Monitoring snow cover variability in an agropastoral area in the Trans Himalayan region of Nepal using MODIS data with improved cloud removal methodology

2011 ◽  
Vol 115 (5) ◽  
pp. 1234-1246 ◽  
Author(s):  
Keshav Prasad Paudel ◽  
Peter Andersen
2021 ◽  
Vol 973 (7) ◽  
pp. 21-31
Author(s):  
Е.А. Rasputina ◽  
A.S. Korepova

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.


Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7416
Author(s):  
Mohd Anul Haq ◽  
Prashant Baral ◽  
Shivaprakash Yaragal ◽  
Biswajeet Pradhan

Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.


2016 ◽  
Vol 47 (12) ◽  
pp. 3955-3977 ◽  
Author(s):  
Pierfrancesco Da Ronco ◽  
Carlo De Michele ◽  
Myriam Montesarchio ◽  
Paola Mercogliano

2019 ◽  
Vol 23 (5) ◽  
pp. 2401-2416 ◽  
Author(s):  
Xinghua Li ◽  
Yinghong Jing ◽  
Huanfeng Shen ◽  
Liangpei Zhang

Abstract. The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.


2008 ◽  
Vol 112 (4) ◽  
pp. 1514-1526 ◽  
Author(s):  
T LIANG ◽  
X HUANG ◽  
C WU ◽  
X LIU ◽  
W LI ◽  
...  

2011 ◽  
Vol 108 (1-2) ◽  
pp. 391-400 ◽  
Author(s):  
Shreedhar Maskey ◽  
Stefan Uhlenbrook ◽  
Sunal Ojha

2010 ◽  
Vol 2 (3) ◽  
pp. 777-793 ◽  
Author(s):  
Yuanzhi Zhang ◽  
Su Yan ◽  
Yu Lu

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