Arid
DOI10.1109/IGARSS.2010.5650098
RETRIEVE SOIL MOISTURE FROM MIXED-PIXELS BASED ON SCALE TRANSFORMATION USING HYPERSPECTRAL DATA
Wu, Daihui; Yan, Binyan; Cui, Yaokui; Fan, Wenjie; Xu, Xiru
通讯作者Fan, Wenjie
会议名称30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing - Global Vision for Local Action
会议日期JUN 25-30, 2010
会议地点Honolulu, HI
英文摘要

Soil moisture is a key parameter for drought monitoring. Crops distribute so fragmentally in China that mixed pixels account for a large proportion in moderate and coarse resolution remote sensing images. The soil moisture retrieved from vegetation-soil mixed pixels is a very important problem for drought monitoring and ecological study. Focusing on vegetation-soil mixed pixels, a new method for retrieving soil moisture from hyperspectral data is provided based on scale transformation method. Yingke Oasis, Zhangye, Gansu province was selected as validation area. A Hyperion/EO-1 data acquired on Jul. 15, 2008 was pre-processed and linearly interpolated to 180m and 1080m resolution images. Then a multi-scale image series was obtained. Using the above method, the soil moisture of pixels whose space resolution is 1080m were calculated. The retrieved results were verified by synchronized ground observation data. The results show that the proposed method is reliable.


英文关键词scale transformation soil moisture mixed pixels hyperspectral remote sensing
来源出版物2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
ISSN2153-6996
出版年2010
页码3875-3878
EISBN978-1-4244-9566-5
出版者IEEE
类型Proceedings Paper
语种英语
国家Peoples R China
收录类别CPCI-S
WOS记录号WOS:000287933804007
WOS关键词AREA INDEX
WOS类目Geosciences, Multidisciplinary ; Remote Sensing
WOS研究方向Geology ; Remote Sensing
资源类型会议论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/298196
作者单位Peking Univ, Inst RS & GIS, Beijing 100871, Peoples R China
推荐引用方式
GB/T 7714
Wu, Daihui,Yan, Binyan,Cui, Yaokui,et al. RETRIEVE SOIL MOISTURE FROM MIXED-PIXELS BASED ON SCALE TRANSFORMATION USING HYPERSPECTRAL DATA[C]:IEEE,2010:3875-3878.
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