DOI: 10.57760/sciencedb.j00001.00369
title: 2013-2020年喜马拉雅山中段和东段Landsat 8积雪覆盖范围数据
title: 2013-2020年喜马拉雅山中段和东段Landsat 8积雪覆盖范围数据

description: Located on the southern edge of the Tibetan Plateau, the Himalayas are the highest mountains in the world, and their snow cover and its changes affect the regional climate, water resources and ecological environment. In order to better understand the changes of snow cover in this area and overcome the problems of insufficient detailed description of snow cover monitoring in mountainous areas with complex terrain by medium and low resolution remote sensing, snow cover monitoring was carried out using Landsat 8 clear sky condition data with a spatial resolution of 30m. Due to the complex terrain of the mountainous area, in view of the general overestimation problem of the normalized snow cover index method in the Himalayas, the support vector machine (SVM) classification method is used to select the snow cover characteristic training samples of different terrain, shadow and other conditions on a scene-by-scene basis for snow accumulation. Classification, combined with auxiliary data such as glacial lakes, surface water bodies and other auxiliary data and spatial neighborhood analysis for data post-processing, a 30m-resolution snow cover dataset was constructed in the middle and eastern sections of the Himalayas from 2013 to 2020. By comparing the high-resolution snow cover classification of Sentinel-2, within a grid of 900*900m, the correlation coefficient of snow cover rate is above 0.95, and the root mean square error is about 0.1%. The snow cover identification results constructed by the two are as follows better consistency. The snow cover data set includes a total of 607 snow cover data covering the central and eastern sections of the Himalayas for 8 years, mainly distributed in the winter snow season from October of the current year to April of the following year. This dataset can provide a basis for verification and optimization of snow cover spatiotemporal and characteristic analysis, and low- and medium-resolution snow cover data, and provide support for studies on climate change, water resources management, and ecological benefits in the Himalayas and downstream regions.
description: Located on the southern edge of the Tibetan Plateau, the Himalayas are the highest mountains in the world, and their snow cover and its changes affect the regional climate, water resources and ecological environment. In order to better understand the changes of snow cover in this area and overcome the problems of insufficient detailed description of snow cover monitoring in mountainous areas with complex terrain by medium and low resolution remote sensing, snow cover monitoring was carried out using Landsat 8 clear sky condition data with a spatial resolution of 30m. Due to the complex terrain of the mountainous area, in view of the general overestimation problem of the normalized snow cover index method in the Himalayas, the support vector machine (SVM) classification method is used to select the snow cover characteristic training samples of different terrain, shadow and other conditions on a scene-by-scene basis for snow accumulation. Classification, combined with auxiliary data such as glacial lakes, surface water bodies and other auxiliary data and spatial neighborhood analysis for data post-processing, a 30m-resolution snow cover dataset was constructed in the middle and eastern sections of the Himalayas from 2013 to 2020. By comparing the high-resolution snow cover classification of Sentinel-2, within a grid of 900*900m, the correlation coefficient of snow cover rate is above 0.95, and the root mean square error is about 0.1%. The snow cover identification results constructed by the two are as follows better consistency. The snow cover data set includes a total of 607 snow cover data covering the central and eastern sections of the Himalayas for 8 years, mainly distributed in the winter snow season from October of the current year to April of the following year. This dataset can provide a basis for verification and optimization of snow cover spatiotemporal and characteristic analysis, and low- and medium-resolution snow cover data, and provide support for studies on climate change, water resources management, and ecological benefits in the Himalayas and downstream regions.