De6.1 report

Sample data set and Report on retrieval
performance based on MODIS and AMSR-E data

Deliverable De6.1

The WorkPackage 6 group1,2,3

1
Cold and Arid Regions Envrironmental and Engineering Research Institute,
CAS, P.R. China
2
Institute Tibetan Plateau Research, Chinese Academy of Science, P.R.China
3
Beijing Normal University, Chinese Academy of Science, P.R.China

Dissemintation level: Programme Participants
Lead beneﬁciary ID: CAREERI

ISSN/ISBN:
c 2010
Edited by the CEOP-AEGIS Project Oﬃce
LSIIT/TRIO, University of Strasbourg
BP10413, F-67412 ILLKIRCH Cedex, France
Phone: +33 368 854 528; Fax: +33 368 854 531
e-mail: management@ceop-aegis.org

No part of this publication may be reproduced or published in any form
or by any means, or stored in a database or retrieval system, without the
written permission of the CEOP-AEGIS Project Oﬃce.

CEOP-AEGIS Report De 6.1

CONTENTS
PART I
A Report for Snow Cover Area Retrieval by MODIS Data
1. Task .................................................................................................................................................................. 1
2. Data .................................................................................................................................................................. 1
3. Algorithm......................................................................................................................................................... 2
4. Validation......................................................................................................................................................... 6
5. References....................................................................................................................................................... 7
PART II
Surface Soil Freeze/Thaw State Dataset Using The Decision Tree Classification Algorithm
1. Task ............................................................................................................................................................ 10
2. Data and method ........................................................................................................................................... 10
2.1 Data .......................................................................................................................................................... 10
2.2 Classification indices............................................................................................................................... 11
2.3 Cluster analysis and decision tree for freeze/thaw status classification............................................. 15
3. Validation....................................................................................................................................................... 16
4. Summary........................................................................................................................................................ 19
5. References...................................................................................................................................................... 20
PART III
Snow Depth Derived From Passive Microwave Remote Sensing Data in China and Snow Data
Assimilation Method
1. Task ................................................................................................................................................................ 24
2. Data ................................................................................................................................................................ 24
3. Method ........................................................................................................................................................... 27
3.1 Snow depth derived from passive microwave remote sensing data ................................................... 27
3.2 Assimilating of passive microwave remote sensing data ..................................................................... 31
4. Accuracy assessment of passive microwave snow products...................................................................... 33
5. Results ........................................................................................................................................................... 35
6. References..................................................................................................................................................... 40
PART IV
Providing Soil Parameter Data Sets for The Entire Plateau from A Microwave Land Data Assimilation
System
1. Task ............................................................................................................................................................ 45
2. Algorithm................................................................................................................................................... 45
3. Data ............................................................................................................................................................ 46
4. Test estimated soil moisture and parameters......................................................................................... 46
5. Evaluation of optimized parameter values ............................................................................................. 48
6. References.................................................................................................................................................. 49

II


III


PART I


Authors: Xiaohua Hao, Jian Wang, Hongyi Li, Zhe Li

Affiliations: Cold and Arid Regions Environment and Engineering Research
Institute, Chinese Academy of Sciences (CAREERI, CAS).



1. Task
Snow is an important, though highly variable, earth surface cover (Klein et al., 1998).
Because of its high albedo, snow is an important factor in determining the radiation balance,
with implications for global climate studies (Foster and Chang, 1993). Midlatitude alpine
snow cover and its subsequent melt can dominate local to regional climate and hydrology,
and more and more notice in the world’s mountains regions snow cover. Because of its
importance, accurate monitoring of snow cover extent is an important research goal in the
science of Earth systems. Satellites are well suited to measurement of snow cover because
the high albedo of snow presents a good contrast with most other natural surfaces except
cloud. Fortunately, the physical properties of snow make it highly amenable to monitoring
via remote sensing. The objective of the MODIS snow mapping is to generate snow cover
area and fractional snow cover products on Qinghai-Tibet Plateau.

2. Data
Mapping of the MODIS snow cover use the elevation data, MODIS series data and
Landsat-ETM+ data.The Digital Elevation Model (DEM) of the area at 500 m spatial
resolution was created from SRTM (Shuttle Radar Topography Mission) data at 3 arc-
seconds, which is 1/1200th of a degree of latitude and longitude, or about 90 meters as a
source of topography correction. From the DEM dataset, information about the slope, aspect
and illumination according to the sun angle and elevation were generated for input to the
topographic corrections algorithms for MODIS image.In the new algorithm, we rely on
MOD09 surface reflectance products (MOD09GA, MYD09GHK) to map the MODIS snow
cover. The data can be obtained from the National Snow and Ice Data Center Distributed
Data Archive. Six MOD09 tiles (h23v05, h24v05, h25v05, h26v05, h2506, h26v06) were
used in the study region. Other MODIS product suite that include cloud mask data (MOD35
and MYD35) and temperature data (MOD11A1 and MYD11A1) were regard as auxiliary
inputs. The MODIS daily snow cover product (MOD10A1 and MYD10A1) is regard as the
reference data of the snow cover from the new algorithms. Landsat-ETM+ data provide a
high-resolution view of snow cover that can be compared with the MODIS and operational
snow-cover products. In the study, Landsat-ETM+ path 143 row 30, path 136 row 38,

1


path134 row 38, path 136 row 39, path134 row 40 path were used to produce a validation
dataset for the MODIS snow cover products. The figure1 shows the detail of study region.

Figure 1. The study region and the Landsat-ETM+ location. A, B ,C, D and E are respectively path 143 row 30, path 136
row 38, path134 row 38, path 136 row 39, path134 row 40.

3. Algorithm
The objective of any radiometric correction of airborne and spaceborne imagery of
optical sensors is the extraction of physical earth surface parameters such as reflectance,
emissivity, and temperature. To getting the true ground reflectance the topography
correction of the MOD09 is necessary in QTP. Law (2004) tested and compared three
topographic correction methods, which are the Cosine Correction, Minnaert Correction and
a CIVCO model. By comparing, he offered an improved CIVCO model. In our study, we
used the improved CIVCO model. The CIVCO method used here is modified from the two
stage normalization proposed by Civco, 1989, and consists of two stages. In the first stage,
shaded relief models, corresponding to the solar illumination conditions at the time of the
satellite image are computed using the DEM data. This requires the input of the solar
azimuth and altitude provided by the metadata of the satellite image. The resulting shaded
relief model would have values between 0 and 1. After the model is created, a
2


transformation of each of the original bands of the satellite image is performed to derive
topographically normalized images using equation (1) and (2).

(1)

( 2)

where !Ref"ij= the normalized radiance data for pixel(i, j) in band("), Ref"ij= the raw
radiance data for pixel(i, j) in band("), µk= the mean value for the entire scaled shaded relief
model (0,1), µij= the scaled (0,1) illumination value for pixel(i, j), C" = the correction
coefficient for band("), N" = the mean on the slope facing away the sun in the uncalibrated
data for the forest category, S" = the mean on the slope facing to the sun in the uncalibrated
data for the forest category, µk = the mean value for the entire scaled shaded relief model ,
µN = the mean of the illumination of forest on the slope facing away from the sun., µS = the
mean of the illumination of forest on the slope facing to the sun.

By the topography correction, we can get the MODIS surface reflectance. It will
improve the accuracy of snow cover mapping in mountainous regions.

The MODIS snow cover products algorithm is essentially designed for the evaluation of
the threshold value of the NDSI (Normalize Difference Snow Index) threshold value. For
MODIS data the NDSI is calculated as:

ê éé à

(3)

The NDSI threshold of the MODIS snow cover products distributed by the NSIDC is
0.40. The NDSI values of the MODIS scenes greater than or equal to 0.40 represent snow
cover pixels. In addition, since water may also have an NDSI 0.4, an additional test is
necessary to separate snow and water. Snow and water may be discriminated because the
reflectance of water is <11% in MODIS band 2. Hence, if the reflectance of MODIS band 4
>11%, and the NDSI 0.40, the pixel is initially considered snow covered. However,
validation of the current NDSI threshold has being accomplished only by the measurements
in the United States and Europe. In China, therefore, there is not reliable NDSI threshold

3


value for the MODIS snow mapping and a credible threshold can be established.

In the study, the snow cover area of A, B and C were selected for this study. First, the
Landsat-ETM+ snow cover maps were produced by the method of the SNOMAP. Then, the
snow cover maps, produced obtained from the way mentioned above, were compared with
the ones derived by the manual photo interpretation classification technique. Overall
agreement which is simply a comparison of the number of snow pixels, is high at 96%.
Thus, the Landsat-ETM+ snow cover maps can be reliable served as the “groudtruth”
with which then the snow cover maps of the study area extracted from the MOD09
measurements by NDSI method were compared. For the MODSI snow cover maps of the
study areas, the NDSI threshold value for snow was increased gradually for 0.30 to 0.40 in
steps of 0.01. At Last, the comparisons focused on comparing the MODIS snow cover maps
following with NDSI threshold value and the Landsat-ETM+ snow cover maps serving as
absolute standard. The result suggests that the MODIS snow cover products distributed by
the NSIDC using NDSI threshold of 0.40 underestimated the SCA (snow-covered area) of
the study areas. In the study areas, the credible NDSI threshold value is respectively 0.34,
0.36and0.38 in A, B and C regions. As computer the average value, it is approximately
0.36,which is less than the one from the 0.40 of NSIDC.

