IMPROVEMENT OF MODIS SNOW COVER ALGORITHM FOR THE HINDU KUSH-HIMALAYAN REGION

Bo-Hui Tanga,* , Shrestha Basantab, Zhao-Liang Lia, c, Gaohuan Liu a, Hua Ouyang a,b,Gurung Deo Rajb, Amarnath Girirajb, and Aung Khun Sanb

a. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,Beijing, 100101, China.

b. International Centre for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal.c. LSIIT, UdS, CNRS, Bld Sebastien Brant, BP10413, 67412 Illkirch, France.* Authors to whom correspondence should be addressed: tangbh@igsnrr.ac.cn

ABSTRACT

This work aimed to refine the Moderate Resolution Imaging Spectroradiometer (MODIS) based snow cover algorithm for the Hindu Kush-Himalayan (HKH) region. Taking into account the effect of the atmosphere and terrain on the satellite observations at the top of the atmosphere (TOA), particularly in heavily rugged Tibet plateau region, the surface reflectances were retrieved from the TOA reflectances after atmospheric and topographic corrections.To reduce the effects of the snow/cloud confusion, a normalized difference cloud index (NDCI) model wasproposed to discriminate snow/cloud pixels, apart from use of the MODIS cloud mask product MOD35. Furthermore, MODIS land surface temperature (LST) product MOD11_L2 have been used to ensure better accuracy of the snow cover pixels. Comparisons of the resultant MODIS snow cover with those obtained respectively from high resolution Landsat ETM+ data and the MODIS snow cover product MOD10_L2 for the Mount Everest region at different seasons, showed overestimation of the MOD10_L2snow cover with the differences of 50%, whereas the improved algorithm can estimate the snow cover for HKH region more precisely with absolute accuracy of 90%.

Index Terms— Snow cover, NDSI, Atmospheric correction, Topographic correction, MODIS

1. INTRODUCTION

MODIS is a passive imaging spectroradiometer, with 36 spectral bands and nominal spatial resolution of 250 m in 2 bands, 500m in 5 bands, and 1 km in 29 bands, which covers the visible and infrared regions from approximately 0.4 to 14.0 m [1]. The MODIS instrument is operational on two Earth Observing System (EOS) spacecraft. The Terra mission was launched with a MODIS instrument in

December 1999 and observations are provided from February 24, 2000, to the present. The Aqua mission was launched in May of 2002 and observations are providedfrom June 24, 2002 to the present.

The MODIS snow and sea ice products (http://modissnow- ice.gsfc.nasa.gov), available globally, are provided at a variety of different resolutions and projections to serve different user groups [2-4], and are distributed through the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado [5]. The snow maps are available at 500 m resolution on a sinusoidal projection, and at 0.05° and 0.25° resolution on a latitude/longitude grid known as the climate-modelling grid (CMG).

It is worthy to note that the algorithm developed by the NSIDC employs at-satellite reflectances in the normalized difference snow index (NDSI) and other thresholds, and the effects of atmosphere and terrain as well as viewing angle have not been corrected for, which may cause some significant errors for estimating snow cover in mountainous areas, especially in heavily rugged like Tibet plateau region. In addition, as pointed out by [6], snow/cloud discrimination is the most significant factor affecting snow detection error. Snow/cloud confusion errors are typically associated with cloud-shadowed land and thin, sparse snow cover. The errormay be caused either by identifying cloud as snow if the cloud is not identified as definite cloud in the cloud mask or, more commonly by missing snow possibly because thin, sparse snow cover was identified as cloud in the cloud mask.

To this end, this work focuses on improving MODIS snow cover estimation algorithm, particularly in the mountainous area of HKH region. Section 2 describes the methodology and issues related to the estimate of snow cover for the HKH region. Section 3 gives some results of the snow cover map derived from MODIS level_1B data for the HKH region, and some preliminary validations with the MODIS snow cover product MOD10_L2 and the Enhanced Landsat Thematic Mapper-plus (ETM+) data. The conclusion is drawn in section 4.

