scholarly journals Snow-Covered Area Retrieval from Himawari–8 AHI Imagery of the Tibetan Plateau

2019 ◽  
Vol 11 (20) ◽  
pp. 2391
Author(s):  
Gongxue Wang ◽  
Lingmei Jiang ◽  
Jiancheng Shi ◽  
Xiaojing Liu ◽  
Jianwei Yang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Temporal Resolution ◽  
Estimation Method ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
The Tibetan Plateau ◽  
Snow Covered Area ◽  
Covered Area ◽  
Fractional Snow Cover

Daily snow-covered area retrieval using the imagery in solar reflective bands often encounters extensive data gaps caused by cloud obscuration. With the inception of geostationary satellites carrying advanced multispectral imagers of high temporal resolution, such as Japan’s geostationary weather satellite Himawari–8, considerable progress can now be made towards spatially-complete estimation of daily snow-covered area. We developed a dynamic snow index (normalized difference snow index for vegetation-free background and normalized difference forest–snow index for vegetation background) fractional snow cover estimation method using Himawari–8 Advanced Himawari Imager (AHI) observations of the Tibetan Plateau. This method estimates fractional snow cover with the pixel-by-pixel linear relationship of snow index observations acquired under snow-free and snow-covered conditions. To achieve reliable snow-covered area mapping with minimal cloud contamination, the daily fractional snow cover can be represented as the composite of the high temporal resolution fractional snow cover estimates during daytime. The comparison against reference fractional snow cover data from Landsat–8 Operational Land Imager (OLI) showed that the root–mean–square error (RMSE) of the Himawari–8 AHI fractional snow cover ranged from 0.07 to 0.16, and that the coefficient of determination (R2) reached 0.81–0.96. Results from the 2015/2016 and 2016/2017 winters indicated that the daily composite of Himawari–8 observations obtained a 14% cloud percentage over the Tibetan Plateau, which was less than the cloud percentage (27%) from the combination of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua.

scholarly journals Height dependence of the tendency for reduction in seasonal snow cover in the Himalaya and the Tibetan Plateau region, 1966–2001

2006 ◽  
Vol 43 ◽  
pp. 369-377 ◽  
Author(s):  
Kunio Rikiishi ◽  
Haruka Nakasato
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
The Tibetan Plateau ◽  
Plateau Region ◽  
Seasonal Snow ◽  
Snow Covered Area ◽  
Covered Area ◽  
Height Dependence ◽  
Seasonal Snow Cover ◽  
The Himalaya

AbstractThe dataset of Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent for the period October 1966-July 2001 is analyzed to examine the height dependence of declining tendencies of seasonal snow cover in the Himalaya and the Tibetan Plateau region (25−45˚ N, 70−110˚E). It is found that the annual mean snow-covered area is decreasing in the Himalaya/Tibet region at a rate of ∼ 1 % a−1, implying that the mean snow-covered area has decreased by one-third from 1966 to 2001. The rate of decrease is largest (1.6%) at the lowest elevations (0−500 m). On the other hand, the length of the snow-cover season is declining at all elevations, with the greatest rate of decline in the 4000−6000 m height range. On the Tibetan Plateau (∼4000−6000 m a.s.l.), the length of the snow-cover season has decreased by 23 days, and the end date for snow cover has advanced by 41 days over this 35 year period. These rates might be somewhat overestimated by the binary definition of snow cover on satellite images. It is likely that the reduction of the snow surface albedo by deposition of Asian dust and anthropogenic aerosols may be at least partly responsible for earlier snowmelt.

Assessment of MODIS-Based Fractional Snow Cover Products Over the Tibetan Plateau

2019 ◽  
Vol 12 (2) ◽  
pp. 533-548 ◽  
Author(s):  
Shirui Hao ◽  
Lingmei Jiang ◽  
Jiancheng Shi ◽  
Gongxue Wang ◽  
Xiaojing Liu
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
The Tibetan Plateau ◽  
Fractional Snow Cover

scholarly journals Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau

2017 ◽  
Vol 9 (12) ◽  
pp. 1332 ◽  
Author(s):  
Hui Liang ◽  
Xiaodong Huang ◽  
Yanhua Sun ◽  
Yunlong Wang ◽  
Tiangang Liang
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
The Tibetan Plateau ◽  
Fractional Snow Cover

scholarly journals A Spectral Analysis of Snow in Mt. Rainier

2018 ◽  
Vol 10 (3) ◽  
pp. 20
Author(s):  
Shrinidhi Ambinakudige ◽  
Pushkar Inamdar ◽  
Aynaz Lotfata
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Thermal Emission ◽  
Major Influence ◽  
Landsat 8 ◽  
Run Off ◽  
Snow Covered Area ◽  
Covered Area ◽  
Local Water ◽  
Normalized Difference Snow Index

Snow cover helps regulate the temperature of the Earth's surface. Snowmelt recharges groundwater, provides run-off for rivers and creeks, and acts as a major source of local water for many communities around the world. Since 2000, there has been a significant decrease in the snow-covered area in the Northern Hemisphere. Climate change is the major factor influencing the change in snow cover amount and distribution. We analyze spectral properties of the remote sensing sensors with respect to the study of snow and examine how data from some of the major remote sensing satellite sensors, such as (Advanced Spaceborne Thermal Emission and Reflection Radiometer) ASTER, Landsat-8, and Sentinel-2, can be used in studying snow. The study was conducted in Mt. Rainier. Although reflectance values recorded were lower due to the timing of the data collection and the aspect of the study site, data can still be used calculate normalized difference snow index (NDSI) to clearly demarcate the snow from other land cover classes. NDSI values in all three satellites ranged from 0.94 to 0.97 in the snow-covered area of the study site. Any pollutants in snow can have a major influence on spectral reflectance in the VIS spectrum because pollutants absorb more than snow.

