Spatiotemporal variations of snow cover in northeast China based on flexible multiday combinations of moderate resolution imaging spectroradiometer snow cover products

2014 ◽  
Vol 8 (1) ◽  
pp. 084685 ◽  
Author(s):  
Shengbo Chen ◽  
Qian Yang ◽  
Hongjie Xie ◽  
Haijun Zhang ◽  
Peng Lu ◽  
...  
Keyword(s):  
Snow Cover ◽  
Northeast China ◽  
Spatiotemporal Variations ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

scholarly journals Influence of Straw Burning on Urban Air Pollutant Concentrations in Northeast China

2019 ◽  
Vol 16 (8) ◽  
pp. 1379 ◽  
Author(s):  
Zhenzhen Wang ◽  
Jianjun Zhao ◽  
Jiawen Xu ◽  
Mingrui Jia ◽  
Han Li ◽  
...  
Keyword(s):  
Northeast China ◽  
Arable Land ◽  
Pollution Index ◽  
Air Pollutant ◽  
Thermal Anomalies ◽  
Pollutant Concentrations ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Straw Burning ◽  
Moderate Resolution Imaging Spectroradiometer

Northeast China is China’s primary grain production base. A large amount of crop straw is incinerated every spring and autumn, which greatly impacts air quality. To study the degree of influence of straw burning on urban pollutant concentrations, this study used The Moderate-Resolution Imaging Spectroradiometer/Terra Thermal Anomalies & Fire Daily L3 Global 1 km V006 (MOD14A1) and The Moderate-Resolution Imaging Spectroradiometer/Aqua Thermal Anomalies and Fire Daily L3 Global 1 km V006 (MYD14A1) data from 2015 to 2017 to extract fire spot data on arable land burning and to study the spatial distribution characteristics of straw burning on urban pollutant concentrations, temporal variation characteristics and impact thresholds. The results show that straw burning in Northeast China is concentrated in spring and autumn; the seasonal spatial distributions of PM2.5, PM10 andAir Quality Index (AQI) in 41 cities or regions in Northeast China correspond to the seasonal variation of fire spots; and pollutants appear in the peak periods of fire spots. In areas where the concentration coefficient of rice or corn is greater than 1, the number of fire spots has a strong correlation with the urban pollution index. The correlation coefficient R between the number of burned fire spots and the pollutant concentration has a certain relationship with the urban distribution. Cities are aggregated in geospatial space with different R values.

scholarly journals Satellite monitoring of blue-ice extent near Byrd Glacier, Antarctica

2004 ◽  
Vol 39 ◽  
pp. 223-230 ◽  
Author(s):  
Ian C. Brown ◽  
Ted A. Scambos
Keyword(s):  
Snow Cover ◽  
Satellite Monitoring ◽  
Landsat Images ◽  
Moderate Resolution ◽  
Summer Maximum ◽  
Resolution Imaging ◽  
Snowfall Event ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Seasonal And Interannual Variations

AbstractWe use satellite images to track seasonal and interannual variations in blue-ice extent over the past 30 years near Byrd Glacier on the East Antarctic plateau. The study areas have low slope and few nearby nunataks, which may increase their climate sensitivity. A threshold-based algorithm sensitive to snow grain-size is used to analyze 56 Moderate Resolution Imaging Spectroradiometer (MODIS) images over three recent summer seasons. Seasonal blue-ice exposure grows rapidly in late spring, and peaks by late December. Exposure is relatively constant between late December and mid-January, then declines in February. We interpret this cycle as due to removal and re-accumulation of patchy snow. Interannual changes in blue-ice area may be estimated by tracking the near-constant summer maximum extent period. Fifteen mid-summer Landsat images, spanning 1974–2002, were analyzed to determine long-term variations. Interannual area changes are 10–30%; however, the MODIS data revealed that the exposed blue-ice area can be sharply reduced for up to 2 weeks after a snowfall event; and in the 2001/02 season, patchy snow cover persisted for the entire summer. The combination of MODIS seasonal and Landsat interannual data indicates that blue-ice areas can be climate-sensitive. The strong feedback between snow cover and surface energy balance implies that blue-ice areas could rapidly decrease due to climate-related increases in snowfall or reduced ablation.

A Study on Flood Forecasting in the Upper Indus Basin Considering Snow and Glacier Meltwater

2017 ◽  
Vol 12 (4) ◽  
pp. 793-805 ◽  
Author(s):  
Tong Liu ◽  
Morimasa Tsuda ◽  
Yoichi Iwami ◽  
◽  
Keyword(s):  
Snow Cover ◽  
Monsoon Season ◽  
Indus Basin ◽  
Maximum Magnitude ◽  
Upper Indus Basin ◽  
Moderate Resolution ◽  
Daily Discharge ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Tarbela Dam

This study considered glacier and snow meltwater by using the degree–day method with ground-based air temperature and fractional glacier/snow cover to simulate discharge at Skardu, Partab Bridge (P. Bridge), and Tarbela Dam in the Upper Indus Basin during the monsoon season, from the middle of June to the end of September. The optimum parameter set was determined and validated in 2010 and 2012. The simulated discharge with glaciermelt and snowmelt could capture the variations of the observed discharge in terms of peak volume and timing, particularly in the early monsoon season. The Moderate Resolution Imaging Spectroradiometer (MODIS) daily and eight-day snow cover products were applied and recommended with proper settings for application. This study also investigated the simulations with snow packs instead of daily snow cover, which was found to approach the maximum magnitude of observed discharge even from the uppermost station, Skardu.This study estimated the glacier and snow meltwater contribution at Skardu, Partab Bridge, and Tarbela as 43.2–65.2%, 22.0–29.3%, and 6.3–19.9% of average daily discharge during the monsoon season, respectively. In addition, this study evaluated the main source of simulation discrepancies and concluded that the methodology proposed in the study worked well with proper precipitation.

scholarly journals Estimating spatial distribution of daily snow depth with kriging methods: combination of MODIS snow cover area data and ground-based observations

2015 ◽  
Vol 9 (5) ◽  
pp. 4997-5020 ◽  
Author(s):  
C. L. Huang ◽  
H. W. Wang ◽  
J. L. Hou
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Snow Depth ◽  
Western China ◽  
Kriging Interpolation ◽  
Snow Cover Area ◽  
Smoothing Effects ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Accurately measuring the spatial distribution of the snow depth is difficult because stations are sparse, particularly in western China. In this study, we develop a novel scheme that produces a reasonable spatial distribution of the daily snow depth using kriging interpolation methods. These methods combine the effects of elevation with information from Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover area (SCA) products. The scheme uses snow-free pixels in MODIS SCA images with clouds removed to identify virtual stations, or areas with zero snow depth, to compensate for the scarcity and uneven distribution of stations. Four types of kriging methods are tested: ordinary kriging (OK), universal kriging (UK), ordinary co-kriging (OCK), and universal co-kriging (UCK). These methods are applied to daily snow depth observations at 50 meteorological stations in northern Xinjiang Province, China. The results show that the spatial distribution of snow depth can be accurately reconstructed using these kriging methods. The added virtual stations improve the distribution of the snow depth and reduce the smoothing effects of the kriging process. The best performance is achieved by the OK method in cases with shallow snow cover and by the UCK method when snow cover is widespread.

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