Integration of multi-source images for improving spatial resolution of snow depth detection in western China Qiming ZHOU
Bo SUN
Dept. of Geography Hong Kong Baptist University Hong Kong S.A.R., China Email:
[email protected] Tel: +852-34115048
Dept. of Geography Hong Kong Baptist University Hong Kong S.A.R., China Email:
[email protected]
Abstract—Snow depth is one of the most important parameters in snow cover studies. Although passive microwave remote sensing imagery has been widely used for detecting snow depth in a large area, the accuracy is limited due to the coarse spatial resolution especially in complex terrain areas. This paper aims to improve the spatial resolution of snow depth detection by integrating multi-source remote sensing data, such as optical snow extent and passive microwave brightness temperature data. With the assist of snow extent product with higher spatial resolution, a linear unmixing method is adopted for improving mixed pixel problem in passive microwave images. Snow extent is not only utilized for identifying the proportion of different components in a mixed pixel but also for indicating their spatial locations. Illustrated with a case study in western China, the spatial resolution of snow depth detection is increased with pixel size from 0.25 degree to 0.05 degree. The proposed method shows that integrating higher resolution optical product is an effective means for improving the spatial resolution of snow depth detection. Keywords- snow cover; snow depth detection; resolution; multi-source data fusion; western China
I.
spatial
INTRODUCTION
As a major component of global water and energy cycles, snow plays an important role in ocean-atmosphere interaction. Snow cover in alpine mountains is always considered as one of the most sensitive indicators of climate change. Apart from snow extent, snow depth is another key parameter for snow storage estimation in snow cover change studies [1]. Passive microwave remotely sensed imagery has been widely used for detecting snow depth at large scales. The measurement of snow depth is based on scattering theory and realized by detecting the difference in emissivity between two frequencies, typically a low (e.g. 18GHz) and a high (e.g. 36GHz) scattering channels [2-3]. To conduct a long-term observation, the most popular data are derived from SMMR, SSM/I and AMSR-E sensors. Various models for the inversion of snow depth based on those images have been developed. A widely used is Chang’s algorithm [4]. However, scholars
indicated that not an existing global algorithm can perform well for all conditions. Li et al.[5] (2007) reported a method using modified Chang’s algorithms for getting more accurate measurements of snow depth in western China and established a long-term snow depth dataset. Although many efforts have been undertaken for improving the measurement of snow depth, the accuracy is limited due to the coarse spatial resolution of passive microwave imagery. Mixed pixel problem still exists. This would affect the precision of snowmelt water equivalent (SWE) estimation especially in mountain areas with a complex terrain. Among various unmixing methods, linear method is the most commonly used one. Singer and McCord [6] (1979) indicated that the signal on a mixed pixel can be considered as a linear combination of the signals of all components at a coarse pixel level. However, existing methods of multi-resolution data fusion using linear unmixing method are focused on determining the proportion of the areas of different endmembers in mixed pixels (e.g. [7]). It is difficult to get the endmembers’ spatial distribution information at sub-pixel level [8]. Bellerby et al. [9] (1998) proposed a method to separate uncontaminated land and sea brightness temperature from mixed pixel by using 8-adjacent pixels. As for snow cover study, it is hard to find a clear border between snow and the other land cover types. In order to get a more accurate snow depth measurement, this study attempts to establish a methodology for improving the spatial resolution of snow depth detection through data fusion of multi-source data including optical and passive microwave remotely sensed images. With the assist of higherresolution optical snow extent products, a linear unmixing method will be adopted for settling mixed pixel problem on coarse-resolution passive microwave images. II.
METHODLOGY
The general method is based on linear unmixing method to modify the result of snow depth detection under the help of snow extent information with a higher spatial resolution. After
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that, snow depth products retrieved from passive microwave imagery will be resampled to a higher spatial resolution.
with pixel size of 0.25 degree can be resampled to a higher resolution with pixel size of 0.05 degree, i.e. an ASMR-E pixel includes twenty-five MODIS-size sub-pixels (Figure 2).
A. Study area Xinjiang Uygur Autonomous Region of China is chosen as the study area (Figure 1). It is located in the west arid region of China covering an area of 1.66 million km2. Since it is situated in the hinterland of Eurasian continent, less precipitation can be found. Snow cover (including glaciers) in alpine mountains is the major water resource for this region. Due to complex topography, snow cover doesn’t show a homogenous distribution especially in the mountain areas. Fine-resolution (25 pixels)
Coarse-resolution (a pixel)
Figure 2. Example of mixed pixel, blue cell means the sub-pixel covered with snow and blank cell means a snow-free sub-pixel.
In fact, an AMSR-E pixel covers a huge extent with the area of 625 km2. The detected snow depth is the average of snow cover in this large area. In other words, brightness temperature represents the energy collected by sensors with the emission of microwave in snow cover area as well as in snowfree area. This can be expressed by the following equation [13]:
TB = ∑ λi ⋅ TBi + ε Figure 1. Study area
B. Data There are three types of snow data will be used, including •
Moderate-Resolution Imaging Spectroradiometer (MODIS) snow extent product [10], issued by National Snow and Ice Data Center (NSIDC);
•
Snow depth dataset of China [11], issued by Cold and Arid Regions Environmental and Engineering Research Institute (CAREERI), Chinese Academy of Science; and
•
Original passive microwave brightness temperature data [12], cropped and reissued by Environmental & Ecological Science Data Center for West China.
