De6.2 report

Preliminary Algorithm Theoretical Basis
Documents for Snow/Ice/Frozen soil Properties

Fraction Cover, Water Equivalent, and Frozen/Thaw
Status

Deliverable De6.2

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
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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 6.2

MODIS SNOW PRODUCTS ALGORITHM
ABSTRACT......................................................................................................................................................... 1
1. INTRODUCTION ............................................................................................................................................. 2
1.1 Identification ......................................................................................................................................... 2
1.2 Overview ................................................................................................................................................ 3
2. ALGORITHM DESCRIPTION OF SNOW COVER .............................................................................................. 5
2.1 Introduction........................................................................................................................................... 5
2.2 Background and Data........................................................................................................................... 6
2.3 Calculation of ground reflectance ....................................................................................................... 8
2.4 Adjust of NDSI .................................................................................................................................... 10
2.5 Additional Algorithms ........................................................................................................................ 11
2.6 Image fusion ........................................................................................................................................ 12
2.7 Backup Algorithm............................................................................................................................... 13
3. ALGORITHM DESCRIPTION OF FRACTIONAL SNOW COVER ..................................................................... 13
4. VALIDATION PLAN ..................................................................................................................................... 13
4.1 Introduction......................................................................................................................................... 13
4.2 Approach ............................................................................................................................................. 14
4.3 Validation Sites.................................................................................................................................... 14
4.4 Auxiliary Measurements .................................................................................................................... 14
4.5 Scaling .................................................................................................................................................. 14
5. ANCILLARY DATA ...................................................................................................................................... 15
6. PROGRAMMING AND PROCEDURAL CONSIDERATIONS ............................................................................ 15
6.1 Programming Issues ........................................................................................................................... 15
6.2 Processing Issues ................................................................................................................................. 15
6.3 Quality Assurance............................................................................................................................... 15
REFERENCES .................................................................................................................................................. 15

PART II

SNOW WATER EQUIVALENT RETRIEVAL ALGORITHM
ABSTRACT....................................................................................................................................................... 19
1. INTRODUCTION ........................................................................................................................................... 19
1.1 Identification ....................................................................................................................................... 19
1.2 Overview .............................................................................................................................................. 20
2. ALGORITHM DESCRIPTION ........................................................................................................................ 21
2.1 Introduction......................................................................................................................................... 21
2.2 Theoretical Basis of the Algorithm.................................................................................................... 24
2.3 Description of Retrieval Concept ...................................................................................................... 25
2.4 Description of Retrieval Algorithm................................................................................................... 25
2.5 Backup Algorithm............................................................................................................................... 26
3. ALGORITHM PROTOTYPING ...................................................................................................................... 26
3.1 Data Analysis....................................................................................................................................... 26
3.2 Prototyping of the Algorithm............................................................................................................. 28
II


4. VALIDATION PLAN ..................................................................................................................................... 29
4.1 Introduction......................................................................................................................................... 29
4.2 Approach ............................................................................................................................................. 29
4.3 Validation Sites.................................................................................................................................... 32
4.4 Auxiliary Measurements .................................................................................................................... 32
4.5 Scaling .................................................................................................................................................. 32
4.6 Data Protocols and Dissemination..................................................................................................... 32
4.7 Proposed Validation Tests.................................................................................................................. 32
5. ANCILLARY DATA ...................................................................................................................................... 32
6. PROGRAMMING AND PROCEDURAL CONSIDERATIONS ............................................................................ 33
6.1 Programming Issues ........................................................................................................................... 33
6.2 Processing Issues ................................................................................................................................. 33
6.3 Quality Assurance............................................................................................................................... 33
References.................................................................................................................................................. 33

PART III

SURFACE SOIL FREEZE/THAW STATE DATASET USING THE DECISION TREE
CLASSIFICATION ALGORITHM
ABSTRACT....................................................................................................................................................... 37

1. INTRODUCTION......................................................................................................................................... 37

1.1 IDENTIFICATION ....................................................................................................................................... 38
1.2 OVERVIEW ................................................................................................................................................ 38

2. ALGORITHM DESCRIPTION .................................................................................................................. 39

2.1 INTRODUCTION ......................................................................................................................................... 39
2.2 TARGETS TO BE OBSERVED ...................................................................................................................... 39
2.3 RADIATIVE TRANSFER PROBLEM ............................................................................................................ 39
2.4 MATHEMATICAL BASIS OF THE ALGORITHM ......................................................................................... 40
2.5 DESCRIPTION OF RETRIEVAL CONCEPT ................................................................................................. 41
2.6 DESCRIPTION OF RETRIEVAL ALGORITHM ............................................................................................ 41
2.7 BACKUP ALGORITHM ............................................................................................................................... 41

3. ALGORITHM PROTOTYPING ................................................................................................................ 41

3.1 DATA ANALYSIS........................................................................................................................................ 41
3.1.1 Analysis of the brightness temperature characteristics of each land surface type .................... 41
3.1.2 Cluster analysis and decision tree for freeze/thaw status classification...................................... 44

4. VALIDATION PLAN................................................................................................................................... 45

4.1 INTRODUCTION ......................................................................................................................................... 45
4.2 APPROACH ................................................................................................................................................ 46
4.3 VALIDATION SITES ................................................................................................................................... 49
4.4 AUXILIARY MEASUREMENTS ................................................................................................................... 49

III


4.5 SCALING .................................................................................................................................................... 49
4.6 DATA PROTOCOLS AND DISSEMINATION ................................................................................................ 49
4.7 PROPOSED VALIDATION TESTS ............................................................................................................... 49

5. ANCILLARY DATA .................................................................................................................................... 49

6. PROGRAMMING AND PROCEDURAL CONSIDERATIONS............................................................ 50

6.1 PROGRAMMING ISSUES ............................................................................................................................ 50
6.2 PROCESSING ISSUES ................................................................................................................................. 50
6.3 QUALITY ASSURANCE .............................................................................................................................. 50

