干旱区生态环境知识资源中心(Arid): Long-Term Monitoring of the Annual Irrigated Cropland Extent in Fragmented and Heterogeneous Arid Landscapes Using Machine Learning and Landsat Imagery

Arid

DOI	10.1029/2023WR036945
	Long-Term Monitoring of the Annual Irrigated Cropland Extent in Fragmented and Heterogeneous Arid Landscapes Using Machine Learning and Landsat Imagery
	Tan, Meibao; Ran, Youhua; Feng, Min; Dong, Guotao; Du, Deyan; Zhu, Gaofeng; Nian, Yanyun; Li, Xin
通讯作者	Ran, YH
来源期刊	WATER RESOURCES RESEARCH
ISSN	0043-1397
EISSN	1944-7973
出版年	2024
卷号	60 期号:6
英文摘要	Understanding the long-term spatiotemporal evolution of irrigated cropland is essential for water resource management, but this knowledge remains elusive in most water-stressed arid areas. In this study, we introduced an integrated framework for long-term and field-scale mapping of annual irrigated cropland in arid and semiarid regions. This framework combines the k-means algorithm with a semiautomatically trained random forest classifier for initial classification and employs the Bayesian Updating of Land Cover algorithm for subsequent postprocessing. Taking the Heihe River basin in northwestern China as the experimental area, we generated 30-m annual irrigated cropland maps spanning from 1990 to 2020 based on Landsat imagery and the Google Earth Engine. Comprehensive validation confirmed the reliability of this approach, with the overall accuracy of the annual maps ranging from 83% to 88.3% (mean: 86.6%). Our data set provides an unprecedentedly long-term and fine-scale perspective for understanding the continuous spatial and temporal dynamics of irrigated cropland in the Heihe River basin, surpassing previous studies in Central Asia and northwestern China. Notably, a rapid expansion of irrigated areas is occurring in the basin, especially in the water-stressed midstream and downstream areas. This finding points to potential ecological risks in the foreseeable future due to water resource constraints. A semiautomatic framework was developed to map irrigated cropland in arid and semiarid areas The overall accuracy of the annual irrigated cropland maps in the Heihe River basin ranged from 83% to 88.3% The area of irrigated cropland in the Heihe River basin increased by 76% over the past 31 years
英文关键词	annual irrigation map arid region Landsat time series Google Earth Engine machine learning
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001250999500001
WOS关键词	HEIHE RIVER-BASIN ; TIME-SERIES ; WATER ; ENVIRONMENT ; MANAGEMENT ; AREAS ; US
WOS类目	Environmental Sciences ; Limnology ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405932
推荐引用方式 GB/T 7714	Tan, Meibao,Ran, Youhua,Feng, Min,et al. Long-Term Monitoring of the Annual Irrigated Cropland Extent in Fragmented and Heterogeneous Arid Landscapes Using Machine Learning and Landsat Imagery[J],2024,60(6).
APA	Tan, Meibao.,Ran, Youhua.,Feng, Min.,Dong, Guotao.,Du, Deyan.,...&Li, Xin.(2024).Long-Term Monitoring of the Annual Irrigated Cropland Extent in Fragmented and Heterogeneous Arid Landscapes Using Machine Learning and Landsat Imagery.WATER RESOURCES RESEARCH,60(6).
MLA	Tan, Meibao,et al."Long-Term Monitoring of the Annual Irrigated Cropland Extent in Fragmented and Heterogeneous Arid Landscapes Using Machine Learning and Landsat Imagery".WATER RESOURCES RESEARCH 60.6(2024).