Notes

Outline

Temporal and Spatial Resolution Requirements for a Soil Moisture Mission 1,2Jeffrey Walker and 2Paul Houser 1The University of Melbourne 2NASA/Goddard Space Flight Center http://www.civag.unimelb.edu.au/~jwalker

Motivation What are the defensible requirements of a remote sensing mission for measurement of surface soil moisture? Polarization – horizontal Wavelength – L-band Look Angle – < 50° Observation Accuracy – >5%v/v Temporal Resolution – ? Spatial Resolution – ?

Methodology Overview Use a LSM to generate a “truth” data set that provides both surface soil moisture “observations” and evaluation data. Degrade the land surface forcing data and initial conditions to simulate uncertainties in this data (assume a perfect model). Run the LSM with degraded data. Run the LSM with degraded data and assimilate the “observations” with various spatial and temporal resolutions imposed using the extended Kalman filter. Compare with the “truth”.

The Catchment-Based LSM

Data Model Input Data Atmospheric forcing data and soil and vegetation properties were taken from ISLSCP-1. Initial Conditions Spin-up catchment-based LSM for 1987 using ISLSCP forcing data. Surface Soil Moisture Observations Surface (2cm) soil moisture data output every day from the “truth” run. Evaluation Data Surface, root zone and total profile soil moisture data output each day plus average evapotranspiration data output each  10 days.

Spatial Disaggregation

Degraded Data Zero mean normally distributed perturbations with standard deviations given below added to the initial conditions, forcing and obs data.

Effect of Degraded Forcing Data Time Series Histogram of Errors Precipitation (mm/day)                        Profile Soil Moisture (%v/v)

Effect of Temporal Resolution

Effect of Spatial Resolution

Spatial Distribution of ET Error

Spatial Distribution of MC Error

Attributes Effecting the Spatial Distribution

Conclusions Daily observations achieved the best results. The greatest impact of temporal resolution was for 1 to 5 days, with greater time between observations having a marginal degradation. Spatial resolution less than the model resolution achieved the best results. Greater resolution produced only slightly worse results. Observations at one-quarter to one-half the spatial resolution of the model/application is a good compromise.