A dataset of microwave brightness temperature and freeze-thaw for medium-to-large lakes over the High Asia region 2002 – 2016

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A dataset of microwave brightness temperature and freeze-thaw for medium-to-large lakes over the High Asia region 2002 – 2016

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A dataset of microwave brightness temperature and freeze-thaw for medium-to-large lakes over the High Asia region 2002 – 2016

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A dataset of microwave brightness temperature and freeze-thaw for medium-to-large lakes over the High Asia region 2002 – 2016

Qiu Yubao1*, Guo Huadong1, Ruan Yongjian1, Fu Xinru1, Shi lijuan1, Tian Bangsen1

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

* Email: qiuyb@radi.ac.cn

Abstract: High Asia is a region dotted with lakes, sensitive to changes that are occurring in the mid-latitudes of the northern hemisphere. It is thus a focus for researchers. Due to widely different dielectric constants of ice and water, satellite passive microwave remote sensing, which has a high revisit rate and is insensitive to the weather, can be used for quick detection of the thawing and freezing of lake ice. Based on the proportions of lake and land areas within the microwave radiometer pixels, the brightness temperature of a lake surface was acquired by linear and dynamic decomposition of the radiometer footprint measurements. The freezing and thawing of 51 sub-pixel level lakes in the High Asia region was closely monitored during the period 2002 – 2016. The data obtained there were then validated against the cloud-free MODIS optical snow product data. The validation test selected three lakes with different sizes located in different parts of High Asia – Hoh Xil Lake, Dagze Co and Kusai Lake. Results suggest that the freezing and thawing times are highly correlated with each other and have correlation coefficients of 0.968 and 0.987, as measured by the optical and microwave remote sensing approach, respectively. The polished dataset contains lake brightness temperatures and lake ice freeze-thaw in different basins of High Asia. These data can be used to carry out inversion of geophysical parameters of other lakes and to enhance the understanding of lake freezing and thawing in High Asia, thus providing a data base of climate and environmental change in High Asia and also of this region’s response to global climate change.

Keywords: High Asia; lake; passive microwave brightness temperature; freeze-thaw parameters; Pixel Unmixing method

Dataset Profile

Chinese title


English title

A dataset of microwave brightness temperature and freeze-thaw for medium-to-large lakes over the High Asia region 2002 – 2016


Qiu Yubao, Guo Huadong, Ruan Yongjian, Fu Xinru, Shi lijuan, Tian Bangsen

Corresponding author

Qiu Yubao (qiuyb@radi.ac.cn)

Time range

2002 – 2016

Geographical scope

High Asia: 24°40′ N – 45°58′ N, 61°57' E – 105°29' E. Himalayas, Kunlun Mountains, Hengduan Mountains, Qilian Mountains, Tianshan Mountain as well as other mountains and plateaus mainly located over the Tibetan Plateau.

Data format


Data volume

12.1 MB

Data service system


Sources of funding

This study is supported by the International Partnership Program of the Chinese Academy of Sciences (131CllKYSB20160061), the National Natural Science Foundation of China (41371351) and the Key Program of National Natural Science Foundation of China (ABCC program, 41120114001)

Database composition

The dataset consists of two parts: (1) 18 GHz passive microwave remote sensing brightness temperature data for 51 lakes in the High Asia region (2002 – 2016, temporal resolution 1 – 2 days); (2) lake ice freeze-thaw determination dataset obtained from the brightness temperature dataset. The files name are, respectively, Lake Brightness Temperature Data Extracted Through the Nearest Neighbor Method and the Pixel Unmixing Method.Zip (12 MB) and Lake Ice Freeze-Thaw Dataset of 51 Lakes over the High Asia (2002 – 2016).xls (0.1 MB).

