scholarly journals Snow Cover Monitoring with Chinese Gaofen-4 PMS Imagery and the Restored Snow Index (RSI) Method: Case Studies

2018 ◽  
Vol 10 (12) ◽  
pp. 1871 ◽  
Author(s):  
Tianyuan Zhang ◽  
Huazhong Ren ◽  
Qiming Qin ◽  
Yuanheng Sun
Keyword(s):  
Snow Cover ◽  
Global Climate ◽  
Limiting Factor ◽  
Landsat 8 ◽  
Climate Variable ◽  
Swir Band ◽  
Novel Method ◽  
Optical Imagery ◽  
Shortwave Infrared ◽  
Signal Saturation

Snow cover is an essential climate variable of the Global Climate Observing System. Gaofen-4 (GF-4) is the first Chinese geostationary satellite to obtain optical imagery with high spatial and temporal resolution, which presents unique advantages in snow cover monitoring. However, the panchromatic and multispectral sensor (PMS) onboard GF-4 lacks the shortwave infrared (SWIR) band, which is crucial for snow cover detection. To reach the potential of GF-4 PMS in snow cover monitoring, this study developed a novel method termed the restored snow index (RSI). The SWIR reflectance of snow cover is restored firstly, and then the RSI is calculated with the restored reflectance. The distribution of snow cover can be mapped with a threshold, which should be adjusted according to actual situations. The RSI was validated using two pairs of GF-4 PMS and Landsat-8 Operational Land Imager images. The validation results show that the RSI can effectively map the distribution of snow cover in these cases, and all of the classification accuracies are above 95%. Signal saturation slightly affects PMS images, but cloud contamination is an important limiting factor. Therefore, we propose that the RSI is an efficient method for monitoring snow cover from GF-4 PMS imagery without requiring the SWIR reflectance.

scholarly journals A MULTI-TEMPORAL APPROACH FOR DETECTING SNOW COVER AREA USING GEOSTATIONARY IMAGERY DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 511-512
Author(s):  
Hwa-Seon Lee ◽  
Kyu-Sung Lee
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Detection Method ◽  
Temporal Variations ◽  
High Temporal Resolution ◽  
Snow Cover Area ◽  
Swir Band ◽  
Multi Temporal ◽  
The Difference ◽  
Shortwave Infrared

In this study, we attempt to detect snow cover area using multi-temporal geostationary satellite imagery based on the difference of spectral and temporal characteristics between snow and clouds. The snow detection method is based on sequential processing of simple thresholds on multi-temporal GOCI data. We initially applied a simple threshold of blue reflectance and then root mean square deviation (RMSD) threshold of near infrared (NIR) reflectance that were calculated from time-series GOCI data. Snow cover detected by the proposed method was compared with the MODIS snow products. The proposed snow detection method provided very similar results with the MODIS cloud products. Although the GOCI data do not have shortwave infrared (SWIR) band, which can spectrally separate snow cover from clouds, the high temporal resolution of the GOCI was effective for analysing the temporal variations between snow and clouds.

scholarly journals A MULTI-TEMPORAL APPROACH FOR DETECTING SNOW COVER AREA USING GEOSTATIONARY IMAGERY DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 511-512
Author(s):  
Hwa-Seon Lee ◽  
Kyu-Sung Lee
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Detection Method ◽  
Temporal Variations ◽  
High Temporal Resolution ◽  
Snow Cover Area ◽  
Swir Band ◽  
Multi Temporal ◽  
The Difference ◽  
Shortwave Infrared

In this study, we attempt to detect snow cover area using multi-temporal geostationary satellite imagery based on the difference of spectral and temporal characteristics between snow and clouds. The snow detection method is based on sequential processing of simple thresholds on multi-temporal GOCI data. We initially applied a simple threshold of blue reflectance and then root mean square deviation (RMSD) threshold of near infrared (NIR) reflectance that were calculated from time-series GOCI data. Snow cover detected by the proposed method was compared with the MODIS snow products. The proposed snow detection method provided very similar results with the MODIS cloud products. Although the GOCI data do not have shortwave infrared (SWIR) band, which can spectrally separate snow cover from clouds, the high temporal resolution of the GOCI was effective for analysing the temporal variations between snow and clouds.

scholarly journals Automated detection and temporal monitoring of crevasses using remote sensing and their implications for glacier dynamics

