MODIS daily cloud-free snow cover products over Tibetan Plateau (2002 – 2015)

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MODIS daily cloud-free snow cover products over Tibetan Plateau (2002 – 2015)

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MODIS daily cloud-free snow cover products over Tibetan Plateau (2002 – 2015)

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MODIS daily cloud-free snow cover products over Tibetan Plateau (2002 – 2015)

Qiu Yubao1*, Guo Huadong1, Chu Duo2, Zhang Huan1,
Shi Jiancheng
1, Shi Lijuan1, Zheng Zhaojun3, Laba Zhuoma2

1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, P. R. China;

2. Institute of Tibetan Plateau Atmospheric and Environmental Sciences, Meteorological Bureau of Tibet Autonomous Region, Lhasa 850000, P. R. China;

3. National Satellite Meteorological Center, Beijing 100081, P. R. China

*Email: qiuyb@radi.ac.cn

Abstract: Snow cover over Tibetan Plateau plays  an  important role in regional water and energy circulation. Snow ablation also affects downstream rivers. Snow parameters and their long-term changes are sensitive factors affecting and responding to regional climate, influencing ecology and disasters. Moderate-resolution imaging spectrometer (MODIS) is widely used for remotely sensing snow due to its high spatio-temporal resolution. However, snow over Tibetan Plateau is distributed patchily and changes rapidly with unexpected atmospheric convection and precipitation. Also, because optical remote sensing is influenced severely by clouds, daily snow cover monitoring is a challenge requiring to remove cloud cover instances. Engaged in Tibetan Plateau's terrain complexity and snow spatio-temporal characteristics, this paper presents a compound method by combining different cloud removal algorithms, giving a MODIS daily cloud-free snow cover algorithm for Tibetan Plateau, as well as MODIS daily cloud-free snow cover products (2002 – 2015). The accuracy of the snow cover products is then verified against experimental data observed from 145 ground stations during two winter periods from October 1, 2009 to April 30, 2011. Results show that, when snow depth exceeds 3 cm, the general classification accuracy is 96.6% and the snow classification accuracy is 89.0%. Accuracy was well controlled in each step, which provided a good algorithm for removing clouds from the MODIS snow cover imagery. A multi-language operational process was developed and the daily, cloud-free climatological snow cover products over Tibetan Plateau were released as a free utility online.

Keywords: daily snow cover products, cloud removal algorithm, cloud free, Tibetan Plateau, MODIS

Dataset Profile:

Chinese title

青藏高原 MODIS 逐日无云积雪面积数据集(2002 –

2015 年)

English title

MODIS daily cloud-free snow cover products over Tibetan Plateau (2002 – 2015)

Corresponding author

Qiu Yubao (qiuyb@radi.ac.cn)

Data author(s)

Qiu Yubao, Guo Huadong, Chu Duo, Zhang Huan, Shi Jiancheng, Shi Lijuan, Zheng Zhaojun, Laba Zhuoma

Time range

July 2002 – April 2015

 

 

Geographical scope

The study spans an area of 25° – 45°N and 67° – 107°E, including the entire Tibet Autonomous Region and Qinghai Province, parts of Sichuan, Yunnan, Xinjiang and Gansu provinces, as well as parts of foreign territories in southern and western Tibetan Plateau

Spatial resolution

500-meter

Data volume

6.9 GB

Data format

Geotiff

Data service system

<http://www.sciencedb.cn/dataSet/handle/55>

 

 

 

Source(s) of funding

Special Fund for Meteorological Scientific Research in the Public Interest "Constructing a Remote Sensing Product Dataset for Snow Pack over Tibetan Plateau" (No. GYHY201206040); State Key Program of National Natural Science Foundation of China (ABCC Grant No. 41120114001); the National Natural Foundation of China (No. 41371351); and "One-Three-Five" Planning Projects of the Chinese Academy of Sciences.

