Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1166/sl.2012.1869 |
Diagnosing Cotton Farmland Quality Using Multi-Temporal Remotely Sensed Data | |
Bai, Junhua1,2,4,5; Li, Jing4,5; Liu, Qinhuo4,5; Wang, Xu3; Li, Shaokun1 | |
通讯作者 | Li, Shaokun |
来源期刊 | SENSOR LETTERS
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ISSN | 1546-198X |
出版年 | 2012 |
卷号 | 10期号:1-2页码:475-483 |
英文摘要 | Farmland quality is a comprehensive indicator for soil, environmental, and health quality. Using remotely sensed imagery, this study explored a method of evaluating farmland quality for cotton. Determining cotton growth conditions with multi-temporal images at the flower-boll stages, the reflectance value from LANDSAT-5 TM4 appropriately classified cotton fields into three ranks of productivity. Our methods successfully classified 417 blocks of approximately 11 705.3 ha of fields using multi-temporal images. On Farm 148, 36.4% of the cotton fields were most productive, 34.1% were moderately productive, and 29.5% were least productive. These classifications were validated with synchronization-based soil and LAI analysis in eight cotton fields of approximately 426.5 ha. The validation showed that the main causes of low land productivity were salinity, soil texture, and soil topography. These results promote the application of remotely sensed imagery to improve the quality of cotton-growing soils and increase the efficiency of managing cotton farmlands. |
英文关键词 | Multi-Temporal Imagery Farmland Quality Diagnosing |
类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
收录类别 | SCI-E |
WOS记录号 | WOS:000303957300069 |
WOS关键词 | SOIL QUALITY ; SENSING IMAGES ; HEALTH ; DESERTIFICATION ; SUSTAINABILITY ; INDICATORS ; SITES ; CHINA |
WOS类目 | Chemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation ; Physics, Applied |
WOS研究方向 | Chemistry ; Electrochemistry ; Instruments & Instrumentation ; Physics |
来源机构 | 北京师范大学 |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/175020 |
作者单位 | 1.Chinese Acad Agr Sci, Inst Crop Sci, Beijing 100081, Peoples R China; 2.Shihezi Univ, Coll Agr Sci, Shihezi 832003, Xinjiang, Peoples R China; 3.Inst Agr Sci, Beijing 102600, Peoples R China; 4.Chinese Acad Sci, Inst Remote Sensing Applicat, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; 5.Beijing Normal Univ, Beijing 100101, Peoples R China |
推荐引用方式 GB/T 7714 | Bai, Junhua,Li, Jing,Liu, Qinhuo,et al. Diagnosing Cotton Farmland Quality Using Multi-Temporal Remotely Sensed Data[J]. 北京师范大学,2012,10(1-2):475-483. |
APA | Bai, Junhua,Li, Jing,Liu, Qinhuo,Wang, Xu,&Li, Shaokun.(2012).Diagnosing Cotton Farmland Quality Using Multi-Temporal Remotely Sensed Data.SENSOR LETTERS,10(1-2),475-483. |
MLA | Bai, Junhua,et al."Diagnosing Cotton Farmland Quality Using Multi-Temporal Remotely Sensed Data".SENSOR LETTERS 10.1-2(2012):475-483. |
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