Table 1. MODIS snow cover accuracy of different NDSI threshold in A, B and C region.
NDSI The overall accuracy, Kappa The overall accuracy, Kappa The overall accuracy, Kappa
threshold coefficient and fractional snow coefficient and fractional snow coefficient and fractional snow
value cover area of A region. cover area of B region. cover area of C region.
0.39 93.00% 0.669 11.37% 86.82% 0.676 27.73% 94.73% 0.708 10.17%

0.38 93.02% 0.672 11.53% 86.81% 0.678 28.36% 94.74% 0.711 10.48%

0.37 93.07% 0.675 11..66% 86.76% 0.679 29.02% 94.62% 0.709 10.79%

0.36 93.11% 0.679 11.83% 86.73% 0.680 29.63% 94.51% 0.707 11.08%

0.35 93.16% 0.683 11.97% 86.63% 0.679 30.25% 94.39% 0.706 11.48%

0.34 93.17% 0.685 12.13% 86.54% 0.679 30.87% 94.26% 0.703 11.82%

0.33 92.89% 0.678 12.66% 86.45% 0.679 31.51% 94.16% 0.702 12.16%
0.32 92.91% 0.681 12.80% 86.28% 0.677 32.13% 94.04% 0.700 12.53%
0.31 92.91% 0.683 12.98% 86.13% 0.676 32.66% 93.88% 0.697 12.89%
0.30 92.90% 0.684 13.18% 86.05% 0.676 33.23% 93.69% 0.692 13.28%

In forested locations, to correctly classify these forests as snow covered, a lower NDSI
threshold is employed. The normalized difference vegetation index (NDVI) and the NDSI
are used together in order to discriminate between snow-free and snow covered forests.

4


(Klein et al., 1998). Last, a threshold of 10% in MODIS band 4 was used to prevent pixels
with very low visible reflectances, for example black spruce stands, from being classified as
snow as has previously been suggested (Dozier, 1989). In addition, the MODIS cloud
masking data product (MOD35) and MODIS temperature mask product (MOD11) were
served as inputs for algorithm.

MODIS cloud masking data product was used to map MODIS snow cover product.
Nevertheless, the ground object under cloud remains unknown. Whether in MODIS terra or
MODIS aqua daily snow cover product, either way, it's always was effected by large cloud.
In the context of remote sensing, image fusion consists of merging images from different
sources, which hold information of a different nature, to create a synthesized image that
retains the most desirable characteristics of each source (Pohl & Genderen, 1998). In my
study, the method was applied to composite the MODIS/Terra and MODIS/Aqua snow
cover product to minimize the effect of cloud. In selecting the image fusion technique for
the daily composites, we decided that it would be most useful to use maximum snow cover.
In other words, if snow were present on any image in any location on the Terra or Aqua. tile
product, it will show up as snow-covered on the daily composite product. Maximum snow
cover is a more useful parameter than minimum or average snow cover. Using either
minimum or average snow cover would result in failure to map some snow cover. The
compositing technique also minimizes cloud cover. The figure 2 shows the flow process of
our new MODIS snow cover map algorithm.

5


MOD09GA
MYD09GA

CIVCO Terrain correction

NDSI 0.36, B2 0.11 other

Snow Snow in forest Klein MODEL b4>0.1
Cloud, Other Cloud Other

LST mask:MOD11A1 Cloud mask: MOD35
Threshold value 283 Land/water mask: MOD03

MODSNOW Maximum Composition MYDSNOW

Snow Cover Map
Figure 2. The flow process chart of the new snow cover algorithms.

4. Validation
Two types of validation are addressed in our study-absolute and relative. To derive
absolute validation, the MODIS maps are compared with ground measurements or
measurements of snow cover from Landsat data, which are considered to be the ‘truth’ for
this work. Relative validation refers to comparisons with other snow maps, most of which
have unknown accuracy. Thus for the studies of relative validation, it is not generally
known which snow map has a higher accuracy.

The accuracy of snow cover products from optical remote sensing is of particular
importance in hydrological applications and climate models. In the study, using in situ
observation data during the five snow seasons at 47 climatic stations from January 1 to
March 31of year 2001 and from November 1 to March 31 of year 2001 to 2005 in northern
Xinjiang area, China, the accuracy of MODIS snow cover products (MOD10A1 and
MOD102) and VEGETATION snow cover products (VGT-S10 snow cover products)
algorithm under varied terrain and land cover types were analyzed. The study shows the
overall accuracy of MOD10A1 MOD10A2 and VGT-S10 snow cover products is high at
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91.3%, 90.6%, 87.9% respectively in all climatic stations. However, the overall accuracy of
the snow cover products in mountain regions is low.

In mountain climatic stations the snow omission of the three products is 32.4 21.7%
36.3% respectively. The cloud limitation ratio of MOD10A1 reaches to 61.8%.;but the
MOD10A2 and VGT-S10 are only 7.6%, 1.8%. The comparison result of user-defined 10-
day MODIS snow products and VGT-S10 snow products shows that the snow identification
ability of MODIS are more accuracy than VGT-S10 snow cover products. However, the
VGT-S10 snow cover products are little affected by cloud than MODIS snow cover
products. We’ll measure the snow properties in the QTP-Naqu. Lake Namtso in future. The
snow density, snow water liquid, snow grain size, snow temperature and snow pit works
were done and the data were used to validate and develop the snow retrieval algorithms.
Figure 3 shows the sampling plan in field.

Figure 3. The sampling plan of snow measurement in field.

In addition, the high-resolution remote sensing data, such as TM, ETM+, ASTER, also
were applied to validate the new MODIS snow cover map.

5. References

Carroll T R. Operational airborne and satellite snow cover products of the National
Operational Hydrologic Remote Sensing Center[C]. Proceedings of the forty-seventh
annual Eastern Snow Conference, Bangor, Maine, CRREL Special Report. June 7-8,
1990: 90-44.

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Civco D L. Topographic Normalization of Landsat Thematic Mapper Digital Imagery[J].
Photogrammetric Engineering and Remote Sensing. 1989, 55(9): 1303-1309.
Dozier, J. Spectral signature of alpine snow cover from the Landsat Thematic Mapper,
Remote Sensing of Environment. 1989, 28: 9-22.
Foster, J.L., D.K. Hall, A.T.C. Chang and A. Rango. An overview of passive microwave
snow research and results. Reviews of Geophysics. 1984, 22: 195-208.
Hao Xiaohua, Wang Jian, Li Hongyi. Evaluation of the NDSI threshold value in mapping
snow cover of MODIS—A case study of snow in the middle Qilian Mountains. Journal
of Glaciology and Geogryology. 2008,30 (1): 132-138.
Hall D K, Riggs G A, Salomonson V V. Development of methods for mapping global snow
cover using moderate resolution imaging spectroradiometer data. Remote Sensing of
Environment. 1995, 54: 127–140.
Hall D K, Riggs G A, Salomonson V V, et al. MODIS snow-cover products[J]. Remote
Sensing of Environment. 2002, 83: 181-194.
Law K H, Nichol J. Topographic correction for differential illumination effects on IKONOS
satellite imagery[C]. ISPRS Congress, Istanbul, Turkey Commission 3. 12-23 July 2004.
Klein A, Hall D K, Riggs G A. Global snow cover monitoring using MODIS. In 27th
International Symposium on Remote Sensing of Environment. June 8-12, 1998: 363-366.
Pohl, C., & Genderen, J. L. V. (1998). Multisensor image fusion in remote sensing:
Concepts, methods and applications. International Journal of Remote Sensing, 19(5),
823#854.
Rango, A. Snow hydrology processes and remote sensing. Hydrological Processes. 1993,
7:121-138.
Singer, F.S. and R.W. Popham. Non-meteorological observations from weather satellites,
Astronautics and Aerospace Engineering. 1963, 1(3): 89-92.
Tucker, C.J. Maximum normalized difference vegetation index images for sub-Saharan
Africa for 1983-1985, International Journal of Remote Sensing, 1986,7: 1383-1384.

8


PART II

Surface soil freeze/thaw state dataset using the decision tree
classification algorithm

Authors: Rui Jin



Surface soil freeze/thaw state dataset using the decision tree
classification algorithm

1. Task

We have developed a new decision tree algorithm to classify the surface soil
freeze/thaw states. The algorithm uses SSM/I brightness temperatures recorded in the early
morning. Three critical indices are introduced as classification criteria—the scattering index
(SI), the 37 GHz vertical polarization brightness temperature (T37V), and the 19 GHz
polarization difference (PD19). And the discrimination of the desert and precipitation from
frozen soil is considered, which improve the classification accuracy. Long time series of
surface soil freeze/thaw statuses can be obtained using this decision tree, which potentially
can provide a basic dataset for research on climate and cryosphere interactions, carbon
cycles, hydrological processes, and general circulation models.