REFERENCES.................................................................................................................................................. 50

IV

PART I

MODIS Snow Products Algorithm

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

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


MODIS Snow Products Algorithm

Abstract

The algorithms of MODIS Terra and MODIS Aqua versions of the snow products have
been developed by the NASA National Snow and Ice Data Center (NSIDC). The MODIS
global snow-cover products have been available through the NSIDC Distributed Active
Archive Center (DAAC) since February 24, 2000 to Terra and July 4, 2002 to Aqua. The
MODIS snow-cover maps represent a potential improvement relative to hemispheric-scale
snow maps that are available today mainly because of the improved spatial resolution and
snow/cloud discrimination capabilities of MODIS, and the frequent global coverage. In
China, the snow distribution is different to other regions. Their accuracy on Qinghai-Tibet
Plateau (QTP), however, has not yet been established. There are some drawbacks about
NSIDC global snow cover products on QTP:

1. The characteristics of snow depth distribution on QTP: Thin, discontinuous. Our research
indicated the MODIS snow-cover products underestimated the snow cover area in QTP
(Hao xiaohua, 2008).
2. The snow on QTP belongs to alpine snow. Errors due to the effects of topography can be
large. Without the terrain correction of a digital elevation model, the NSIDC global snow
products can underestimate the snow cover in QTP.
3. The snow products can separate snow from most obscuring clouds. However, there are
still many cloud pixels in daily snow cover product.

The study developed a new daily snow cover algorithm through improving the NSIDC
snow algorithms and combining MODIS-Terra and MODIS-Aqua data in QTP. The study
also developed a method of mapping fractional snow cover from MODIS in QTP. The new
snow cover products will provide daily snow cover at 500-m resolution in QTP. The new
snow cover algorithm employs the CIVCO topographic correction, a grouped-criteria
technique using the Normalized Difference Snow Index (NDSI) and other spectral threshold
tests and image fusion technology to identify and classify snow on a pixel-by-pixel basis.
The usefulness of the NDSI is based on the fact that snow and ice are considerably more
reflective in the visible than in the shortwave IR part of the spectrum, and the reflectance of

1


most clouds remains high in the short-wave IR, while the reflectance of snow is low. In
order to reduce the effect on cloud, snow cover over MODIS-Terra and MODIS-Aqua is
composed as maximum snow extent. At last, a MODIS-Terra fractional snow cover data
were added to the product base on linear relationship between NDSI and fractional snow
cover.

Validation of the MODIS snow cover and fractional snow cover products is an on-going
process. Two types of validation are addressed in the study-absolute and relative. To derive
absolute validation, the MODIS maps are compared with field measurements. Relative
validation refers to comparisons with other high resolution image snow cover maps, which
are considered to be the ‘truth’ snow maps. We have validated the daily snow cover product
MOD10A1 and 8-day snow cover product MOD10A2 using snow depth from 47 climate
stations in North Xinjiang, China. The accuracy of MODIS snow cover mapping algorithm
under varied topography, snow depth and land cover types was analyzed. Analysis showed
that the MODIS snow cover underestimated the snow cover area in alpine regions.
Vegetation cover has an important influence in the accuracy of MODIS snow cover maps.
We also validated the MOD10A1 by Landsat-ETM+ images with 30-m resolution in QTP.
Results suggest that the snow mapping algorithm of MODIS also underestimates the snow
cover. We intend to design a field experiment focused on validating our snow cover
products in QTP this winter. Recent advances in the area of snow remote sensing have lead
to further algorithm development to more accurately measure snow cover from different
sensors. In future, a blended snow product to map snow cover area utilizing MODIS,
AMSR-E passive microwave data, QuikSCAT scatterometer data and ICESTA laser radar
data will be developed.

1. Introduction
1.1 Identification

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

2


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.

1.2 Overview

Remote sensing of snow cover is more than 40 years old. Snow was observed in the
first image obtained from the TIROS-1 weather satellite following its April 1960 launch
(Singer and Popham, 1963). However, it was in the mid-1960s that snow was successfully
mapped from space on a weekly basis following the launch of the ESSA-3 satellite. ESSA-3
carried the Advanced Vidicon Camera System (AVCS) that operated in the spectral range of
0.5 - 0.75 mm with a spatial resolution at nadir of 3.7 km. Using a variety of sensors,
including the Scanning Radiometer (SR), Very High Resolution Radiometer (VHRR) and
AVHRR sensors, snow cover has been mapped in the Northern Hemisphere on a weekly
basis since 1966 by NOAA (Matson et al., 1986; Matson, 1991). Initially, the weekly
NOAA National Environmental Satellite Data and Information System (NESDIS)
operational product was determined from visible satellite imagery from polar-orbiting and
geostationary satellites and surface observations. Where cloud cover precluded the analyst’s
view of the surface for an entire week, the analysis from the previous week was carried
forward (Ramsay, 1998). The maps were hand drawn, and then digitized using an 89 89
line grid overlaid on a stereographic map of the Northern Hemisphere. In 1997, the older,
weekly maps were replaced in 1997, by the IMS product. The IMS product provides a daily
snow map that is constructed through the use of a combination of techniques including
visible, near-infrared and passive-microwave imagery and meteorological-station data at a
spatial resolution of about 25 km (Ramsay, 1998 and 2000). Regional snow products, with
1-km resolution, are produced operationally in 3000 - 4000 drainage basins in North
America by the National Weather Service using NOAA National Operational Hydrologic
Remote Sensing Center (NOHRSC) data (Carroll, 1990 and Rango, 1993). Passive-
microwave sensors on-board the Nimbus 5, 6, and 7 satellites and the Defense
Meteorological Satellite Program (DMSP) have been used successfully for measuring snow
extent at a 25 to 30 km resolution through cloud-cover and darkness since 1978 (Chang et
al., 1987). Passive-microwave sensors also provide information on global snow depth
(Foster et al., 1984). The NOAA/AVHRR and the DMSP Special Sensor Microwave Imager
(SSM/I) are currently in operation. The Landsat Multispectral Scanner (MSS) and TM
sensors, with 80-m and 30-m resolution, respectively, are useful for measurement of snow

3


covered area over drainage basins (Rango and Martinec, 1982). Additionally, Landsat TM
data are useful for the quantitative measurement of snow reflectance (Dozier et al., 1981;
Dozier, 1984 and 1989; Hall et al., 1995; Winther, 1992).