 

 

 

 

Dataset composition

The dataset consists of two parts: the "MODIS daily cloud-free snow cover products over Tibetan Plateau from 2002 to 2015", and the vector data of the research area. They are, respectively:

  1. MODIS_Dysno_Cloud-free_2002-2015.zip (6.9 GB), i.e., the daily cloud-free snow data;

  2. Tibet_Range.zip (24 KB), i.e., the auxiliary vector data storing the boundary of the study area over Tibetan Plateau.

1. Introduction

As an important part of the cryosphere, snow is one of the most active natural elements on the Earth's surface, profoundly impacting regional and even global climate change, energy balance, and water cycle1. For Tibetan Plateau – the highest plateau in the world which is known as the "roof of the world" and  "the third pole", snow is an important feature of the land surface. With strong reflectivity and poor thermal conductivity, snow changes the radiation balance of the ground surface and the heat condition of the atmosphere by absorbing a  lot of heat when melting, which causes changes in atmospheric circulation, thus impacting regional climate2. Hydrological effects of snowmelt also have profound impacts on surface runoff and atmospheric thermal conditions3. Snow's impact on the Asian monsoon and the droughts and floods in South and East Asia has been a focus of domestic and international climatologists for more than a century4–5.

Satellite remote sensing technology has gradually become the main approach for snow monitoring because of its short revisit period and large coverage area. Frequently used snow remote sensing products include Landsat, SPOT6, 7, AVHRR8, VEGETATION9, MODIS snow products10, SMMR SSM/I11, 12, AMSR-E13–15  and other microwave snow products, wherein MODIS daily snow data (MOD10A1 and MYD10A1) are most widely used due to its high spatio-temporal resolution.

However, since optical remote sensing products are highly influenced by weather conditions, clouds restrict the acquisition of daily cloud-free snow products. This study combines the cloud removal algorithms for MODIS standard snow products from scholars in China and the international community16–25. Based on the snow-cover and topography characteristics of Tibetan Plateau, this paper presents a snow identification methodology according to the geographical and climatological findings in the region. Cloud pixels were reclassified as snow or land through eight steps: daily combination of MODIS morning and afternoon observations, adjacent three-day temporal synthesis, "permanent" snow and land recognition, neighboring four-pixel method, elevation filtering, correction of misclassified shaded areas, maximum snow and land masking, and finally, snow lines fitting. By the seventh step, the cloud area has fallen to an average of about 5.1% with a minimum loss in the snow classification precision. By the final step – expected snow lines fitting, all clouds have been removed and the daily cloud-free MODIS snow products can be obtained. The products provide a reference for dynamically monitoring the Plateau's snow at daily scale.

2. Data collection and processing

2.1 Original data products

(1) Original MODIS snow cover products MOD10A1 and MYD10A1, downloaded from the official website of the US National Snow and Ice Data Center (http://nsidc.org) which span from July 2002 to April 2015. The track numbers of the raw data are shown in Figure 1.

Figure 1 MODIS track over Tibetan Plateau (in the rectangular box)

(2) Digital Elevation Model (DEM) data, downloaded from the website of the Geospatial Data Cloud (http://www.gscloud.cn). There are 54 scenes in total.

2.2 Data processing

The characteristics of Tibetan Plateau – its large area, complex and diverse terrain, and heterogeneity of snow distribution – determine the regional applicability of cloud-removal methods. In the last step, Tibetan Plateau is zoned into seven explicit sub-regions, according to the slope complexities and differences, namely, Indo-Gangetic Plain, North of Tibetan Plateau, Hinterland of Tibetan Plateau, Mountainous area in eastern Tibetan Plateau, Pamirs, Tianshan Mountains, and Himalayas, as illustrated in Figure 2.

Figure 2 Zoning map of snow features over Tibetan Plateau

There are eight steps for reclassifying clouds from MODIS snow products of Tibetan Plateau; the cloud is gradually reduced to obtain daily cloud-free products. Figure 3 shows the flow chart of the algorithm.