2. Data and method

2.1 Data

The daily F13 SSM/I brightness temperatures during the period from Oct. 1, 2002 to
Sep. 30, 2003 were provided by the National Snow and Ice Data Center (NSIDC) at the
University of Colorado in the Equal Area Scalable Earth Grid (EASE-Grid) format
(Armstrong et al., 1994). The global level 3 products were used in this study, and the spatial
resolution is 25 km. The SSM/I radiometer passes over the same region twice daily at 6:00
(descending orbit) and 18:00 (ascending orbit) local time. Because the surface soil
temperature at 6:00 local time approximates the daily minimal surface temperature, the
descending orbit data was selected to capture the daily freeze/thaw cycle (Zhang &
Armstrong, 2001). The atmospheric influence was not corrected for the SSM/I brightness
temperature since it has an insignificant effect (Judge et al., 1997).
Due to the coarse spatial resolution of passive microwave remote sensing, “pure”
training samples from SSM/I data need to be collected to analyze the brightness temperature
characteristics of different land surface types and to determine the threshold of each node in
the decision tree. We selected four types of samples, including frozen soil, thawed soil,
desert and snow. The latter two sample types have volume scattering characteristics similar
to those of frozen soil. Grody’s method was adequately validated by previous research

10


(Grody & Basist, 1996), so it was adopted directly to identify precipitation. The ancillary
data used to ensure the purity of samples include the daily MODIS snow cover product with
0.05º resolution (MOD10C1) (Hall et al., 2006), the map of geocryological regionalization
and classification in China (Zhou et al., 2000), and the Chinese land use map at 1:1,000,000
scale.
All the training samples were randomly selected according to the following criteria,
and a training sample corresponds to a SSM/I pixel. The frozen soil samples were selected
in the seasonally frozen ground region and the permafrost region from the map of
geocryological regionalization and classification in China from winter data. The thawed soil
samples were picked from the unfrozen region, and the short-term frozen ground region
from summer data. The desert samples came from the hinterland of Taklimakan according
to the Chinese land use map. The snow samples were determined if the snow fraction
derived from MODIS snow cover products was larger than 0.75 in a 25 km EASE-grid pixel.
The number of samples of frozen soil, thawed soil, desert and snow are 207, 317, 467 and
362, respectively.
The 4 cm deep soil temperatures observed by the Soil Moisture and Temperature
Measuring System (SMTMS) of the GEWEX-Coordinated Enhanced Observing Period
(CEOP) (http://monsoon.t.u-tokyo.ac.jp/ceop2/index.html) (Koike, 2004) were used as
validation data. Table 1 shows the locations of the CEOP stations used in the paper.

2.2 Classification indices

There are three critical indices used in the decision tree:
(1) Scattering Index (SI): The SI was proposed based on a regression analysis of the
training data covering various land surface types and atmospheric conditions (Grody, 1991),
expressed as follows:

, (1)
where, T19V, T22V and T85V are vertical polarization brightness temperatures at 19,
22 and 85 GHz, respectively. F represents the simulated 85 GHz vertical polarization
brightness temperature under the ideal condition of no scattering effect. SI is the deviation
of the actual SSM/I T85V observation from F. Because the volume scattering darkening of
frozen soil at 85 GHz is stronger than that at lower frequencies, SI is a more reliable index
than SG for distinguishing between scatterering and non-scatterering samples.

11


(2) 37 GHz vertical polarization brightness temperature (T37V): A correlation analysis
was carried out between the SSM/I brightness temperature at each channel and the SMTMS
4 cm deep soil temperature, revealing that T37V has the highest correlation coefficient of
0.87 with the 4 cm deep soil temperature. T37V was therefore used as a criterion to indicate
the thermal regime of the surface soil.
(3) 19 GHz Polarization Difference (PD19 = T19V - T19H). The polarization
difference at 19 GHz reveals the surface roughness. A rougher surface decreases the
coherent reflection and increases incoherent scattering, resulting in the tendency of the
surface reflectivity to be independent of polarization, diminishing the polarization difference.
The PD19 was used to identify the desert, which has a relatively small roughness.
2.3 Analysis of the brightness temperature characteristics of each land surface type
The variation of the time series of the above three indices was analyzed for each
sample type, providing a priori knowledge necessary to create a decision tree.
(1) Frozen/thawed soil
Figure 1 shows the time series of T37V, SI and PD19 at the Tuotuohe and MS3608
stations from June 29, 1997 to August 31, 1998. The SMTMS 4 cm deep soil temperatures
and soil moistures are also shown as ancillary information to indicate the surface soil
freeze/thaw status. Both stations are located in the seasonally frozen ground region. The soil
moisture of MS3608 was higher than that of Tuotuohe.
Although the hydrothermal conditions are different between the two stations, the three
indices have many characteristics in common when the soil is frozen or thawed. In the
middle of October, the 4 cm deep soil temperature fell below the soil freezing point; the
liquid water in the soil changed its phase to ice and suddenly dropped. The 37 GHz
brightness temperature therefore decreased, and the SI increased due to volume scattering
darkening. When the reverse phase change process occurred during middle to late April of
the next year, the 4 cm deep soil temperature increased; the 37 GHz brightness temperature
accordingly increased and the SI decreased due to dominant surface scattering. The frozen
soil scatters with an SI between 10 and 3 because the volume fraction of soil matrix and ice
particles in the frozen soil is very large, about 0.5 to 0.8, which results in the attenuation of
the volume scattering effect. The high value of SI at the MS3608 station in December 1997
resulted from the snow cover. The PD19 of frozen soil fluctuated modestly with soil
temperature and soil moisture, and was commonly smaller than 25.

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(a) Tuotuohe

(b) MS3608
Fig. 1 Time series of T37V, SI and PD19 of frozen/thawed soil at Tuotuohe (a) and MS3608 (b) stations.

(2) Desert
Two years (1999-2000) of SSM/I brightness temperatures and daily mean air
temperatures were acquired for the Tazhong station (Table 1), located in the hinterland of
the Taklimakan desert and operated by the CMA (China Meteorological Administration).
There were no soil temperature observations at the Tazhong station. The polarization
difference of the desert at each SSM/I channel was larger than that of other land types
because it is smoother (Neale et al., 1990). Fig. 2 shows that the PD19 of the desert was
above 30 for most of the year, the SI was mainly between 5 and 10, and the brightness
temperature variation of the desert agreed well with the air temperature variation due to the
very low moisture content in the desert. Compared to dry snow and frozen ground, the
desert is a weaker scatterer due to the large volume fraction, and the homogeneous particle
size and dielectric properties. The effective emissivity of the desert at 37 GHz vertical

13


polarization was about 0.95 on average, calculated by dividing the 37 GHz vertical
polarization brightness temperature by the daily mean air temperature.

Table 1. Stations used in algorithm development and validation (Wang et al., 2000, Zhou et al., 2000)

Station Situation Altitude(m) Geocryological regionalization Landscape
AMDO 91.63ºE; 4700 predominantly continuous permafrost subhumid alpine
32.24ºN
MS3608 91.78ºE; 4610 predominantly continuous and island permafrost subhumid alpine
31.23ºN
MS3637 91.66ºE; 4820 predominantly continuous and island permafrost subhumid alpine
31.02ºN
D66 93.78ºE; 4600 predominantly continuous permafrost semi-arid desert steppe
35.52ºN
D105 91.94ºE; 5020 predominantly continuous permafrost N/A
33.07ºN
D110 91.88ºE; 5070 predominantly continuous permafrost subhumid swamp
32.69ºN meadow
BJ 91.90ºE; 4509 predominantly continuous and island permafrost N/A
31.37ºN
Tuotuohe 92.43ºE; 4535 predominantly continuous permafrost semi-arid alpine
34.22ºN
Tazhong 83.4ºE; 1099 desert desert
39.0ºN

Fig. 2 Time series of T37V, SI and PD19 of the desert at Tazhong station, Taklamakan Desert.
(3) Snow cover
The microwave radiative characteristics of snow cover are very similar to those of
frozen soil, including a low temperature, a low complex dielectric constant, and strong
volume scattering (Edgeton et al., 1971). The shallow and dry snow is transparent to
microwaves, so most of the brightness contribution comes from the underlying soil, which
may cause confusion in separating shallow snow and frozen soil. The snow depth for each
snow sample was calculated using Equation 2 (Che et al., 2008). The SI of shallow snow
samples (<10 cm) are generally between 0 and 20, close to the SI of frozen soil. An increase

14


in the snow depth enhances the volume scattering effect in snow. Therefore, the SI of snow
deeper than 10 cm is above 30, and even reaches 80 for deep snow.
(2)

Furthermore, the patchily-distributed shallow snow cover over China cannot
effectively play a role in the heat preservation and insulation of the underlying soil. The soil
under the snow cover remained frozen most of the time (Cao et al., 1997). The snow cover
was therefore not targeted as a classification type in this decision tree.

2.3 Cluster analysis and decision tree for freeze/thaw status classification

The spatial distribution of the randomly selected training samples shows that each type
converges as a cluster in the 3-dimensional space composed by the three indices (Fig. 3a).
The decision rules in the decision tree (Fig. 4) were determined from the mean and standard
deviation of each index calculated for each type. These rules are:
(1) The PD19 of desert is 36.28±2!2.22 (mean±2!standard deviation), obviously larger
than that of other land surface types. A threshold of PD19>30 was used to identify most
desert (Fig. 3b), and the remaining desert can be further separated in the sub-branches of the
decision tree by using PD19>25. (2) Both frozen soil and snow are strong scatterers with
high SI values. The threshold of SI"5.0 was used to separate more than 95% (18.69±2$6.04)
(Fig. 3c) of frozen soil samples into the left branch of the decision tree (Fig. 4). (3) In terms
of brightness temperature, the T37V of frozen soil is 232.57±2$9.40, while that of thawed
soil is 259.1±2$5.33. The threshold of T37V=252 K can separate frozen and thawed soil
samples with the least misclassification (Fig. 3a and d). (4) Because of the strong scattering
from ice particles, some of the precipitation pixels would be divided into the left branch of
the decision tree after using SI"5.0. However, the precipitation is still warmer than frozen
ground. Grody’s index T22V"165+0.49$T85V was therefore directly adopted to identify
deep convective precipitation with ice particles. Furthermore, the discriminant
T85V/T19V<0.9 was used to identify hail clouds and rainstorms (He & Chen, 2006). For
precipitation with weak scattering, the discrimination of 254K%T22V%258K and SI%2 were
used in the right branch of the decision tree (Grody & Basist, 1996). The decision tree to
classify soil surface freeze/thaw status was finally set up in Fig. 4.

15


Fig. 3 Cluster analysis on the samples of frozen soil, thawed soil, desert and snow (a) and the statistical
characteristics of PD19 (b), SI (c) and T37V (d) for different land surface types.