The Moderate Resolution Imaging Spectroradiometer (MODIS), a major NASA EOS
instrument, was launched aboard the Terra satellite on December 18, 1999 (10:30 AM
equator crossing time, descending) for global monitoring of the atmosphere, terrestrial
ecosystems, and oceans. On May 4, 2002, a similar instrument was launched on the EOS-
Aqua satellite (1:30 PM equator crossing time, descending) (Salomonson et al., 2001).
MODIS data are now being used to produce snow-cover products from automated
algorithms at Goddard Space Flight Center in Greenbelt, MD. The products are transferred
to the National Snow and Ice Data Center (NSIDC) in Boulder, CO, where they are archived
and distributed via the Warehouse Inventory Search Tool (WIST). The MODIS snow
products are produced as a series of six products, including MOD10_L2, MOD10L2G,
MOD10A1, MOD10A2, MOD10C1 and MOD10C2. MOD10_L2 is swath product that is
generated using the MODIS calibrated radiance data products (MOD02HKM and
MOD021KM), the geolocation product (MOD03), and the cloud mask product (MOD35_L2)
as inputs. The MODL2G product is the result of mapping all the MOD10_L2 swaths
acquired during a day to grid cells of the Sinusoidal map projection. The Earth is divided
into an array of 36 x 18, longitude by latitude, tiles, about 10°x10° in size in the Sinusoidal
projection. The daily snow product MOD10A1 is a tile of data gridded in the sinusoidal
projection. Tiles are approximately 1200 x 1200 km (10°x10°) in area. Snow data arrays are
produced by selecting the most favorable observation (pixel) from the multiple observations
mapped to a cell of the MOD10_L2G gridded product from the MOD10_L2 swath product.
In addition to the snow data arrays mapped in from the MOD10_L2G, snow albedo is
calculated. There are four SDSs (or data fields) of snow data; snow cover map, fractional
snow cover, snow albedo and QA in the data product file. The MOD10A2 is eight-day
composited of MOD10A1. The MOD10A2 is generated by merging all the MOD10A1
products (tiles) for an eight-day period. MOD10C1 and MOD10C2 snow product gives a
global view of snow cover at 0.05° resolution global climate modeling grid (CMG) by a
geographic projection. The detail of MODIS products can be found from MODIS Snow
Products User Guide (Riggs et al. 2003). MODIS snow-cover products represent potential
improvement to or enhancement of the currently available operational products mainly
because the MODIS products are global and 500-m resolution, and have the capability to
separate most snow and clouds. The MODIS snow-mapping algorithms are automated,

4


which means that a consistent data set may be generated for longterm climate studies that
require snow-cover information. MODIS Terra and MODIS Aqua versions of the snow
products are generated. Bias to Terra is because the snow detection algorithm is based on
use of near infrared data at 1.6 µm. A primary key to snow detection is the characteristic of
snow to have high visible reflectance and low reflectance in the near infrared, MODIS band
6. MODIS band 6 (1.6 µm) on Terra is fully functional however, MODIS band 6 on Aqua is
only about 30% functional; 70% of the band 6 detectors non-functional. That situation on
Aqua caused a switch to band 7 (2.1 µm) for snow mapping in the swath level algorithm. In
addition, a fractional snow cover data array has been added to the product from collection 5.

In our study, mapping snow cover in mountainous regions remains an omission
limitation to the MODIS snow products from NSIDC (Hao Xiaohua et al. 2008). The
MODIS snow cover products rely on analysts to fine-tune the maps. So we describe and
validate a method that retrieves snow-covered area in Xinjiang and Qinghai-Tibet Plateau
regions, China by Terra MOD09 surface reflectance data. Develop an improved algorithm
suited for mapping MODIS snow cover and fraction snow cover on Qinghai-Tibet Plateau.

2. Algorithm Description of snow cover
2.1 Introduction
The new snow cover algorithm employs the CIVCO topographic correction, a grouped-
criteria technique using the Normalized Difference Snow Index (NDSI) and other spectral
threshold tests and image fusion technology to identify and classify snow on a pixel-by-
pixel basis. The new algorithm was selected for the following reasons:
(1) The new snow cover algorithm is more accurate than algorithm of NSIDC on Qinghai-
Tibet Plateau.
(2) It corrects the effect of atmospheric and topographic conditions.
(3) It can minimize the limitation of the cloud.
(4) It runs automatically and fast. It is straightforward, computationally frugal, and thus
easy for the user to understand exactly how the product is generated.

Snow has strong visible reflectance and strong short-wave IR absorbing characteristics.
The Normalized Difference Snow Index (NDSI) is an effective way to distinguish snow
from many other surface features. Both sunlit and some shadowed snow is mapped
effectively. A similar index for vegetation, the Normalized Difference Vegetation Index
(NDVI) has been proven to be effective for monitoring global vegetation conditions
throughout the year (Tucker, 1979 and 1986). Additionally, some snow/cloud discrimination
5


is accomplished using the NDSI. Other promising techniques, such as traditional supervised
multispectral classifications, spectral-mixture modeling, or neural-network analyses have
not yet been shown to be usable for automatic application at the global scale. However,
these techniques may progress to regional applications.

