北京市农林科学院机构知识库

INVERSION OF PADDY LEAF AREA INDEX USING BEER-LAMBERT LAW AND HJ-1/2 CCD IMAGE

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文献类型：外文期刊

作者： Gu, Xiaohe ¹ ; Zhang, Jingcheng ¹ ; Yang, Guijun ¹ ; Song, Xiaoyu ¹ ; Zhao, Jinling ¹ ; Cui, Bei ¹ ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： LAI;Beer-Lambert Law;Extinction Coefficient;HJ-1/2 CCD;Paddy

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： Monitoring crop leaf area index (LAI) timely and accurately by remote sensing is crucial to assess crop growth, manage field water-fertilizer and predict yield. The Huaihe River Basin was chose as study area to carry out field survey. By using decision tree classification and HJ-1/2 CCD image, the spatial distribution of paddy was identified. The extinction coefficient of paddy surface was confirmed with in-situ samples. The Beer-Lambert law was introduced to develop the inversion model of paddy LAI. The accuracy of inversion model was evaluated with in-situ samples, including coefficient of determination (R-2), RMSE and overall accuracy, while contrasting with the model of single-variable and multi-variables. Results showed that the inversion model based on Beer-Lambert law reached highest accuracy with the average R-2 of 0.684 and the average RMSE of 0.592. The average R-2 of multi-variables was 0.636, while the average RMSE was 0.661. The model of single-variable has lowest accuracy with average R-2 of 0.595 and average RMSE of 0.732. It indicated that the retrieval accuracy of LAI was improved with more variables inputted. The model based on Beer-Lambert law simulated the physical process of radiative transfer of paddy that differed from the two other models. The overall accuracy of Beer-Lambert law model exceeded 95 percent, while those of the two other models were 91.0 percent and 88.2 percent respectively. So the inversion model of paddy LAI based on Beer-Lambert law could eliminate the influence of water background and improve the accuracy of paddy LAI by remote sensing.

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