Remote sensing-based monitoring of coverage and depth of snow in northern Xinjiang
4
Citation
0
Reference
20
Related Paper
Citation Trend
Abstract:
The snow disaster often takes place in the north of Xinjiang.So it is of great significance to exactly monitor the snow distribution and snow depth in the northern Xinjiang,which can provide scientific basis for snow disaster prevention and reduction.In recent years,NDSI is mainly used to abstract the snow cover with MODIS data.The NDSI is a spectral ratio that takes advantage of the spectral difference of snow in short-wave infrared and visible spectral bands.It can only discern one pixel into snow or other features,and can not satisfy accurate drainage basin snow cover mapping and snow parameter extracting.In this study,linear spectrum mixing model was used to abstract snow fraction in the north of Xinjiang.Then we established the relationship between snow fraction and NDSI and evaluated whether NDSI can be used to estimate the cover rate of snow within a 250m pixel.The result showed that they had good linear relationship.The mean absolute error for 25 true measured points was 0.06.Moreover,we analyzed the correlation between snow depth and the reflected spectrum of snow and compared the true measured snow reflected spectrum with the image reflected spectrum.The most sensitive bands to snow depth were chosen.At last,the snow depth-inversing model was built.Cite
Snow is one kind of special underlying surface which has high reflectivity, low thermal conductivity and snowbroth hydrological effect, Xinjiang Uygur Autonomous Region in China is a snow-prone area, hereafter referred simply to Xinjiang area, frequent snow disaster leads to local people's production and life affected, so accurate monitoring of snow has very important significance. This paper describes the current status of snow research using remote sensing technology at home and abroad, the basic knowledge and application of EOS/MODIS is simply introduced, NDSI (Normalized Difference Snow Index) is a measuring method which bases on the difference of visible and NIR (near infrared) bands reflectivity of snow, it can discriminate snow and snow free. In the case study area, the algorithm which combined NDSI and bands brightness temperature difference is taken to extract snow cover, comparing with meteorological data, the snow cover extracted from MODIS data prove to be accurate.
Cite
Citations (0)
Cite
Citations (11)
Abstract Snow and Ice–cover area information is important for a wide variety of scientific climate studies, water source and management applications. The NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, provides improved capabilities to observe snow-ice coverarea from space and has been successfully using a Normalized Difference Snow Index (NDSI) methodes, This technique is the same procedure for the Normalized Difference Vegetation Index (NDVI), along with threshold tests, to provide global, automated binary maps of snow cover. The NDSI is a spectral band ratio that takes advantage of the spectral differences of snow in short-wave infrared and visible. MODIS spectral bands to identify snow versus other features in a scene. This methodes has evaluated whether there is a “signal” in the NDSI that could be used to estimate the area of snow satellite images within a 500 m MODIS pixel, the percentage of snow cover was calculated for 500 m cells. Snow cover area in the head water sourse of the rivers can be useful to estimating the amount and the quantity of the water for the rivers. The south of Turkey area have the basin of the two rivers (Tigris and Euphrates). The main fresh water sorce to Syria and Iraq.
Moderate-resolution imaging spectroradiometer
Spectroradiometer
Cite
Citations (3)
Using CBERS-2 CCD data as ground truth images and based on the characters of geographic environment and climate in Northeast China,this paper modified the parameters of Salomoson's subpixel snow cover fraction model,retrieved the subpixel snow cover fraction from MODIS data and analyzed the stability and accuracy of the revised model with different ways.The result shows that the modified Salomoson model has stability in different geomorphology-landscape unit,and the little fluctuation comes from the difference of snow physical character,atmospheric effects,and the errors of snow classification images and image registration.In the Northeast Plain,when NDSI ranges from 0.52 to 0.65,the accuracy of modified model is high.However,because NDSI is based on the non-linear transformation of band reflectance,snow cover fraction is little underestimated.The pixels,snow cover fraction of which is overestimated,are located in suburb and rural residential area.In contrast,the pixels,covering cities,towns and roads have a higher accuracy.The main reason is that human activity frequency influences the degree of spectral character difference between snow part and non-snow part in one MODIS pixel.In comparison with MODIS snow products(MOD10A2),snow cover fraction provides much richer information than the traditional snow cover mapping methods,but both of them don't give the exact estimation of snow cover fraction in forest zone.
Subpixel rendering
Ground truth
Fraction (chemistry)
Cite
Citations (2)
Abstract Taking the Northern Xinjiang region as an example, we develop a snow depth model by using the Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR‐E) horizontal and vertical polarization brightness temperature difference data of 18 and 36 GHz bands and in situ snow depth measurements from 20 climatic stations during the snow seasons November–March) of 2002–2005. This article proposes a method to produce new 5‐day snow cover and snow depth images, using Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products and AMSR‐E snow water equivalent and daily brightness temperature products. The results indicate that (1) the brightness temperature difference (Tb 18h –Tb 36h ) provides the most accurate and precise prediction of snow depth; (2) the snow, land and overall classification accuracies of the new images are separately 89.2%, 77.7% and 87.2% and are much better than those of AMSR‐E or MODIS products (in all weather conditions) alone; (3) the snow classification accuracy increases as snow depth increases; and (4) snow accuracies for different land cover types vary as 88%, 92.3%, 79.7% and 80.1% for cropland, grassland, shrub, and urban and built‐up, respectively. We conclude that the new 5‐day snow cover–snow depth images can provide both accurate cloud‐free snow cover extent and the snow depth dynamics, which would lay a scientific basis for water management and prevention of snow‐related disasters in this dry and cold pastoral area. After validations of the algorithms over other regions with different snow and climate conditions, this method would also be used for monitoring snow cover and snow depth elsewhere in the world. Copyright © 2011 John Wiley & Sons, Ltd.
