1. Combining COSMOS and Microwave Satellite Data
Integrating Observational Data to Improve Soil Moisture Profile Monitoring
Bobby B. Chrisman
University of Arizona – Dept. of Hydrology and Water Res
9/20/2018

2. Problem Statement
Problem: One soil moisture (SM) measurement is not representative of the entire root zone
Objective: Integrate data products to generate profiles without complex land surface modeling and without numerous inputs/state variables
Potential Next Step: synthesis of COSMOS and Satellite Radar measurements or another surface moisture data product to produce SM profile
Snapshot
Timeseries
Problem Notes: The data products on their own do not account for depth variability, this requires land surface modeling or other techniques (typically complex). The root-zone soil moisture profiles are important for many fields including hydrologists, ecologists, biologists, atmospheric scientists, etc.. The profile gives a the necessary depth variability to answer the questions of how much water and where, soil capacity, plant uptake availability, etc…
Objective Notes: Integrate the methods by taking advantage of the disconnect between the two. The surface measurement constrains the surface value, while the COSMOS signal constrains the integrated (weighted) root-zone value.
9/20/2018

3. Proposed Methodology + → Snapshot profile calculation:
Use data products that constrain values of the SM profiles, at least within some given error.
Use data products that have varying penetration depths
Take advantage of this disconnect of the penetration depths to put a constrain on what the SM profile is
Calculate SM profile + →
9/20/2018

4. Proposed Methodology
An adaptation of Al-Hamdan’s Maximum Entropy Model to calculate the soil moisture profile:
Surface SM value – measured
Mean SM value – measured
Depth SM value (SM at base of profile) – estimated SM
Surface and depth SM values provide end members and the mean value determines the curvature!
depth
9/20/2018

5. Proposed Methodology Snapshot profile calculation: Wet Case Dry Case
Constrain surface meas.
Constrain integrated column meas.
Estimate value range at depth z*
Produce multiple SM profiles within constraints and meas. error
Replicate a COSMOS equivalent integrated value using the weighting function
Closest COSMOS equivalent to COSMOS = best fit profile
Dynamic Case
SM profiles
+ Z*
+ weighting function
Largest effect Z* Z*
Zero effect
9/20/2018

6. Proposed Methodology Inherent Error
Potential for a non-unique solution
Multiple profiles (and significantly different profiles) give similar COSMOS equivalent value with given weighting function
COSMOS is not a soil column mean value
Circular Method
Possible Solution: Time series vs. snapshot
Add simple state variables and information on if drying or wetting: probability of SM change and P
Add initialization of SM profile and use COSMOS only to compare to COSMOS equivalent
Essentially adding more constrains

7. Proposed Methodology
9/20/2018

8. Spatial Disconnect? Needs to be addressed for method to be used!
Upscaling of COSMOS stationary probes
Downscaling of SMOS/SMAP data
Locally dense COSMOS network
Rover to match spatial extent
Other surface moisture measurements to match COSMOS

9. Data Products

10. Data Products
9/20/2018

11. Results Not ready just yet!
Timseries, stationary (1)-> forced surface measurement with 5 cm TDT probe at Santa Rita test bed site
Timseries, stationary (2) -> SMOS surface moisture data over uniform agricultural fields in IOWA
Snapshot, rover -> SMOS surface moisture data and COSMOS rover
How to check it’s correct?
9/20/2018

12. Conclusions
Can provide a simple means of integrating data products to produce a better product
Valuable synthesis between networks and soil moisture campaigns
Ease of access
Ease of use
Method will become more accurate as COSMOS and Satellite Radar develop
9/20/2018