2.2 Background and Data
2.2.1 Area of interest

The Qinghai-Tibet Plateau is the highest plateau over the world. It not only had an
important influence on the atmospheric circulation of the northern hemisphere, but also
directly affected the climatic and eco-environmental evolution of China in the Quaternary
period (Huairen and Xin, 1985).The Qinghai-Tibet Plateau is the largest, nonpolar cold
desert in the world, with an average elevation above 4000 m. The presence of snow cover
plays a key role in the cold desert ecosystem by affecting the hydrology, ecology and
climate. Snow cover in Qinghai-Tibet Plateau is highly variable both spatially and
temporally. Thin, discontinuous sheets of snow can occur year round (Zheng et al. 2000). In
the absence of snow, soils are more vulnerable to freezing and potentially decreased rates of
microbial transpiration, which can alter the soil’s ability to sequester carbon. Due to the
remoteness and topographic complexity of the Qinghai-Tibet Plateau, remote sensing offers
the most practical tool for monitoring its snow cover area.

2.2.2 Elevation 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.

2.2.3 MODIS data

In the new algorithm, we rely on MOD09 surface reflectance products (MOD09GA,
MYD09GHK) to get the MODIS snow cover. MOD09 (MODIS Surface Reflectance) is a
seven-band product computed from the MODIS Level 1B land bands 1 (620-670 nm), 2
(841-876 nm), 3 (459-479), 4 (545-565 nm), 5 (1230-1250 nm), 6 (1628-1652 nm), and 7
(2105-2155 nm). MOD is the MODIS/Terra data and MYD is the MODIS/Aqua data. The
product is an estimate of the surface spectral reflectance for each band as it would have been

6


measured at ground level as if there were no atmospheric scattering or absorption. It corrects
for the effects of atmospheric gases, aerosols, and thin cirrus clouds. 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.

2.2.4 Landsat-ETM+ data and analysis

The ETM+ was launched on April 15, 1999, on the Landsat-7 satellite
(http://www.landsat.gsfc.nasa.gov/project/satellite.html). The ETM+ has eight discrete
bands ranging from 0.45 to 12.5 Am, and the spatial resolution ranges from 15 m in the
panchromatic band, to 60 m in the thermal-infrared band. All of the other bands have 30-m
resolution. 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, 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.

7


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.

2.3 Calculation of ground reflectance

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. The imagery available in the MOD09 (MODIS surface
reflectance product) provides measurements of surface reflectance with the atmosphere
correction by ‘6S’ model. However, in rugged terrain and in the case of multi-temporal
dataset these measurements are affected strongly by changes of topographic conditions. Our
research indicates that such variability reduces the identification of snow in shadow. To
getting the true ground reflectance the topography correction of the MOD09 is necessary in
QTP.

The problem of differential terrain illumination on satellite imagery has been
investigated for at least 20 years. At present, there are many methods in terrain correction,
such as physical models, Semi-empirical and empirical models. Although physical models can
be quite successful to eliminate atmospheric and topographic effects they inherently rely on an

8


accurate spectral and radiometric sensor calibration and on the accuracy and appropriate spatial
resolution of a digital elevation model (DEM) in rugged terrain and the computer is complex. The
MODIS data is large quantity. The empirical based approach offers the fast and accurate
correction. 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 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.
9


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.

2.4 Adjust of NDSI

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 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

10


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%

2.5 Additional Algorithms

In forested locations, many snow covered pixels have an NDSI lower than 0.4. 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. The NDSI-NDVI field is
designed to capture as much of the variation in NDSI-NDVI values observed in the snow
covered forests as possible while minimizing inclusion of non-forested pixels. It was
designed to include forestcovered pixels that have NDSI values lower than 0.4, yet have
NDVI values lower than would be expected for snow-free conditions (Klein et al., 1998).
For MODIS data the NDVI is calculated as:

ê éé à

( 4)

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).

The NDSI can separate snow from most obscuring clouds, it does not always identify or

11


discriminate optically-thin cirrus clouds from snow. Clouds are masked by using the
MODIS cloud masking data product (MOD35).

One of the problems facing the MODIS snow-mapping algorithm is the mapping of
snow in regions where it is known not to exist. One of the more common locations for this
problem is in dark, dense forests, particularly in the tropics. The nature of the snow-
mapping algorithm is such that it is particularly sensitive to small changes in the NDSI or
NDVI over dark, dense vegetation. To correct false-snow mappings in tropical forests, the
MODIS temperature mask product (MOD11) was used to improve the accuracy of snow
cover map. A tentative threshold of 277 K has been set. When this threshold is applied in
tropical regions, e.g., the Congo, it eliminates from 93% to 98% of the false snow (Barton,
et al. 2001).

2.6 Image fusion

MODIS cloud masking data product was used to map MODIS snow cover product.
Nevertheless, inaccurate detection of clouds in the MOD35 cloud mask product revealed to
be problematic in high-elevation regions such as the QTP, China (Hall et al. 2002). The
Collection 5 of the MODIS snow products has been infused and expanded with information
regarding characteristics and quality of snow products at each level. It improves the cloud
mask product, thus permitting more snow covet to be mapped. However, the accurate
monitoring of SCA using optical imagery of high spatial resolution is seriously reduced by
cloud cover due to the similar reflective nature of snow and clouds. 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

12


compositing technique also minimizes cloud cover. The figure 2 shows the flow process of our
new MODIS snow cover map algorithm.

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.

2.7 Backup Algorithm
Future enhancements to MODIS snow cover maps include improving snow cover
resolution, fusing the polygenetic remote sensing data and producing more abundant applied
snow products.

3. Algorithm Description of fractional snow cover
The work are doing.

4. Validation Plan
4.1 Introduction
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

13


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
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.

4.2 Approach
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.

4.3 Validation Sites
QTP-Naqu. Lake Namtso.

4.4 Auxiliary Measurements
Snow density, snow water liquid, snow grain size, snow temperature and snow pit works.

4.5 Scaling

14


5. Ancillary Data
DEM data , snow depth from climate stations.

6. Programming and Procedural Considerations
6.1 Programming Issues
The difficulty in establishing the accuracy of any of these maps is that it is not known
which map is the ‘‘truth’’ (if any) and the techniques used to map snow cover in the various
maps are different, resulting in different products.

