Description of Snow Depth Retrieval Algorithm for ADEOS II AMSR
Dr. Alfred Chang and Dr. Richard Kelly
NASA/GSFC
1. Introduction
The development of a snow depth retrieval algorithm for ADEOS II AMSR has been developed from current
applications of snow depth retrievals from the Special Sensor Microwave Imager (SSM/I). While the SSM/I is
not exactly identical in spatial and waveband configuration to the planned ADEOS II AMSR, it is sufficiently
comparable over its four frequencies to be a useful instrument with which to develop a snow retrieval algorithm
that is based on the microwave emission properties of snow. The AMSR will have an improved spatial
resolution and expanded waveband range compared with the SSM/I and it is expected, therefore, that algorithm
development will continue post launch.
2. Underlying principles for snow depth retrieval
There are two aspects to successful snow cover retrieval from space using passive microwave radiometers. First,
the snowpack must be detected and second it must be quantified in terms of its snow depth. Fresh of dry snow
(containing a negligible amount of liquid water) is a forward scatter of naturally upwelling radiation. Comparedwith non-snow surfaces, therefore, a snowpack has a distinctive electromagnetic signature at frequencies above 25
GHz. When viewed using passive microwave radiometers from above the snowpack, the scattering of upwellingradiation depresses the brightness temperature of the snow at increasingly high frequencies. This scattering
behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it isthen possible to estimate the snow depth of the pack using the degree of scattering. Chang et al. (1987) proposeda scheme to estimate the snow depth of a dry, homogeneous, single layer snowpack using radiative transfer theory
and the difference between two horizontally-polarized brightness temperature channels at high and low
frequencies such that:
SD = a ∆TB)+ b [cm],(1)

where b is generally regarded as zero and a = 1.59 cm K-1 and the assumption is made that the snow grain radius
is 0.3 mm and snow density is 300 kg m-3 . The ∆TB term is the difference in brightness temperature between 19
GHz and 37 GHz channels (horizontal polarization). This model works well under the non-complex snow
conditions (flat land, no significant forest cover, single layer dry snow) and has been the basis for several SD or
snow water equivalent (SWE) retrieval algorithms (e.g. Goodison and Walker, 1994, Foster et al., 1997).
However, for global applications there are several additional factors that need to be taken into consideration andincorporated into a retrieval scheme for successful snow depth estimation. These are described in the following
section.
3. Confounding factors
Snowpack properties
It is known that at passive microwave wavelengths, shallow snow (less than 10 cm) is transparent to naturally
upwelling microwave wavelengths (Armstrong and Brodzik, 1999). This factor can lead to non-detection of thesnowpack and hence underestimation of the snow volume. This factor will be most prominent in the early andlate parts of the winter season. Use of a high frequency channel (e.g. 89 GHz on AMSR) will assist with thisdetection although great care must be exerted when using this channel on account of potential atmosphericcontamination.
Wet snow can confound snow depth retrievals by depressing the scattering behaviour of the snow. Ultimately,
this leads to underestimation of the pack. Unfortunately, at present there is little that can be done to overcome
this problem directly although at least the detection of wet snow is possible by using a combination of information
about the surface temperature (Sun et al., 1996), polarization difference at 37 GHz (Walker and Goodison, 1994)
and immediate snow cover history.

Equation (1) above indicates a static parameterization based on radiative transfer properties of a snowpack (0.3mm radius grains and 300 Kg m-3 density). While this can be applied in some areas around the world at certain
times, the nature of snow is such that it can be temporally and spatially dynamic in evolution (Colbeck, 1986).
To retain the a coefficient in (1) suggests that globally, snow covers are homogeneous in character which clearly is
not the case. Although Josberger et al. (1995) found that snowpack properties can be homogeneous at regional
scales, for continental applications this will not be the case. The a coefficient should, therefore, be varied both
spatially and temporally and so we have computed a set of coefficients for each month of the year. The spatial
distribution of the coefficients is achieved using the seasonal snowpack classification of Sturm et al. (1995) which
divides the northern hemisphere into 6 dominant regional snow types: taiga, tundra, alpine, maritime, ephemeraland prairie. The a coefficients are re-computed for each of these regions based on dominant snowpack
characteristics thought to dominate in each region.
False scatterers
Precipitation acts as a confounding effect on snowpack retrievals. This is because precipitation clouds consist ofhydrometeors that act like a snowpack on the ground and scatter upwelling microwave radiation away from theradiometer’s field of view. Currently it is not possible to detect the presence of snow or retrieve snow depth
from beneath precipitating clouds but it is possible to detect precipitation and therefore flag the presence ofrainfall. The method used was developed by Grody and Basist (1996).
Forest cover
A major problem in large areas of the globe is the effect that forest cover has on the retrieval of snow depth frompassive microwave radiometers (Chang et al., 1996). Dense coniferous (and perhaps deciduous forest at
early/late times in the season) depress the microwave scattering signal from snow within the forest causing an
under-retrieval of the snow depth. Attempts have been made to overcome the problem (e.g. Foster et al., 1997)
but the problem is not easily resolvable at the moment. The Robinson and Kukla (1985) global albedo data set
has been used to estimate forest cover through the following relationship:
ff = -150albedo + 120