Extraction and assessment of snowline altitude over the Tibetan plateau using MODIS fractional snow cover data (2001 to 2013)

2014 ◽  
Vol 8 (1) ◽  
pp. 084689 ◽  
Author(s):  
Zhiguang Tang ◽  
Jian Wang ◽  
Hongyi Li ◽  
Ji Liang ◽  
Chaokui Li ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
The Tibetan Plateau ◽  
Fractional Snow Cover

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.0)

2015 ◽  
Vol 8 (10) ◽  
pp. 8481-8518
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Threshold Value ◽  
Landsat 8 ◽  
Snow Covered Area ◽  
Covered Area ◽  
Independent Information ◽  
Landsat 7 ◽  
Definition Of

Abstract. Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed e.g. in case of the frequently used Normalised-Difference Snow Index (NDSI). The definition of this value is often not adequately defined by either a general value from literature or over the impression of the user but not by reproducible independent information. PRACTISE V.2.0 addresses this important aspect and does show additional improvements. The Matlab based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilised as in-situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow-affected photographs was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results have shown that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches.

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.1)

2016 ◽  
Vol 9 (1) ◽  
pp. 307-321 ◽  
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Threshold Value ◽  
Landsat 8 ◽  
Snow Covered Area ◽  
Covered Area ◽  
Independent Information ◽  
Landsat 7 ◽  
Normalized Difference Snow Index ◽  
Definition Of

Abstract. Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed, e.g. in case of the frequently used normalized-difference snow index (NDSI). The definition of this value is often not adequately defined by either a general value from literature or over the impression of the user, but not by reproducible independent information. PRACTISE V.2.1 addresses this important aspect and shows additional improvements. The Matlab-based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilized as in situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow-affected photographs, was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results show that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible, and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches.

A Spectral Analysis of Snow in Mt. Rainier

2018 ◽  
Vol 10 (3) ◽  
pp. 18
Author(s):  
Shrinidhi Ambinakudige ◽  
Pushkar Inamdar ◽  
Aynaz Lotfata
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Thermal Emission ◽  
Major Influence ◽  
Landsat 8 ◽  
Run Off ◽  
Snow Covered Area ◽  
Covered Area ◽  
Local Water ◽  
Normalized Difference Snow Index

Snow cover helps regulate the temperature of the Earth's surface. Snowmelt recharges groundwater, provides run-off for rivers and creeks, and acts as a major source of local water for many communities around the world. Since 2000, there has been a significant decrease in the snow-covered area in the Northern Hemisphere. Climate change is the major factor influencing the change in snow cover amount and distribution. We analyze spectral properties of the remote sensing sensors with respect to the study of snow and examine how data from some of the major remote sensing satellite sensors, such as (Advanced Spaceborne Thermal Emission and Reflection Radiometer) ASTER, Landsat-8, and Sentinel-2, can be used in studying snow. The study was conducted in Mt. Rainier. Although reflectance values recorded were lower due to the timing of the data collection and the aspect of the study site, data can still be used calculate normalized difference snow index (NDSI) to clearly demarcate the snow from other land cover classes. NDSI values in all three satellites ranged from 0.94 to 0.97 in the snow-covered area of the study site. Any pollutants in snow can have a major influence on spectral reflectance in the VIS spectrum because pollutants absorb more than snow.

scholarly journals A revisit of parametrization of summer downward longwave radiation over the Tibetan Plateau from high temporal resolution measurements

2019 ◽  
Author(s):  
Mengqi Liu ◽  
Xiangdong Zheng ◽  
Jinqiang Zhang ◽  
Xiangao Xia
Keyword(s):  
Tibetan Plateau ◽  
Temporal Resolution ◽  
Longwave Radiation ◽  
Surface Energy Budget ◽  
Coefficient Of Determination ◽  
Cloud Base ◽  
High Temporal Resolution ◽  
The Tibetan Plateau ◽  
Clear Sky ◽  
Downward Longwave Radiation

Abstract. The Tibetan Plateau (TP) is one of hot spots in the climate research due to its unique geographical location, high altitude, highly sensitive to climate change as well potential effects on climate in East Asia. Downward longwave radiation (DLR), as a key component in the surface energy budget, is of practical implications for many research fields. Several attempts have been made to measure hourly or daily DLR and then model it over the TP. This study uses 1-minute radiation and meteorological measurements at three stations over the TP to parameterize DLR during summer months. Three independent methods are used to discriminate clear-sky observations by making maximal use of collocated measurements of downward shortwave and longwave radiation as well as Lidar backscatter measurements with high temporal resolution. This guarantees a reliable separation of clear-sky and cloudy samples that favors for proper parameterizations of DLR under these two contrast conditions. Clear-sky and cloudy DLR models with original parameters are firstly assessed. These models are then locally calibrated based on 1-minute observations. DLR estimation is notably improved since specific conditions over the TP are accounted for by local calibration, which is indicated by smaller root mean square error (RMSE) and larger coefficient of determination (R2). The best local parametrization can estimate clear-sky DLR with RMSE of 3.8 W⸱m-2. Overestimation of clear-sky DLR by previous study is evident, likely due to potential residue cloud contamination on the clear-sky samples. Cloud base height under overcast conditions is shown to be intimately related to cloudy DLR parameterization, which is considered by this study in the locally calibrated parameterization over the TP for the first time.

Sign in / Sign up

Export Citation Format

Share Document