6.2 Processing Issues
6.3 Quality Assurance

References
Barton, J.S., D.K. Hall and G.A. Riggs, unpublished document, 2001: Thermal and geometric thresholds in the
mapping of snow with MODIS, July 11, 2001.
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.
Chang, A.T.C., J.L. Foster and D.K. Hall. Microwave snow signatures (1.5 mm to 3 cm) over Alaska, Cold
Regions Science and Technology. 1987, 13:153-160.
Civco D L. Topographic Normalization of Landsat Thematic Mapper Digital Imagery[J]. Photogrammetric
Engineering and Remote Sensing. 1989, 55(9): 1303-1309.
Dozier J, Schneider S R, McGinnis J D F. Effect of grain size and snowpack water equivalence on visible and
near-infrared satellite observations of snow[J]. Water Resources Research.1981,17(4): 1213-1221.
Dozier, J. Snow reflectance from Landsat-4 thematic mapper. I.E.E.E. Transactions on Geoscience and
Remote Sensing, 1984,22: 323-328.
Dozier, J. Spectral signature of alpine snow cover from the Landsat Thematic Mapper, Remote Sensing of
Environment. 1989, 28: 9-22.
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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.
Foster, J.L., A.T.C. Chang. Snow cover. In Atlas of Satellite Observations Related to Global Change R.J.
Gurney, C.L. Parkinson, and J.L. Foster (eds.), Cambridge University Press, Cambridge. 1993: 361-370.
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.
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Quaternary geology, Geological Publishing House, P.R. China,1985,2:135–144.
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.
Matson, M., C.F. Ropelewski and M.S. Varnadore. An atlas of satellitederived northern hemisphere snow
cover frequency, National Weather Service, Washington, D.C. 1986, 75 pp.
Matson, M.. NOAA satellite snow cover data, Palaeogeography and Palaeoecology. 1991, 90: 213-218.
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.
Ramsay, B. The interactive multisensor snow and ice mapping system. Hydrological Processes. 1998,
12:1537-1546.
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the 57th Eastern Snow Conference, Syracuse, NY, East Snow Conference. 2000: 161-170.
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Symposium on Hydrological Aspects of Alpine and High Mountain Areas, IAHS Publication.
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Salomonson V V, Guenther B, Masuoka, E A. A summary of the status of the EOS Terra Misson MODIS and
attendant data product development after one year of on-orbit performance. In: Proceedings of the

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International Geoscience and Remote Sensing Symposium/IGARSS’2001, Sydney, Australia, 9-13 July, 2001.
Singer, F.S. and R.W. Popham. Non-meteorological observations from weather satellites, Astronautics and
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watershed during the snow melt season, Nordic Hydrology. 1992, 23: 273-290.

17

PART II

Snow Water Equivalent Retrieval Algorithm

Authors: Tao Che



Snow Water Equivalent Retrieval Algorithm

Abstract
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. The
snow depth distribution is indirectly validated by MODIS snow cover products by
comparing the snow extent area from this work. The final snow depth datasets from 1978 to
2006 show that the inter-annual snow depth variation is very significant. The spatial and
temporal distribution of snow depth is illustrated and discussed, including the steady snow
cover regions in China and snow mass trend in these regions. Though the area extent of
seasonal snow cover in the Northern Hemisphere indicates a weak decrease in a long time
period, there is no clear trend in change of snow cover area extent in China. However, snow
mass over the Qinghai-Tibet Plateau and Northwestern China has increased, while it has
weakly decreased in Northeastern China. Overall, snow depth in China during the past three
decades shows significant inter-annual variations with a weak increasing trend.

1. Introduction

1.1 Identification

Snow plays an important role at the climatic system due to its high surface albedo and
heat insulation effect which influences energy exchange between the land surface and the
atmosphere. It also influences the hydrological processes though snow water storage and
release. To obtain the large scale and long time period snow depth datasets, the passive
microwave remote sensing data (e.g. SMMR and SSM/I) have shown their capability in the
past three decades (Armstrong and Brodzik, 2002). The deeper the snowpack, the more
snow crystals are available to scatter microwave energy away from the sensor. Hence,
microwave brightness temperatures are generally lower for deep snowpack while they are

19


higher for shallow snowpack (Chang and others, 1987). Based on this fact, both snow depth
and snow water equivalent retrieval algorithms were developed by using brightness
temperature difference between 18 and 37 GHz (spectral gradient, e.g. Chang and others,
1987). With the utility of the Chang algorithm in the global scale, it was shown that a single
algorithm cannot describe all kinds of snow conditions (Foster and others, 1997). Regional
algorithms to retrieve snow depth have been developed in the past decade for North
America and Eurasia snowpack (Foster and others, 1997; Tait, 1998; Kelly and others,
2003).

1.2 Overview
In fact, the global snow depth retrieval algorithms overestimate snow depth in China
according to the records of meteorological station observations (Chang and others, 1992).
Snow depth retrieved from passive microwave remote sensing data can be influenced by the
condition of snowpacks, such as snow crystal (England, 1975; Chang and others, 1976;
Foster and others, 1997), snow density (Wiesmann and Matzler, 1999; Foster and others,
2005), and vegetation (Foster and others, 1997). Tait (1998) reported the different
algorithms for different snow features. For this reason, it is necessary to develop an
algorithm favorable to snow depth study in China.

It is reported that snow grain size and density determine the coefficient of spectral
gradient for snow depth retrieval. For example, using the Chang algorithm with a grain size
of 0.3 mm, the coefficient is 1.59, and with a grain size of 0.40 mm, the coefficient becomes
0.78 (Foster and others, 1997). Josberger and Mognard (2002) reported that while the
snowpack was constant, the spectral gradient continued to increase with time due to the
metamorphism of snow. Larger snow grains cause increased microwave scattering with the
result that an algorithm based on a fixed value for grain size will tend to overestimate snow
depth. (Armstrong and others, 1993). So, the spectral gradient will increase with the time
lapses due to the grouping snow grain size and snow density.

