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

doi:10.3785/j.issn.1008-9209.2021.04.261

浙江大学学报（农业与生命科学版）

2021, Vol. 47

Issue (4): 439-450 DOI: 10.3785/j.issn.1008-9209.2021.04.261

作物表型分析技术及应用专题

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

董震^1,²(

),杨贵军¹(

),孙林²,杨浩¹,朱耀辉¹,雷蕾¹,陈日强¹,张成健¹,刘淼¹

^1.北京农业信息技术研究中心，农业农村部农业遥感机理与定量遥感重点实验室，北京 100097
^2.山东科技大学测绘与空间信息学院，山东青岛 266590

Application of multi-layer discrete anisotropic radiative transfer model in vertical distribution inversion of maize leaf area index

Zhen DONG^1,²(

),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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摘要：

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

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

收稿日期: 2021-04-26 出版日期: 2021-09-02

CLC:

TP 751

基金资助: 广东省重点领域研发计划(2019B020214002)

通讯作者: 杨贵军 E-mail: jmhshdzh@163.com;yanggj@nercita.org.cn

作者简介: 董震（https://orcid.org/0000-0002-0666-1972），E-mail：jmhshdzh@163.com

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引用本文:

董震,杨贵军,孙林,杨浩,朱耀辉,雷蕾,陈日强,张成健,刘淼. 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[J]. 浙江大学学报（农业与生命科学版）, 2021, 47(4): 439-450.

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.

链接本文:

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

图1 玉米3层垂直分布模拟场景

图2 DART模型输入参数敏感性分析结果LAI：叶面积指数；Sz：太阳天顶角；LAD：叶倾角分布；LCC：叶片叶绿素含量；H：株高。

图3 不同叶倾角分布（LAD）和体素大小（Vsize）组合下PAR模拟结果精度评价

图4 各波长处反射率模拟结果的R2

图5 冠层反射率模拟结果精度评价

图6 PAR模拟结果精度评价

分层 Layer	回归模型 Regression model	模拟数据集 Simulated dataset		实测数据集 Measured dataset
分层 Layer	回归模型 Regression model	RMSE/(m²/m²)	NRMSE/%	RMSE/(m²/m²)	NRMSE/%
上层 Top layer	$y = e [－ 4.12 + 271.87 × D V 560,620]$	0.084	6.4	0.121	9.9
中层 Middle layer	$y = e [－ 31.49 + 31.16 × R 920,970]$	0.365	14.2	0.656	19.4
下层 Bottom layer	$y = e [－ 16.66 + 17.74 × R 790,950]$	0.338	9.5	0.580	14.9

表1 LAIc单参数反演模型筛选结果

分层 Layer	回归模型 Regression model	模拟数据集 Simulated dataset		实测数据集 Measured dataset
分层 Layer	回归模型 Regression model	RMSE	NRMSE	RMSE	NRMSE
上层 Top layer	$y = e [24.31 + 51.09 × N D 530,720]$	3.11	8.1	3.86	9.4
中层 Middle layer	$y = e [2.33 － 118.8 × D V 790,850]$	1.35	9.6	2.41	15.9
下层 Bottom layer	$y = e [43.55 － 42.24 × R 780,920]$	0.79	9.3	1.08	13.7

表2 PARf单参数反演模型筛选结果 (%)

图7 不同模型下LAI反演结果

表3 LAI反演模型的精度评价

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