1. Introduction

The Tibetan Plateau comprises the main body of High Asia, which consists, overall, of the Himalayas, Kunlun Mountains, Hengduan Mountains, Qilian Mountains, Tianshan Mountains as well as other Asian mountain ranges. The elevation of the whole region ranges from 2,000 m to 8,844 m. The region contains the densest concentration of high-altitude lakes in the world, with about 1,210 of the lakes having an area larger than 1 km2.1 High Asia has drawn much scholarly attention for the study of climate change2-3 as the region is highly sensitive to changes in global climate.4 Alpine lakes are sensitive to climate change particularly at the time when freezing and thawing occur5, and the timings of these events are often considered to reveal the characteristics of regional climatic variations.6

Figure 1 Distribution of the lakes included in this dataset

Characterized by high elevations and low temperatures, High Asia is a sparsely populated region with few travelers. As lack of monitoring sites makes it difficult to obtain the freeze-thaw parameters of lakes from ground monitoring,7-8 remote sensing data are usually needed to supplement any ground data. Satellite passive microwave data can be obtained all year round, in all weather conditions and they have a short revisit period.9 Microwave data’s high sensitivity to the ice-water phase change10 makes it an especially suitable way to monitor the freezing and snowing of ice and snow and these data have been widely applied to monitor, for example, continental permafrost changes,11 snow changes,12 and sea ice changes.13

The coarse resolution of passive microwave radiometers and the effects of mixed pixels seriously restrict the monitoring of lake freezing and thawing.14-15 At present, because of the microwave pixel size, only 35 large lakes in the entire northern hemisphere can be monitored using existing passive microwave systems.16 In the High Asia region, lakes that can be monitored directly using passive microwave data include Qinghai Lake, Namco Lake, Siling Lake and other large lakes.14-15

To obtain a clear understanding of the impact of regional climate and environmental change on lake freezing and thawing in the High Asia region, we need to know more about the freeze-thaw cycle. Given the limited amount of ground observations and the restrictions of optical remote sensing (subject to cloud influences), passive microwave data constitute the best source for monitoring and understanding lake freezing and thawing in the region.16

In this study, the nearest neighbor method and linear decomposition of mixed pixels were used. Passive microwave data AMSR-E, AMSR2 and MWRI, which are commonly used for large-scale surface monitoring, were used as auxiliary inputs. By applying these data into the monitoring, we obtained information relating to the freezing and thawing of 51 lakes in different parts of the High Asia region from 2002 to 2016 (Figure 1). This information provides an important supplement to existing information on lake freezing and thawing in the High Asia region.

2. Data acquisition and processing methods

2.1 Data collection

Three main kinds of data were used in this study: passive microwave brightness temperature data AMSR-E, AMSR2 and MWRI (Microwave Radiation Imager); Tibetan Plateau lake dataset; and daily MODIS cloud-free snow-area data for the Tibetan Plateau. These data were used to extract lake regional brightness temperature values, to calculate the dynamic area ratio of lake to lakeshore, and to test the lake ice freeze-thaw parameters, respectively. Further details of these datasets are given below.

2.1.1 Passive microwave brightness temperature data: AMSR-E, AMSR2 and MWRI

The passive microwave AMSR-E sensor has 12 imaging channels with horizontal and vertical polarization at frequencies of 6.9, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz; AMSR2 has 14 channels at frequencies of 6.9, 7.3, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. The difference in emissivity between ice and water is more significant in the 18.7 GHz channel. Also, given its resolution and the atmospheric and surface effects, V-polarized 18.7 GHz V data is commonly used for monitoring lake freezing and thawing.14-15 The resolution of AMSR-E data is 27 km × 16 km whereas that of AMSR2 data is 22 km × 14 km. AMSR-E data can be found at: and AMSR2 data at: < http://gcom-w1.jaxa.jp/index.html >.

Together, AMSR-E and AMSR2 did not provide continuous observations and there was a data gap from October 4, 2011 to July 2, 2012. This gap was made up by China’s FY-3 MWRI sensor launched in 2010, which has been in operation since then. MWRI sensors have 10 imaging channels at frequencies that match those of AMSR-E, except for the 6.9 GHz channel. As MWRI data have a low resolution – the resolution of its 18.7 GHz data is 30 km × 50 km – in this study, the nearest neighbor method was applied to process the MWRI data to supply missing data for Qinghai Lake, Namco Lake and Siling Lake. MWRI data are available at: .