Liquid water content in snow layer (Ulaby and others, 1986; Matzler, 1994) and large
water bodies (Dong, 2005) can also lead to large errors in retrieving snow water equivalent.
These two factors should be considered before the linear regression for the coefficient
modification as in the Chang algorithm. Microwave radiation will not determine snow depth
accurately when snow is wet (Matzler, 1994). The dry snow and wet snow criteria were

20


used to discriminate the wet snow brightness temperature data, while the lake and land-sea
boundary were collected for removing the meteorological stations that near to the large
water body. After the work of Neale and others (1990), the NOAA-NASA SSM/I Pathfinder
(NNSP) program also uses SSM/I data to derive land surface classifications and to establish
criteria of dry snow and wet snow (Singh and Gan, 2000).

Grody (1991) reported it was necessary to remove the rain signal to identify snow cover.
When it is raining, snow parameters may not be retrieved. For obtaining the long-time series
dataset of snow depth, the Grody’s decision tree method based on the passive microwave
remote sensing data can be adopted so that the snow depth retrieval algorithm only is
focused on the snow pixels.

In this study, we will modify the Chang snow algorithm to make it suitable for snow
depth retrieval in China using SMMR and SSM/I remote sensing data and snow depth data
recorded at the China national meteorological stations. We will further analyze the accuracy
and uncertainty of the new snow product produced from the modified Chang algorithm. The
daily snow depth datasets in China from 1978/1979 to 2005/2006 will be produced, and
their spatial and temporal characteristics will be analyzed.

2. Algorithm Description

2.1 Introduction

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.

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

21


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 are
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)
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 1 and 2).

22


Figure 1. 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 2 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).
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

23


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

Figure 3 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

2.2 Theoretical Basis of the Algorithm

To obtain the large scale and longtime period snow depth datasets, the passive
microwave remote sensing data (e.g. SMMR and SSM/I) have shown their capability in the
past three decades (Armstrong and Brodzik, 2002). The deeper the snowpacks, the more
snow crystals are available to scatter microwave energy away from the sensor. Hence,
microwave brightness temperatures are generally lower for deep snowpacks while they are
higher for shallow snowpacks (Chang and others, 1987). Based on this fact, both snow
depth and snow water equivalent retrieval algorithms were developed by using brightness
temperature difference between 18 and 37 GHz (spectral gradient, e.g. Chang and others,
1987). With the utility of the Chang algorithm in the global scale, it was shown that a single
algorithm cannot describe all kinds of snow conditions (Foster and others, 1997). Regional
algorithms to retrieve snow depth have been developed in the past decade for North
America and Eurasia snowpacks (Foster and others, 1997; Tait, 1998; Kelly and others,
2003).

24


2.3 Description of Retrieval Concept

2.4 Description of Retrieval Algorithm

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
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

25


daily snow depth dataset, the intervals between swaths were filled up by the most recent
data available.


3. Algorithm Prototyping

3.1 Data Analysis

Passive microwave remote sensing data

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 4). 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.

MODIS snow cover area products

Hall and others (2002) described the Moderate Resolution Imaging Spectroradiometer
(MODIS) snow cover area algorithm for the EOS Terra satellite. At present, the MODIS

26


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.

27


Figure 4. Position of meteorological stations within main snow cover regions in China (NWC:
Northwestern China, QTP: Qinghai-Tibet Plateau, NEC: Northeastern China, and other region).

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

3.2 Prototyping of the Algorithm
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.
28


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

4. Validation Plan

4.1 Introduction

The validation used meteorological observations data, considering the influences from
vegetation, wet snow, precipitation, cold desert and frozen ground. The snow depth
distribution is indirectly validated by MODIS snow cover products by comparing the snow
extent area from this work.

4.2 Approach

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.

Accuracy assessment (Snow cover)

29


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
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):

(4)
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)).

30


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.

Uncertainty

Effect of Vegetation
Vegetation cover has a significant influence on snow depth estimation from remote
sensing data (Foster and others, 1997, 2005). In this study, we used the forest cover
parameter to remove this influence (Foster and others, 1997). In fact, this method is not
appropriate out for dense forest regions. We overlap the stable snow cover map with the
Chinese Vegetation Map and find dense forests with a large forest cover fraction (greater
0.5) mainly distribute in the Xing’aling regions (Heilongjiang Province and the Eastern Inter
Mongolia) with about 160 EASE-Grid pixels (100,000km2). Although snow depth derived
from the modified algorithm may be questionable, the total area of the dense forest regions
is very limited.

Effect of Snow Crystal

31


The snow grain size can influence the algorithm coefficient of snow depth retrieval (e.g.
formula (1) and (2)). With a snow grain size of 0.3mm the coefficient is 1.59, but with a
snow grain size of 0.4mm the coefficient becomes 0.78 (Foster and others, 1997). Snow
crystal size can depend on the snowfall condition, such as the wind and temperature. It also
varies with snow metamorphism after the snow is on the ground. In this study, we
characterized this influence using a statistical regression method and adjusted the seasonal
offsets. These offsets can not interpret the regional differences of snow conditions.

Effect of Liquid Water Content

The snow depth can not be retrieved when snow is wet because the liquid water within
snow layer will remove the volume scatter of microwave signals. Therefore, only morning
brightness temperature data were used to minimize the errors associated with melting snow
in the afternoon.

