scholarly journals Spatial and temporal variations of the surface albedo and other factors influencing Urumqi Glacier No. 1 in Tien Shan, China

2017 ◽  
Vol 63 (241) ◽  
pp. 899-911 ◽  
Author(s):  
XIAOYING YUE ◽  
JUN ZHAO ◽  
ZHONGQIN LI ◽  
MINGJUN ZHANG ◽  
JIN FAN ◽  
...  
Keyword(s):  
Surface Albedo ◽  
Temporal Variations ◽  
Surface Energy Budget ◽  
Tien Shan ◽  
Spatial And Temporal Variations ◽  
Spatial And Temporal Variability ◽  
Landsat 8 ◽  
Topographic Effects ◽  
Landsat Images ◽  
Factors Influencing

ABSTRACTGlacier albedo controls the surface energy budget, the variability of which affects the glacier surface melt rate and, in turn, impacts the mass balance of the glacier. During 2013 and 2014, spatial and temporal variations of albedo were investigated using 18 Landsat images of Urumqi Glacier No. 1. Factors influencing these spatiotemporal profiles were analyzed. An established retrieval process, including geolocation, radiometric calibration, atmospheric, topographic, and anisotropic correction and narrow- to broadband conversion, was applied for the first time to Landsat-8 images. Differences between Landsat image derived albedo values and albedo values measured using a handheld spectroradiometer ranged from −0.024 to 0.049. Spatial and temporal variations of surface albedo were significant, especially in the ablation area. The variability of the values of ice albedo ranged from 0.06 to 0.44 due to topographic effects and light-absorbing impurities. The results suggest that this retrieval method can be used to investigate the spatial and temporal variability of surface albedo from Landsat-8 images on mountain glaciers. Moreover, as constant albedo values for ice and snow cannot be assumed, the distribution of albedo was not completely dependent on altitude under conditions of more intense ablation, and by reason of light-absorbing impurities during the melt season.

scholarly journals Consequences of the 2019 Greenland Ice Sheet Melt Episode on Albedo

2021 ◽  
Vol 13 (2) ◽  
pp. 227
Author(s):  
Arthur Elmes ◽  
Charlotte Levy ◽  
Angela Erb ◽  
Dorothy K. Hall ◽  
Ted A. Scambos ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Surface Albedo ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Surface Energy Budget ◽  
Landsat 8 ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Seasonal Snow Cover

In mid-June 2019, the Greenland ice sheet (GrIS) experienced an extreme early-season melt event. This, coupled with an earlier-than-average melt onset and low prior winter snowfall over western Greenland, led to a rapid decrease in surface albedo and greater solar energy absorption over the melt season. The 2019 melt season resulted in significantly more melt than other recent years, even compared to exceptional melt years previously identified in the moderate-resolution imaging spectroradiometer (MODIS) record. The increased solar radiation absorbance in 2019 warmed the surface and increased the rate of meltwater production. We use two decades of satellite-derived albedo from the MODIS MCD43 record to show a significant and extended decrease in albedo in Greenland during 2019. This decrease, early in the melt season and continuing during peak summer insolation, caused increased radiative forcing of the ice sheet of 2.33 Wm−2 for 2019. Radiative forcing is strongly influenced by the dramatic seasonal differences in surface albedo experienced by any location experiencing persistent and seasonal snow-cover. We also illustrate the utility of the newly developed Landsat-8 albedo product for better capturing the detailed spatial heterogeneity of the landscape, leading to a more refined representation of the surface energy budget. While the MCD43 data accurately capture the albedo for a given 500 m pixel, the higher spatial resolution 30 m Landsat-8 albedos more fully represent the detailed landscape variations.

scholarly journals Evaluating the spatial and temporal variations of aquatic weeds (Biomass) on Lower Volta River using multi-sensor Landsat Images and machine learning

Heliyon ◽  
2021 ◽  
Vol 7 (5) ◽  
pp. e07080
Author(s):  
Clement Nyamekye ◽  
Samuel Anim Ofosu ◽  
Richard Arthur ◽  
Gabriel Osei ◽  
Linda Boamah Appiah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Aquatic Weeds ◽  
Landsat Images

scholarly journals Spatial and temporal variations of oxygen isotopes in snowpacks and glacial runoff in different types of glacial area in western China

2006 ◽  
Vol 43 ◽  
pp. 269-274 ◽  
Author(s):  
Yuanqing He ◽  
Hongxi Pang ◽  
W.H. Theakstone ◽  
Dian Zhang ◽  
Aigang Lu ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Precipitation Amount ◽  
Summer Precipitation ◽  
Temporal Variations ◽  
Tien Shan ◽  
Spatial And Temporal Variations ◽  
Glacier Area ◽  
Altitude Effect ◽  
Amount Effect ◽  
Yulong Mountain

AbstractIn order to improve understanding of spatial and temporal variations of stable isotopes in atmospheric precipitation, snow cover and glacier meltwater in different regions of China, samples were collected for isotopic analysis in four areas: Yulong mountain, Yunnan Himalaya (temperate-glacier area); Samdain Kangsang mountain, Nyainqêntanglha Shan (subpolar-glacier area); the headwater area of the Ürümqi river, Tien Shan (subpolar-glacier area); and Muztag mountain, Pamirs (polar-glacier area). Sampling was undertaken in both summer and winter between 2000 and 2003. The δ18O values show a ‘temperature–altitude effect’ in new winter snow on Yulong mountain, reflecting the condensation and fractionation processes associated with the winter monsoon, but a different, more complex pattern in residual snow deposited during the summer monsoon; this old snow is influenced by the ‘precipitation amount effect’, solar radiation and evaporation, and the water content of the snowpack. The summer precipitation at Samdain Kangsang mountain is associated with the summer monsoon. There is a marked precipitation amount effect during the long passage of the southwest/India monsoon from the distant moisture source to Samdain Kangsang mountain, and the summer precipitation is strongly depleted of the heavy isotope. Above 6000 m, the high radiation flux causes much evaporation from the snow surface. The associated 1 8O enrichment of the snow is reflected in a ‘reverse altitude effect’. The δ18O values in the summer snowpack of the Tien Shan and Muztag mountain decrease with increasing altitude and decreasing air temperature, indicating a temperature–altitude effect. Post-depositional processes cause isotopic changes during the transformation of snow/firn/ice to meltwater; the effects are much stronger at temperate than at polar glaciers. Moreover, changes in the isotopic signal at both temperate and polar glaciers can result from evaporation, sublimation, ablation and drifting.

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

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