2016 ◽  
Vol 57 (71) ◽  
pp. 81-91 ◽  
Author(s):  
Anshuman Bhardwaj ◽  
Lydia Sam ◽  
Shaktiman Singh ◽  
Rajesh Kumar
Keyword(s):  
Flow Characteristics ◽  
Surface Velocity ◽  
Average Error ◽  
Landsat 8 ◽  
Differential Gps ◽  
Swir Band ◽  
Glacier Dynamics ◽  
Glacier Flow ◽  
Shortwave Infrared ◽  
Gps Observations

AbstractDetailed studies on temporal changes of crevasses and their linkage with glacier dynamics are scarce in the Himalayan context. Observations of temporally changing surficial crevasse patterns and their orientations are suggestive of the processes that determine seasonal glacier flow characteristics. In the present study, on a Himalayan valley glacier, changing crevasse patterns and orientations were detected and mapped on Landsat 8 images in an automated procedure using the ratio of Thermal Infrared Sensor (TIRS) band 10 to Optical Land Imager (OLI) shortwave infrared (SWIR) band 6. The ratio was capable of mapping even crevasses falling under mountain shadows. Differential GPS observations suggested an average error of 3.65% and root-mean-square error of 6.32m in crevasse lengths. A year-round observation of these crevasses, coupled with field-based surface velocity measurements, provided some interesting interpretations of seasonal glacier dynamics.

Impact of LULC Changes on LST in Rajshahi District of Bangladesh: A Remote Sensing Approach

2020 ◽  
Vol 3 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Abdulla Al Kafy ◽  
Abdullah Al-Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Md. Soumik Sikdar ◽  
Mohammad Hasib Hasan Khan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Agricultural Land ◽  
Global Climate ◽  
Urban Expansion ◽  
Water Bodies ◽  
Landsat 8 ◽  
Lulc Changes ◽  
The Impact

Urbanization has been contributing more in global climate warming, with more than 50% of the population living in cities. Rapid population growth and change in land use / land cover (LULC) are closely linked. The transformation of LULC due to rapid urban expansion significantly affects the functions of biodiversity and ecosystems, as well as local and regional climates. Improper planning and uncontrolled management of LULC changes profoundly contribute to the rise of urban land surface temperature (LST). This study evaluates the impact of LULC changes on LST for 1997, 2007 and 2017 in the Rajshahi district (Bangladesh) using multi-temporal and multi-spectral Landsat 8 OLI and Landsat 5 TM satellite data sets. The analysis of LULC changes exposed a remarkable increase in the built-up areas and a significant decrease in the vegetation and agricultural land. The built-up area was increased almost double in last 20 years in the study area. The distribution of changes in LST shows that built-up areas recorded the highest temperature followed by bare land, vegetation and agricultural land and water bodies. The LULC-LST profiles also revealed the highest temperature in built-up areas and the lowest temperature in water bodies. In the last 20 years, LST was increased about 13ºC. The study demonstrates decrease in vegetation cover and increase in non-evaporating surfaces with significantly increases the surface temperature in the study area. Remote-sensing techniques were found one of the suitable techniques for rapid analysis of urban expansions and to identify the impact of urbanization on LST.

scholarly journals EXTRACTING BUILT-UP FEATURES IN COMPLEX BIOPHYSICAL ENVIRONMENTS BY USING A LANDSAT BANDS RATIO

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 79-85
Author(s):  
A. H. Ngandam Mfondoum ◽  
P. G. Gbetkom ◽  
R. Cooper ◽  
S. Hakdaoui ◽  
M. B. Mansour Badamassi
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Experimental Site ◽  
Soil Features ◽  
Mixed Pixels ◽  
Shortwave Infrared ◽  
Normalization Process

Abstract. This paper addresses the remote sensing challenging field of urban mixed pixels on a medium spatial resolution satellite data. The tentatively named Normalized Difference Built-up and Surroundings Unmixing Index (NDBSUI) is proposed by using Landsat-8 Operational Land Imager (OLI) bands. It uses the Shortwave Infrared 2 (SWIR2) as the main wavelength, the SWIR1 with the red wavelengths, for the built-up extraction. A ratio is computed based on the normalization process and the application is made on six cities with different urban and environmental characteristics. The built-up of the experimental site of Yaoundé is extracted with an overall accuracy of 95.51% and a kappa coefficient of 0.90. The NDBSUI is validated over five other sites, chosen according to Cameroon’s bioclimatic zoning. The results are satisfactory for the cities of Yokadouma and Kumba in the bimodal and monomodal rainfall zones, where overall accuracies are up to 98.9% and 97.5%, with kappa coefficients of 0.88 and 0.94 respectively, although these values are close to those of three other indices. However, in the cities of Foumban, Ngaoundéré and Garoua, representing the western highlands, the high Guinea savannah and the Sudano-sahelian zones where built-up is more confused with soil features, overall accuracies of 97.06%, 95.29% and 74.86%, corresponding to 0.918, 0.89 and 0.42 kappa coefficients were recorded. Difference of accuracy with EBBI, NDBI and UI are up to 31.66%, confirming the NDBSUI efficiency to automate built-up extraction and unmixing from surrounding noises with less biases.