4.3 Validation Sites
The specific validation sites still under-investigation which will be presented in later
vrsion

4.4 Auxiliary Measurements

Still under-investigation which will be presented in later version

4.5 Scaling


4.6 Data Protocols and Dissemination

4.7 Proposed Validation Tests


5. Ancillary Data

32


The ancillary data need in this algorithm is: meteorological station snow depth
observations, MODIS snow cover area products, vegetation distribution map in China and
lake distribution map/Land-sea boundary. Detailed information for each dataset can be fund
in Section 3.1 Data Analysis

6. Programming and Procedural Considerations
The whole part still under-investigation which will be presented in later version

6.1 Programming Issues
6.2 Processing Issues
6.3 Quality Assurance

References

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grain-size relationships with relevance for passive microwave studies, Ann. Glaciol.,
17, 171–176.

2. Armstrong, R. L., K. W. Knowles, M. J. Brodzik and M. A. Hardman. 1994, updated
current year. DMSP SSM/I Pathfinder daily EASE-Grid brightness temperatures, [list
dates of data used]. Boulder, Colorado USA: National Snow and Ice Data Center.
Digital media

3. Armstrong, R.L., and M.J. Brodzik. 2002. Hemispheric-scale comparison and
evaluation of passive-microwave snow algorithms. Ann. Glaciol,. 34, 38-44.
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Microwave emission from snow and glacier ice. J. Glaciol., 16, 23-39.

5. Chang, A. T. C., J. L. Foster, and D. K. Hall. 1987. Nibus-7 SMMR derived global snow
cover parameters. Ann. Glaciol,. 9, 39-44.

6. Chang, A. T. C., D. A. Robinson, L. Peiji, and C. Meisheng. 1992. The use of
microwave radiometer data for characterizing snow storage in western China. Ann.

33


Glaciol., 16, 215-219.

7. Congalton, R. 1991. A review of assessing the accuracy of classification of remotely
sensed data. Remote Sens. Environ,.37, 35-46,.

8. Dong, J. R., J. P.Walker, and P. R. Houser. 2005. Factors affecting remotely sensed
snow water equivalent uncertainty. Remote Sens. Environ, 97, 68-82.

9. England, A.W. 1975. Thermal microwave emission from a scattering layer. J. Geophys.
Res., 80 (32), 4484-4496.

10. Foster, J. L., A. T. C. Chang, and D. K. Hall, 1997. Comparison snow mass estimates
from a prototype passive microwave snow algorithm, a revised algorithm and snow
depth climatology. Remote Sens. Environ. 62, 132-142, 1997.

11. Foster, J.L., C.J. Sun, J.P. Walker, R. Kelly, A.C.T. Chang, J.R. Dong, H. Powell. 2005.
Quantifying the uncertainty in passive microwave snow water equivalent observations.
Remote Sens. Environ. 94, 187-203.

12. Grody, N C. 1991. Classification of snow cover and precipitation using the Special
Sensor Microwave Imager. J. Geophys. Res., 96, 7423-7435.

13. Grody, N. C., and A. N. Basist. 1996. Global identification of snowcover using SSM/I
measurements. IEEE Trans. Geosci. Remote Sensing.34, 237-249.

14. Hall, D. K., G. A. Riggs, V. V. Salomonson, N. E. DiGirolamo, and K. J. Bayr. 2002.
MODIS snow-cover products. Remote Sens. Environ.83, 181-194.

15. Hu, X. Y. 2001. The Vegetation Atlas of China (1:1,000,000). Beijing: Science press.

16. Josberger, E. G., and Mognard, N. M. 2002. A passive microwave snow depth
algorithm with a proxy for snow metamorphism. Hydrological Processes, 16(8), 1557-
1568.

17. Kelly, R.E., A.C.T. Chang, and T. Leung T. 2003. A prototype AMSR-E global snow
area and snow depth algorithm. IEEE Trans. Geosci. Remote Sens., 41(2), 230-242.

18. Knowles, K., E. Njoku, R. Armstrong, and M.J. Brodzik. 2002. Nimbus-7 SMMR
Pathfinder daily EASE-Grid brightness temperatures. Boulder, CO: National Snow and

34


Ice Data Center. Digital media and CD-ROM.

19. Li, P. J. and D. S. Mi. 1983. Distribution of snow cover in China. Journal of glaciology
and cryopedology, 5(4), 9-18. (In Chinese)

20. Matzler, C. 1994. Passive microwave signatures of landscapes in winter. Meteorol.
Atmos. Phys. 54, 241–260.

21. Neale, C. M. U., M. L. McFarland, and K. Chang. 1990. Land-surface-type
classification using microwave brightness temperatures from the special sensor
microwave/imager. IEEE Trans. Geosci. Remote Sens. 28(5), 829-837.
22. Qin, D., S. Liu, and P. Li. 2006. Snow cover distribution, variability, and response to
climate change in Western China. J. Climate, 19(9), 1820-1833.

23. Rikiishi, K. and N. Nakasato. 2006. Height dependence of the tendency for reduction in
seasonal snow cover in the Himalaya and the Tibetan Plateau region, 1966-2001. Ann.
Glaciol., 43, 369-377.

24. Singh, P. R., and T. Y. Gan. 2000. Retrieval of snow water equivalent using passive
microwave brightness temperature data. Remote Sens. Environ, 74, 275-286.

25. Tait, A.B. 1998. Estimation of snow water equivalent using passive microwave
radiation data. Remote Sens. Environ.64, 286-291.

26. Ulaby, F., R.Moore, , and A. Fung. 1986. Microwave Remote Sensing, Artech House,
Dedham, MA, Vol. III, 1602-1634.

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Remote Sens. Environ, 70, 307-316.

35


PART III

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

Abstract

A new decision tree algorithm to classify the surface soil freeze/thaw states has been
developed. 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.

1. Introduction

Globally, about 50&106 km2 of surface soil undergoes freeze/thaw cycles annually
(Kimball et al., 2001; Zhang et al., 2003a). The soil freeze/thaw status has a profound
influence on the energy and water exchange between the land surface and the atmosphere,
the hydrological cycle, crop growth, and the carbon cycle (Cao & Chang, 1997; Goodison et
al., 1998; Judge et al., 1997; Zhang & Armstrong, 2001; Zuerndorfer et al., 1990;
Zuerndorfer & England, 1992). The timing, duration, and area of surface soil freeze/thaw
status can be taken as an indicator of climate change because of its sensitivity (Goodison et
al., 1998; Li et al., 2008; Zhang & Armstrong, 2001; Zhang et al., 2003b).

