USDA-ARS,
© Phil Hurvitz, Introduction to Geographic Information Systems and their Potential Uses as Management Tools in Commercial Shellfish Farming Introduction.
1 Overview Importing data from generic raster files Creating surfaces from point samples Mapping contours Calculating summary attributes for polygon features.
Slide 1 NATO UNCLASSIFIEDMeeting title – Location - Date Satellite Inter-calibration of MODIS and VIIRS sensors Preliminary results A. Alvarez, G. Pennucci,
1 _________________________________________________________________________________________________________________________________________________________________.
Hyperspectral remote sensing
Ozone time series and trends Various groups compute trends in different ways. One goal of the workshop is to be able to compare time series and trends.
Yield Cleaning Software and Techniques OFPE Meeting
R I T Rochester Institute of Technology Geometric Scene Reconstruction Using 3-D Point Cloud Data Feng Li and Steve Lach Advanced Digital Image Processing.
BME 353 – BIOMEDICAL MEASUREMENTS AND INSTRUMENTATION MEASUREMENT PRINCIPLES.
Chapter 5: Introductory Linear Regression
Use of digital imagery in FPRA Effectiveness Evaluation Program: A Case Study Stéphane Dubé, NIFR Soil Scientist Fred Berekoff, PG District Stewardship.
SOIL SAMPLING Dr. Dave Franzen Extension Soil Specialist North Dakota State University.
Protocols for Mapping Soil Salinity at Field Scale: EC a Survey Considerations D.L. Corwin 1 and S.M. Lesch 2 1 USDA-ARS, U.S. Salinity Laboratory Riverside,
GEOGRAPHICAL INFORMATION SYSTEM
Qing Zhu1, Henry Lin1, Xiaobo Zhou1, J.A. Doolittle2 and Jun Zhang1
Week 14 Chapter 16 – Partial Correlation and Multiple Regression and Correlation.
Stewart Reed Oklahoma State University
Using Soil Moisture and Matric Potential Observations to Identify Subsurface Convergent Flow Pathways Qing Zhu, Henry Lin, and Xiaobo Zhou Dept . Crop.
In-Field Soil Sampling
Laboratory in Oceanography: Data and Methods
CHAPTER 3 Describing Relationships
CHAPTER 3 Describing Relationships
CHAPTER 3 Describing Relationships
Precision Ag Precision agriculture (PA) refers to using information, computing and sensing technologies for production agriculture. PA application enables.
Presentation transcript:

Arrowhead conference Scott M. Lesch 1 & Dennis L. Corwin 2 1. Principal Statistician, Dept. of Environmental Science, UCR 2. Research Soil Scientist, USDA-ARS US Salinity Laboratory Part I: Salinity Assessment & Prediction Software (ESAP: with multiple application examples) Part II: GIS Applications & Case Studies

Arrowhead conference  A series of integrated Windows based shareware software programs which can be used for the prediction of field scale, spatial soil salinity information (and/or other soil properties) from conductivity survey data.  ESAP has been specifically designed to facilitate cost-effective, technically sound, soil salinity assessment and data interpretation techniques.  ESAP can be down-loaded free of charge from the USDA-ARS US Salinity Laboratory website. A NRCS certified version (suitable for installing on NRCS desktop and laptop computers) is also available. ESAP Software Version 2.35 Brief Overview of the ESAP Software Suite

Arrowhead conference ESAP Modeling Software (3 core programs)…  ESAP-RSSD  examine, analyze, & summarize ECa survey data  generating optimal soil sampling designs from sensor data  ESAP-SaltMapper  1-D transect plots and 2-D raster maps  tile line maps, calculate tile line locations, diagnose potential tile line problems  ESAP-Calibrate  convert survey data into predicted soil salinity (a/o other soil properties)  diagnose & identify primary soil properties influencing survey data  generate multiple field summary statistics  generate prediction data (for making spatial maps)

Arrowhead conference ESAP Support Software (2 support programs)…  ESAP-SigDPA  performs signal data pre- processing chores, data QA/QC and validity checks, scale conversions, and row (transect) identification & assignment.  DPPC-Calculator  a convenient to use calculator version of the 1989 Rhoades DPPC model.  can be used for direct prediction of salinity from spot 4-probe or EM survey data, given additional soil temperature, texture, and moisture measurements (or estimates).

Arrowhead conference ESAP is primarily designed for Statistical Calibration: (i.e., Ordinary Regression Modeling) A (spatially referenced) multiple linear regression model which typically includes both soil conductivity and trend surface parameters. Typical EM38 model (for predicting Soil Salinity): EC e = b 0 + b 1 (EM V ) + b 2 (EM H ) + b 3 (x) + b 4 (y) where EM V and EM H represent the EM38 vertical and horizontal signal readings, and x and y represent the spatial survey coordinates.

