A review on different methodologies employed in
current SWE products from spaceborne passive
microwave observations
Nastaran Saberi, Richard Kelly
Interdisciplinary 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?
Which
properties?
How?
Snow Properties Retrieval
Using Passive Microwave RS
• Passive microwave observations
Brightness temperature: TB
– Appropriate frequency channels:
10 GHz-19GHz-37GHz
X , Ku, Ka (IEEE)
o Goal: Modeling microwave-medium AMSR2 instrument
http://www.drroyspencer.com/2012/05/
interactions to retrieve snow physical properties
Snow Properties Retrieval
Using Passive Microwave RS
l = f (x)
Snow
properties
retrieval
Empirical
approaches
SD & TB
differences
Emission
modeling
Semiempirical
Physical
DMRT-ML
(Matzler et al., 1982)
l : observation(TB )
x : unknown(SD / GS / Rho)
MEMLS
(Wiesmann and Matzler, 1999)
HUT
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
3
2
4
d
1
TB (d -, q ) = TBgs + TBs = TB (0+, q )e-(ke -qks )secq d +
kaTs
(1- e-(ke -qks )secq d )
ke - qks
MEMLS Snowpack emission model
A. 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 LWC
o Temperature
o Depth
o Density
For substratum, reflectivity and temperature is needed
DMRT-ML
Leung Tsang et al. (2000) , Picard et al. (2012)
 QCA-CP
 DMRT (ks, ke)
 DISORT (RT)
o Mono disperse, stickiness
o Poly disperse, Rayleigh
DMRT-ML inputs (for each layer of snowpack)
o Grain size (Optical)
o Temperature
o Depth
o Density
o Substratum model
Emission Models Summary
Empirical
Physical
Calibration
Sensitivity
Analysis
Simplicity
Complexity
Parsimonious modeling:
physics-based | semi-empirical |
empirical
• Differences HUT, MEMLS,
DMRT-ML:
– Radiative transfer
solution
– Wave propagation
– Substratum
– Representation of a
snow grain
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 models
Problem: dependency on precipitation data
• Satellite passive microwave derived SWE
datasets
Challenge: complex topography
GlobSnow - SWE
AMSR2 Snow Depth and Snow Water
Equivalent
Product by R. Kelly
AMSR2 - SWE V2
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)
V2
Snow Detection based on
history of snow
Grain size & Density are
dynamic
Forest correction is model-based
Snow Depth/SWE
Retrieval using DMRT-ML
Atmospheric correction
Lake ice addressed
RFI determination (10 Ghz)
Methods that Use Machine Learning
Techniques
Neural network
Emission model: HUT/MEMLS/DMRT
TB 19V
SWE/SD
TB 37V
Training Process
TB<->SD/SWE
Training dataset
Passive microwave observation
(TB)
Inversion by NN
SD/SWE
MicroWave Radiation Imager (MWRI) on Feng-Yun 3
Grass Land
Bare Soil
Farm Land
Forest
SD=fgrass×SDgrass+ fbarren×Sdbaren+fforest×Sdforest + ffarmland×Sdfarmland
SDfarmland= 4.235+0.432×d18h36h+1.074×d89v89h;
Validation Process
Validation Process
General 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