MOISST Workhsop, 2014

Soil Moisture Estimation Using Active DTS at MOISST Site June 4, 2014

Chadi Sayde, Daniel Moreno, John Selker

Department of Biological and Ecological Engineering Oregon State University, USA

John S. Selker

Protective Outer Jacket

Kevlar Fibers for Strength

Plastic Buffer Coating

Glass Fiber 50 μm

Optical fiber

DTS Laser unit

Fiber diameter 0.9 mm Black and white jackets

25 cm Every 1 s (1 Hz)

Measuring Temperature with DTS

Electrical resistance

heater

Te

mp

era

ture

( C

)

Field test in Hermiston-Oregon: Thermal responses of

soil at 30 cm depth to a 10 W/ 1 min heat pulse

Heat Injection

0

5

10

15

20

25

0 20 40 60 80 100 120 140

Time ( s )

Inte

sit

y (

W/m

)

0 100 200 300 400

0

5

10

15

Time from start of heat pulse (sec.)

Te

mp

era

ture

in

cre

ase

(C

)

0.05 m3/m3

0.12 m3/m3

Cu

mula

tiv

e T

emp

erat

ure

incre

ase

(°C

)

Time from start of heat pulse (sec.)0 100 200 300 400

0

5

10

15

Time from start of heat pulse (sec.)

Te

mp

era

ture

in

cre

ase

(C

)

0.05 m3/m3

0.12 m3/m3

Cu

mula

tiv

e T

emp

erat

ure

incre

ase

(°C

)

Time from start of heat pulse (sec.)

Measuring soil moisture content

Actively heated

Heat injected in soil

along fiber optic cable

DTS reads temperature

changes during heat pulse

along fiber optic cable

Soil water content inferred

from thermal response

of soil to the heat pulse

Heat Pulse Interpretation:

The Integral Method

dtTT

jt

t

cum

0

0 100 200 300 400

0

5

10

15

Time from start of heat pulse (sec.)

Te

mp

era

ture

in

cre

ase

(C

)

0.05 m3/m3

0.12 m3/m3

Cu

mula

tiv

e T

emp

erat

ure

incre

ase

(°C

)

Time from start of heat pulse (sec.)0 100 200 300 400

0

5

10

15

Time from start of heat pulse (sec.)

Te

mp

era

ture

in

cre

ase

(C

)

0.05 m3/m3

0.12 m3/m3

Cu

mula

tiv

e T

emp

erat

ure

incre

ase

(°C

)

Time from start of heat pulse (sec.)

4

Calibration Curve

Calibration curve relating the degree of

saturation (S) to Tcum normalized by its

value at saturation

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 0.2 0.4 0.6 0.8 1

λ(W

m-1

K

-1)

S (-)

S vs. λ measured from non-disturbed

samples collected from the calibration soil

column using KD2 probe

5

0

0.02

0.04

0.06

0.08

0.1

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

erro

r (

m3/m

3)

θ (m3/m3)

Sensortran 5100

Silixa Ultima

Error in soil water content estimation due

to the DTS system performance

Funding agency: NASA

Location: Stillwater, OK

Objectives:

Better understanding of spatio-temporal

variation of soil water content

Calibration / Validation remote sensing

data

Downscaling remote sensing data

6

Interpretation of satellite soil moisture

products with ultra-high resolution fiber optic

and cosmic ray ground-based measurements.

Oregon State University

Monitoring Stations

4L

1L

3L

2H

2L

1H

• 4900 m of FO cables

• 4600 m under ground

• soil moisture measured at

36,800 locations Simultaneously

• 3 depths: 5, 15, 25 cm

• Solar power

• Remote communication

Fiber Optics Cable Path

8

9

10

Oregon State University

Monitoring Stations

4L

1L

3L

2H

2L

1H

Equipment Data Logger Sensors Measurements

Stations # 1H,

2H, 2L

Decagon

EM50G

Decagon 5-TM

and MPS-2

Soil moisture,

Temp. and water

potential

Station # 1L, 3L Campbell

CR-800

Same as

above

Same as above

East 30

sensors

Specific Heat

Station # 4L Stevens

Datalog 3000

Hydra Probe Soil moisture,

temperature, EC

Fiber Optics Cable Path

Precipitation recorded at the site and Soil Water Contents

measured at Stations 1H and 2H

12

1HH

2HH

Tcum vs. soil water content

measured at stations 1H

and 2H in August, 2013

13

Spatial Variability of Soil

Thermal properties

14

Precipitation recorded at the site and Soil Water Contents

measured at Stations 1H and 2H

15

16

17

18

HR bottom cable

HR middle cable

Jun

e 1

, 20

13

– T

cum

fro

m 4

min

h

eat

pu

lse

19

Histogram of Tcum on June 1, 2013 Saturated soil after heavy rainfall

20

Generate distributed

calibration curves:

Thermal response curve

generated from non disturbed

samples

Strategic detailed surveying of

soil water content and soil

thermal properties

Calibration curve could be

produced by few measured

Tcum-θ couples per location

Vegetation and topography

indices

Future work: Increasing Calibration

Accuracy

21

Ultra High Resolution LIDAR

mapping of the site

22 Source: USGS

July 20-25: • 1X1 mile to be mapped • 3 LIDAR types: 2 copters, ground • Horizontal resolution <6cm • Color aerial mosaic and orthophotos

<2.5 cm • Near infrared and NDVI

Products: • Surface micro-topography • Canopy height, above ground biomass • Vegetation type?

Active DTS Soil Moisture product available

in the summer

Distributed calibration

Dynamic calibration: Increased accuracy

with more data integrated

High resolution LIDAR micro-topography

and vegetation height maps: Improving the accuracy of DTS products

Effects of micro-topography on Hydrologic processes

in the field

Upscaling DTS soil moisture

Summary

23

Acknowledgements

This research was funded by NASA, award NNX12AP58G, with the support of the Center for Transformative Environmental Monitoring Programs (CTEMPS) funded by the National Science Foundation, award EAR 0930061. We thank Tyson Ochsner’s team, OSU Range Research Station team, Christine Hatch, and Steele-Dunne for their curtail field support

Thank You!