Arrowhead conference Model based summary statistics & predictions:  Given our estimated regression model, we can calculate:  the survey grid (field) mean, with an associated confidence interval  the percentage of survey sites exceeding specific thresholds (for example, % area of field with salinity levels between 2 and 4 dS/m)  individual (spatially referenced) soil property predictions, which in turn can be used to generate spatial maps

Arrowhead conference Example 1: dense grid of EC a (EM38) survey data is imported into RSSD software and an optimized sampling plan is generated (EC e soil samples will then be used to calibrate EM38 survey data).

Arrowhead conference Regression model is then fit to calibration data and used to predict depth specific EC e information from the EM signal data. (for this example: R 2 = 0.91 & 0.89 for 0-2 & 2-4 ft depths)

Arrowhead conference Final Product: predicted 0-2 ft & 2-4 ft Salinity Maps

Arrowhead conference and predicted 0-2 ft & 2-4 ft summary statistics… Geometric Mean Estimates (w/95% CI’s) 0-2 ft depth:3.40 dS/m (2.66, 4.36) 2-4 ft depth: 4.39 dS/m (3.21, 6.04) Range Interval Estimates (% area of field w/in specific range classes) 0-2 ft depth 2-4 ft depth Range (dS/m) % % < >

Arrowhead conference Additional ESAP-Calibrate software features  The prediction of additional soil physical / chemical properties is often possible (e.g., texture, water content, SAR, boron, etc.).  Steady-state leaching fraction maps can often be estimated (using salinity or chloride data).  If desired, estimates of Tons of Salt per acre/ft can be made (via TDS samples, composite cation- anion analysis, or estimated TDS=f(EC) relationship).  Can be used to test for changing spatial salinity conditions over time.  Can be used to estimate relative yield loss due to salinity for various crops, or calibrated directly to actual crop yield data (to produce absolute yield loss estimates and/or yield maps).

Arrowhead conference Example 2: Projected Yield Loss Maps 23.7% (broccoli) 10.4% (wheat)

Arrowhead conference Example 3: Post-leaching 0-2 ft Salinity Map (and statistics) Geo-Mean Statistics: Post: 2.91 dS/m Pre: 3.40 dS/m (about a 17% reduction) RIE Statistics: RangePostPre % 28.3% % 31.2% % 23.8% > 816.5% 16.7% Leaching Design: 20 E/W basins (18m x 380m) – basin spill-over design. Apprx 45 ac-ft water (ECe = 1.1 dS/m) applied over 6 weeks (estimated evap loss = 11 ac-ft) Post-leaching survey performed 4 weeks after final water application…

Arrowhead conference Pre- v.s. post-leaching 0-2 ft salinity maps…

Arrowhead conference Example 3 (cont.): Calculated 0-2 ft Salt Displacement Map

Arrowhead conference Mapping multiple soil properties (Example 4: Soil salinity and % Clay content) 2003 survey of Coachella Valley lettuce field. EC e %Clay (0-2 ft) (2-4 ft) Mean Std Min Max Corr(lnEM, lnECe | 0-2 ft) = 0.78; Corr(lnEM, %Clay | 2-4 ft) = 0.83 for this field…

Arrowhead conference Deeper (2-4 ft) texture pattern appears to explain the corresponding 0-2 ft soil salinity map in this example...

Arrowhead conference Mapping multiple soil properties (Example 5: Soil salinity and Water content) 2001 survey of Palo Verde alfalfa field. EC e VH2o Mean Std Min Max Corr(lnEM, lnECe) = 0.83; Corr(lnEM, VH2o) = 0.84 for this field…

Arrowhead conference Corresponding bulk average (0-0.9 m) volumetric water content map… Side note: this field was suffering from deficient irrigation scheduling; yield losses in alfalfa correspond to dry areas (below wilting point)…

Arrowhead conference Example 6: Mapping tile line effects in an IID alfalfa field (using the ESAP SaltMapper program).

Arrowhead conference Observed tile line influence on spatial EM-38 signal levels across an Imperial alfalfa field.

Arrowhead conference Example of “filtering” EMv transect data, in order to better identify underlying cyclic pattern (and identify positions of local minima)…

Arrowhead conference 2D filtered EM signal data shows spatial tile line pattern and general tile line locations…

Arrowhead conference Accurate locations of each tile line can generally be determined by plotting the positions of the local minima within each survey transect.

Arrowhead conference The user must first interactively identify the corresponding line positions (using the on-screen “threading” procedure)…

Arrowhead conference The exact physical locations of tile lines can then be identified and mapped using a built in ANOCOVA modeling procedure... Using EM38 data from two separate surveys (performed 1 year apart), we were able to map the individual tile lines to within 1 m accuracy…

Arrowhead conference End of Part I…