Remotely-Sensed Estimates of Soil Moisture

1
Remotely-Sensed Estimates of Soil Moisture to
Infer Soil Texture and Hydraulic Properties
Figure 1
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FONTArial, Project Synopsis text size16-20
Near-surface soil moisture is a critical
component of land-surface energy and water
balance models, but accurate soil moisture
prediction requires soil texture and hydraulic
property information, which is poorly
characterized over most of the globe.
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Project synopsis should be short and simply
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Optimized vs. Measured Soil Textures
Figure 2
Physically meaningful soil texture information
can be retrieved from a few but
appropriately-timed soil moisture measurements
from microwave remote sensing.
2

• Name Christa Peters-Lidard, NASA/GSFC
• E-mail Christa.D.Peters-Lidard_at_nasa.gov
• Phone [phone]
• References
• Santanello, J.A., Jr., C. D. Peters-Lidard, M.
  Garcia, D. Mocko, M. Tischler, MS. Moran, and
  D.P. Thoma, 2007. Using Remotely-Sensed
  Estimates of Soil Moisture to Infer Soil Texture
  and Hydraulic Properties across a Semi-arid
  Watershed, In Press, Remote Sensing of the
  Environment.
• Peters-Lidard, Christa D., David M. Mocko, Joseph
  A. Santanello, Jr., Michael A. Tischler, M. Susan
  Moran, Matthew Garcia, and Y. Wu, 2007. The role
  of precipitation uncertainty for soil property
  estimation using soil moisture retrievals in a
  semi-arid environment. Submitted to Water
  Resources Research.
• Garcia, M., C.D. Peters-Lidard, and D.C.
  Goodrich, 2007. Spatial interpolation of
  precipitation in a dense gauge network for
  monsoon storm events in the southwestern US.
  Submitted to Water Resources Research.
• Data Sources This is a joint effort composed of
  multiple agencies including the USDA-Agricultural
  Research Service (watershed, remote-sensing
  data), NASA-GSFC (land-surface modeling within
  the Land Information System (LIS
  http//lis.gsfc.nasa.gov), coupled with Parameter
  Estimation (PEST)) and US Army Corps of
  Engineers-Engineering Research and Development
  Center (financial support, operational testing,
  user interface development),
• Near-surface (0-5cm) soil moisture observations
  (Figure 1) derived from successive aircraft
  flights using NASAs L-Band Push-Broom Microwave
  Radiometer (PBMR a precursor to the Hydros/SMAP
  mission) were acquired during the Monsoon 90
  experiment in SE Arizona, and used to calibrate
  soil hydraulic properties in the Noah
  land-surface model executed within LIS at a very
  high horizontal spatial resolution of 40 meters.
• Technical Description of Image
• Figure 1 Simulated (top), PBMR-observed
  (middle), and difference (bottom) 0-5 cm soil
  moisture using a) default (USDA SSURGO) soils and
  b) soil properties calibrated using LIS/NoahPEST
  on DOY 214. The soil moisture bias and RMS error
  are greatly reduced after the soil property
  calibration. Limited remote microwave retrievals
  of near-surface soil moisture can be used to
  calibrate the soil texture and hydraulic
  properties using this combined observation,
  modeling and parameter estimation approach. (Fig.
  1). Santanello et al., 2007
• Figure 2 Percentages of sand, silt, and clay
  estimated using this approach at the eight sites
  compared with in-situ soil measurements from
  Schmugge et al. (1994). The LIS/NoahPEST system
  correctly estimates highly-sandy soils in this
  semi-arid watershed. Soil texture estimated using
  this approach corresponds well with in situ
  observations of sand, silt, and clay at various
  sites across the watershed. By estimating within
  a continuous range of soil properties such as
  sand, silt, and clay percentages rather than
  applying disconitnuous soil texture classes, the
  physical accuracy and consistency of the
  estimated soil properties is assured and can be
  more easily assessed against in situ
  measurements.

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