Accuracy assessment of cloud removal methods for Moderate-resolution Imaging Spectroradiometer(MODIS)snow data in the Tianshan Mountains,China  

作　　者：WANG Qingxue MA Yonggang XU Zhonglin LI Junli 

机构地区：[1]College of Ecology and Environment,Xinjiang University,Urumqi 830046,China [2]Xinjiang Key Laboratory of Oasis Ecology,Xinjiang University,Urumqi 830046,China [3]College of Geography and Remote Sensing Sciences,Xinjiang University,Urumqi 830046,China [4]Xinjiang Jinghe Observation and Research Station of Temperate Desert Ecosystem,Ministry of Education,Urumqi 830046,China [5]Key Laboratory of Oasis Ecology of Education Ministry,Urumqi 830046,China [6]Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,China [7]University of Chinese Academy of Sciences,Beijing 100049,China [8]Key Laboratory of GIS&RS Application,Xinjiang Uygur Autonomous Region,Urumqi 830011,China

出　　处：《Journal of Arid Land》2025年第4期457-480,共24页干旱区科学(英文版)

基　　金：funded by the Third Xinjiang Scientific Expedition Program(2021xjkk1400);the National Natural Science Foundation of China(42071049);the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2019D01C022);the Xinjiang Uygur Autonomous Region Innovation Environment Construction Special Project&Science and Technology Innovation Base Construction Project(PT2107);the Tianshan Talent-Science and Technology Innovation Team(2022TSYCTD0006).

摘　　要：Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.

关 键 词：real time camera cloud removal algorithm snow cover Moderate-resolution Imaging Spectroradiometer(MODIS)snow data snow monitoring 

分 类 号：P426.6[天文地球—大气科学及气象学]