scholarly journals Variation of snow cover in the Nyang River basin of southeastern Tibetan Plateau, China

2021 ◽  
Author(s):  
Rui Zhang ◽  
Zongxue Xu ◽  
Depeng Zuo ◽  
Chunguang Ban
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Water Equivalent ◽  
Global Climate ◽  
Remote Sensing Data ◽  
Significant Negative Correlation ◽  
Spatial And Temporal Variation ◽  
Snow Cover Area ◽  
Northern Edge ◽  
Slight Upward Trend

Abstract Snow cover is highly sensitive to global climate change and strongly influences the climate at global and regional scales. Because of limited in situ observations, snow cover dynamics in the Nyang River basin (NRB) have been examined in few studies. Five snow cover indices derived from observation and remote sensing data from 2000 to 2018 were used to investigate the spatial and temporal variation of snow cover in the NRB. There was clear seasonality in the snow cover throughout the entire basin. The maximum snow-covered area was 8,751.35 km2, about 50% of the total basin area, and occurred in March. The maximum snow depth (SD) was 5.35 cm and was found at the northern edge of the middle reaches of the basin. Snow cover frequency, SD, and fraction of snow cover area increased with elevation. The decrease in SD was the most marked in the elevation range of 5,000–6,000 m. Above 6,000 m, the snow water equivalent showed a slight upward trend. There was a significant negative correlation between snow cover and temperature. The results of this study could improve our understanding of changes in snow cover in the NRB from multivariate perspectives. It is better for water resources management.

Anticipating the computational performance of Earth System Models for pre-exascale systems

2021 ◽  
Author(s):  
Xavier Yepes-Arbós ◽  
Miguel Castrillo ◽  
Mario C. Acosta ◽  
Kim Serradell
Keyword(s):  
Computational Efficiency ◽  
Climate Model ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Limiting Factor ◽  
Earth System ◽  
Computational Point ◽  
System Models ◽  
Computational Performance ◽  
Earth System Models

<p>The increase in the capability of Earth System Models (ESMs) is strongly linked to the amount of computing power, given that the spatial resolution used for global climate experimentation is a limiting factor to correctly reproduce climate mean state and variability. However, higher spatial resolutions require new High Performance Computing (HPC) platforms, where the improvement of the computational efficiency of ESMs will be mandatory. In this context, porting a new ultra-high resolution configuration into a new and more powerful HPC cluster is a challenging task, involving technical expertise to deploy and improve the computational performance of such a novel configuration.</p><p>To take advantage of this foreseeable landscape, the new EC-Earth 4 climate model is being developed by coupling OpenIFS 43R3 and NEMO 4 as atmosphere and ocean components respectively. An important effort has been made to improve the computational efficiency of this new EC-Earth version, such as extending the asynchronous I/O capabilities of the XIOS server to OpenIFS. </p><p>In order to anticipate the computational behaviour of EC-Earth 4 for new pre-exascale machines such as the upcoming MareNostrum 5 of the Barcelona Supercomputing Center (BSC), OpenIFS and NEMO models are therefore benchmarked on a petascale machine (MareNostrum 4) to find potential computational bottlenecks introduced by new developments or to investigate if previous known performance limitations are solved. The outcome of this work can also be used to efficiently set up new ultra-high resolutions from a computational point of view, not only for EC-Earth, but also for other ESMs.</p><p>Our benchmarking consists of large strong scaling tests (tens of thousands of cores) by running different output configurations, such as changing multiple XIOS parameters and number of 2D and 3D fields. These very large tests need a huge amount of computational resources (up to 2,595 nodes, 75 % of the supercomputer), so they require a special allocation that can be applied once a year.</p><p>OpenIFS is evaluated with a 9 km global horizontal resolution (Tco1279) and using three different output data sets: no output, CMIP6-based fields and huge output volume (8.8 TB) to stress the I/O part. In addition, different XIOS parameters, XIOS resources, affinity, MPI-OpenMP hybridisation and MPI library are tested. Results suggest new features introduced in 43R3 do not represent a bottleneck in terms of performance as the model scales. The I/O scheme is also improved when outputting data through XIOS according to the scalability curve.</p><p>NEMO is scaled using a 3 km global horizontal resolution (ORCA36) with and without the sea-ice module. As in OpenIFS, different I/O configurations are benchmarked, such as disabling model output, only enabling 2D fields, or either producing 3D variables on an hourly basis. XIOS is also scaled and tested with different parameters. While NEMO has good scalability during the most part of the exercise, a severe degradation is observed before the model uses 70% of the machine resources (2,546 nodes). The I/O overhead is moderate for the best XIOS configuration, but it demands many resources.</p>

