气候变化领域集成服务门户(CCPortal): A Neural Network Classification Framework for Monthly and High Spatial Resolution Surface Water Mapping in the Qinghai-Tibet Plateau from Landsat Observations

CCPortal

DOI	10.1175/JHM-D-22-0211.1
	A Neural Network Classification Framework for Monthly and High Spatial Resolution Surface Water Mapping in the Qinghai-Tibet Plateau from Landsat Observations
	Ran, Qinwei; Aires, Filipe; Ciais, Philippe; Qiu, Chunjing; Wang, Yanfen
发表日期	2023
ISSN	1525-755X
EISSN	1525-7541
起始页码	1635
结束页码	1657
卷号	24 期号:10
英文摘要	The Qinghai-Tibet Plateau, known as the Asian Water Tower, has a significant area of water bodies that provide a wide range of valuable ecosystem services. In the context of climate change, the formation condition of surface water and water extent is changing fast. Thus, there is a critical need for monthly detection algorithms at high spatial reso-lution (;30 m) with good accuracy. Multiple sensors' observations are available, but producing reliable long time series surface water mapping at a subannual temporal frequency still remains a challenge, mainly due to data limitations. In this study, we proposed a neural-network-based monthly surface water classification framework relying on Landsat 5/7/8 images in 2000-20 and topographic indices, and retrieved monthly water mask for the year 2020. The surface water was mainly distributed in the central and western parts of the plateau and the maximum area of permanent surface water (water frequency . 60%) was 26.66 3 103 km2 in 2020. The overall, producer, and user accuracies of our surface water map were 0.96, 0.94, and 0.98, respectively, and the kappa coefficient reached 0.90, demonstrating a better performance than existing products [i.e., Joint Research Centre (JRC) Monthly Water History with overall accuracy 0.94, producer accuracy 0.89, user accuracy 0.99, and kappa coefficient 0.89]. Our framework efficiently solved the problem of missing data in Landsat images referring to the JRC and a priori information and performed well in dealing with ice/snow cover issues. We showed that higher uncertainties exist on wetlands and recommended exploring relationships between water and wetlands in the future.
关键词	Water resources Remote sensing Satellite observations Classification
英文关键词	LAKE ICE PHENOLOGY; GOOGLE EARTH ENGINE; COVER CLASSIFICATION; TIME-SERIES; FEATURE-SELECTION; DECISION TREE; INDEX; EXTRACTION; DYNAMICS; IMAGERY
WOS研究方向	Meteorology & Atmospheric Sciences
WOS记录号	WOS:001106633000001
来源期刊	JOURNAL OF HYDROMETEOROLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283220
作者单位	Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Universite PSL; Observatoire de Paris; Universite Paris Saclay; CEA; Centre National de la Recherche Scientifique (CNRS); Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
推荐引用方式 GB/T 7714	Ran, Qinwei,Aires, Filipe,Ciais, Philippe,et al. A Neural Network Classification Framework for Monthly and High Spatial Resolution Surface Water Mapping in the Qinghai-Tibet Plateau from Landsat Observations[J],2023,24(10).
APA	Ran, Qinwei,Aires, Filipe,Ciais, Philippe,Qiu, Chunjing,&Wang, Yanfen.(2023).A Neural Network Classification Framework for Monthly and High Spatial Resolution Surface Water Mapping in the Qinghai-Tibet Plateau from Landsat Observations.JOURNAL OF HYDROMETEOROLOGY,24(10).
MLA	Ran, Qinwei,et al."A Neural Network Classification Framework for Monthly and High Spatial Resolution Surface Water Mapping in the Qinghai-Tibet Plateau from Landsat Observations".JOURNAL OF HYDROMETEOROLOGY 24.10(2023).