[%]

(2)

where ff is the forest fraction in percent and the two coefficients describe a straight line. This linear relationshipis based in the fact that for land surfaces that are not water bodies, low albedos (<0.5) are likely to be indicative ofdense forest whilst higher albedos (>0.5) are probably indicative of low stand or no vegetation. The range offorest cover is calibrated to between 0 % and 90 %and is then re-scaled linearly to 0 to 1 where 1 represents
100 % and 0 represents 0 % such that the data can be incorporated into (1):SD = a ∆TB) / (1-ff) + b

[cm].

(3)

A full account of this relationship can be found in Foster et al. (1997). This relationship is currently underfurther refinement since the Robinson and Kukla (1985) data set is at 1.0º spatial resolution which is coarser than
the scale used in this project (9.28 km equal area grid). In addition, there are better products available andbecoming available to provide more up-to date information about the global distribution of forest cover. For
example, direct forest cover data are available through the International Geosphere and Biosphere Project (IGBP)(although these are derived from annual average land cover data sets based on the reflective properties of the land
surface from AVHRR data). It is expected that in the future, more dynamic forest cover information will beavailable through MODIS reflectance or land category products that are produced every 16 days. This will
provide a better characterization of the forest cover plus it will give improved information about stem volume(rather than percentage cover in a pixel) which is thought to be the key effect on microwave retrievals of snow inforested areas (Kurvonen et al., 1998).
Mountainous terrain
Retrieval of snow depth from complex mountain topography is a challenge for low spatial resolution passive
microwave radiometers. This is because within a given footprint in a mountain zone, the variability of snow
depth is much greater than in flat terrain. Consequently, to avoid underestimation and overestimation, suchterrain is flagged and avoided in the current version of the algorithm.
used to flag mountain topography.

The GTOPO30 product from the USGS is

4. Implementation of the algorithm
The algorithm, coded in C, is currently implemented from a Unix Bourne Shell. The procedure is shown by theflow chart in Figure 1 below. The first step is to determine the kind of surface present (flat land, water body onland, ice, ocean, mountainous terrain, snow climatologically (im)possible, forest cover). The climatologicalpossibility of snow cover presence is obtained from Dewey and Heim (1981 and 1983). Unless the surface is flat
land without heavy forest cover, the procedure flags the surface type and does not attempt to compute the snow
depth. For flat land without heavy forest cover, the algorithm proceeds to Step 2, which reads, in AMSR channelbrightness temperatures.
Step 3 determines that the AMSR data are within a reasonable range and that there are no gross data errors in thebrightness temperature. Step 4 obtained the surface temperature estimate based global circulation model
estimates from the Japanese Meteorological Agency. This step is used to determine whether or not the surface islikely to be too warm and, therefore, the probability is low for the presence of snow. Step 5 screens for
precipitation and Step 6 determines whether there is wet or dry snow present and Step 7 estimates the snow depth.
If the snow is dry, the algorithm computes the snow depth using equation (3) above. The applied a coefficient in(3) dependent on whether or not the underlying soil is dry or wet, the Sturm et al. (1995) seasonal snow class andalso on the time of year.
5. Validation of algorithm
Two data sets were assembled to validate the algorithm developed for ADEOS II AMSR snow depth estimation.
First, a four-year record from 1992 – 1995 of daily GTS meteorological station snow depth measurements andcoincident SSM/I brightness temperatures prepared by NASDA, Japan were reanalyzed (see Chang and Koike,
2000). The second data set used was a month of daily global WMO meteorological station snow depth data for
January 2001.
1992-1994 data set validation
This data set comprises 86 GTS meteorological stations distributed in the northern hemisphere and quality
controlled (originally there were 100 but anomalous and erroneous readings were screened). The data comprisefour years of gauged daily snow depth measurements from January 1992 to December 1995. Coincident SSM/I
brightness temperatures at each station are also stored in the data set.
Snow depth was estimated using the simple algorithm from equation 1 (hereafter referred to as the “1.59”
algorithm) and also using the spatially and temporally dynamic algorithm from equation 3 (hereafter referred to as
the Chang algorithm). The estimated snow depths from both algorithms were compared with the gauge data andthe mean absolute error (an indicator of the magnitude of algorithm error) and the mean error (a measure ofalgorithm bias) for both algorithms for the entire four-year period was computed. Figure 1 shows two
histograms of the results. The mean absolute error (MAE) histogram shows that both algorithms perform
similarly well at the global scale. The average MAE for the Chang algorithm is 16.6 cm while for the “1.59”
algorithm it is 16.1 cm. This would suggest that the errors are similar in magnitude for each station over the four
years. However, inspection of the bias in the adjacent histogram demonstrates that the Chang algorithm bias ismuch closer to 0 cm than the “1.59” algorithm (-5.9 cm and –0.3 cm for Chang and “1.59” respectively). Thissuggests that although the absolute errors are similar, the underestimation of snow depth traditionally found in thepassive microwave estimates, is reduced in the new algorithm. The reason for this could be on account of thefact that the new Chang algorithm incorporates the “forest effect” in its estimates thus reducing the bias
(underestimation) commonly found in retrievals. Figure 2 shows the MAE and ME for sites where the forest
fraction is greater than 30%. Again a similar situation emerges with similar average MAE estimates for bothalgorithms (19.6 cm and 21.3 cm for the “1.59” and Chang algorithms respectively). However, the average bias
in the Chang algorithm is much less under high forest fractions with an average ME of –13.7 cm and 1.3 cm forthe “1.59” and Chang algorithm respectively.