2.1.2 Tibetan Plateau Lake Dataset

The dataset showing changes in lakes on the Tibetan Plateau with an area more than 1 km2 over the past 60 years1 was obtained from Scientific Data. This dataset covers three different periods: (a) 1960s data consisting of surveying and mapping, partly based on first-time lake survey results; (b) CBERS-1 CCD data from 2005, partly based on second-time lake survey results; (c) GF-1 WFV data from 2014. During the production of this dataset, the lake area data from 2005 and 2014 were superimposed on passive microwave data to obtain lake and land areas for 2002  2011 The AMSR-E passive microwave data uses the 2005 Tibetan Plateau Lake Dataset as lake area input data and the AMSR2 passive microwave data for 2012 – 2016 uses the 2014 Tibetan Plateau Lake Dataset to make these calculations. The Tibetan Plateau Lake Dataset can be found at: .

2.1.3 MODIS Daily Cloud-Free Snow Product for the Tibetan Plateau

This dataset consists of a daily cloudless two-value snow product developed from MOD10A1 and MYD10A1 data.17 The dataset can be downloaded from . It includes classified features of the following types for the Tibetan Plateau area: land, snow, lake water, lake ice, and lake uncertain (meaning that the lake state cannot be determined during reclassification after cloud removal). The lake water and lake ice information from this dataset can be compared with the freeze-thaw information obtained from the microwave remote sensing for validation.

2.2 Data processing methodology

2.2.1 The nearest-neighbor method

A satellite sensor scanning the Earth is in a state of relatively constant motion; each time the satellite revisits a lake, the scanning position, which is nearest to the lake center, changes, leading to small differences in the area pixel of AMSR-E (27 km × 16 km), AMSR2 (22 km × 14 km) and MWRI (50 km × 30 km). Thus the areas of the lake and lakeshore measured by the sensor also constantly change (except for lakes whose area is much smaller than the pixel resolution).

A satellite transit scanning diagram is shown in Figure 2.

Figure 2 Apparent changes in the ratio of lake area to land caused by satellite transiting and scanning

To obtain brightness temperature information for the lakes, in this study the pixel nearest to the lake center – whether AMSR-E, AMSR2 or MWRI – was used. First, the coordinates of the lake center were determined using high-resolution Landsat 5 data. Then, a 25 km × 25 km rectangle was drawn around the central point. A rectangle of this size covers an area similar to that of an 18.7 GHz passive microwave pixel and thus the coordinates of the pixel and the lake center will be within the same pixel. To apply the nearest-neighbor method, the lake center is denoted O(X,Y),TB(Xi, Yi) is the center of any pixel, and A(Xɑ, Yɑ) denotes the vertex of the rectangle. When TB(Xi, Yi) satisfies formulae (1), (2) and (3), (Xmin, Ymin) is the point required.

The lakeshore pixel brightness temperature used in the mixed pixel decomposition was obtained from the high-resolution Landsat image. Two or three passive microwave pixels nearest to the lake were chosen as the pure land point P(X1,Y1) and their average value was then calculated.

2.2.2 The pixel unmixing method

Passive microwave data have a low spatial resolution and a single pixel usually includes several different types of features. The mixed pixel effect is, therefore, quite serious. A mixed pixel decomposition algorithm was applied to radiometrically calibrated AMSR-E L2A and AMSR2 L1R brightness temperature data,18 which is based on the lake area to land area ratio. When applying this method, we assumed that the continental information captured by a passive microwave radiometer is homogeneous and that the land information is related to the area of the object only. Ignoring other less important factors, the passive microwave mixed-pixel decomposition model can be expressed as:19-20


In equation (4), is the lake brightness temperature for a single pixel of the passive microwave data, is the brightness temperature for the lake part of a mixed pixel and is the brightness temperature for the land part of a mixed pixel. 𝑎 and 𝑏 are, respectively, the area weights for the lake and land pixels within a passive microwave pixel. The lake area brightness temperature and land area brightness temperature in the above equation are themselves influenced by factors such as the atmosphere.