1737978-1-4244-9566-5/10/$26.00 ©2010 IEEE IGARSS 2010

2. METHODOLOGY

2.1. Atmospheric correction

It is well known that the reflectance derived from radiance measured by sensors at TOA may be increased or decreased when compared to the surface reflectance as a function of the reflectance of the target and of its environment, sensor spectral band, viewing and solar geometry, and atmospheric characteristics. Note that the snow cover algorithm developed by NSIDC uses the TOA reflectances in the Normalized Difference Snow Index (NDSI) and other thresholds, the effects of atmosphere have not been corrected for, which may cause some significant errors for estimating snow cover in mountainous areas, especially in heavily rugged like Tibet plateau region. In this work, anupdated Simplified Method for the Atmospheric Correction (SMAC) model [7] is used to perform the atmospheric correction.

2.2. Topographic correction

The topographic correction of remotely sensed imagery over mountain regions is as important as atmospheric correction. Remote sensing plays a unique role in mountain studies. However, remotely sensed data over mountainous area are contaminated by topographic shading and shadowing, which are not desirable for land surface characterization. The NDSI ratio algorithm can eliminate topographic effect partly.But it cannot produce satisfactory results particularly in heavily rugged region since the radiometric variations caused by topography are wavelength-dependent and their differences at different channels are not simply increased or decreased by a constant.

Taking into account MODIS imagery with 500m coarse resolution and the required computational effort as well as the poor quality of existing Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data with 90 m resolution, the Hapke shadowing function [8] is used to reduce the effect of topography in the Tibet plateau region for estimating the snow cover in this work.

2.3. Determination of snow cover

After atmospheric and topographic corrections, the true surface reflectances for the MODIS data are obtained. The NDSI, defined as the difference of reflectances observed in a visible band such as MODIS band 4 (0.545-0.565 m) and a short-wave infrared band such as MODIS band 6 (1.628-1.652 m) divided by the sum of the two reflectances,is then calculated using equation (1).

4 6

4 6

b bNDSI

b b (1)

Note that clouds typically have high reflectances in

visible and near-infrared wavelengths, while reflectance of snow decreases in shortwave infrared wavelengths. To reduce the effects of the snow/cloud confusion, a NDCImodel in terms of MODIS bands 1 (0.620–0.670 m) and 6 (1.628–1.652 m) has been proposed to discriminate snow/cloud pixels as expressed in equation (2), apart from the use of the MODIS cloud mask product MOD35.

1 6

1 6

b bNDCI

b b (2)

The mapping of snow cover becomes limited in areas where snow cover is obscured by dense forest canopies. A forested landscape is never completely snow-covered because tree branches, trunks, and canopies may not be covered with snow. Often, in boreal forests, snow that falls on the coniferous tree canopy will not stay on the canopy for the entire winter because of sublimation. Thus, even in a continuously snow-covered area, much of the forested landscape will not be snow-covered. Furthermore, snow that falls onto the ground through the canopy may not be visible from above. Note that a significant difference exists between the high reflectance of snow and the low reflectance of soil, leaves, and bark. In addition, reflectance in the visible spectrum like MODIS band 1 (0.620–0.670 m) often increases with respect to the near-infrared reflectance like MODIS band 2 (0.841–0.876 m). Consequently, in order to give special considerations for dense forests, the Normalized Difference Vegetation Index (NDVI) as equation (3) is calculated as a complement to discriminate between snow-free and snow- covered forests.

2 1

2 1

b bNDVI

b b (3)

In addition, to eliminate much of the spurious snow cover, confused with cloud cover, aerosol effects and snow/sand confusion on coastlines and etc., the MODIS LST product MOD11_L2 is also used as a “thermal mask”to ensure the discrimination accuracy of the snow cover pixels. If the temperature of a pixel is larger than 283 K, then the pixel will not be mapped as snow.

After calculations of NDSI, NDCI, and NDVI, we can determine the snow cover pixels according to these three values and other distinct criteria. Taking into account that the 0.4 NDSI thresholds is not well suited to the detection of snow in mountain forests, as many snow covered forests have a NDSI value below 0.4. The value of NDSI is adjusted to 0.3 for HKH region. Note that reflectances used are the atmospheric and topographic corrected reflectances, to prevent pixels containing very dark targets such as fir-spruce forests, the threshold values of the surface reflectances in MODIS bands 2 (0.841–0.876 m) and 4 (0.545–0.565 m) are adjusted to >9%. A pixel in a non-densely forested region will be mapped as snow if the NDSI is 3 and reflectances in MODIS bands 2 and 4 are >9%, and LST<283 K. If a pixel in a densely forested region, the NDVI and NDSI are used together to determine

1738

it.

