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Classification and Estimation of Irrigation Waters Based on Remote Sensing Images: Case Study in Yucheng City (China)
Lu, Qingshui1,2; Liang, Shangzhen3; Xu, Xinliang4
2018-10-01
Source PublicationSUSTAINABILITY
ISSN2071-1050
Volume10Issue:10Pages:12
Corresponding AuthorLu, Qingshui(luqs@lreis.ac.cn)
AbstractThe downstream plain of the Yellow River is experiencing some of the most severe groundwater depletion in China. Although the Chinese government has issued policies to ensure that the Yellow River can provide enough irrigation waters for this region, groundwater levels continue to decrease. Yucheng City was selected as a case study. A new method was designed to classify the cropland into various irrigated cropland. Subsequently, we analyzed data regarding these irrigated-cropland categories, irrigation norms, and the minimum amount of irrigation water being applied to cropland. The results showed that 91.5% of farmland can be classified as double irrigated (by both canal/river and well water), while 8.5% of farmland can be classified as well irrigated. During the irrigation season, the sediments brought in by the river have blocked portions of the canals. This has led to 23% of the double-irrigated cropland being irrigated by groundwater, and it is thus a main factor causing reductions in groundwater supply. These blocked canals should be dredged by local governments to mitigate local groundwater depletion. The method for classifying irrigated cropland from high-resolution images is valid and it can be used in other irrigated areas with a declining groundwater table for the sustainable use of groundwater resources.
KeywordYellow River Downstream Plain irrigated cropland category well irrigation canal irrigation minimum irrigation water amount
DOI10.3390/su10103503
WOS KeywordLAND
Indexed BySCI
Language英语
Funding ProjectNatural Science Foundation of Beijing[9182004] ; National Natural Science Foundation of China[31670471] ; National Social Science Fund Project of China[17AJL008]
Funding OrganizationNatural Science Foundation of Beijing ; National Natural Science Foundation of China ; National Social Science Fund Project of China
WOS Research AreaScience & Technology - Other Topics ; Environmental Sciences & Ecology
WOS SubjectGREEN & SUSTAINABLE SCIENCE & TECHNOLOGY ; Environmental Sciences ; Environmental Studies
WOS IDWOS:000448559400145
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52477
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLu, Qingshui
Affiliation1.Univ Jinan, Coll Business, Jinan 250022, Shandong, Peoples R China
2.Univ Jinan, Inst Green Dev, Jinan 250022, Shandong, Peoples R China
3.Water Bur Yucheng City, Yucheng 251200, Shandong, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resource Res, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Lu, Qingshui,Liang, Shangzhen,Xu, Xinliang. Classification and Estimation of Irrigation Waters Based on Remote Sensing Images: Case Study in Yucheng City (China)[J]. SUSTAINABILITY,2018,10(10):12.
APA Lu, Qingshui,Liang, Shangzhen,&Xu, Xinliang.(2018).Classification and Estimation of Irrigation Waters Based on Remote Sensing Images: Case Study in Yucheng City (China).SUSTAINABILITY,10(10),12.
MLA Lu, Qingshui,et al."Classification and Estimation of Irrigation Waters Based on Remote Sensing Images: Case Study in Yucheng City (China)".SUSTAINABILITY 10.10(2018):12.
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