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”.

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.


	^1,2Jeffrey Walker and ²Paul Houser
	¹The University of Melbourne
	²NASA/Goddard Space Flight Center

	http://www.civag.unimelb.edu.au/~jwalker


	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 – ?


	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”.


	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.


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


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


	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.