3. Validation

In order to evaluate the accuracy of the decision tree algorithm, the daily classification

results were first validated by SMTMS 4 cm deep soil temperature observations at the local

time of 6:00 am for eight stations on the Qinghai-Tibetan Plateau measured during CEOP-

EOP3. Only the classification of frozen or thawed soil was validated. The number of

validated pixels was 1695, and the number of misclassifications was 219. The average

classification accuracy reached 87% (Table 2).

16


Fig. 4 Flow chart of the decision tree for the surface soil freeze/thaw status classification.

As for the misclassification, among 219 pixels, 18 cases of thawed soil were

misclassified into the desert type due to the high PD19 value of the flat and dry surfaces.

This kind of misclassification can be avoided using a reliable desert map. The freeze or

thaw statuses of the remaining 201 pixels were misclassified. We first analyzed this kind of

misclassification from the viewpoint of soil temperature; it was found that 40% and 73% of

the misclassification occurred when the 4 cm deep soil temperature was in the range of -0.5

°C-0.5 °C and -2.0 °C-2.0 °C, respectively, according to the frequency histogram of

misclassification pixels numbered against 4 cm deep soil temperatures (Fig. 5a). Then we

determined that from the viewpoint of timing, most misclassifications occurred during the

transition period between the cold and warm seasons. For instance, the proportions of error

in April-May and September-October to the total number of misclassifications were about

33% and 38%, respectively (Fig. 5b). It is understandable that most of the misclassifications

were in the transition periods because the heterogeneity within pixels is more significant at

these times. Furthermore, the frozen soil is defined according to the temperature regime.

However, most of the water in the soil still remains in the liquid state when the soil

temperature is just below the soil freezing point, which shows similar dielectric properties

as the thawed soil and would result in misclassification between frozen and thawed soil.
17


Table 2. Validation of the classification results by 4 cm deep soil temperature observations at selected CEOP
stations.
Station Validation data Misclassified data Accuracy (%)
AMDO 219 25 88.58
MS3608 207 24 88.41
MS3637 209 27 87.08
D66 217 15 93.09
D105 209 39 91.34
D110 211 41 80.57
BJ 207 19 90.82
Tuotuohe 216 29 86.57
Total 1695 219 87.08

Fig. 5 Frequency histograms of the soil temperature and occurrence time for the misclassified pixels.

We also conducted a grid-to-grid validation by the Kappa statistics using the map of

geocryological regionalization and classification in China (Zhou et al., 2000) as a reference

(Fig. 6b), a widely used method to measure the agreement between the reference data and

the classified result in grid format (Congalton, 1991). For comparability, we first obtained

the actual number of frozen days for one year—during the period from October 1, 2002 to

September 31, 2003—over China based on the pentad compositions by counting the frozen

days for each pixel (Fig. 6a). Then, the map of the frozen soil area was delineated by

assuming that the pixels that were frozen for more than 15 days should be seasonally frozen

soil or permafrost. The pixels that were frozen for less than 15 days represent short time

frozen soil (Zhou et al., 2000). The new frozen soil area map derived from the decision tree

classification result using the SSM/I data was compared with the reference map. The results

show that the overall classification accuracy was 91.66%, which was calculated from the

error matrix, and the Kappa index was 80.5%. The boundary between the frozen and thawed

18


soil in the new map (Fig. 6a) was consistent with the southern limit of seasonally frozen

ground in the reference map (Fig. 6b).

Fig. 6 actual number of frozen days in China (a) and Map of geocryological regionalization and classification
in China (b) for the period from Oct. 1, 2002 to Sep. 31, 2003.

4. Summary
A decision tree algorithm was developed to identify the surface soil freeze/thaw states
taking the influence of the desert and precipitation into account. The more reliable SI was
introduced into this decision tree instead of SG to identify the scatterers. The average
accuracy of the classification result was 87%, which was validated against the 4 cm deep
soil temperature observations. Most misclassifications occurred when the soil temperatures
were near the soil freezing point and during the transition period between the warm and cold
seasons. A grid-to-grid Kappa analysis was also conducted to evaluate the consistency
between the map of the actual number of frozen days obtained using the decision tree

19


classification algorithm and the map of geocryological regionalization and classification in
China. The results showed that the overall classification accuracy was 91.7%, while the
Kappa index was 80.5%.
Both validation results show that this new decision tree algorithm based on SSM/I
brightness temperature can produce a long time series of surface soil freeze/thaw status from
the launch of SSM/I in 1987 until now with an accuracy capable of providing a dataset to
analyze the timing, duration and areal extent of surface soil freeze/thaw status for the
research on climate and cryosphere interactions, carbon cycles, and hydrological processes
in cold regions.

5. References
Allison, I., Barry, R. G., & Goodison, B. E. (2001). Climate and Cryosphere (CliC) project
science and co-ordination plan. WCRP-114/WMO/TD No.1053
Armstrong, R. L., Knowles K. W., & Brodzik M. J. et al. (1994). DMSP SSM/I Pathfinder
daily EASE-Grid brightness temperatures. Boulder, Colorado USA: National Snow and
Ice Data Center. Digital media.
Bartsch, A., Kidd, R. A. & Wagner, W. et al. (2007). Temporal and spatial variability of the
beginning and end of daily spring freeze/thaw cycles derived from scatterometer data.
Remote Sensing of Environment, 106 (3): 360-374
Cao, M. S., Li, X., & Wang, J. et al. (2006). Remote sensing of cryosphere. Beijing: Science
Press. in Chinese
Cao, M. S., Chang, A. C. T. (1997). Monitoring terrain soil freeze/thaw condition on
Qinghai Plateau in spring and autumn using microwave remote sensing. Journal of
Remote Sensing, 1(2): 139-144. in Chinese
Che, T., Li, X., Jin. R. et al. (2008). Snow depth derived from passive microwave remote
sensing data in China. Annals of Glaciology, 49: 145-154
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely
sensed data. Remote sensing of Environment, 37: 35-46
Dobson, M. C., Ulaby, F. T., Hallikainen, M. et al. (1985). Microwave dielectric behavior of
wet soil- Part : four-component dielectric mixing models. IEEE Transactions on
Geoscience and Remote Sensing, GE-23: 35-46
Edgeton, A. T., Stogryn, A., & Poe, G. (1971). Microwave radiometric investigations of
snowpack. Final Rep. 1285R-4 of Contract 14-08-001
England, A. W., Galantowicz, J. F. & Zuerndorfer, B. W. (1991). A volume scattering
explanation for the negative spectral gradient of frozen soil. International Geoscience
and Remote Sensing Symposium, 3: 1175-1177
England, A. W. (1990). Radiobrightness of diurnally heated, freezing soil. IEEE
Transactions on Geoscience and Remote Sensing, 28(4): 464-476
Fiore Jr, J. V. & Grody, N. C. (1992). Classification of snow cover and precipitation using
SSM/I measurement: case studies. International Journal of Remote Sensing, 13(17):
3349-3361
Frolking, S., McDonald, K. C. & Kimbal, J. S. et al. (1999). Using the space-borne NASA
scatterometer (NSCAT) to determine the frozen and thawed seasons. Journal of
Geophysical Research, 104(D22): 27895-27907
Givri, J. R. (1997). The extension of the split window technique to passive microwave
surface temperature assessment. International Journal of Remote Sensing, 18(2): 335-
353
20


Goodison, B. E., Brown, R. D. & Grane, R. G. (1998). EOS Science Plan: Chapter 6
Cryospheric System. NASA
Grody, N. C. & Basist, A. N. (1996). Global identification of snowcover using SSM/I
measurement. IEEE Transactions on Geoscience and Remote Sensing, 34(1): 237-248
Grody, N. C. (1991). Classifiaction of snow cover and precipitation using the special sensor
microwave imager. Journal of Geophysical Research, 96(D4): 7423-7435
Hall D. K., George, A. R. & Vincent, V. S. (2006). MODIS/Terra Snow Cover Daily L3
Global 0.05deg CMG V005. Boulder, Colorado USA: National Snow and Ice Data
Center. Digital media
He, W. Y. & Chen, H. B. (2006). Analyses of evolutional characteristics of a hailstorm
precipitation from TRMM observation. Acta Meteorological Sinica, 64(3): 364-376. in
Chinese
Hoekstra, P. & Delaney, A. (1974). Dielectric Properties of Soils at UHF and Microwave
frequency. J. Geophys Res., 79: 1699-1708
Jin, R. & Li, X. (2002). A review on the algorithm of frozen/thaw boundary detection by
using passive microwave remote sensing. Remote Sensing Technology and Application,
17(6): 370-375. in Chinese
Jin, R. (2007). Soil Frozen/Thawed Status Detection by Using SSM/I and Active Layer Data
Assimilation System. Ph.D thesis. Graduate University of Chinese Academy of
Sciences
Jin, Y. Q. (1997). Analysis of SSM/I data over the desert areas of China. Journal of Remote
Sensing, 1(3): 192-197. in Chinese
Judge, J., Galantowicz, J. F. & England, A. W. et al. (1997). Freeze/thaw classification for
prairie soils using SSM/I radiobrightnesses. IEEE Transaction On Geoscience and
Remote Sensing, 35(4): 827-832
Kimball, J. S., McDonald, K. C., Keyser, A. R. et al. (2001). Application of NASA
scatterometer (nscat) for determining the daily frozen and nonfrozen landscape of
Alaska. Remote Sensing of Environment, 75: 113-126
Koike, T. (2004). Coordinated Enhanced Observing Period (CEOP) - an initial step for
integrated global water cycle observation. World Meteorological Organization
Bulletin, 53(2): 115-121
Li, X., Cheng, G. D. & Jin, H. J. et al. (2008). Cryospheric change in China. Global and
Planetary Change, 62 (34): 210-218, doi:10.1016/j.gloplacha.2008.02.001.
Neale, C. M. U., McFarland, M. J., Chang, K. (1990). Land-surface-type classification using
microwave brightness temperature from the Special Sensor Microwave/Imager. IEEE
Transactions on Geoscience and Remote Sensing, 28(5): 829-838
Ulaby, F. T., Moore, R. K. & Fung, A. K. (1986). Microwave remote sensing: active and
passive. Dedham MA: Artech House.
Wang S. L., Yang M. X., Toshio K. et al. (2000). Application of time-domain-reflectometer
to researching moisture variation in active layer on the Tibetan Plateau. Journal of
Glaciology and Geocryology, 22(1): 78-84. in Chinese
Wegmuller, U. (1990). The effect of freezing and thawing on the microwave signatures of
bare soil. Remote Sensing of Environment, 33: 123-135
Williams, P. J. & Smith, M. W. (1989). The frozen earth. New York: Cambridge University
Press
Yang Meixue, Yao Tandong & He Yuanqing. (2000). The role of soil moisture-energy
distribution and melting-freezing processes on seasonal shift in Tibetan plateau.
Journal of Mountain Science, 20(5): 553-558
Zhang, L. X., Zhao, S. J. & Jiang, L. M. (2009). The time series of microwave radiation
from representative land surface in the upper reaches of Heihe River during alternation