During the mixed-pixel decomposition and also the calculation of the corresponding value of the AMSR-E pixel, the lake area weight a and land area weight b are buffering a 27 km × 16 km rectangular area around the scanning point that is nearest to the lake center during the passing of the satellite. For AMSR2, the corresponding rectangle has an area of 22 km × 14 km. The weights for the lake and shore area for each revisit of the satellite are obtained through superimposed dynamical analysis. Finally, the proportions of lake area to land area are substituted into equation (4) and the brightness temperature value of the lake area in each pixel can be obtained.

Using WGS84 coordinates (World Geodetic System 1984), it is difficult to buffer a rectangular area around a central point at one time. The corresponding rectangles for AMSR-E and AMSR2 measure 27 km × 16 km and 22 km × 14 km, respectively. Therefore, during the experiment, two perfect circles were drawn around one point – the radius of the larger circle being equal to half the length of the rectangle and the radius of the smaller circle being equal to half the width of the rectangle. The minimum rectangle was then drawn based on the transverse tangent cylindrical conformal projection coordinate system. Figure 3 shows the relevant diagram for an AMSR-E pixel. Finally, the rectangle IJKL for a satellite transit was obtained. Dynamic composition was then used to obtain the lake and land area proportions from the lake imagery.

Figure 3 Schematic diagram of dynamic rectangular buffering of AMSR-E pixel

2.2.3 TIMESAT software and visual discrimination for extraction of freeze-thaw information

After the brightness temperature of the lake pixels was processed using mixed-pixel decomposition, the TIMESAT (time series of satellite sensor data) software was used to extract freeze-thaw information for the lakes being studied. The TIMESAT software was developed for use with long-term series of NDVI data to monitor changes in the cyclical growth of plants.21-22 TIMESAT has the capability to quickly process long-term series of data that include seasonal changes. Therefore, by embedding the brightness temperature data in the algorithm of the software, we were able to extract the lake freeze-thaw parameters by simple visual analysis (Figure 4).

Figure 4 Freeze-thaw information extracted using TIMESAT software

3. Sample description

3.1 Lake brightness temperature data

The decomposition data were stored as Excel spreadsheets. These included AMSR-E data for 51 lakes from 2002 to 2011, AMSR2 data for 51 lakes from 2012 to 2016 and MWRI data for 3 lakes from 2011 to 2016, making 105 sheets of data in total. All the spreadsheets were stored in two different formats, the details of which are as follows.

Type 1: These data have not undergone mixed-pixel decomposition and consist of lake brightness temperatures detected using the nearest-neighbor method only (Table 1). The data are shown in two columns, A and B. The dates are listed in column A while column B includes the V-polarized 18.7 GHz brightness temperatures.

Table 1 Lake Brightness Temperatures recording




18.7GHz V




















Figure 5 Long-term series of lake brightness temperatures obtained using the nearest-neighbor method (Qinghai Lake)

Type 2: Lake ice freeze-thaw changes detected by combining the nearest neighbor method and the pixel unmixing method (Table 2). These data consist of eight columns in total, of which column A lists the dates, columns B and C give the X and Y coordinates, respectively, of the pixel centers, column D lists the V-polarized 18.7 GHz mixed brightness temperatures of the lake and lakeshore and columns E and F give, respectively, the lake area and lakeshore area proportions for the pixel nearest to the lake center. Column G consists of the 18.7 GHz V-polarized brightness temperature values for the adjacent area of lakeshore and column H lists the lake area brightness temperature values after decomposition.

