北京市农林科学院机构知识库

Retrieval of LAI and leaf chlorophyll content from remote sensing data by agronomy mechanism knowledge to solve the ill-posed inverse problem

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文献类型：外文期刊

作者： Li, Zhenhai ¹ ; Nie, Chenwei ¹ ; Yang, Guijun ¹ ; Xu, Xingang ¹ ; Jin, Xiuliang ¹ ; Gu, Xiaohe ¹ ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 10097, Peoples R China

关键词： Winter wheat;LAI;leaf chlorophyll content;radiative transfer model;genetic algorithm;prior knowledge

期刊名称：REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XVI

ISSN： 0277-786X

年卷期： 2014 年 9239 卷

页码：

收录情况： SCI

摘要： Leaf area index (LAI) and LCC, as the two most important crop growth variables, are major considerations in management decisions, agricultural planning and policy making. Estimation of canopy biophysical variables from remote sensing data was investigated using a radiative transfer model. However, the ill-posed problem is unavoidable for the unique solution of the inverse problem and the uncertainty of measurements and model assumptions. This study focused on the use of agronomy mechanism knowledge to restrict and remove the ill-posed inversion results. For this purpose, the inversion results obtained using the PROSAIL model alone (NAMK) and linked with agronomic mechanism knowledge (AMK) were compared. The results showed that AMK did not significantly improve the accuracy of LAI inversion. LAI was estimated with high accuracy, and there was no significant improvement after considering AMK. The validation results of the determination coefficient (R-2) and the corresponding root mean square error (RMSE) between measured LAI and estimated LAI were 0.635 and 1.022 for NAMK, and 0.637 and 0.999 for AMK, respectively. LCC estimation was significantly improved with agronomy mechanism knowledge; the R-2 and RMSE values were 0.377 and 14.495 mu g cm(-2) for NAMK, and 0.503 and 10.661 mu g cm(-2) for AMK, respectively. Results of the comparison demonstrated the need for agronomy mechanism knowledge in radiative transfer model inversion.

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