3. RESULTS AND VALIDATIONS

3.1. Study region

The HKH region with the latitude ranging from 15.96° N to 39.32° N and the longitude ranging from 60.85° E to 105.04° E, is the youngest, highest, and one of the most fragile mountain systems in the world. It extends 3,500 km over all or part of eight countries from Afghanistan in the west to Myanmar in the east with total estimated area of 3,441,719 km2 (http://www.icimod.org/index.php?page=43).Known as the ‘water tower’ of Asia, the region boasts the largest concentration of snow and glaciers outside the Polar Regions and contains the headwaters of the 10 largest river basins in Asia, which provide water to 1.3 billion people, a fifth of the world’s population. The HKH region is a reservoir of biodiversity including all or part of four Global Biodiversity Hotspots and provides the basis for livelihoods to a population of around 210.53 million people.

3.2. Preprocessing of satellite data

Image processing is conducted using interactive data language (IDL) platform, which is developed particularly for the HKH region and is currently operational to produce snow cover mapping for this region. On the basis of the clear-sky confidence level (clear, probably clear, uncertain, cloudy) assigned to each pixel in the MODIS cloud mask product, MOD35_L2, the cloudy pixels are then screened out in the first step of the process for the MODSI level_1B data. In addition, the pixels with the value of NDCI ranging from 0.1 to 0.5 and the values of apparent reflectance in band 6 larger than 0.4 are also screened out.

3.3. Mapping of snow cover for HKH region

This work aims to improve the MODIS snow cover algorithm and to accurately map the snow cover for the HKH region. In the first step, all the MODIS satellite data are geo-referenced and mosaicked to the same project, and then are clipped with the HKH boundary file. The true surface reflectances are derived from the TOA apparent reflectances by performing atmospheric and topographic corrections with 90m resolution SRTM DEM data. The slope and aspect are calculated by using this DEM data and then are linearly interpolated to 500m resolution to match the MODIS level-1B data. The values of NDSI, NDCI, and NDVI are calculated for each pixel of the imagery. The snow cover pixel is then determined by different criteria. Figure 1 shows, as an example, the snow cover mappingestimated by the proposed algorithm for the whole HKH region with 500m resolution on May 13, 2002. The white colored areas are snow cover, the green colored areas are snow free land, and the grey colored areas is clouds.

Figure 1. Mapping of snow cover using the proposed algorithm for the HKH region from MODIS level_1B data with 500m resolution on May 13, 2002.

3.4. Preliminary validation of resultant snow cover

In order to validate the proposed algorithm for HKH region, we first estimated the snow cover for Bhutan at different seasons in 2002. Figure 2 shows one of the comparisons between MODIS snow cover product and the resultant snow cover estimated by proposed algorithm for January 5, 2002. Figure 2(a) is a true color composite (TCC) of MODIS bands 1, 4, and 3. Figure 2(b) and 2(c) are the MODIS snow cover imageries with 500m resolution estimated using the proposed algorithm and obtained from MODIS product MOD10_L2, respectively. From these imageries we can see explicitly that NSIDC maps many pixels as snow covers in southern Bhutan which does not exist in TCC.

(a) (b)

(c)Figure 2. Comparison of snow cover maps for Bhutan for January 5, 2002 (a: composited image by MODIS bands 1, 4 and 3; b: snow cover map estimated using the proposed algorithm; c: snow cover map obtained from MODIS product MOD10_L2).

In order to further validate the proposed algorithm, we also select the Mount Everest region as study area. Several Landsat imageries with 30-m resolution at different seasons have been used to estimate snow cover, which is

1739

subsequently compared with the MODIS snow cover product MOD10_L2 and the resultant MODIS snow cover estimated by the proposed algorithm. Figure 3 shows, as anexample, the comparison of estimated snow cover using the proposed algorithm with the snow covers obtained from ETM+ imageries and MODIS snow cover product MOD10_L2, respectively, for January 5, 2002.

