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Mapping high-resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother


Authors: Jianzhi Dong
Owners: CTEMPs OSU-UNR
Resource type:Composite Resource
Created:Nov 30, 2017 at 9:49 p.m.
Last updated: Apr 09, 2018 at 8:57 p.m. by CTEMPs OSU-UNR

Abstract

This study demonstrated a new method for mapping high-resolution (spatial: 1 m, and temporal: 1 h) soil moisture by assimilating distributed temperature sensing (DTS) observed soil temperatures at intermediate scales. In order to provide robust soil moisture and property estimates, we first proposed an adaptive particle batch smoother algorithm (APBS). In the APBS, a tuning factor, which can avoid severe particle weight degeneration, is automatically determined by maximizing the reliability of the soil temperature estimates of each batch window. A multiple truth synthetic test was used to demonstrate the APBS can robustly estimate soil moisture and properties using observed soil temperatures at two shallow depths. The APBS algorithm was then applied to DTS data along a 71 m transect, yielding an hourly soil moisture map with meter resolution. Results show the APBS can draw the prior guessed soil hydraulic and thermal properties significantly closer to the field measured reference values. The improved soil properties in turn remove the soil moisture biases between the prior guessed and reference soil moisture, which was particularly noticeable at depth above 20 cm. This high-resolution soil moisture map demonstrates the potential of characterizing soil moisture temporal and spatial variability and reflects patterns consistent with previous studies conducted using intensive point scale soil moisture samples. The intermediate scale high spatial resolution soil moisture information derived from the DTS may facilitate remote sensing soil moisture product calibration and validation. In addition, the APBS algorithm proposed in this study would also be applicable to general hydrological data assimilation problems for robust model state and parameter estimation.

Raw project data is available by contacting ctemps@unr.edu

Subject Keywords

DTS,soil moisture,distributed temperature sensing,CTEMPs,soil temperature

How to cite

Dong, J. (2018). Mapping high-resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother, HydroShare, http://www.hydroshare.org/resource/ac905a9500b6401d82b9f7511dcbd3d2

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
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  • Public Resource  Public
  • Sharable Resource  Shareable

Coverage

Spatial:

 Coordinate System/Geographic Projection:  WGS 84 EPSG:4326
 Coordinate Units:  Decimal degrees
Place/Area Name: Oklahoma State University Range Research Station

Longitude
-97.2311°
Latitude
36.0640°

Temporal:

 Start Date:
 End Date:

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Authors

The people or organizations that created the intellectual content of the resource.

Name Organization Address Phone Author Identifiers
Jianzhi Dong Water Resources Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, Netherlands

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