Arid
DOI10.1166/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
ISSN1546-198X
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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