Spatial-Temporal Analysis of Field Evapotranspiration Based on Complementary Relationship Model and IKONOS Data
文献类型: 外文期刊
作者: Yang Guijun 1 ; Zhao Chunjiang 1 ; Xu Qingyun 2 ;
作者机构: 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
2.Liaoning Tech Univ, Inst Surveying & Mapping, Fuxing 123000, Peoples R China
关键词: complementary relationship model;IKONOS;evapotranspiration;lysimeter
期刊名称:2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
ISSN: 2153-6996
年卷期: 2013 年
页码:
收录情况: SCI
摘要: Mapping high spatial-temporal resolution evapotranspiration (ET) over large areas is important for water resources planning, precision irrigation and monitoring water use efficiency. However, both the traditional field measurement and aerodynamic estimation mainly focus on obtaining local ET. Remote sensing data often can be used to retrieve large area instantaneous ET at low spatial resolution over region or global scale. Therefore, using traditional measurements and high resolution image data to generate high spatial-temporal resolution ET is becoming an important research direction. In this paper, the complementary relationship model (CR) was employed together with meteorological data to estimate actual ET, and the results were validated by lysimeter observation. Furthermore, CR model was combined with high resolution image, IKONOS data, to estimate instantaneous field scale ET and they also were transferred into daily ET. The cumulative evapotranspiration (ET) of winter wheat during the reproductive phase from March to June of 2011 was 469.12 mm, essentially corresponding to the annual precipitation in the Beijing area. The most high accuracy of estimated ET by CR model is also on May(R-2=0.863, RMSE=0.103 mm). The transferred daily ET by self-preservation of evaporative fraction(EF) method were consistent with lysimeter measurements for all four months(R-2=0.937, RMSE=0.668 mm). It was proved in this study that CR model can be used to estimate precision field scale ET with meteorological data and high resolution remote sensing data together in a region with limited ground data availability.
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