Cryosphere

2004 ◽  
Author(s):  
Kenneth M. Hinkel ◽  
Andrew W. Ellis
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Global Climate ◽  
Ice Sheets ◽  
Past Century ◽  
High Latitudes ◽  
Ice Shelves ◽  
The Past ◽  
Third Assessment Report ◽  
The Antarctic

The cryosphere refers to the Earth’s frozen realm. As such, it includes the 10 percent of the terrestrial surface covered by ice sheets and glaciers, an additional 14 percent characterized by permafrost and/or periglacial processes, and those regions affected by ephemeral and permanent snow cover and sea ice. Although glaciers and permafrost are confined to high latitudes or altitudes, areas seasonally affected by snow cover and sea ice occupy a large portion of Earth’s surface area and have strong spatiotemporal characteristics. Considerable scientific attention has focused on the cryosphere in the past decade. Results from 2 ×CO2 General Circulation Models (GCMs) consistently predict enhanced warming at high latitudes, especially over land (Fitzharris 1996). Since a large volume of ground and surface ice is currently within several degrees of its melting temperature, the cryospheric system is particularly vulnerable to the effects of regional warming. The Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) states that there is strong evidence of Arctic air temperature warming over land by as much as 5 °C during the past century (Anisimov et al. 2001). Further, sea-ice extent and thickness has recently decreased, permafrost has generally warmed, spring snow extent over Eurasia has been reduced, and there has been a general warming trend in the Antarctic (e.g. Serreze et al. 2000). Most climate models project a sustained warming and increase in precipitation in these regions over the twenty-first century. Projected impacts include melting of ice sheets and glaciers with consequent increase in sea level, possible collapse of the Antarctic ice shelves, substantial loss of Arctic Ocean sea ice, and thawing of permafrost terrain. Such rapid responses would likely have a substantial impact on marine and terrestrial biota, with attendant disruption of indigenous human communities and infrastructure. Further, such changes can trigger positive feedback effects that influence global climate. For example, melting of organic-rich permafrost and widespread decomposition of peatlands might enhance CO2 and CH4 efflux to the atmosphere. Cryospheric researchers are therefore involved in monitoring and documenting changes in an effort to separate the natural variability from that induced or enhanced by human activity.

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.

scholarly journals A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling

2020 ◽  
Vol 12 (21) ◽  
pp. 3577
Author(s):  
Siyong Chen ◽  
Xiaoyan Wang ◽  
Hui Guo ◽  
Peiyao Xie ◽  
Jian Wang ◽  
...  
Keyword(s):  
Snow Cover ◽  
Conditional Probability ◽  
Interpolation Method ◽  
Space Time ◽  
Landsat 8 ◽  
Snow Cover Area ◽  
Northern Xinjiang ◽  
Central Pixel ◽  
Snow Cover Extent ◽  
Snow Condition

Seasonal snow cover is closely related to regional climate and hydrological processes. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products from 2001 to 2018 were applied to analyze the snow cover variation in northern Xinjiang, China. As cloud obscuration causes significant spatiotemporal discontinuities in the binary snow cover extent (SCE), we propose a conditional probability interpolation method based on a space-time cube (STCPI) to remove clouds completely after combining Terra and Aqua data. First, the conditional probability that the central pixel and every neighboring pixel in a space-time cube of 5 × 5 × 5 with the same snow condition is counted. Then the snow probability of the cloud pixels reclassified as snow is calculated based on the space-time cube. Finally, the snow condition of the cloud pixels can be recovered by snow probability. The validation experiments with the cloud assumption indicate that STCPI can remove clouds completely and achieve an overall accuracy of 97.44% under different cloud fractions. The generated daily cloud-free MODIS SCE products have a high agreement with the Landsat–8 OLI image, for which the overall accuracy is 90.34%. The snow cover variation in northern Xinjiang, China, from 2001 to 2018 was investigated based on the snow cover area (SCA) and snow cover days (SCD). The results show that the interannual change of SCA gradually decreases as the elevation increases, and the SCD and elevation have a positive correlation. Furthermore, the interannual SCD variation shows that the area of increase is higher than that of decrease during the 18 years.

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