Figure 1
AMSR Snow depth
algorithm logic

Preliminaries

Get Ancillary Data:•Land/sea/ice•Topography•Snow class (Sturm et al., 1995)•Snow (Im)possible
Locate AMSR data and read in one scan line of AMSR data.

Step 1 (For each AMSR sample)
Test for:•Ocean, land water body, ice•Snow Impossible•Mountain•Otherwise continue to Step 2

FLAG
FLAG
FLAG

Step 2 Obtain AMSR brightness temperatures
Continue to Step 3
Step 3 Do range check of brightness temperatures in all channels•Bad data•Good data continue to Step 4

FLAG

Step 4 Obtain surface temperature from Japan Meteorological
Agency global circulation model data:•If T ≥ 275 K, surface too warm
•If T < 275 K continue to Step 5

FLAG

Step 5 Precipitation screen (Grody and Basist (1996)):

Scat = max(Tb18V-Tb36V -3, Tb23V-Tb89V -3, Tb36V-Tb89V - 1)
•If (Tb23V > 258) or (Tb23V > 254 and Scat < 2) or(Tb23V > (165+0.49Tb89V) then rainfall is present•Otherwise continue to Step 6

FLAG

Step 6 Wet or dry snow screen (Walker and Goddison, 1994)•If (Tb36V – Tb36H > 10) and (T ≥ 270 K) wet snow
•Otherwise continue to Step 7

FLAG

Step 7 Wet or dry soil screen and Snow Depth (SD) Estimate•If {[(Tb36V – Tb18V)/18] ≥ -0.3} and (T ≥ 270 K) and(T ≤ 273 K) Wet soil snow depth estimate:[cm]
SD = 1.66(Tb18V-Tb36V)•Otherwise dry soil snow depth estimate:SD = a(Tb18V-Tb36V-5) / (1 – ff)•Goto Step 1 for next sample[cm]January 2001 data set validation
A similar exercise was conducted for a relatively constrained (temporally) data set but which contained many
more gauged snow depth measurements. Data were obtained from the WMO’s network of approximately 600
stations (many in the GTS network) in the northern hemisphere. MAE and ME calculations were made at four
distinct spatial scales to determine whether the Chang and “1.59” algorithms might perform differently.
Computations were made at a global scale, North America data, Canadian data and Northern Great Plains data.
The results are shown in the time plots in Figure 3. It is clear that the MAE values are, again, similar for boththe “1.59” and Chang algorithms. However, the bias in the Chang algorithm is reduced significantly in the
Chang algorithm estimates. This is the case at all spatial scales with the anomalous exception of the ME for the
Northern Great Plains where the “1.59” algorithm does marginally better.
Again, it was suggested that the reason for the improved bias revealed in the Chang results is due to the fact thatthe forest effect is incorporated in the algorithm. Figure 4 is similar to Figure 3 except that again, computationsof MAE and ME were performed only for pixels with forest fraction greater than 30%. The results support theargument that the Chang algorithm does reduce the overall bias in the estimates caused by forest cover.
Figure 5 Shows visually representation of the two algorithms for 1 January 2001. The left panel is the result ofapplying the “1.59” algorithm and the right panel is a representation of the Chang algorithm. The same screens(rainfall, mountains etc.) are applied in both algorithms so that the differences between the two retrievals are thespatial variation in snow depth.