21


of freezing and thawing. Journal of Glaciology and Geocryology, 31(2): 198-205. in
Chinese
Zhang, T., Barry R. G., Knowles, K. Ling, F. & Armstrong R. L. (2003a) Distribution of
seasonally and perennially frozen ground in the Northern Hemisphere, in Proceedings
of the 8th International Conference on Permafrost, Zurich, Switzerland, edited by
Phillips M., Springman S. M. & Arenson L. U., pp. 1289-1294, A. A. Balkema,
Brookfield, Vt.
Zhang, T., Armstrong, R. L. & Smith, J. (2003b). Investigation of the near-surface soil
freeze-thaw cycle in the contiguous United States: algorithm development and
validation. Journal of Geophysical Research, 108(D22), doi: 10.1029/2003JD003530
Zhang, T. & Armstrong, R. L. (2001). Soil freeze/thaw cycles over snow-free land detected
by passive microwave remote sensing. Geophysical Research Letters, 28(5): 763-766
Zhao, Y. S. (2003). Analysis principium and methods of remote sensing application.
Beijing: Science Press, 202-208. in Chinese
Zhou, Y. W., Guo, D. X., Qiu, G. Q. et al. (2000). Geocryology in China. Beijing: Science
Press. in Chinese
Zuerndorfer, B., England, A. W., Dobson, M. C. et al. (1990). Mapping freezing/thaw
boundary with SMMR data. Agricultural and Meteorology, 52: 199-225
Zuerndorfer, B. & England, A. W. (1992). Radiobrightnesses decision criteria for f
reeze/thaw boundaries. IEEE Transaction On Geoscience and Remote Sensing, 30(1):
89-102

22


PART III

Snow depth derived from passive microwave remote sensing data in
China and snow data assimilation method

Authors: Tao Che



Snow depth derived from passive microwave remote sensing data in
China and snow data assimilation method

1. Task
Snow, one of the most important components in the cryosphere system, plays a crucial
role in influencing variability in the global climate system over a variety of temporal and
spatial scales (Peixoto and Oort, 1992; Ghan and Shippert, 2006). In this study, we report
spatial and temporal distribution of seasonal snow depth derived from passive microwave
satellite remote sensing data (e.g. SMMR from 1978 to 1987 and SMM/I from 1987-2006)
in China. We first modified the Chang algorithm and then validated it using meteorological
observations data, considering the influences from vegetation, wet snow, precipitation, cold
desert and frozen ground. Furthermore, the modified algorithm is dynamically adjusted
based on the seasonal variation of grain size and snow density.

We also report a snow data assimilation system, which can directly assimilate the
passive microwave remote sensing data into the snow process model by the Ensemble
Kalman Filter (EnKF). The Microwave Emission Model of Layered Snowpacks (MEMLS)
is used to transfer the snow state variables to the brightness temperature data, so that the
EnKF algorithm can create the Kalman gain matrix according to the brightness temperature
data simulated and observed. The errors from simulation and observation is estimated by the
comparisons and experiences. The experiment is implemented at a single site, where the
forcing data from the JMA-GSM operational global data assimilation system (3D-Var), the
brightness temperature data from the AMSR-E, the snow process model from the common
land model (CLM). The paper also discusses several important issues to enhance the current
system, such as the utility of VIS/NIR albedo products, the balance between ensemble size
and computation, dynamic error estimation, microwave radiative transfer models of
atmosphere and snowlayer, and so forth. This work is the preliminary research, and in the
future we will focus on development of snow data assimilation system in regional scale.

2. Data
! Passive microwave remote sensing data

24


The Scanning Multichannel Microwave Radiometer (SMMR) is an imaging 5-
frequency radiometer (6, 10, 18, 21, and 37 GHz) flown on the Nimbus-7 earth satellites
launched in 1978. The SSM/I sensors on the DMSP satellite collect data for 4 frequencies:
19, 22, 37, and 85 GHz. Both vertical and horizontal polarizations are measured for all
except 22 GHz, for which only the vertical polarization is measured. At NSIDC (National
Snow and Ice Data Center), the SMMR and SSM/I brightness temperatures are gridded to
the NSIDC Equal-Area Scalable Earth grids (EASE-Grids). Because China is located in a
mid-latitude region, we used the brightness temperature data with the global cylindrical
equal-area projection (Armstrong and others, 1994; Knowles and others, 2002).

! Meteorological station snow depth observations

Snow depth observations at national meteorological stations from the China
Meteorological Administration (CMA) were used to modify and validate the coefficient of
the Chang algorithm. We used 178 stations within the main snow cover regions in China,
covering the Northeastern China, Northwestern China, and the QTP (Qinghai-Tibet Plateau)
(Figure 1). For modification of the Chang algorithm, we collected snow depth data from the
daily observations in 1980 and 1981 for SMMR, and 2003 for SSM/I, respectively. Then,
snow depth data in 1983 and 1984 (for SMMR) and 1993 (for SSM/I) were used to validate
the modified algorithm.

Figure 1. Position of meteorological stations within main snow cover regions in China (NWC: Northwestern
China, QTP: Qinghai-Tibet Plateau, NEC: Northeastern China, and other region).
! MODIS snow cover area products

25


Hall and others (2002) described the Moderate Resolution Imaging Spectroradiometer
(MODIS) snow cover area algorithm for the EOS Terra satellite. At present, the MODIS
snow products are created as a sequence of products beginning with a swath (scene) and
progressing, through spatial and temporal transformations, to an eight-day global gridded
product. In the NASA Goddard Space Flight Center (GSFC), the daily Climate Modeling
Grid (CMG) snow product gives a global view of snow cover at 0.05 degree resolution.
Snow cover extent is expressed as a percentage of snow observed in the raw MODIS cells at
500 m when mapped into a grid cell of the CMG at 0.05 degree resolution. These MODIS
snow cover products can be downloaded from NASA Earth Observing System Data
Gateway. In this study, we projected the 0.05 degree daily CMG product to register with the
EASE-Grids projection for the accuracy assessment of snow area extent derived from
passive microwave satellite data.

! Vegetation distribution map in China

Snow depth retrieval from passive microwave remote sensing data will be influenced
by vegetation, in particular, the dense forest. Hu (2001) published the vegetation atlas of
China (1:1,000,000), which is the most detailed and accurate vegetation map of the whole
country up to now. It was based on the result of the nationwide vegetation surveys and their
associated researches in 50 years since 1949 and the relevant data from the aerial remote
sensing and satellite images, as well as geology, pedology and climatology. In this study, we
digitized and vectorized the vegetation atlas of China, and projected it into cylindrical
equal-area projection to register the EASE-GRID data. The forest area fraction will be used
to reduce the forest influence for the snow depth retrieval from passive microwave
brightness temperature data.

! Lake distribution map/Land-sea boundary

Based on the results of Dong and others (2005), large water bodies will seriously
influence the brightness temperature. Before the modification of snow depth retrieval
algorithm, those brightness temperature data and meteorological station data nearby the
lakes or ocean were removed to eliminate the mixed pixel effect. We used the 1:1,000,000
lake distribution maps from the Lake Database in China, which was produced by the
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (CAS) and
was shared for scientific and educational group at Data-Sharing Network of Earth System
Science, CAS (http://www.geodata.cn). The Data-Sharing Network also archived the

26


1:4,000,000 coastline maps. These spatial data also was projected to register the EASE-
GRID data.

! Experiment sites and data of snow data assimilation

The snow data assimilation experiment was implemented in Eastern Siberia Taiga area,
which is one of nine cold regions from the CEOP/CAMP. There are seven reference sites in
Eastern Siberia Taiga area. Snow depth and air temperature were observed in winter (from
October to next April).

The CLM forcing data usually include precipitation, shortwave radiation, infrared
radiation, as well as wind speed, air temperature, specific humidity and atmospheric
pressure at the observational height. In general, it is difficult to collect all of these
atmosphere data, particularly in cold regions. In this experiment, the JMA-GMS model
outputs were pre-processed as the forcing data (Hirai, 2006). We collected the forcing data
from October 2002 to May 2004. These before October 2003 were used for the spin-up of
CLM, while others for the snow data assimilation periods. The air temperature data in these
sites only were used for the comparison with JMA-GMS model outputs, while snow depth
data for validation of simulation and assimilation results. Satellite brightness data were from
the AMSR-E.

