TY  - Data
T1  - Data set of seamless MODIS snow coverage ratio in the source area of the Yellow River from 2000 to 2021
A1  - Yang Ying
A1  - Tang Zhongxi
A1  - Xing De
A1  - Hou Jinliang
DO  - 10.12072/ncdc.NIEER.db2115.2022
PY  - 2022
DA  - 2022-05-24
PB  - National Cryosphere Desert Data Center
AB  - Production background: The source area of the Yellow River is the main water producing area and water source conservation area of the Yellow River Basin. Snow melt water is one of the important water sources in the source area, and high-precision snow cover area datasets are the foundation for ecological and hydrological simulation, climate change research, and other research in the source area. However, a large amount of cloud coverage in MODIS snow products results in almost half of the information being missing. Due to the seasonal snow cover in the source area of the Yellow River showing shallow snow layers, patchy distribution, and rapid melting, traditional statistical methods are difficult to accurately capture the spatiotemporal characteristics of snow cover in the source area. Advanced deep learning techniques can better explore the spatiotemporal characteristics hidden behind the dataProduction method: This dataset utilizes daily MODIS Normalized Snow Index (NDSI) products from 2000 to 2021, and uses the MODIS NDSI cloud pixel reconstruction model based on Partial Convolutional Neural Network (PCNN) developed by Xing et al. (2022) to first generate spatiotemporal continuous MODIS NDSI data products; Secondly, the standard algorithm of NASA's Snow Cover Ratio (FSC) product was used to prepare a seamless daily MODIS FSC remote sensing monitoring dataset for the Yellow River source area from 2000 to 2021Data content: The element included in the data is FSC, with a spatial coverage of the entire Yellow River source area. The data starts from the 2000-2001 snow season (i.e. November 1, 2000) and ends on April 30, 2021, 2020. It includes 21 complete snow seasons. The spatial resolution is 0.005 degrees (approximately 500m), and the temporal resolution is daily. The naming convention is: YYYYDDD.tif, where YYYY represents the year and DDD represents the Julian day (001-365)Data advantages, characteristics, and application scope: Based on the verification of snow 
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/d44dd669-1649-47f3-90a0-ae354d0d2a1f
ER  -