Table 2 Lake brightness temperatures obtained using the pixel decomposition method














TB of

Percentage of lake area

Percentage of land area

TB of

TB of


31.888 47

87.526 56


0.644 517

0.355 483




31.881 92

87.531 67


0.620 583

0.379 417




31.846 58

87.546 26


0.459 976

0.540 024




31.896 07

87.557 38


0.625 501

0.3744 99




31.922 45

87.563 47


0.540 223

0.459 777




31.905 04

87.519 09


0.523 359

0.476 641




31.876 72

87.584 86


0.610 791

0.389 209




31.916 36

87.509 19


0.566 584

0.433 416




31.869 82

87.493 92


0.603 482

0.396 518





Figure 6 Long-term time series of lake brightness temperatures obtained using mixed-pixel decomposition (Bairebucuo as an example)

3.2 Lake ice freezing and thawing dataset

These data were stored in Excel format. There were 51 sheets in total, each of which corresponded to a particular lake. Taking Qinghai Lake as an example (Table 3), the eight columns give the Chinese name of the lake (column A), the English name of the lake (column B), the X and Y coordinates of the lake center (columns C and D, respectively), the start and end dates of the lake ice freeze-up (columns E and F, respectively) and the lake ice break-up (columns G and H, respectively).

Table 3 Freeze-thaw date of Qinghai Lake









Lake nameChinese

Lake nameEnglish

Latitude of lake center

Longitude of lake center

Freeze-up starts

Freeze-up ends

Break-up starts

Break-up ends


Qinghai Lake

36.886 03

100.178 55






Qinghai Lake

36.886 03

100.178 55






Qinghai Lake

36.886 03

100.178 55






Qinghai Lake

36.886 03

100.178 55






Qinghai Lake

36.886 03

100.178 55






Qinghai Lake

36.886 03

100.178 55






Qinghai Lake

36.886 03

100.178 55






Qinghai Lake

36.886 03

100.178 55






Qinghai Lake

36.886 03

100.178 55






Qinghai Lake

36.886 03

100.178 55






Qinghai Lake

36.886 03

100.178 55






Qinghai Lake

36.886 03

100.178 55






Qinghai Lake

36.886 03

100.178 55






Qinghai Lake

36.886 03

100.178 55





4. Quality control and evaluation

4.1 Missing data

4.1.1 Revisit intervals of the passive microwave remote sensors

There are gaps in passive microwave data. The revisit cycles of the passive microwave remote sensors are 1 – 2 days in low latitude regions and up to several times a day in high latitude regions (Figure 7). For the High Asia region, which lies between high and middle latitudes, the time interval between lake brightness temperature acquisitions is 1 or 2 days.

Figure 7 Passive microwave data coverage

4.1.2 Notes on missing data

The raw data of this dataset were acquired by three passive microwave sensors, AMSR-E, AMSR2 and MWRI. Of these, AMSR-E was launched on May 4, 2002 and began to make Earth observations on June 19, 2002. It continued to acquire data until October 4, 2011 when the antenna lost power; after that, no further observations were made. AMSR2 was launched on May 18, 2012 and began to obtain Earth observation data on July 3, 2012. This sensor is still in operation. MWRI has been in operation since its launch in 2010 and these data can be used to fill in the gap between AMSR-E and AMSR2 data. However, due to the low resolution of MWRI data, the nearest-neighbor method was used in this study to process MWRI data to supply missing data for Qinghai Lake, Nam Co and Siling Co only. For other lakes, there were gaps in the brightness temperature dataset and lake ice freeze-thaw dataset for the period October 2011 to July 2012.

4.2 Error analysis

4.2.1 Positioning accuracy of the sensors

During the use of passive microwave sensors for lake ice monitoring, only the single pixel nearest to the lake center during the passing of the sensor was used, so the positioning accuracy of the pixel directly influenced the accuracy of ice freezing and thawing detection. AMSR-E Level-2A data, for example, had a positioning error of about 5 to 7 km.23 The position offset also introduces an error into the decomposition of lake brightness temperature mixed pixels. In addition, the brightness temperature error in the lake ice freezing period is about 3 – 4 K and it reaches 7 – 8 K during the lake ice thawing period.