Although they all have a high temporal resolution, multiday composite data will be adopted so that images can cover the whole study area and heavy cloud can be removed from the final results as well. In this study, 8-day composite snow cover extent product with spatial resolution of 0.05 degree (MOD10C2) will be employed. Passive microwave data are collected at spatial resolution level of 0.25 degree. In order to make a consistence of ground snow cover condition, the observation dates of all data are chosen at December 31, 2008. C.
Data fusion The purpose of data fusion is to integrate the advantages of optical snow extent product’s higher spatial resolution and passive microwave product’s ability of snow depth detection. The general idea is to settle mixed pixel problem in passive microwave data. In this study, coarse-resolution ASMR-E data
(1)
Where, •
TB means the total brightness temperature received by sensor;
λ
•
i means the proportion of component i in a mixed pixel;
•
TBi means the brightness temperature of component i;
•
ε means the residuals.
To simplify the analysis, two components – “snow” and the “others” are considered in this study. According to formula (1), brightness temperatures received by sensor should be:
TB = λSNOW ⋅ TBSNOW + λOTHERS ⋅ TBOTHERS
(2)
To bring the formula (2) into the modified Chang’s algorithm provided by CAREERI, the adjusted snow depth estimation without the affect of brightness temperatures of the other components in a mixed pixel can be calculated by the following formula,
SD ' =
SD − 0.66 ⋅ (1 − λ SNOW ) ⋅ (TB19 H ,OTHERS − TB36 H ,OTHERS )
λ SNOW
(3)
D. Data processing All imagery data will be clipped to match the study area. The proportion of snow cover in AMSR-E pixel can be
retrieved from MODIS snow extent product. i.e., the proportion of snow cover in an AMSR-E pixel can be represented by the average of snow cover proportions of the corresponding 25 MODIS pixels. Seldom research works have reported the difference of brightness temperatures between 19GHz and 36GHz for other land cover types. In this study, statistical method will be used for finding a general mean value of the difference between the two frequency channels for the other land cover types. By taking AMSR-E snow depth product as a mask, snow-free land cover will be classified from AMSR-E brightness temperature data. According to the classified snow-free land cover, the difference image of brightness temperatures between 18GHz and 36GHz will be obtained and the mean value will be calculated.
(a)
After that, snow depth product with modified snow depth values will be resampled to higher spatial resolution with pixel size of 0.05 degree. III.
RESULTS AND ANALYSIS
Figure 3 shows the difference of brightness temperatures between 18GHz and 36GHz of snow-free land cover types. Gray region represents no data because of the gap between satellite orbits. According to statistics, the mean value is minus 2.67 without some exceptional values which are treated as errors coming from snow cover classification. (b) Figure 4. Snow depth estimations, (a) is retrieved from Environmental & Ecological Science Data Center at 0.25 degree spatial resolution, (b) is the modified results of snow depth at 0.05 degree spatial resolution.
IV.
Figure 3. Difference of brightness temperatures of snow-free land cover types
We assume the MODIS product has a more accurate result of snow cover detection. The pixel with a probability of snow cover larger than 50% will be considered as a snow-cover pixel in the final result. Figure 4 illustrates snow depth estimations before and after data fusion. After combining with higherresolution MODIS snow extent, the results of snow depth detection have been modified, and the spatial resolution of snow depth has increased.
DISCUSSION AND CONCLUSION
For snow depth detection, an assumption is that snow cover in an AMSR-E pixel shows the same thickness of snow. When separating an AMSR-E pixel into smaller sub-pixels, some sub-pixel show the snow depth but adjacent ones might show nothing. That is how the shape of sawtooth appears. Due to this reason, the result shows a less gradual change of snow depth in terms of its spatial distribution (Figure 4 (b)). Besides, scholars indicated that it is hard for optical remote sensing sensors to detect a thin layer of snow. Since we take snow cover detection result of optical MODIS snow extent product as a standard, snow cover with thinner thickness in the bottom of the study area disappeared in the modified snow depth product. Determination of the difference of brightness temperatures between the two channels for snow-free land cover type is the key to modify snow depth measurement of passive microwave data. Although we can measure the difference value in other seasons without snow cover, ground land cover situation might change. Besides, the situation of satellite and sensor as well as atmosphere status would be changed also. As it is difficult to find pure endmembers in linear unmixing method, it is also hard to get the difference value of brightness temperatures of the other land cover types for each pixel. A mean value for the whole study area is calculated. This might lead to an unsuitable estimation of the adjustment of snow depth measurement, because land cover situation can be various in a large area.
This study has proposed a method of integrating higherresolution optical snow product and passive microwave remote sensing data for increasing the spatial resolution of snow depth detection. High resolution snow extent product is used for identify the proportion of components in coarse resolution pixels. Besides, the difference of brightness temperatures of snow-free land cover types is calculated for modifying snow depth measurements. By taking MODIS and AMSR-E data for example, the proposed method has proven to be effective to enhance the spatial information of snow depth detection. As for future work, an evaluation system of the accuracy of snow depth estimation should be undertaken. In-site observation data of snow depth will be collected for accuracy assessment. In addition, a good strategy of obtaining the difference of brightness temperatures between 18 GHz and 36GHz should be developed so as to make snow depth estimation be more accurate.
[2]
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[5]
[6]
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[8]
[9]
ACKNOWLEDGMENT Snow depth dataset of China and AMSR-E/Aqua Daily Gridded Brightness Temperatures of China are provided by Environmental & Ecological Science Data Center for West China, National Natural Science Foundation of China (http://westdc.westgis.ac.cn).
[10]
[11]
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