Soil Moisture Estimation Using Active DTS at MOISST Site

Transcription

1 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

2 Measuring Temperature with DTS Optical fiber Kevlar Fibers for Strength Plastic Buffer Coating Protective Outer Jacket Glass Fiber 50 μm Fiber diameter 0.9 mm Black and white jackets DTS Laser unit Every 1 s (1 Hz) 25 cm

3 Intesity ( W/m) Temperature increase ( C) Temperature ( C ) Cumulative Temperature increase ( C) Measuring soil moisture content Actively heated Heat injected in soil along fiber optic cable Electrical resistance heater 15 DTS reads temperature changes during heat pulse along fiber optic cable Heat Injection 0.05 m3/m m3/m3 Soil water content inferred from thermal response of soil to the heat pulse 5 0 Field test 0 in Hermiston-Oregon: Thermal 300responses 400 of 0 20 Time 40 from start start 60 of heat of pulse heat (sec.) 80 pulse (sec.) soil at 30 cm depth to a 10 W/ 1 min heat pulse Time ( s )

4 Cumulative Temperature increase ( C) Temperature increase ( C) Heat Pulse Interpretation: The Integral Method 15 T cum t j t0 T dt m3/m m3/m Time from start of heat pulse (sec.) Time from start of heat pulse (sec.) 4

5 error (m 3 /m 3 ) λ (W m -1 K -1 ) Calibration Curve Sensortran 5100 Silixa Ultima θ (m3/m3) S (-) Calibration curve relating the degree of saturation (S) to T cum normalized by its value at saturation S Error vs. λ in measured soil water from content non-disturbed estimation due samples to the DTS collected system from performance the calibration soil column using KD2 probe 5

6 Interpretation of satellite soil moisture products with ultra-high resolution fiber optic and cosmic ray ground-based measurements. 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

7 Fiber Optics Cable Path 1L 2H 1H 4L 3L 2L 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

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11 Fiber Optics Cable Path 1L 2H 1H 4L 3L 2L Equipment Data Logger Sensors Measurements Stations # 1H, 2H, 2L Station # 1L, 3L Station # 4L Decagon EM50G Campbell CR-800 Stevens Datalog 3000 Decagon 5-TM and MPS-2 Same as above East 30 sensors Hydra Probe Soil moisture, Temp. and water potential Same as above Specific Heat Soil moisture, temperature, EC Oregon State University Monitoring Stations

12 Precipitation recorded at the site and Soil Water Contents measured at Stations 1H and 2H 1HH 2HH 12

13 Tcum vs. soil water content measured at stations 1H and 2H in August,

14 Spatial Variability of Soil Thermal properties 14

15 Precipitation recorded at the site and Soil Water Contents measured at Stations 1H and 2H 15

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18 June 1, 2013 Tcum from 4 min heat pulse HR bottom cable HR middle cable 18

19 Histogram of Tcum on June 1, 2013 Saturated soil after heavy rainfall 19

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21 Future work: Increasing Calibration Accuracy 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 21

22 Ultra High Resolution LIDAR mapping of the site 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? Source: USGS 22

23 Summary 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 23

24 Thank You! 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 We thank Tyson Ochsner s team, OSU Range Research Station team, Christine Hatch, and Steele-Dunne for their curtail field support