4.2.2 Processing error

After analysis of the data, the decomposition of passive microwave mixed pixels was used for lakes whose area was greater than 0.3 times that of a single AMSR-E pixel. When the lake area is between 0.2 and 0.3 times that of an AMSR-E pixel, the method is affected by the shape of the lake as well as the characteristics of the lake and its surrounding land area. This leads to a great deal of uncertainty in the results. In addition, there are limitations to the passive microwave mixed pixel linear decomposition method. For example, the vegetation around the lake, which is obviously influenced by the season, affects the lake brightness temperature decomposition and produces deviations in the extracted results. If the difference in elevation between the lake and its surrounding land is large – as is the case for lakes surrounded by mountains – or if there is a large difference between the brightness temperature of the lake and its surrounding land, there can be large discrepancies in the mixed pixel decomposition results. The angles of the slopes of the surrounding mountains and the topography can also influence the results.

4.3 Comparison and verification

Results were compared with and validated against the MODIS Tibetan Plateau daily snow cover dataset.17 The use of this data helped not only to distinguish snow from land with cloud cover but also to reclassify cloud-covered lake areas. The lake surfaces were reclassified as “lake”, “lake ice” or, in a few cases, “uncertain”. A threshold of 90% of the lake consisting of lake water was used to define lake ice melting and a threshold of 20% consisting of lake water was used to define lake freezing.7 This helped to reduce the errors accumulated during the direct visual determination of lake freezing and thawing introduced by the cloud removal reclassification.8

Dagze Co, Kusai Lake and Hoh Xil Lake were selected for verification test. The lake freeze-thaw results extracted from the AMSR-E data and those extracted from the cloud-free MODIS data were correlated with each other, showing Pearson correlation coefficients of 0.968 and 0.968, respectively (Figure 8). It can therefore be concluded that the lake ice freezing and thawing results derived from the passive microwave remote sensing data were highly reliable.


Figure 8 Comparison and verification based on MODIS cloud-free data products

5. Usage notes

This dataset includes time-series of brightness temperatures and lake-ice freeze-thaw data for 51 medium to large lakes in the High Asia region from 2002 to 2016. This dataset can be used for inversion of lake parameters such as lake-ice thickness changes. It also helps to improve the understanding of lake-ice freezing and thawing in the High Asia region more generally. The dataset provides a foundation for studying climate and environmental changes in High Asia as well as the region’s response to global climate changes.


We thank the National Snow and Ice Data Center for providing the AMSR-E L2A swath brightness temperature data, the Japan Aerospace Exploration Agency for providing the AMSR2 L1R swath brightness temperature data, the National Satellite Meteorological Center for providing the MWRI L2A swath brightness temperature data, the Science Data Bank for providing the Tibetan Plateau MODIS daily cloud-free snow-cover data, and Scientific Data for providing the Tibetan Plateau Lake Dataset. This work is supported by the National Natural Science Foundation of China (No. 41371351) and the International Partnership Program of Chinese Academy of Sciences (No. 131CllKYSB20160061).


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Data citation

1. Qiu Y, Guo H, Ruan Y et al. A dataset of microwave brightness temperature and freeze-thaw for medium-to-large lakes over the High Asia region (2002 – 2016). Science Data Bank. DOI: 10.11922/sciencedb.374

Authors and contributions

Qiu Yubao, PhD, Associate Professor; research area: environmental remote sensing applications. Contribution: algorithm development.

Guo Huadong, PhD, Professor; research area: application of remote sensing technology. Contribution: global change research guidance.

Ruan Yongjian, MSc; research area: passive microwave remote sensing technology and its application. Contribution: data production and freezing and thawing detection.

Fu Xinru, MSc; research area: remote sensing technology and its applications. Contribution: data preprocessing and analysis.

Shi Lijuan, PhD; research area: passive microwave technology. Contribution: data preprocessing and interpretation.

Tian Bangsen, Assiatant Professor; research area: microwave remote sensing applications. Contribution: data processing and interpretation.



How to cite this article: Qiu Y, Guo H, Ruan Y et al. A dataset of microwave brightness temperature and freeze-thaw for medium-to-large lakes over the High Asia region 2002 – 2016. China Scientific Data 2 (2017), DOI: 10.11922/csdata.170.2017.0117