The MEMLS was linked with the CLM to transfer the snow state variables to the
brightness temperature, so that the satellite brightness temperature can be directly
assimilated into the snow assimilation scheme. The model step of the assimilation system
was one hour, and the AMSR-E pass times were rounded to be compatible with the model
times. At the observation time of brightness temperature, the assimilation scheme was
applied when the snow depth > 2cm. 2cm is threshold at which passive microwave
brightness temperatures can effectively detect snowpacks.

3. Method
3.1 Snow depth derived from passive microwave remote sensing data

! The coefficient of spectral gradient algorithm

Based on theoretical calculations and empirical studies, Chang and others (1987)
developed an algorithm for passive remote sensing of snow depth over relative uniform
snowfields utilizing the difference between the passive microwave brightness temperature
of 18 and 37 GHz in horizontal polarization.

27


SD = 1.5*(TB(18H) – TB(37H)) 1

SD is snow depth in cm, and TB(18H) and TB(37H) are brightness temperature at 18
and 37 GHz in horizontal polarization, respectively. Here, brightness temperature at 37GHz
is sensitive to snow volume scattering, while that at 18GHz includes the information from
the ground under the snow. Therefore, the basic theory of the spectral gradient algorithm is
the snow volume scattering, which can be used to estimate the snow depth after the
coefficient (slope) was modified by the snow depth observations in the field.

Based on Foster and others’s results (1997) of forest influence, the forest area fraction
was considered here:

SD = a*(TB(18H) – TB(37H))/(1-f) 2

where a is the coefficient, while f is the forest area fraction.

In this study, snow depth observations at the meteorological stations in 1980 and 1981
were regressed with the spectral gradient of SMMR at 18 and 37GHz in horizontal
polarization. Before regression, the adverse factors should be taken into account, such as
liquid water content within the snowpack, which lead to a large uncertainty due to the big
difference between dry snow and water dielectric characteristics. The brightness
temperature data influenced by liquid water content were eliminated based on the following
dry snow criteria: TB(22V)-TB(19V) 4, TB(19V)-TB(19H)+TB(37V)-TB(37H)>8,
225<TB(37V)<257, and TB(19V) 266 (Neale and others 1990). Mixed pixels with large
water bodies were removed according to the Chinese lake distribution map and the Chinese
coastline maps.

According to the regression between the spectral gradient of TB(18H) and TB(37H)
and the snow depth measured at the meteorological stations, the coefficient (slope) is 0.78
and the standard deviations from the regression line is 6.22cm for SMMR data. For the
SSM/I brightness temperature data, the 19GHz channel replaced the 18GHz of SMMR.
Results show that the coefficient is 0.66 and the standard deviations from the regression line
is 5.99cm. So, the modified algorithm is:

SD = 0.78*(TB(18H) – TB(37H))/(1-f) (for SMMR data from 1978 to 1987)

SD = 0.66*(TB(19H) – TB(37H))/(1-f) (for SSM/I data from 1987 to 2006)
(3)

28


There are 2217 snow depth observations available in 1980 and 1981, while 6799
observations in 2003 due to the SSM/I has an improved swath width and acquiring period
than the SMMR has (See Figure 2 and 3).

Figure 2. Snow depth estimated from passive microwave brightness temperature data and observed in
meteorological stations: (a) SMMR in 1980 and 1981 and (b) SSM/I in 2003.

Figure 3 Percentage of error frequency distribution of snow depth estimated from passive microwave
brightness temperature data and observed in meteorological stations. (a) SMMR in 1980 and 1981 and (b)
SSM/I in 2003.
! A simple dynamically adjusted algorithm

Snow density and grain size are two sensitive factors affecting microwave emission
from snowpacks (Foster and others, 1997, 2005), because it can partly affect the volume
scattering coefficient of snow. Although Josberger and Mognard (2002) developed a
dynamic snow depth algorithm, it is difficult to use the algorithm to mapping snow depth
estimation in China because the lack of reliable ground and air temperature data for each
passive microwave remote sensing pixel. In this study, we adopted a statistical regression
method to adjust the coefficient dynamically based on the error increasing ratio within the
snow season from October to April. The original Chang algorithm underestimated the snow
depth in the beginning of snow season and overestimated snow depth in the end of snow
season (Figure 4). As the results of statistic, the average offsets can be obtained in every
month for SMMR and SSM/I, respectively (Table 1).

29


Figure 4 Error increases from snow density and grain size variations within the snow season from October to
next April based on the estimations of SMMR and SSM/I data and observations in meteorological stations.
Here (a): SMMR and (b): SSM/I

Table 1 Average offsets to remove the influence from snow density and grain size variations for each month
within the snow season based on the linear regression method

Average offset (cm)
Month
SMMR SSM/I
Oct -3.64 -4.18
Nov -3.08 -3.58
Dec -1.91 -1.93
Jan -0.19 0.29
Feb 1.51 2.15
Mar 2.65 3.31
Apr 3.32 3.80
! Retrieval of Snow Depth

The spectral gradient algorithm for the snow depth retrieval is based on the volume
scattering of snowpacks, which means other scattering surfaces can influence the results as
well. However, it will overestimate the snow cover area if the spectral gradient algorithm is
directly used to retrieve snow depth (Grody and Basist,1996). This is because that the snow
cover produces a positive difference between low and high-frequency channels, but the
precipitation, cold desert, and frozen ground show a similar scattering signature. Grody and
Basist (1996) developed a decision tree method for the identification of snow. The
classification method can distinguish the snow from other scattering signatures (i.e.
precipitation, cold desert, frozen ground).

Within the decision tree flowchart, there are four criteria related to the 85GHz channel.
For its utility of SMMR brightness temperature data which do not have the 85GHz channel,
we only adopted other relationships, such as the TB(19V)-TB(37V) as the scattering

30


signature rather than the TB(22V)-TB(85V). For the SMMR measures, the simplified
decision tree can be described as following relationships:

1. TB(19V)-TB(37V)>0, for scattering signature;

2. TB(22V)>258 or 258%TB(22V)&254 and TB(19V)-TB(37V)%2, for precipitation;

3. TB(19V)-TB(19H)&18 and TB(19V)-TB(37V)%10, for cold desert;

4. TB(19V)-TB(19H) 8K and TB(19V)-TB(37V) 2K and TB(37V)-TB(85V) 6K,
for frozen ground.

For the more detail description of the decision tree method, please see Grody and
Basist (1996).

In this study, we adopted the Grody’s decision tree method to obtain snow cover from
SMMR (1978-1987) and SSM/I (1987-2004). Then, the snow depth data were calculated
only on those pixels by the snow depth retrieval algorithm. The return periods of SMMR
and SSM/I measurements are about every 3-5 days depending on the latitude. To obtain the
daily snow depth dataset, the intervals between swaths were filled up by the most recent
data available. The flow chart to obtain the snow depth data in China can be described by
Figure 5.

Figure 5 Flow chart of snow depth data in China derived from passive microwave brightness temperature data.

3.2 Assimilating of passive microwave remote sensing data

31


The data assimilation algorithm is the linkage between the model operator and the
observation operator within the snow data assimilation system. By uncertainty analysis of
simulation and observation, it can give us the optimal estimation of snow state variables. At
present, the usual optimal algorithms in land data assimilation is Kalman Filter (KF) and its
improved methods (Kalman, 1960; Evensen, 1994, 2003, and 2004), and the particle filter
(Arulampalam et al, 2002).

A KF combines all available measurement data, plus prior knowledge about a system
and measuring devices, to produce an estimate of the desired variables in such a manner that
error is minimized statistically. When a system can be described through a linear model and
when system and measurement noise are white and Gaussian, the best estimates can be
obtained from the KF method. The forecasting equation can be described as:

(4)

Here, is the snow state vector from the snow process model, and is the

analyzed state vector during the previous time step. The error covariance matrix can be
estimated by

(5)

is a prior covariance matrix. So the updating scheme is

(6)

where is the observation operator such as the MEMLS in this study, while the is the
Kalman gain matrix:

(7)

The updated error covariance

(8)

The updating scheme of KF needs the error covariance matrix for the model prediction
and observations. However, the snow process model is a nonlinear and discontinuous
model, so that it is difficult to develop a linear model and therefore not able to create the
error estimation from the KF scheme directly. To solve this problem, the KF was improved
and expanded by Evensen (1994) as the Ensemble Kalman Filter (EnKF). By adopting the
Monte Carlo sampling method, the statistics of forecasting and measurement can be

32


obtained. Consequently, the error statistics within equations (2) and (5) can be approximated
as (Evensen, 2003):

(9)

(10)

The e within means the error covariance by estimation from ensemble.

Therefore, the nonlinear snow process model also can be analyzed within an EnKF
based LDAS. By using the EnKF scheme, the LDAS can assimilate the passive microwave
brightness temperature data into the snow process model.

4. Accuracy assessment of passive microwave snow products
! Accuracy assessment (Snow depth)

To assess the accuracy of snow depth retrieved from the modified algorithm, we used
measured snow depth data at the meteorological stations in 1983 and 1984 to compare with
the SMMR results, and that in 1993 for the SSM/I results. Both of the absolute errors less
than 5cm hold about 65% of all data (Figure 6). The standard deviations are 6.03cm and
5.61cm for SMMR and SSM/I, respectively.

