Retrieval of Snow Water Equivalent

Using Passive Microwave Brightness

Temperature Data

By:Purushottam Raj Singh & Thian Yew GanDept. of Civil & Environmental Engineering

University of Alberta, Canada

RESEARCH OBJECTIVE

To Develop new SWE retrieval algorithms

using Passive Microwave Brightness

Temperature Data of SSM/I Sensor for a

prairie like environment of North America

*SWE = Snow Water Equivalent (cm)

INTRODUCTION

• Snow: Dominant source of Water Supply, contributes up to 70% in many parts of Canada

• Seasonal Variation of SWE: Critical to an effective management of Water Resources

• Snow course & snow gauge data: Point measurements & Limited

• Airborne Data for SWE: Expensive

PASSIVE MICROWAVE RADIOMETRY

• Passive Microwave (PM): can penetrate clouds & provide information during night

• Daily PM data available on a global basis

• Satellite Microwave data: To retrieve SWE Chang et al.,1976; Goodison et al.,1986; etc.

• Basis of microwave detection of snow: Redistribution of upwelling radiation (RTM, SM)

STUDY SITE

• Red River Basin (120,000 Km^2)

• Elevation Range: 237-552m

Figure 1. The Red River basin study area of eastern North Dakota and northwestern Minnesota.

DATA• Airborne SWE Data(88, 89 & 97)->NWS

• SSM/I Brightness Temperature

Year SSM/I Ascending/Descend Source

1988: DMSP(F8) 6:13 18:13 -> NSIDC

1989: DMSP(F8) 6:13 18:13 -> NSIDC

1997: DMSP(F10) 22:24 10:24 -> MSFC

1997: DMSP(F13) 17:46 5:46 -> MSFC

• Other DataLand Use/Cover & DEM(30 arc”) -> USGS

Temperature & Precipitation -> HPCC

Total Precipitable Water(1 deg.) -> TOVS

0

50

100

150

200

Nov Dec Jan Feb Mar AprMonth

Sno

wfa

ll(c

m)

• Airborne SWE Data: ->NWS

Year 1988 1989 1997

# of Airborne Data: 65 241 192

# of Gridded Data: 52 175 197

# of Dry Snow Cases: 16 121 119

Mean SWE(cm) 3.43 9.25 13.55

• Cumulative Snowfall1997

1989

1988

Cumulative snowfall in cm.

Selection Criteria for Dry Snow Cases

• V37<250°K; V19-V37=>9 °K ! Goodison et al.,’86

• V37-H37 => 10 °K ! Walker & Goodison’93

• P_factor > 0.026

• V37 > 225 °K (DMSP-F8)

• P_factor < 0.041 (F10/F13)

Where,

V37: 37GHz Vertical Polarization Brightness Temperature(°K)P_factor or polarization factor = (V37-H37)/(V37+H37)

! From Present Study

RETRIEVAL ALGORITHMS

Goodison et al.,’94: SWE=K1+K2(V19-V37) ..(1)

Chang et al.,’96: SWE=K3+K4(H19-H37)(1-AF) ..(2)

Proposed: (a) Conventional Regression (b) PPR

(a) SWE = K5(V19-H37) + K6(AMSL) + K7(1-AF) +

K8(1-AW)TA + K9 (TPW) ..(3)

)4..()x(yy)b( Tmm

o

m

M

1m

• Projection Pursuit Regression (PPR):)4()x(

M

1myy T

mm

o

m

)5()x(yE

2

Tmm

Mo

1mmy

)6()y(Var

)y,x,,,(LU

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

1 2 3 4 5 6 7 8 9

Number of Terms of PPR, Mo

Un

exp

lain

ed V

ari

an

ce, U

Figure 2.Calibration Results: Fraction of unexplained variance (U) versus the number of terms (Mo) for the PPR model using selected dry snow cases, ascending set of SSM/I data of 1989.

Figure 3. Scatterplots of SWE from Airborne Gamma Ray Vs. SWE Retrieved from SSM/I using Existing (Eq. 1) and Proposed (Eq. 3: Multi-variate Regression) Algorithms.

DISCUSSION OF RESULT

Figure 4. Scatterplots of SWE from Airborne Gamma Ray Vs. SWE Retrieved from SSM/I using Existing (Eq. 2) and Proposed (Eq. 4: Projection Pursuit Regression) Algorithms.

DISCUSSION OF RESULT (Contd.)

Necessity to Add Shift-Parameter(or “offset’)

• Shift-Parameter (SP) required at validation stage.

• Existing retrieval algorithms: show some improvement with SP.

• SP depends on the overall SWE of each year.

• Example: (Number encircled are SP for Calibration Year)

Year: 1988 1989 1997

Mean SWE(cm) 3.43 9.25 13.55

Shift-Para(1) -5.00 0.00 + 4.00

Shift-Para(2) 0.00 +5.00 + 9.00

Figure 5. Distinct patterns of inter-annual SWE retrieved from exist- ing algorithms (Eqs. 1 & 2) when plotted against one of the proposed algorithm (Eq.3). Marked improvement with Shift Parameter (SP).

DISCUSSION OF RESULT (Contd.)

Reason behind Shift-Parameter

• Snowfall, temperature gradient & snow metamorphism process vary from year to year

• Scatter-induced darkening is not a function of Scattering albedo alone. It is also a function of Snow-Depth (England, 1975).

• Also Retrieval algorithms of statistical nature are biased towards the mean.

* Scattering of TB by snow grains within the dielectric layer gives rise to Scattering albedo

Figure 6. Scatter-induced darkening (TBo) versus scattering albedo (o) for various thickness (D) of dry fresh snowpack at 273 K, a case of free space microwave wave-length (o) of 10 cm (adapted from England, 1975).

Reason behind Shift-Parameter (contd.)

CONCLUSIONS

• Reasonably accurate SWE retrieved from SSM/I data from different satellites using Proposed algorithms and calibration techniques like Projection Pursuit Regression (PPR) & multi-variate regression.

• Introduce a Shift-parameter (SP) to retrieval algorithms. Magnitude of SP depends on the overall SWE difference between calibration & validation years.

• Introduce new criteria for selecting dry snow cases that are affected by depth-hoar, and/or large water bodies.

FOR FURTHER DETAILS ON THIS POSTER PRESENTATION

Singh, P. R., and Gan, T. Y. (2000), Retrieval of snow water equivalent using passive microwave brightness temperature data. Remote Sensing of Environment. 74(2):275-286.

Thank You