Figure 3 Flow chart of the algorithm for MODIS daily cloud-free snow products

Data preprocessing

By combining the MODIS Reprojection Tool (MRT) open source software (downloadable at: ) with customized algorithms, the MOD10A1 and MYD10A1 images were mosaicked and clipped through reprojection. ENVI 5.1 software was used for the mosaicking and clipping of DEM data, yielding MOD10A1, MYD10A1, and DEM data in the WGS84 geographic coordinate system.

Steps for cloud removal

Steps for cloud removal are described in Table 1.

Table 1  Description of the cloud removal process

 

Figure 4 Minimum snow area, minimum land area and snow changes from January 9, 2011 to January 17, 2011

Note: white indicates the variation of snow area over eight days.

3. Sample description

3.1 Naming format

The file names of the "MODIS daily cloud-free snow products over Tibetan Plateau" dataset conform to the format "MODIS_Dysno_Cloudfree_ YYYYMMDD.tif". The following information of the dataset can be acquired from the name.

(1) "MODIS_Dysno_Cloudfree" represents MODIS daily cloud-free snow products over Tibetan Plateau;

(2) "YYYY" indicates the year;

(3) "MM" indicates the month;

(4) "DD" indicates the day.

3.2 Classification description

The image classification code is consistent with MOD10A1, whose pixel  values are given in Table 2.

Table 2  Image pixel classifications

3.3 Data sample

Figure 5 is an example of a daily cloud-free snow product over Tibetan Plateau from December 22 to December 31, 2002, calculated via the above mentioned algorithms and procedures.

Figure 5 Maps of cloud-free snow products over Tibetan Plateau

4. Accuracy evaluation

Daily snow depth data – 61,480 pairs of observed values provided by 145 ground stations – were selected as samples for data quality evaluation. The data span  424 time phases in two snow seasons over Tibetan Plateau from October 1, 2009 to April 30, 2011. The confusion matrix and formulas (1) and (2) were adopted to evaluate the snow classification images and analyze the MODIS data (MOD10AI, MYD10A1) under clear weather. The accuracy of all types of synthetic products during cloud processing, including general classification accuracy and snow classification accuracy, was analyzed. For this purpose, the following samples were considered during accuracy evaluation:  ① sample (a) with snow images and records from ground stations (snow depth > 0); ② sample (b), recorded snow from ground stations classified as snow-free, namely omission; ③ sample (c), recorded snow-free from ground stations classified as snow; and ④ sample (d) with snow-free images and records from ground stations (Table 3).

Table 3 Confusion matrix for accuracy verification

General classification accuracy, namely accuracy, reflects the capacity of classification algorithms in identifying snow as snow-covered and land as snow- free across the whole research area. Snow classification accuracy, namely precision, reflects the proportion of real snow pixels among all the snow pixels identified by the classification algorithm. They may be expressed by the following formulas:

Raw data of MOD10A1, MYD10A1 and all processed products in the confusion matrix are indicated in Figure 4. The general classification accuracy of MODIS data was above 98%, while the snow classification accuracy approximated to 82%. Since there were few pixel elements modifying the erroneous judgment of lake and lake ice, statistical results were omitted. The following factors might result in errors during precision validation: thin snow cover, dispersed snow distribution, differentiated spatial scales employed for generating snow depth data at ground stations and MODIS snow image pixels, optics’ penetrating effect in thin snow-covered areas, and spectral mixing effect caused by mottled thin snow. When snow cover is thin (for example, less than 3 cm) during snow-melt periods, snow underestimation might occur, in which circumstance snow is observed within the small area of ground stations but the MODIS image (500 m * 500 m) displays to be snowless. It might also be that the station is located in a city and the snow quickly melts due to the temperature which is higher than its surrounding areas, in which circumstance the MODIS image overestimates the situation and erroneously suggests snow. In the case where snow is thicker than 3 cm, the samples recorded as snow by both images and ground observations  are denoted by a3, and the samples recorded as snow by images but as land by ground observations are denoted by b3. A calculation by formulas (1) and (2) demonstrated an improved general accuracy of all synthetic products, with Oa and Sa of MODIS data respectively rising to about 99% and 94%.