Moderate-resolution imaging spectroradiometer
Cite
Citations (18)
Moderate-resolution imaging spectroradiometer
Spectroradiometer
Atmospheric correction
Cite
Citations (69)
The extension of the snow cover and the distribution of different snow types can be considered an indicator of global changes and a key parameter in the global radiation balance of the Earth. Moreover, in the mountain regions the possibility to monitor the snow characteristics using remote sensing images can support hydrological studies. The reflectance of snow is determined in part by the size and shape of snow crystals, especially in the short wave infrared (SWIR) wavelength region; for this reasons it is possible to use remote sensed images to map differences in the snow cover. The Specific Surface Area (SSA) of snow is a crucial variable for understanding snow chemistry and air snow exchanges of chemical species that can also be related to snow reflectance. This study shows how field spectral measurement and SSA data of snow samples can be used as input data for classifying Landsat TM SWIR images in order to obtain maps of different snow types. This method can be a very useful tool to monitor the snow metamorphism, air-snow exchanges and climate.
Snow field
Cite
Citations (4)
Snow is the most important freshwater resource in northern Xinjiang, which is a typical inland arid ecosystem in western China. Snow mapping can provide useful information for water resource management in this arid ecosystem. An applicable approach for snow mapping in Northern Xinjiang Basin using MODIS data was proposed in this paper. The approach of linear spectral mixture analysis (LSMA) was used to calculate snow cover fractions within a pixel, which was used to establish a regression function with NDSI at a 250-meter grid resolution. Field campaigns were conducted to examine whether NDSI can be used to extend the utility of the snow mapping approach to obtain sub-pixel estimates of snow cover. In addition, snow depths at 80 sampling sites were collected in the study region. The correlation between image reflectivity and snow depth as well as the comparison between measured snow spectra and image spectra were analyzed. An algorithm was developed on the basis of the correlation for snow depth mapping in the region. Validation for another dataset with 50 sampling sites showed an RMSE of 1.63, indicating that the algorithm was able to provide an estimation of snow depth at an accuracy of 1.63cm. The results indicated that snow cover area can reach 81% and average snow depth was 13.8 cm in north Xinjiang in January 2005. Generally speaking, the snow cover and depth had a trend of gradually decreasing from north to south and from the surroundings to the center. Temporally, the cover reached a maximum in early January, and the depth reached a maximum was ten days later. Snow duration was so different in different regions with the Aletai region having the longest and the Bole having the shortest. In the period of snow melting, snow depth decreased earlier, afterward snow cover dwindled. Our study showed that the spatial and temporal variation of snow cover was very critical for water resource management in the arid inland region and MODIS satellite data provide an alternative for snow mapping through dedicated development of mapping algorithms suitable for local application.
Cite
Citations (2)
For the needs of snow cover monitoring using multi-source remote sensing data, in the present article, based on the spectrum analysis of different depth and area of snow, the effect of snow depth on the results of snow cover retrieval using normalized difference snow index (NDSI) is discussed. Meanwhile, taking the HJ-1B and MODIS remote sensing data as an example, the snow area effect on the snow cover monitoring is also studied. The results show that: the difference of snow depth does not contribute to the retrieval results, while the snow area affects the results of retrieval to some extents because of the constraints of spatial resolution.
Snow field
Cite
Citations (1)
Snow cover has an immense value as a natural resource of water used particularly for irrigation purposes. The amount of snow accumulated in a (mountain) watershed determines the runoff after the onset of melt during spring. Hence, the measurements of snow cover extent and snow characteristics (snow depth and snow water equivalent) in real time are very important. Conventional methods of data collection (for example, snow surveys or isolated stations using in situ sensors) are time consuming and spatially limited. Consequently, the resultant snow water equivalent measurements are enormously different from the actual snow water equivalent. At high elevation and in remote areas of the globe where very little in situ data exists, remote sensing is the only mean by which to observe the snow cover distribution. Microwave radiation penetrating through clouds and snow covered area could provide snow depth and snow water equivalent information about a snow pack. However, due to the coarse spatial resolution of the passive microwave sensors, in a single footprint, the presence of snow surface area along with forest fraction and water bodies contribute considerable signal variation. The principal objective of this study is to analyze the degree to which SSM/I brightness temperature can be affected by the snow parameters. The sensitivity analyses of the snow surface on the brightness temperature estimation with semi-empirical model using Special Sensor Microwave Imager (SSM/I) frequencies are investigated. In the sensitivity analysis the parameters of interest were snow density, snow salinity, snow wetness, snow grain size, snow temperature and snow depth.
Snow field
Snowmelt
Cite
Citations (1)