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

1Cold 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 ParticipantsLead beneficiary ID: CAREERI

ISSN/ISBN:c!2010

Edited by the CEOP-AEGIS Project O!ceLSIIT/TRIO, University of Strasbourg

BP10413, F-67412 ILLKIRCH Cedex, FrancePhone: +33 368 854 528; Fax: +33 368 854 531

e-mail: [email protected]

No part of this publication may be reproduced or published in any formor by any means, or stored in a database or retrieval system, without thewritten permission of the CEOP-AEGIS Project O!ce.

CEOP-AEGIS Report De 6.1

II

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


III


PART I

A Report for Snow Cover Area Retrieval by MODIS Data

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

A Report for Snow Cover Area Retrieval by MODIS Data

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,


2

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


3

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:

Erreur ! Des objets ne peuvent pas être créés à partir

des codes de champs de mise en forme. (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


4

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

threshold

value

The overall accuracy, Kappa

coefficient and fractional snow

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.


5

(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.


6

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

MOD09GA MYD09GA

CIVCO Terrain correction

NDSI≥0.36, B2＞0.11

Snow，Cloud, Other

Klein MODEL，b4>0.1

LST mask:MOD11A1 Threshold value≤283

Cloud mask: MOD35， Land/water mask: MOD03

other

Snow in forest，Cloud，Other

MYDSNOW MODSNOW

Snow Cover Map

Maximum Composition


7

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.


8

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.


PART II

Surface soil freeze/thaw state dataset using the decision tree

classification algorithm

Authors: Rui Jin Affiliations: Cold and Arid Regions Environment and Engineering Research



10

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


11

(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.


12

(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.


13

(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


14

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;

32.24ºN 4700 predominantly continuous permafrost subhumid alpine

MS3608 91.78ºE; 31.23ºN

4610 predominantly continuous and island permafrost subhumid alpine

MS3637 91.66ºE; 31.02ºN

4820 predominantly continuous and island permafrost subhumid alpine

D66 93.78ºE; 35.52ºN

4600 predominantly continuous permafrost semi-arid desert steppe

D105 91.94ºE; 33.07ºN

5020 predominantly continuous permafrost N/A

D110 91.88ºE; 32.69ºN

5070 predominantly continuous permafrost subhumid swamp meadow

BJ 91.90ºE; 31.37ºN

4509 predominantly continuous and island permafrost N/A

Tuotuohe 92.43ºE; 34.22ºN

4535 predominantly continuous permafrost semi-arid alpine

Tazhong 83.4ºE; 39.0ºN

1099 desert desert

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


15

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.


16

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).


17

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.


18

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


19

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


20

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.

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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

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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

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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

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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

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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

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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.

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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

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22

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.

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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


PART III

Snow depth derived from passive microwave remote sensing data in

China and snow data assimilation method

Authors: Tao Che Affiliations: Cold and Arid Regions Environment and Engineering Research



24

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


25

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


26

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


27

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.


28

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)


29

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).


30

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


31

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


32

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


33

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


34

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)).


35

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.


36

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)


37

(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.


38

(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.


39

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)


40

(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.

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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)


45

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.


46

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).


47

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).


48

Figure 2 Observed daily-mean near-surface (at 3 cm depth) soil moisture variations at 16

Mongolian AMPEX stations during 2003/4/30-2003/9/30.

Figure 3 Comparisons of AMPEX daily-mean station-averaged near-surface soil volumetric

water content with (a) LDAS-UT output, and (b) LSM (SiB2) simulation at CEOP Mongolian site during 2003/4/30-2003/9/30.

5. Evaluation of optimized parameter values


49

Table 1 shows the observed values and the optimized values of soil parameters. The soil

porosity value ( ) and soil water potential at saturation ( ) are quite comparable to the

observation averaged over the individual sites, but the estimates of the soil hydraulic

conductivity ( ) are one order of magnitude lower than the observed one, and the

estimates of the pore size distribution index ( ) are also much higher than the observed one.

Therefore, the optimized values of and are tuned values rather than physical ones.

The results suggest that the near-surface soil moisture retrieved from the satellite data is not

sufficient to physically estimate all the soil parameters, but it is possible to estimate the

most sensitive parameters such as the soil porosity.

Table 1 The observed and estimated soil parameter values.

Sand% Clay%

(m)

(10-6 m/s)

Observed MGSa 0.334 - - 3.37 - 18.7 MGSb 0.388 - - 3.62 -0.12 51.9 DRSa 0.393 - - 3.06 - 44.0 DRSb 0.428 - - 3.00 -0.11 88.6 BTSb 0.456 - - - - 49.3 E4a 0.358 - - 2.93 - 21.3 G6a 0.341 - - 2.70 - 8.9 C2a 0.299 - - 3.10 - 19.3 C4a 0.395 - - 2.82 - 28.5 Ave 0.377 - - 3.08 -0.115 36.7

LDAS-UT estimated 0.368 47 28 7.34 -0.18 4.9

a: from Table 1 of Yamanaka et al. (2005); b: from Table I-2 and Table I-3 of Appendix I in Kaihotsu (2005). 6. References Duan, Q., V.K. Gupta, and S. Sorooshian, 1993: A shuffled complex evolution approach for

effective and efficient global minimization, J. Optimiz. Theory App., 76, 501-521.

Kaihotsu, I., 2005: Grand truth for evaluation of soil moisture and geophysical/vegetation

parameters related to ground surface conditions with AMSR and GLI in the Mongolian

Plateau (pp.1-113). JAXA, Japan.

Yamanaka, T., I. Kaihotsu, D. Oyunbaatar, T. Ganbold, 2005: Regional-scale variability of

the surface soil moisture revealed by the AMPEX monitoring network. Grand truth for


50

evaluation of soil moisture and geophysical/vegetation parameters related to ground

surface conditions with AMSR and GLI in the Mongolian Plateau, JAXA, Japan, 33-42.

Yang, K., T. Watanabe, T. Koike, X. Li, H. Fujii, K. Tamagawa, Y. Ma, and H. Ishikawa,

2007: Auto-calibration system developed to assimilate AMSR-E data into a land surface

model for estimating soil moisture and the surface energy budget. J. Meteor. Soc. Japan,

85A, 229-242.

Yang, K., T. Koike, I. Kaihotsu, and J. Qin, 2009: Validation of a dual-pass microwave land data assimilation system for estimating surface soil moisture in semi-arid regions, Journal of

Hydrometeorology 10, 780-793, DOI: 10.1175/2008JHM1065.1.

Acknowledgments

The work described in this publication has been supported by the EuropeanCommission (Call FP7-ENV-2007-1 Grant nr. 212921) as part of the CEOP-AEGIS project (http://www.ceop-aegis.org) coordinated by the Universityof Strasbourg, France.

de6.1 report

Technology

snow data assimilation

sample data set

soil parameter data

snow depth

snow cover area retrieval

chinese academy of science

classification indices

contentspart ia report