1992-1995 GTS-SSM/I Daily Data
Mean Absolute Error

1992-1995 GTS-SSM/I daily data
Mean Error (Bias)

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Error (cm)Figure 1. Global validation (mean absolute error and bias) of AMSR (chang) algorithm compared with standard‘static’ algorithm (see equation 1) for 86 global GTS snow depth gauge stations.

1992-1995 GTS-SSM/I Daily Data
Mean Absolute Error

1992-1995 GTS-SSM/I Daily Data
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Figure 2. Global validation (mean absolute error and bias) of AMSR (chang) algorithm compared with standard‘static 1.59’ algorithm (see equation 1) for 86 global GTS snow depth gauge stations where forest fraction isgreater than 30%.

Global25
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January 2001 (day)Figure 3. Comparison of ‘Chang’ and ‘1.59’ snow depth algorithm validation. MAE is the mean absolute error
and ME is the mean error (bias). The comparison is for applications of the algorithms in January 2001 and fromall stations at global, North America, Canada and Northern Great Plains scales.
Global3030

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January 2001 (day)Figure 4. Comparison of ‘Chang’ and ‘1.59’ snow depth algorithm validation. MAE is the mean absolute error
and ME is the mean error (bias). The comparison is for applications of the algorithms in January 2001 for EASE
grid pixels that are characterized with forest cover greater than 30% and from all stations at global, North America,
Canada and Northern Great Plains scales.

Figure 5. Visualisation of the “1.59” snow depth algorithm (left) and the Chang snow depth algorithm (right) for 1
January 2001. The colour scale units are in cm.

6. Future developments
Results from the work to date are very encouraging and suggests that the new algorithm development should
improve the old static coefficient methodology described by equation 1. However, the retrieval residuals or
errors still need further constraint. Despite a retrieved error (MAE) of 16.6 cm globally, translating this into a
SWE produces a value of 1.6 mm for fresh snow (with a density of 100 kg m-3 ) and more for seasonal snow withhigher density. It is clear, however, that the spatial distribution of the coefficient a in equation 3 is important forsuccessful snow depth retrievals. While the Sturm et al. (1995) seasonal snow classification is undoubtedly an
invaluable global descriptor that can help, newer, more sensitive descriptors are needed that can provide higher
spatial resolution for coefficient spatial extrapolation. This is one area that we are currently developing using
geostatistics and land cover data sets and results to date are promising (with further reduced ME and reduced
MAE values). In addition, it is clear that successful estimation of daily local snow depth variability will benefit
from a dynamic temporal component that could be included in the algorithm to determine variations in snowpack
properties. While problematic, this aspect is the subject of current development activities.
An important future development that will assist with the algorithm validation and development is ourparticipation in the Cold Land Processes Field Experiment (CLPX) in North American planned for the 2002-2003
winter season in Colorado. This experiment is part of the NASA Global Water and Energy Cycle (GEWEC)
initiative, the Global Energy and Water Cycle Experiment (GEWEX) and the GEWEX Americas Prediction
Project (GAPP). The broad objectives of the CLPX are to develop our understanding of cold land processes by
increasing our ability to characterize the spatial and temporal variability of snow, ice and frozen ground in thenatural environment, and to identify and quantify the various uncertainties associated with remote sensing
observations and models of cold land processes, thereby improving our abilities to predict the behavior of various
cold land processes (NASA, 2001). As part of this, a significant number of field experiments are planned for
February and March 2002 and 2003 to measure snowpack parameters intensively in three 25 km x 25 km and 1
km x 1 km cells in Colorado. The measurement suite will include snow depth, SWE, and surface wetness androughness and various snow descriptors obtained from snow pits. It is planned that this experiment will also
have access to ground-based and airborne radiometers plus a suite of satellite observations. The aircraft flightlines will cover several the AMSR foot-prints (25 km x 25 km). Consequently, it is anticipated that several of theoutstanding uncertainties associated with snow depth and SWE retrieval form microwave radiometry will beaddressed. This information will assist directly with out efforts for the development of ADEOS II AMSR snow
depth retrieval algorithm.
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