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浙江大学学报（农业与生命科学版）
Journal of Zhejiang University (Agriculture and Life Sciences)  2021, Vol. 47 Issue (4): 439-450    DOI: 10.3785/j.issn.1008-9209.2021.04.261
Special Topic: Crop Phenotyping Technologies and Applications     
Application of multi-layer discrete anisotropic radiative transfer model in vertical distribution inversion of maize leaf area index
Zhen DONG1,2(),Guijun YANG1(),Lin SUN2,Hao YANG1,Yaohui ZHU1,Lei LEI1,Riqiang CHEN1,Chengjian ZHANG1,Miao LIU1
1.Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
2.College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, Shandong, China
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Abstract  

In order to more accurately monitor the vertical distribution of the leaf area index (LAI) of maize, we propose a conditionally constrained LAI vertical distribution inversion method based on the simulation dataset constructed by the discrete anisotropic radiative transfer (DART) model. First, the simulation effects of DART model on canopy reflectance and photosynthetically active radiation (PAR) were evaluated based on the three-layer vertical distribution scenario, and constructed the corresponding simulation dataset with PAR. Second, a single parameter inversion model for LAI and PAR was built based on the simulated dataset. Finally, using the single parameter inversion model as a priori knowledge, the inversion of the vertical distribution of maize canopy LAI based on the hyperspectral vegetation index was realized by solving the constraint problem. The results showed that the accuracy of the constraint optimization inversion model was higher than that of the single parameter inversion model. The coefficient of determination (R2) of LAI inversion results for top layer of maize increased by 0.022, root-mean-square error (RMSE) decreased by 0.016 m2/m2, and normalized root-mean-square error (NRMSE) decreased by 1.3%. The R2 of LAI inversion results for middle layer of maize increased by 0.08, RMSE decreased by 0.219 m2/m2, and NRMSE decreased by 10.1%. The R2 of LAI inversion results for bottom layer of maize increased by 0.069, RMSE decreased by 0.041 m2/m2, and NRMSE decreased by 4.6%. Therefore, it can be concluded that the inversion of vertical distribution of LAI in maize canopy using the conditional constraint optimization method can effectively improve the inversion accuracy.

Key words： agricultural remote sensing      discrete anisotropic radiative transfer model      photosynthetically active radiation      leaf area index      vertical distribution     
Received: 26 April 2021      Published: 02 September 2021
CLC:  TP 751  
Corresponding Authors: Guijun YANG     E-mail: jmhshdzh@163.com;yanggj@nercita.org.cn
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Zhen DONG
Guijun YANG
Lin SUN
Hao YANG
Yaohui ZHU
Lei LEI
Riqiang CHEN
Chengjian ZHANG
Miao LIU
Cite this article:

Zhen DONG,Guijun YANG,Lin SUN,Hao YANG,Yaohui ZHU,Lei LEI,Riqiang CHEN,Chengjian ZHANG,Miao LIU. Application of multi-layer discrete anisotropic radiative transfer model in vertical distribution inversion of maize leaf area index. Journal of Zhejiang University (Agriculture and Life Sciences), 2021, 47(4): 439-450.

URL:

http://www.zjujournals.com/agr/10.3785/j.issn.1008-9209.2021.04.261     OR     http://www.zjujournals.com/agr/Y2021/V47/I4/439


多层离散各向异性辐射传输模型在玉米叶面积指数垂直分布反演中的应用

为更准确地监测玉米叶面积指数（leaf area index, LAI）垂直分布，以多层离散各向异性辐射传输（discrete anisotropic radiative transfer, DART）模型构建的模拟数据集为基础，提出一种条件约束的LAI垂直分布反演方法。首先，基于3层垂直分布场景，评价DART模型对玉米冠层反射率和光合有效辐射（photosynthetically active radiation, PAR）的模拟效果，并构建相应的模拟数据集。其次，基于模拟数据集构建LAI和PAR单参数反演模型。最后，以单参数反演模型为先验知识，通过求解约束化问题实现基于高光谱植被指数的玉米冠层LAI垂直分布反演。结果表明：相较于单参数反演模型，约束优化条件下的反演模型精度更高。玉米上层LAI反演结果的决定系数（R2）提高0.022，均方根误差（root-mean-square error, RMSE）降低0.016 m2/m2，归一化均方根误差（normalized root-mean-square error, NRMSE）降低1.3%；玉米中层LAI反演结果的R2提高0.08，RMSE降低0.219 m2/m2，NRMSE降低10.1%；玉米下层LAI反演结果的R2提高0.069，RMSE降低0.041 m2/m2，NRMSE降低4.6%。说明利用条件约束优化的方法进行玉米冠层LAI的垂直分布反演，能有效提高反演精度。


关键词： 农业遥感,  离散各向异性辐射传输模型,  光合有效辐射,  叶面积指数,  垂直分布 
Fig. 1 Simulation scenario of maize three-layer vertical distribution
Fig. 2 Sensitivity analysis results of input parameters of DART modelLAI: Leaf area index; Sz: Solar zenith angle; LAD: Leaf angle distribution; LCC: Leaf chlorophyll content; H: Height.
Fig. 3 Accuracy evaluation of PAR simulation results under different leaf angle distribution (LAD) and voxel size (Vsize) combinations
Fig. 4 R2 of the reflectivity simulation results at each wavelength
Fig. 5 Accuracy evaluation of canopy reflectance simulation results
Fig. 6 Accuracy evaluation of PAR simulation results

分层

Layer

回归模型

Regression model

模拟数据集

Simulated dataset

实测数据集

Measured dataset

RMSE/(m2/m2)NRMSE/%RMSE/(m2/m2)NRMSE/%
上层 Top layery=e[－4.12+271.87×DV560,620]0.0846.40.1219.9
中层 Middle layery=e[－31.49+31.16×R920,970]0.36514.20.65619.4
下层 Bottom layery=e[－16.66+17.74×R790,950]0.3389.50.58014.9
Table 1 Screening results of LAIc single parameter inversion model

分层

Layer

回归模型

Regression model

模拟数据集

Simulated dataset

实测数据集

Measured dataset

RMSENRMSERMSENRMSE
上层 Top layery=e[24.31+51.09×ND530,720]3.118.13.869.4
中层 Middle layery=e[2.33－118.8×DV790,850]1.359.62.4115.9
下层 Bottom layery=e[43.55－42.24×R780,920]0.799.31.0813.7
Table 2 Screening results of PARf single parameter inversion model
Fig. 7 LAI inversion results of different models

反演模型

Inversion model

分层

Layer

R2RMSE/(m2/m2)NRMSE/%

单参数模型

Single parameter model

上层

Top layer

0.9000.1219.9

中层

Middle layer

0.6940.64229.7

下层

Bottom layer

0.5550.37535.0

约束化模型

Constrained model

上层

Top layer

0.9220.1058.6

中层

Middle layer

0.7740.42319.6

下层

Bottom layer

0.6240.33430.4
Table 3 Accuracy evaluation of LAI inversion model
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