A new decision tree algorithm was developed to classify the soil freeze/thaw state with
SSM/I data. New indices are introduced, and the discrimination of the desert and
precipitation from frozen soil is considered. 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

37


processes, and general circulation models (Allison et al., 2001; Jin & Li, 2002; Judge et al.,
1997; Zhang & Armstrong, 2001; Zuerndorfer et al., 1990).

1.1 Identification

1.2 Overview

Many studies were published during the 1980s and 1990s on detecting the surface soil
freeze/thaw state using passive microwave radiometers such as SMMR and SSM/I. There
are two major types of near-surface soil freeze/thaw states classification algorithm
comprising the dual-indexes algorithm (Zuerndorfer et al., 1990; Zuerndorfer et al., 1992;
Judge et al., 1997; Zhang and Armstrong, 2001; Zhang et al., 2003), and change detection
algorithm (Smith et al., 2004). All above algorithms were based on the unique microwave
radiative characteristics associated with frozen soils, such as lower thermo-dynamical
temperature, higher emissivity and volume scattering darkening effect.

(1) Dual-indexes Algorithm

The dual-indexes algorithm using T37 brightness temperature and the spectral gradient
(SG) between T37 and T18/T19 was most widely used in 1990s. The dual-index algorithm
was easily for the operational application with the unified thresholds throughout the
research region, however the thresholds of both indices were determined through a
statistical analysis of training samples, which need to be recalibrated when applied in other
regions (Jin and Li, 2002).

(2) Change Detection Algorithm

The change detection algorithm for surface soil freeze/thaw states classification was
originated from the active microwave remote sensing based on the time series of the
backscattering coefficient. Smith developed an algorithm applicable to passive microwave
remote sensing (Smith et al., 2004) by using the difference between the brightness
temperature at 37 and 19 (or 18) GHz to identify the transition from frozen to thawed soil.
However, the gradual process of soil temperature with freezing, the coarse spatial resolution
of the passive microwave radiometers, and the opposite effect of increased emissivity and
decreased thermal temperature of frozen soil on the brightness temperature may resulted in
no abrupt changes in brightness temperature or spectral signals at the daily scale.

38


Furthermore, both of above algorithms only separate frozen and thawed soil. The
desert in the winter season and snow were both easily misclassified as frozen soil because of
their similar volumetric scattering characteristics (Fiore Jr & Grody, 1992; Cao & Chang,
1997). In addition, precipitation may mask the radiation emitted from the land surface
(Grody & Basist, 1996). Therefore, it is necessary to distinguish these types to improve the
classification accuracy of frozen/thawed soil.

2. Algorithm Description

2.1 Introduction

A new decision tree algorithm was developed to classify the soil freeze/thaw state with
SSM/I data. New indices, i.e. scattering index, polarization difference, are introduced, and
the discrimination of the desert and precipitation from frozen soil is considered, which will
improve the classification accuracy of the surface soil freeze/thaw states.

2.2 Targets to be observed

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
(Grody & Basist, 1996), so it was adopted directly to identify precipitation.

2.3 Radiative Transfer Problem

The soil brightness temperature Tb can be simply expressed as the product of the soil
effective temperature Teff and the emissivity e if we consider the soil as a semi-infinite
medium (Ulaby et al., 1986). When the soil freezes, its thermodynamic temperature
decreases, but the emissivity increases due to the decreased permittivity. Therefore, the
change in radiobrightness may be either positive or negative, mainly depending on the soil
moisture (Zuerndorfer et al., 1990; Zuerndorfer & England, 1992). For dry soil, the soil
emissivity changes little between the thawed and frozen states, so the brightness temperature
generally decreases with soil temperature. For moist soil, the emissivity increases
significantly when it changes from the thawed to the frozen state, but the Teff may only

39


drop a few Kelvin, so the Tb may increase (Dobson et al., 1985; Jin & Li, 2002; Zuerndorfer
et al., 1990). According to the above analysis, although the brightness temperature of frozen
soil is low, the brightness temperature cannot be taken as an unambiguous index to identify
the soil freeze/thaw status (Zuerndorfer et al., 1990). Moreover, the brightness temperature
of moist regions near rivers and lakes is also low because of abundant moisture and the
corresponding lower emissivity, which may cause confusion in distinguishing between
frozen soil and very moist soil when using the brightness temperature alone (England, 1990).

Both the permittivity and the dielectric loss factor decrease with soil freezing (Hoekstra
et al., 1974). The dielectric loss factor is reduced more than the permittivity, resulting in a
decrease of the loss tangent ( ), which means that the emission depth will be
greater and there will be volume scattering. The effective emission depth Ze (1-e-1 of the
total emission in the zenith direction originates above Ze) is about 10% of the free space
wavelength in moist soil, and increases to more than 30% of the free space wavelength
when the soil is frozen (Zuerndorfer et al., 1990). The higher the microwave frequency the
more heterogeneous the soil column is, and the stronger the scattering volume will be (Cao
& Chang, 1997; England et al., 1991; Zuerndorfer et al., 1990). The brightness temperature
of frozen soil at high frequencies is therefore generally lower than that at low frequencies.

In summary, the microwave emissions and scattering characteristics have several
differences between frozen and thawed soil, such as a lower thermodynamic temperature
and brightness temperature, a higher emissivity, and a stronger volume scatter darkening
effect that can be used to select proper indices to identify the soil freeze/thaw state.

2.4 Mathematical Basis of the Algorithm

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)

40


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.

(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.

2.5 Description of Retrieval Concept
2.6 Description of Retrieval Algorithm

3. Algorithm Prototyping

3.1 Data Analysis

3.1.1 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.

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

41

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