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 jackets25 cm Every 1 s (1 Hz)
Electrical resistance heater T e m p e ra tu re ( 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 ) Int e s it y ( W /m ) 0 100 200 300 400 0 5 10 15
Tim e from s tart of heat puls e (s ec.)
T e m p e ra tu re i n c re a s e ( C) 0.05 m3/m3 0.12 m3/m3 C u m u lativ e T em p er at u re in c rea se ( ° C)
Time from start of heat pulse (sec.)
0 100 200 300 400
0 5 10 15
Tim e from s tart of heat puls e (s ec.)
T e m p e ra tu re i n c re a s e ( C) 0.05 m3/m3 0.12 m3/m3 C u m u lativ e T em p er at u re in c rea se ( ° 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
dt
T
T
jt
t
cum
0 0 100 200 300 400 0 5 10 15Tim e from s tart of heat puls e (s ec.)
T e m p e ra tu re i n c re a s e ( C) 0.05 m3/m3 0.12 m3/m3 C u m u lativ e T em p er at u re in c rea se ( ° C)
Time from start of heat pulse (sec.)
0 100 200 300 400
0 5 10 15
Tim e from s tart of heat puls e (s ec.)
T e m p e ra tu re i n c re a s e ( C) 0.05 m3/m3 0.12 m3/m3 C u m u lativ e T em p er at u re in c rea se ( ° C)
Time from start of heat pulse (sec.)
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 e r r o r ( m 3/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
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
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
Precipitation recorded at the site and Soil Water Contents
measured at Stations 1H and 2H
12
1H
HTcum vs. soil water content
measured at stations 1H
and 2H in August, 2013
Spatial Variability of Soil
Thermal properties
Precipitation recorded at the site and Soil Water Contents
measured at Stations 1H and 2H
18 HR bottom cable HR middle cable
June
1,
2013
–
Tcum
fr
om
4
mi
n
hea
t pul
se
19
Histogram of
Tcum on June 1,
2013
Saturated soil
after heavy
rainfall
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
T
cum-θ
couples per location
Vegetation and topography
indices
Future work: Increasing Calibration
Accuracy
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
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