Table 4 shows that the cloud removal procedure prior to elevation filtering had less influence on the classification accuracy; daily observation combination and elevation filtering slightly improved the general snow classification precision, whereas maximum snow and land masking brought down the general classification accuracy of synthesis products; in the case of the data with snow-depth being above 3 cm, the accuracy decreased to 98.24% from 98.97% (average accuracy value of MOD10A1 and MYD10A1) while the snow classification accuracy increased from 94.48% to 94.73%, with a larger amount of cloud removed; fitting expected snow lines had the lowest accuracy and was suitable for the last step of cloud removal; nevertheless, when the snow depth was above 3 cm, the general classification accuracy and snow classification accuracy of cloud-free snow products still reached 96.6% and 89% respectively, higher than previously published documentary records24 and basically met the accuracy requirements of MODIS standard snow products under clear weather. All of this indicates that the algorithm for cloud removal may be applied in the dynamic, daily, cloud-free snow monitoring over Tibetan Plateau.

Table 4  Accuracy verification of all product types

5. Usage notes

The dataset, based on moderate-resolution snow products MOD10A1 and MYD10A1, applies to studies on water and energy circulation, ecology, and disasters on Tibetan Plateau. It is especially of great significance for research on glacier snow model, climate change, run-off change, long-term laws for snow spatio-temporal distribution, ecological benefits, snow disasters, future tendencies and other aspects of the Tibetan Plateau environment.

Acknowledgments

Our thanks go to the US National Snow and Ice Data Center (http://nsidc.org) for providing MOD10A1 and MYD10A1 products, and the GSCloud (http://www. gscloud.cn) for providing DEM data.

References

1. Bin C, Qiu Y, Shi L et al. Comparative validation of snow depth algorithms using AMSR-E passive microwave data in China. Journal of Glaciology and Geocryology 35 (2013): 801 – 813.

2. Liu Y. Sea-Land Thermal Differences on the Impact of Climate Change in China. Beijing: Meteorological Press, 2005.

3. Barnett TP, Dümenil L, Schlese U et al. The effect of Eurasian snow cover on regional and global climate variations. Journal of the Atmospheric Sciences  46 (2010): 661 – 686.

4. Qiu Y, Guo H, Bin C et al. Comparison on snow depth algorithms over China using AMSR-E passive microwave remote sensing. IEEE International Geoscience and Remote Sensing Symposium (2014): 851 – 854.

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7. Laba Z, Qiu Y, Chu D et al. Remote sensing analysis of Tibet Karuola Glacier area change from 1972 to 2010. Remote Sensing Technology and   Application 30 (2015): 784 – 792.

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9. Xiao X, Zhang Q, Boles S et al. Mapping snow cover in the pan-Arctic zone, using multi-year (1998 – 2001) images from optical VEGETATION sensor. International Journal of Remote Sensing 25 (2004): 5731 – 5744.

10. Hall DK, Riggs GA, Salomonson VV et al. MODIS snow-cover products. Remote Sensing of Environment 83 (2002): 181 – 194.

11. Grody NC & Basist AN. Global identification of snow cover using SSM/I measurements. IEEE Transactions on Geoscience & Remote Sensing 34 (1996): 237 – 249.

12. Che T,  Xin L,  Jin R  et  al. Snow depth derived from passive   microwave remote-sensing data in China. Annals of Glaciology 49 (2008): 145 – 154.

13. Feng Q, Zhang X & Liang T. Dynamic monitoring of snow cover based on MOD10A1 and AMSR-E in the north of Xinjiang Province, China. Acta Prataculturae Sinica 18 (2009): 125 – 133.