Figure 6 Percentage of error frequency distribution of validation by the snow depth observations in
meteorological stations and the spectral gradient of SMMR in 1983 and 1984 (a, the number of data is 2070)
and SSM/I in 1993 (b, the number of data is 6862).
! Accuracy assessment (Snow cover)

We collected MODIS snow cover products from December 3, 2000 to February 28,
2001 to compare with the results of this study. Though MODIS snow cover products can not
provide snow depth information, we can compare the agreement or disagreement of MODIS
and SSM/I snow extent in each of SSM/I pixels by resampling the MODIS snow cover

33


products into the EASE-Grids projection. For a SSM/I pixel, when the snow depth is larger
than 2cm, we consider the pixel to be snow covered. For the resampled MODIS pixel, the
snow cover area is a fraction of snow covered, and when the snow cover area is larger than
50% we consider it as a snow cover pixel. Congalton (1991) described several accuracy
assessment methods of remotely sensed data. First of all, we considered the MODIS snow
cover products as the truth because the optical remote sensing has higher spatial resolution
and better comprehensive algorithm than the passive microwave remote sensing. Then, we
established the error matrixes of the SSM/I results for each day according to MODIS snow
cover products. Finally, two methods (overall accuracy and kappa analysis) were used to
assess the accuracy.

The two data sets have a good agreement by the overall accuracy analysis. The overall
accuracy is about from 0.8 to 0.9 after using Grody’s decision tree method (Grody and
Basist, 1996), while the accuracy from 0.7 to 0.8 without using the method (Figure 7(a)).
The results show that the overall accuracy can be improved by Grody’s decision tree
method by 10%.

The Kappa analysis is a more strict method to assess the coincidence in two data sets.
The Khat statistic was defined as (Congalton, 1991):

(11)

Where r is the number of rows in the error matrix, xii is the number of MODIS
observations in row i and column i, xi+ and x+i are the marginal totals of row i and column
i, respectively. N is the total number of data. The results of Khat statistics show that the
accuracy can be improved by Grody’s decision tree method by 20% (Figure 7(b)).

34


Figure 7 Accuracy assessment of overall accuracy and Kappa analysis methods based on the MODIS daily
snow cover area products from December 1, 2000 to February 28, 2001. Solid line is the results with Grody’s
decision tree method to identify the snow cover, and Dash line is the results without the decision tree method.
(a) Overall accuracy, and (b) Kappa coefficient.

5. Results
Based on the daily snow depth data from 1978 to 2006, snow cover in China is mainly
located in three regions, the QTP, the Northwestern China, and the Northeastern China,
while other regions only hold a little of snow mass (Figure 8).

Figure 9 clearly illustrates the snow state variables output from the snow data
assimilation system. Figure 9(a) compares the snow depth assimilated and the in-situ
observations. The root mean squared errors (RMSE) of snow depth are 0.175 (for
simulation) and 0.087 (for assimilation), while the bias errors of snow depth are 70.2% (for
simulation) and 23.7% (for assimilation), respectively. Figure 9(b), (c), (d) compare the
snow temperature, liquid water content and density of CLM simulation and assimilation
results.

35


The scatter plots of snow depth from in situ observations against the CLM simulations
and also the assimilated results are illustrated in Figure 10. The Figure 10a is the scatter
plots of snow depth simulated against observations, while the figures 10b and 10c are snow
depth assimilated against observations for all of snow season and accumulation period,
respectively.

(a)

36


(b)
Figure 8 (a) Annual average snow depth distributions in China from 1978 to 2006 based on the SMMR and
SSM/I data. (b) Average snow depth distributions in China from 1978 to 2006 during winter (December,
January, and February) based on the SMMR and SSM/I data.

Figure 9 Assimilation results of snow state variables in the research period. (a) the snow depth from in-situ
observations, CLM single simulations, and the snow data assimilation system outputs, (b), (c), and (d) the
snow temperature, liquid water content, and density from assimilation system outputs, respectively.

37


(a) (b)

Figure 10 Scattering plots of snow depth of in-
situ observations against the CLM simulations and
the assimilated results. (a) in-situ observations
against the CLM simulations, (b) in-situ
observations against the assimilated results in the
whole period, (c) in-situ observations against the
assimilated results only in the accumulation period.

(c)

First of all, the outputs of snow depth were significantly improved by assimilating the
AMSR-E brightness temperatures. The initial states of the snow process model were
continuously updated by the satellite observations, which reduced the uncertainties of
simulation.

On the other hand, the assimilation results included more information than the retrieval
of satellite observations only. More snow state variables can be obtained from the snow data
assimilation system, such as the snow temperature, liquid water content, and snow density.
38


These data come from the simulations of the snow process model, which can be implicitly
improved by assimilating the observations.

For evaluation of assimilation results, MEMLS was used to recalculate the brightness
temperature at 18.7 and 36.5GHz in horizontal and vertical polarization based on the
snowpacks before and after the assimilation. The TBDs at 18 and 36 GHz predicted by
MEMLS before and after the assimilation along with the ASMSR-E observed ones are
illustrated in Figure 11. The RMSEs of TBDs before assimilation are 21.105 (H) and 14.625
(V), while they after assimilation are 2.515 (H) and 1.905 (V), respectively. The bias errors
before assimilation are 69.0% (H) and 59.2% (V), while they after assimilation are 7.2% (H)
and 7.5% (V). here (H) and (V) present the horizontal and vertical polarization, respectively.

(a)

39


(b)
Figure 11 Comparisons of TBDs (Brightness temperature differences) between AMSR-E observations and
MEMLS simulations before and after assimilation, here (a) for horizontal polarization, and (b) for vertical
polarization.

6. References
Armstrong, R. L., Brodzik, M. J. (2002), Hemispheric-scale comparison and evaluation of passive-
microwave snow algorithms. Annals of Glaciology, 34: 38~44.
Anderson, E. A. (1976). A point energy and mass balance model of a snow cover, NOAA Tech. Rep.
NWS, 19, Office of Hydrol., Natl. Weather Serv., Silver Spring, Md.
Andreadis K. M. & Lettenmaier D. P. (2006). Assimilating remotely sensed snow observations into a
macroscale hydrology model. Advances in Water Resources. 29, 872-886.
Aoki, T., Hachikubo A., & Hori M. (2003). Effects of snow physical parameters on shortwave broadband
albedos, J. Geophys. Res., 108(D19), 4616, doi:10.1029/2003JD003506.
Arulampalam M. S., Maskell S., Gordon N., & Clapp T. (2002). A Tutorial on Particle Filters for Online
Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Geoscience and Remote Sensing,
50, 174-188.
Bonan, G. B., (1996). A land surface model (LSM version1.0) for ecological, hydrological, and
atmospheric studies: Technical description and user’s guide. NCAR Tech. Note NCAR/TN-
417+STR, 1-150.
Chang A. T. C., Foster J. L., & Hall D. K. (1987). Nibus-7 SMMR derived global snow cover
parameters, Annual of Glaciology, 9, 39-44.
Chang A. T. C., Gloersen P., Schmugge T, Wilheit T. T. & Zwally H. J.(1976). Microwave emission
from snow and glacier ice. Journal of Glaciology, 16, 23-39.

40


Clark M. P., Slater A. G., Barrett A. P., Hay L. E., McCabe G. J., Rajagopalan B., & Leavesley G. H.
(2006). Assimilation of snow covered area information into hydrologic and land-surface models.
Advances in Water Resources, 29, 1209-1221.
Cline, D., Armstrong R., Davis R., Elder K., & Liston G.. 2002, Updated July 2004. CLPX GBMR Snow
Pit Measurements. Edited by M. Parsons and M.J. Brodzik. In CLPX-Ground: Ground Based Passive
Microwave Radiometer (GBMR-7) Data, T. Graf, T. Koike, H. Fujii, M. Brodzik, and R. Armstrong.
2003. Boulder, CO: National Snow and Ice Data Center. Digital Media.
Dai, Y., & Zeng Q. C. (1997). A land surface model (IAP94) for climate studies, Part I: Formulation and
validation in off-line experiments. Adv. Atmos. Sci., 14, 433-460.
Dai, Y., et al., (2001). Common Land Model: Technical documentation and user’s guide [Available
online at http://climate.eas.gatech.edu/dai/clmdoc.pdf].
Dai, Y., Zeng X., Dickinson R. E., Baker I., Bonan G., Bosilovich M., Denning S., Dirmeyer P., Houser
P., Niu G., Oleson K., Schlosser A., & Yang Z. L., (2003). The Common Land Model (CLM). Bull.
of Amer. Meter. Soc., 84, 1013-1023.
Dickinson, R. E., Henderson-Sellers A., Kennedy P. J., & Wilson M. F. (1993). Biosphere-Atmosphere
Transfer Scheme (BATS) version 1e as coupled to Community Climate Model. NCAR Tech. Note
NCAR/TN-387+STR, 1-72 .
Dong J. R., Walker J. P., & Houser P. R. (2005). Factors affecting remotely sensed snow water
equivalent uncertainty. Remote Sensing of Environment, 97, 68 – 82.
Evensen G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-
Carlo methods to forecast error statistics. Journal of Geophysical Research, 99, 10143-10162.
Evensen G. (2003). The Ensemble Kalman Filter: theoretical formulation and practical implementation.
Ocean Dynamics, 53, 343-367.
Evensen G. (2004). Sampling strategies and square root analysis schemes for the EnKF. Ocean
Dynamics, 54, 539-560.
Foster J L, Sun C J, Walker J. P., Kelly R., Chang A. C. T., Dong J. R., Powell H. (2005). Quantifying
the uncertainty in passive microwave snow water equivalent observations. Remote Sensing of
Environment, 94, 187-203.
Foster J. L. & Rango A. (1982). Snow cover conditions in the northern hemisphere during the winter of
1981. Journal of Climatology, 20, 171–183.
Foster J. L., Chang A. T. C., & Hall D. K. (1997). Comparison snow mass estimates from a prototype
passive microwave snow algorithm, a revised algorithm and snow depth climatology, Remote
Sensing of Environment. 62,132–142.
Fung A. K. (1994). Microwave Scattering and Emission Models and Their Applications. Norwood, MA:
Artech House, 1-573.
Ghan, S J & Shippert, T. (2006). Physically based global downscaling: Climate change projections for a
full century. Journal of Climate, 19, 1589-1604.
Hall, D. K. Riggs G. A., Salomonson V. V., DiGirolamo N. E. & Bayr K. J. (2002). MODIS snow-cover
products. Remote Sensing of Environment, 83, 181-194.

