A review on different methodologies employed in current SWE products from spaceborne passive

microwave observations

Nastaran Saberi, Richard KellyInterdisciplinary Center on Climate Change (IC3) and Department of Geography and

Environmental Management, University of Waterloo, ON, Canada

Outline

• Introduction– Snow physical properties retrieval using

passive microwave observations• Emission Modeling (HUT, MEMLS, DMRT)

• SWE Products & Validation Process

• Research questions and summary

Why measuring snowpack physical

properties?

How?

Which properties?

Snow Properties Retrieval Using Passive Microwave RS

• Passive microwave observationsBrightness temperature: TB

– Appropriate frequency channels:10 GHz-19GHz-37GHz

X , Ku, Ka (IEEE)

o Goal: Modeling microwave-medium

interactions to retrieve snow physical propertiesAMSR2 instrumenthttp://www.drroyspencer.com/2012/05/

Snow properties retrieval

Empirical approaches

SD & TB differences

Emission modeling

Physical

DMRT-ML

Semi-empirica

l

MEML

S

HUT

(Matzler et al., 1982)

(Wiesmann and Matzler, 1999)

Snow Properties Retrieval Using Passive Microwave RS

Emission Modeling

http://www.iup.uni-bremen.de/iuppage/psa/members/MariaHoerhold.php

HUT snowpack emission model Pulliainen et al. (1999)

Semi-empirical model, adapted for remote sensing observations

1

2

3

d

4

MEMLS Snowpack emission modelA. Wiesmann and C. Matzler (1999)

A semi-empirical six-flux radiative transfer model

For a multi layered snowpack

MEMLS inputs (for each layer of snowpack) o Grain size (correlation length)o LWCo Temperatureo Deptho Density

For substratum, reflectivity and temperature is needed

DMRT-MLLeung Tsang et al. (2000) , Picard et al. (2012)

QCA-CP

DMRT (ks, ke)

DISORT (RT)o Mono disperse, stickinesso Poly disperse, Rayleigh

DMRT-ML inputs (for each layer of snowpack) o Grain size (Optical)o Temperatureo Deptho Densityo Substratum model

Emission Models Summary

• Differences HUT, MEMLS, DMRT-ML:

– Radiative transfer solution

– Wave propagation– Substratum – Representation of a

snow grain

Empirical Physical

Complexity

Sensitivity Analysis

Simplicity

Calibration

Parsimonious modeling: physics-based | semi-empirical | empirical

Snow Depth & Snow Water Equivalent Products

Snow Depth and Snow Water Equivalent Products

SWE estimation using in-situ data =>sparse observing networks

• Data assimilation • Reanalysis snow cover using land surface or

snow modelsProblem: dependency on precipitation data

• Satellite passive microwave derived SWE datasetsChallenge: complex topography

Glo

bSno

w -

SW

E

AMSR2 Snow Depth and Snow Water Equivalent

Product by R. Kelly

V1 & V2 :Predicated on the AMSR-E algorithm (2003/4)

V1: Regression based, came in response to deficiencies in static algorithm (Kelly ,2009).

V2: Physical modeling based (kelly, 2003)

V2Grain size & Density are dynamic

Forest correction is model-based

Atmospheric correction

Lake ice addressed

RFI determination (10 Ghz)AMSR

2 - S

WE

V2

Snow Detection based on history of snow

Snow Depth/SWE Retrieval using DMRT-ML

Methods that Use Machine Learning Techniques

Neural network

Emission model: HUT/MEMLS/DMRT

Training ProcessTB<->SD/SWE

Passive microwave observation (TB)

Inversion by NNSD/SWE

Training dataset

TB 19V

TB 37V

SWE/SD

MicroWave Radiation Imager (MWRI) on Feng-Yun 3

Grass Land Bare Soil

Farm Land Forest

SD=fgrass×SDgrass+ fbarren×Sdbaren+fforest×Sdforest + ffarmland×SdfarmlandSDfarmland=4.235+0.432×d18h36h+1.074×d89v89h;

Validation Process

Validation ProcessGeneral overview of SWE dataset assessment by SnowPEx

Statistical Assessment Tools

Summary & Research Questions!

Challenges in gathering In-situ data

• High spatial variability in snow physical properties and limitation in accessibility

=> Quantifying errors

In electromagnetic modeling

• Adaptation of physical model to metamorphic processes and a layered snowpack structure, also adapting to spaceborne scale => Key emission controllers of seasonal snow evolution

Thank you

Aknowledgements: Karem Chokmani