14. Lu X, Wang X, Cui C et al. Snow depth retrieval based on AMSR-E data in northern Xinjiang Region, China. Journal of Glaciology & Geocryology 35 (2013): 40 – 47.

15. Dai L, Tao C, Jian W et al. Snow depth and snow water equivalent estimation from AMSR-E data based on a priori snow characteristics in Xinjiang, China. Remote Sensing of Environment 127 (2012): 14 – 29.

16. Gafurov A & Bárdossy A. Cloud removal methodology from MODIS snow cover product. Hydrology and Earth System Sciences 13 (2009): 1361 – 1373.

17. Gao Y, Xie H, Yao T et al. Integrated assessment on multi-temporal and multi-sensor combinations for reducing cloud obscuration of MODIS snow cover products of the Pacific Northwest USA. Remote Sensing of Environment 114 (2010): 1662 – 1675.

18. Paudel KP & Andersen P. Monitoring snow cover variability in an agropastoral area in the Trans Himalayan region of Nepal using MODIS data with improved cloud removal methodology. Remote Sensing of Environment 115 (2001): 1234 – 1246.

19. Dietz AJ, Kuenzer C & Conrad C. Snow cover variability in Central Asia between 2000 and 2011 derived from improved MODIS daily snow cover products. International Journal of Remote Sensing 34 (2013): 3879 – 3902.

20. Dietz AJ, Wohner C & Kuenzer C. European snow cover characteristics between 2000 and 2011 derived from improved MODIS daily snow cover products. Remote Sensing 4 (2012): 2432 – 2454.

21. López-Burgos V, Gupta HV & Clark M. Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach. Hydrology and Earth System Sciences  7 (2013): 1809 – 1823.

22. Gao Y, Xie H, Lu N et al. Toward advanced daily cloud-free snow cover  and snow water equivalent products from Terra-Aqua MODIS and Aqua AMSR-E measurements. Journal of Hydrology 385 (2010): 23 – 35.

23. Thirel G, Salamon P, Burek P et al. Assimilation of MODIS snow cover area data in a distributed hydrological model. Hydrology & Earth System Sciences Discussions 8 (2011): 1329 – 1364.

24. Huang X, Hao X, Wang W et al. Algorithms for cloud removal in MODIS daily snow products. Journal of Glaciology & Geocryology 34 (2012):1118 – 1126.

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Data citation

1. Qiu Y, Guo H, Chu D et al. MODIS daily cloud-free snow cover products  over Tibetan Plateau (2002 – 2015). Science Data Bank. DOI: 10.11922/ sciencedb.170.55

Authors and contributions

Qiu Yubao, PhD, Associate Professor; research area: applied research of environmental remote sensing. Contribution: algorithms and process modeling.

Guo Huadong, PhD, Professor; research area: applied research of environmental remote sensing. Contribution: geo-processing guidance.

Chu Duo, PhD, Professor; research area: applied research of environmental remote sensing. Contribution: snow process modeling and verification.

Zhang Huan, MSc; research area: applied research of remote sensing. Contribution: data pre-processing and data production.

Shi Jiancheng, PhD, Professor; research area: applied research of remote sensing. Contribution: data modeling.

Shi Lijuan, PhD; research area: applied research of remote sensing. Contribution: data pre-processing and data production.

Zheng Zhaojun, PhD, Associate Professor; research area: applied research of snow remote sensing. Contribution: data pre-processing and input.

Laba Zhuoma, MSc; research area: applied research of remote sensing. Contribution: data collection and file editing.

 

 

How to cite this article: Qiu Y, Guo H, Chu D et al. MODIS daily cloud-free snow cover products over Tibetan Plateau (2002 – 2015). China Scientific Data 1 (2016), DOI: 10.11922/csdata.170.2016.0003

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