三江源国家公园星空地一体化生态监测数据平台
Ecological Data Center of Sanjiangyuan National Park
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An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018

Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).

三江源国家公园界线矢量数据集

三江源国家公园包括长江源、黄河源、澜沧江源3 个园区,总面积为12.31 万平方公里,介于东经89°50'57"—99°14'57",北纬32°22'36"—36°47'53",占三江源国土面积的31.16%。 本数据集是基于《三江源国家公园总体规划》中的三江源国家公园区位图进行数字化而产生。数据包含长江源园区、黄河源园区和澜沧江园区的边界。 数据格式为Shapefile格式。推荐使用arcmap打开数据。

三江源国家公园长序列地表冻融数据集——双指标算法(1979-2015)

本数据集采用SMMR(1979-1987)、SSM/I(1987-2009)和SSMIS(2009-2015)逐日亮温数据,由双指标(TB_37v,SG)冻融判别算法生成,分类结果包含冻结地表、融化地表、沙漠及水体四种类型。数据覆盖范围为三江源区域,空间分辨率为25.067525 km,EASE Grid投影方式,以Geotif格式存储。像元数值表征地表冻融的状态:1代表冻结,2代表融化,3代表沙漠,4代表水体。因为该数据集中所有tif文件描述的是三江源国家公园范围,所以这些文件的行列号信息是不变的,摘录如下(其中cellsize单位为m): ncols 52 nrows 28 cellsize 25067.525 nodata_value 0

三江源国家公园2001-2018遥感物候产品数据集

该数据集是基于MODIS 16天合成的NDVI产品(MOD13Q1 collection6)估算的三江源国家公园区域的植被生长季开始(Start of Season: SOS)和生长季结束的日期(End of Season: EOS)。共用了两种常见的物候期估算方法,分别是基于多项式拟合的阈值提取法(文件名中有poly字符)和基于双逻辑曲线(double logistic function)拟合后的拐点提取法(文件名中有sig字符)。该数据可以用来分析植被物候期与气候变化的关系。时间范围为2001年至2018年。空间分辨率为250m。数据中包含4个子文件夹,CJYYQ_phen是三江源国家公园长江源园区的物候结果,HHYYQ_phen是三江源国家公园黄河源园区的物候结果,LCJYYQ_phen是三江源国家公园澜沧江源园区的物候结果,SJY_phen是整个三江源区域的物候。 数据格式为geotif,建议使用arcmap或者Python+GDAL浏览和处理数据。

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