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Station 4: Remote Sensing Sources of Imagery 2006 Wheat (Source: SWOOP 2006 Gov’t) 2010 Wheat then Soybeans (Source: SWOOP 2010 Gov’t) Orthophotography 50cm resolution Early spring Leaf off Natural Colour Near Infrared Earth Observation Satellites = Free (30m) or for purchase (20m down to sub-metre) Soybeans Soybeans Bare Ground Unmanned Aerial System (UAS): August 21, 2015 Wheat Stubble & Red Clover Red, Green, NIR Camera RGB (Natural Colour) @ 5cm resolution Sept 4, 2013 Sept 27, 2013 April 23, 2014

Station 4: Remote Sensing Sources of Imagery

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Page 1: Station 4: Remote Sensing Sources of Imagery

Station 4: Remote Sensing Sources of Imagery

2006 Wheat (Source: SWOOP 2006 Gov’t)

2010 Wheat then Soybeans (Source: SWOOP 2010 Gov’t)

• Orthophotography • 50cm resolution • Early spring • Leaf off • Natural Colour • Near Infrared

Earth Observation Satellites = Free (30m) or for purchase (20m down to sub-metre)

Soybeans Soybeans Bare Ground

Unmanned Aerial System (UAS):

August 21, 2015 Wheat Stubble & Red Clover

Red, Green, NIR Camera RGB (Natural Colour)

@ 5cm resolution

Sept 4, 2013 Sept 27, 2013 April 23, 2014

Page 2: Station 4: Remote Sensing Sources of Imagery

Station 4: Remote Sensing Fundamentals

http://www.nue.okstate.edu/GreenSeeker/NDVI-PartA-mayo.pdf

Leaf Pigment Cell Structure

Algebra:Vegetation Indices

missionscience.nasa.gov

Agronomic Question: What is causing the red clover to catch more vigorously in some areas & not in others?

Raw Image → Map Product → Management Decision

UAS eBee (August 21, 2015) Wheat Stubble & Red Clover

www.airinov.fr

Page 3: Station 4: Remote Sensing Sources of Imagery

Station 4: Remote Sensing Analysis “Map Making from the Images”

2014 Soybeans – Processed Image

In-season Analysis

Multi-Year Soybean Yield Map (2005/2013/2014)

Bare Ground Analysis – Spring 2010 - Wheat

Page 4: Station 4: Remote Sensing Sources of Imagery

Station 4: Plant Sensors – How do they work? Quantifying Plant Vigour using Light Reflectance

Chlorophyll +

Biomass +

Cell Structure =

NDVI

Reading over 100 times per second!

Common Application:

Real-Time VRA (Nitrogen)

• Benchmark (N-Rich) recommended to initiate field job.

• Using desired algorithm/formula, sensing system will administer product for the goal to reach the vegetation benchmark.

Emits Own Light Source:

Red LED + Near Infared

Page 5: Station 4: Remote Sensing Sources of Imagery

Station 4: Plant Sensors – Zone Maps

Identifying Visual Opportunities Beyond the Human Eye

Patterns of Change

Aug 2 Aug 15

Vegetative Contoured NDVI Zones: • Strong Asset for Pinpointing Issues (disease, deficiencies, etc.)

• Unbias In-Season Performance Record

Page 6: Station 4: Remote Sensing Sources of Imagery

Image source: http://adamchukpa.mcgill.ca/gwpss/Papers/GWPSS_2011_Lund.pdf

Wear Plate

Sapphire Window

Two Disks

Row Unit for Optical Measurements

Veris® OpticMapper Visible/Near Infrared

(vis/NIR) LEDs

R² = 0.49

0

10

20

30

0 10 20 30

Pre

dic

ted

SO

C (

g k

g-1

)

Measured SOC (g kg-1)

R² = 0.81

0

50

100

150

200

250

0 50 100 150 200 250

Pre

dic

ted

PM

eh

lich

-3(m

g k

g-1

)

Measured PMehlich-3 (mg kg-1)

R² = 0.81

0

200

400

600

800

1000

0 200 400 600 800 1000

Pre

dic

ted

c

lay (

g k

g-1

)

Measured clay (g kg-1)

R² = 0.72

0

250

500

750

1000

0 250 500 750 1000

Pre

dic

ted

s

an

d (

g k

g-1

)

Measured sand (g kg-1)Veris® P4000 Hydraulic Motion

Platform

Power Generator

Sapphire Window

Sensing Probe

Visible/Near Infrared

Near Infrared

Enclosed Spectrometers 0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

350 750 1150 1550 1950

Ab

so

rba

nc

e

Vis-NIR wavelengths (nm)

Vis-NIR Spectra

Digital Microscope

Microscope Enclosure

Raw Data (OpticMapper)

Soil Image (200x Magnification)

Aggregates Quartz

Machine Vision

Predicted sand (g/kg)

Me

as

ure

d s

an

d (

g/k

g)

Soil Sample

Incident Light

Light Emitting Diode (LED)

Photo Detecting Array

Digital Data Communication

Display

Computer

• Ultraviolet (UV) • Visible (vis) • Infrared (IR) • Near Infrared (NIR) • Mid Infrared (mid-IR)

Diffuse Reflectance

Spectroscopy

Hui Huang et al. (2014) ASABE

Dhawale et al. (2014) ASABE

Dhawale et al. (2014) ASABE Dhawale et al. (2014) ASABE

Station 4: Optical Soil Sensing

Dhawale et al. (2013) CSBE

Void Space

Page 7: Station 4: Remote Sensing Sources of Imagery

ReferenceISE

Analyte

Indicator

Cations

• H+

• K+

• Na+

Anions

• NO3-

• HPO4-

• H2PO42-

ISE

Guard

Sensor Deployment Mechanism

Frame

Laptop Computer for Control System

Standard Hitch

Water

Soil Preparation Mechanism

GPS

On-the-spot Soil Analyser (OSA)

Station 4: Soil Potentiometry

An apparent electrical conductivity mapping unit comprised of 6 coulters

that provide two depths of investigation

A soil pH mapping unit that includes a soil

sampling mechanism with two ion-selective electrodes and a cleaning water

supply system

Veris® MSP

Ion Selective Electrode

Auger Water Tank

Ion Meter

Sample Rinsing Nozzles

Manual Probe

R2 = 0.80

SE = 0.22 pH

5.0

5.5

6.0

6.5

7.0

7.5

5.0 5.5 6.0 6.5 7.0 7.5

Measured pH

Lab

ora

tory

pH

R² = 0.87

0

5

10

15

20

25

30

35

0 5 10 15 20 25 30 35

NO

3- ,

mg

kg

-1(L

ab

ora

tory

)

NO3-, mg kg-1 (Field)

Prepared soil Sample Surface

Deployed Sensors Between Disc Space

Spring Loaded Sensors During Measurement

Horizontal Stop Position of Blades

Mounting Bracket

Guard

Rotating discs

Cutting Blade

Page 8: Station 4: Remote Sensing Sources of Imagery

Station 4: Agricultural Geophysics

27-07-2013

ECa 0 – 3.18 m

ECa 0 – 1.55 m

ECa 0 – 1.03 m

ECa 0 – 0.54 m

Mapping Apparent Soil Electrical

Conductivity using Electromagnetic

Induction

Mapping Apparent Soil Electrical

Conductivity using Galvanic Contact

Resistivity

Ground Penetrating Radar

Gamma-ray Spectrometry

RTK-Elevation Mapping

May 8, 2004 September 4, 2013 September 27, 2013 April 24, 2014

Elevation Slope Aspect