globalchange  > 气候变化事实与影响
DOI: 10.1016/j.jag.2013.05.007
Scopus记录号: 2-s2.0-84897827083
论文题名:
Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data
作者: Duan S; -B; , Li Z; -L; , Wu H; , Tang B; -H; , Ma L; , Zhao E; , Li C
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 26, 期:1
起始页码: 12
结束页码: 20
语种: 英语
英文关键词: Dual-angle observations ; Hyperspectral ; Leaf area index ; Look-up table ; PROSAIL
Scopus关键词: accuracy assessment ; data inversion ; energy balance ; leaf area index ; maize ; mass balance ; observational method ; potato ; radiative transfer ; spectral analysis ; unmanned vehicle
英文摘要: Leaf area index (LAI) is a key variable for modeling energy and mass exchange between the land surfaceand the atmosphere. Inversion of physically based radiative transfer models is the most establishedtechnique for estimating LAI from remotely sensed data. This study aims to evaluate the suitability ofthe PROSAIL model for LAI estimation of three typical row crops (maize, potato, and sunflower) fromunmanned aerial vehicle (UAV) hyperspectral data. LAI was estimated using a look-up table (LUT) basedon the inversion of the PROSAIL model. The estimated LAI was evaluated against in situ LAI measurements.The results indicated that the LUT-based inversion of the PROSAIL model was suitable for LAI estimationof these three crops, with a root mean square error (RMSE) of approximately 0.62 m2m-2, and a relativeRMSE (RRMSE) of approximately 15.5%. Dual-angle observations were also used to estimate LAI andproved to be more accurate than single-angle observations, with an RMSE of approximately 0.55 m2m-2 and an RRMSE of approximately 13.6%. The results demonstrate that additional directional informationimproves the performance of LAI estimation. © 2013 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79637
Appears in Collections:气候变化事实与影响

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作者单位: State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; ICube, UdS, CNRS, 300 Bld Sebastien Brant, BP10413, 67412 Illkirch, France; Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100080, China

Recommended Citation:
Duan S,-B,, Li Z,et al. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,26(1)
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