41


Hall, D. K., Sturm, M., Benson, C. S., Chang, A. T. C., Foster, J. L., Garbeil, H. & Chacho E. (1991).
Passive microwave remote and in-situ measurements of Arctic and Subarctic snow covers in Alaska.
Remote Sensing of Environment, 38, 161–172.
Hirai M. (2006). Global JMA model output (JMA-GSM).
http://www.atd.ucar.edu/projects/ceop/dm/model/
Jin Y. Q., & Liang Z. C. (2003). Iterative solution of multiple scattering and emission from an
inhomogeneours scatter media. Journal of applied physics, 93, 2251-2256.
Jordan, R. (1991). A One-dimensional temperature model for a snow cover, Special. Report. 91-1b, Cold
Regions Res. and Eng. Lab., Hanover, N. H.
Kalman R E. (1960). A new approach to linear filtering and prediction problems. Trans. ASME, Series
D, J. Basic Eng., 82, 35-45.
Kelly, R .E. J., & Chang, A. T. C. (2003). Development of a passive microwave global snow depth
retrieval algorithm for SSM/I and AMSRE data. Radio Science, 38(4), 8076,
doi:10.1029/2002RS002648.
Kelly, R. E., Chang, A., Tsang, L., & Foster, J. (2003). A prototype AMSR-E global snow area and snow
depth algorithm. IEEE Transactions on Geoscience and Remote Sensing, 41(2), 230– 242.
Loth, B., & Graf H. F. (1993). Snow cover model for global climate simulation, J. Geophys. Res., 98,
10,451– 10,464,.
Lynch-Stieglitz, M. (1994). The development and validation of a simple snow model for the GISS GCM.
Journal of Climate, 7, 1842-1855.
Matzler C, & Wiesmann A. (1999). Extension of the microwave emission model of layered snowpacks to
coarse-grained snow. Remote Sensing of Environment, 70, 317-325.
Matzler C, Wiesmann A., & strozzi T. (2000). Simulation of microwave emission and backscattering of
layeredsnowpacks by a radiative transfer model, and validation by surface-based experiments.
IGARSS00, v4, 1548-1550.
Oleson K. W., Dai Y. J., Bonan G., et al (2004). Technical description of the community land model
(CLM). NCAR technical note.
Pan M., et al. (2003). Snow process modeling in the North American Land Data Assimilation System
(NLDAS): 2. Evaluation of model simulated snow water equivalent. J. Geophys. Res., 108(D22),
8850, doi:10.1029/2003JD003994.
Peixoto, J. P., & Oort A. H. (1992). Physics of Climate. American Institute of Physics, New York.
Schlosser, C. A., & Mocko D. M. (2003). Impact of snow conditions in spring dynamical seasonal
predictions, J. Geophys. Res., 108(D16), 8616, doi:10.1029/2002JD003113.
Sheffield J, et al. (2003). Snow process modeling in the North American Land Data Assimilation System
(NLDAS): 1. Evaluation of model-simulated snow cover extent. J. Geophys. Res., 108(D22), 8849,
doi:10.1029/2002JD003274.
Slater A. G., Schlosser C. A., Desborough C. E., Pitman A. J., Henderson-Sellers A., Robock A. E et al.
(2001). The Representation of Snow in Land Surface Schemes: Results from PILPS2 (d). Journal of
Hydrometeorology, 2, 7-25.

42


Steppuhn H. (1981). Snow and agriculture. In D.M. Gray and D.N. Male, editors, Handbook of Snow:
Principles, Processes, Management and Use, 60–125. Pergamon Press.
Stone, R. S., Longenecker D., Duttoo E.G., & Harris J.M.. (2001). The advancing date of spring
snowrnelt in the Alaskan Arctic. Eleventh ARM Science Team Meeting Proceedings, Atlanta,
Georgia, 19-23.
Sun, C., Walker, J. P., & Houser, P. R. (2004). A methodology for snow data assimilation in a land
surface model, J. Geophys. Res., 109, D08108, doi:10.1029/2003JD003765.
Sun, S., Jin J. M., & Xue Y. (1999), A simplified layer snow model for global and regional studies, J.
Geophys. Res., 104, 19,587– 19,597.
Tsang L., Chen C., Chang A. T. C., Guo J., & Ding K. H. (2000). Dense media radiative transfer theory
based on quasicrystalline approximation with applications to passive microwave remote sensing of
snow. Radio Science, 35, 731-749.
Ulaby, F., Moore, R., & Fung, A. (1981), Microwave Remote Sensing, Artech House, Dedham, MA,
Vol. I, pp1-5
Ulaby, F., Moore, R., & Fung, A. (1986), Microwave Remote Sensing, Artech House, Dedham, MA,
Vol. III, pp1602-1634.
Wiesman A, & Matzler C. (1999). Microwave emission model of layered snowpacks. Remote Sensing of
Environment, 70, 307-316.
Wiesmann, A., Matzler, C., & Weise, T. (1998), Radiometric and structural measurements of snow
samples. Radio Sci., 33, 273-289.
Wu T. D., & Chen K. S. (2004). A Reappraisal of the Validity of the IEM Model for Backscattering
From Rough Surfaces [J]. IEEE Transaction on Geoscience and Remote Sensing, 42 (4), 743-753.
Xue Y., Sun S., Kahan D., & Jiao Y, (2003). Impact of parameterizations in snow physics and interface
processes on the simulation of snow cover and runoff at several cold region sites, J. Geophys. Res.
108, D22, doi:10.1029/2002JD003174.

43


PART IV

Providing soil parameter data sets for the entire plateau from a
microwave land data assimilation system

Authors: Kun Yang

Affiliations: Institute of Tibetan Plateau Research, Chinese Academy of Sciences
(ITP, CAS)


Providing soil parameter data sets for the entire plateau from a
microwave land data assimilation system

1. Task

Soil thermal and hydraulic parameters are the basic parameters for land surface
modelling, hydrological modelling, and land data assimilation system. Most of current
models use available dataset of soil parameters that are derived from soil survey. However,
their accuracy is often questionable due to very limited soil samples available. This is
particularly true for the Tibetan Plateau. This task will estimate soil parameters from a land
data assimilation system developed by University of Tokyo (LDAS-UT) presented in Yang
et al. (2007).

2. Algorithm

Figure 1a shows the flowchart of the LDAS-UT system. It assimilates the AMSR-E 6.9
GHz and 18.7 GHz brightness temperatures into a LSM, with a RTM as an observation

operator. At first, the LSM produces the near-surface soil moisture ( ), the ground
temperature (Tg), and the canopy temperature (Tc), which are then fed into the RTM to
simulate the brightness temperatures. The difference between simulated Tb (Tbp,est) and
observed Tb (Tbp,obs) is sensitive to the near-surface soil moisture, which is then adjusted
to minimize the difference by a global optimization scheme (Duan et al., 1993)
Figure 1b shows a dual-pass assimilation algorithm adopted in LDAS-UT. Pass 1, so-
called calibration pass, aims at tuning system parameters; Pass 2, so-called assimilation
pass, is to estimate soil moisture. The principle behind this algorithm is that the responding
time scale of a system state to the system parameters is different from the responding time
scale to the initial condition. The system parameters have a long-term impact on state
variables (such as soil moisture), and therefore, a long time window (several months or
longer) is required to calibrate the parameters. By contrast, initial near-surface soil moisture
has a short-term effect on the system state variables, and therefore, a short time window (~1
day) is selected to estimate its value by minimizing a cost function. It should be noted that
the parameter calibration presented in LDAS-UT relies on satellite microwave data instead
of surface observations, and thus, it may have a wide applicability.

45


3. Data
A dense soil moisture network was deployed through the AMPEX (Advanced Earth
Observing Satellite II (ADEOS-II) Mongolian Plateau Experiment for ground truth) project
in order to collect data for development and validation of AMSR/AMSR-E soil moisture
retrieval algorithms (Kaihotsu, 2005). The CEOP Mongolia reference site covers a flat area
of in a semi-arid grassland of Mandal Govi, where soil moisture at 3 cm
depth was measured at 16 stations and meteorological data at 4 stations.

4. Test estimated soil moisture and parameters

Figure 2 shows that the observed soil moisture values are quite diverse in space. Figure
3 shows the comparison of soil moisture among the LDAS-UT estimate, LSM estimate, and
the station-averaged observations. Clearly, the LDAS-UT estimate is agreeable with the
observations fairly well, whereas the LSM simulation with default parameter values
overestimates soil moisture.

A further analysis indicates that the improvement of soil moisture estimations in LDAS-
UT is realized through both the parameter calibration and the data assimilation. We also
evaluated the effect of the accuracy of forcing data on soil moisture estimate and found a
general decrease of the accuracy of the estimate when the forcing data become worse.
Nevertheless, LDAS-UT produces better estimates than the LSM does in all cases. It is also
surprising that LDAS-UT produces fairly good estimate of soil moisture when precipitation
is set to be zero in the forcing data (not shown).The detail of this application can be found in
Yang et al. (2009).

46


Figure 1 (a) LDAS-UT system structure; (b) schematic of the dual-pass assimilation
technique. , , and are the ground temperature, canopy temperature, and near-
surface soil water content, respectively. is the brightness temperature, the cost
function, and the data assimilation window. is soil reflectivity, the optical
thickness of the vegetation. The subscript p denotes the polarization, obs the observed value,
and est the estimated value (see details in Yang et al., 2007).

47

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