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

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

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

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

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

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

Proposed Methodology 9/20/2018

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

Data Products

Data Products 9/20/2018

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

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