(a) (b)

(c)Figure 3. Comparison of snow cover maps (a: obtained from ETM+ data; b: estimated using the proposed algorithm; c: obtained from MOD10_L2 product).

Table 1 gives the statistics of the snow cover areas derived from different satellite data for this study site fordifferent seasons in 2002. Considering the ETM+ snow cover as “true” values, from this table, we can see clearly that NSIDC’s snow cover algorithm overestimates the snow cover areas for the Mount Everest region at worst larger than 50%, whereas the improved algorithm can estimate the snow cover for HKH region more feasible with absolute accuracy of 90%. So it was concluded that the proposed algorithmused in this study area, can estimate the snow cover more accurately.

Table 1. Statistics of snow cover area obtained from different methods for Mount Everest region for different seasons in 2002.

Date ETM+ (km2)

Estimated(km2)

Absolute error (%)

MOD10_L2 (km2)

Absolute error (%)

Jan. 5 596.62 543.00 8.9 760.00 27.4

Apr. 11 1201.70 1310.75 9.1 1871.25 55.7

May 13 1464.62 1307.50 10.7 1758.75 20.1

Oct. 04 1480.97 1329.50 10.2 1807.75 22.1

4. CONCLUSION

In this work, a refinement of the MODIS based snow cover algorithm for the HKH region has been proposed. The results of comparing the snow cover areas estimated using the proposed algorithm with those obtained from Landsat

ETM+ data and the MODIS snow cover product for the Mount Everest region at different seasons showed that MODIS snow cover product overestimates the snow cover areas at worst larger than 50%, whereas the improved algorithm can estimate the snow cover for this region more feasible with absolute accuracy of 90%.

5. ACKNOWLEDGMENT

This work was jointly supported by the project “Too Much Water, Too Little Water” funded by Swedish International Development Cooperation Agency (Sida) and implemented by Mountain Environment and Natural Resources’Information System (MENRIS, ICIMOD), the project of European Commission (Call FP7-ENV-2007-1 Grant nr. 212921) as part of the CEOP-AEGIS project (http://www.ceop-aegis.org/) coordinated by the Université de Strasbourg, and the project of National Natural Science Foundation of China (Grant number 40801140).

6. REFERENCE

[1] Barnes, W.L., Pagano, T.S., & Salomonson, V.V. (1998). Prelaunch characteristics of the Moderate Resolution Imaging Spectroradiometer (MODIS) on EOS-AM1. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1088–1100.[2] Hall, D. K., Riggs, G. A., Salomonson, V. V., DeGirolamo, N. E., Bayr, K. J., & Jin, J. M. (2002). MODIS Snow-cover products. Remote Sensing of Environment, 83, 181– 194.[3] Hall, D. K., Riggs, G. A., & Salomonson, V. V. (2006). MODIS/Terra Snow cover daily L3 global 500 m grid V005, updated daily. National Snow and Ice Data Center.[4] Riggs, G. A., Hall, D. K., & Salomonson, V. V. (2006). MODIS Snow Products User Guide Collection 5. http://modis-snow-ice.gsfc.nasa.gov/sugkc2.html.[5] Scharfen, G. R., Hall, D. K., Khalsa, S. J. S., Wolfe, J. D., Marquis, M. C., Riggs, G. A., & McLean, B. (2000). Accessing the MODIS snow and ice products at the NSIDC DAAC. In Proceedings of IGARSS’00 , Honolulu, HI, 23-28 July 2000, 2059–2061.[6] Hall, D. K., & Riggs, G. A. (2007). Accuracy assessment of the MODIS snow products. Hydrological Processes, 21(12), 1534 1547. doi:10.1002/hyp.6715.[7] Rahman, H. & Dedieu, G. (1994). SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. International Journal of Remote Sensor, 15(1), 123-143.[8] Hapke, B. (1984). Bidirectional reflectance spectroscopy. 3. Correction for macroscopic roughness. Icarus, 59, 41-59.

1740