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遥感技术与应用  2023, Vol. 38 Issue (1): 66-77    DOI: 10.11873/j.issn.1004-0323.2023.1.0066
定量遥感专栏     
二向反射遥感反演最优角度采样方法研究
张腾1,2(),游冬琴1(),闻建光1,2,唐勇1
1.中国科学院空天信息创新研究院 遥感科学国家重点实验室,北京 100083
2.中国科学院大学 资源与环境学院,北京 100049
The Optimal Angular Sampling for Bi-directional Reflectance Distribution Function Retrieval
Teng ZHANG1,2(),Dongqin YOU1(),Jianguang WEN1,2,Yong TANG1
1.State Key Laboratory of Remote Sensing Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100083,China
2.College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China
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摘要:

地物具有二向反射特性,可由二向反射分布函数(Bi-directional Reflectance Distribution Function, BRDF)刻画,它是光学定量遥感反演的基础。BRDF的反演依赖于多角度观测,由于卫星、航空和地基观测的角度有限,如何设计可行的稀疏角度采样,对实现BRDF的高质量反演至关重要。研究引入角度信息量,基于模型模拟和传感器观测的多角度数据,通过计算不同角度组合用于核驱动模型BRDF反演的角度信息量与反演误差,探究了角度信息量与反演误差之间的关系,确定了BRDF反演的最优观测平面和角度数目,进而得到不同太阳天顶角对应的优选角度组合。验证结果表明:优选出的角度组合在大多数地表上可实现高质量的BRDF反演。研究成果可为多角度观测实验、多角度卫星载荷设计以及地表二向反射反演提供重要参考。

关键词: BRDF核驱动模型遥感反演角度采样    
Abstract:

The anisotropic land surface reflectance is characterized by Bi-directional Reflectance Distribution Function (BRDF), which is the basis of quantitative optical remote sensing. The inversion of BRDF relies on multi-angular observations. Due to the limited observations from satellites, aerocrafts and goniometers, it is very critical to design a feasible sparse angular sampling to achieve high-quality BRDF inversion. In this study, based on RossThick-LiSparse Reciprocal (RTLSR) kernel-driven model, we designed the optimal angular sampling by using the PROSAIL model simulated reflectance and observations from POLDER and in situ by employing the angular information content to quantify the information which the observation geometry can contribute to the inversion. Firstly, the information content and BRDF inversion accuracy with different angle combinations are calculated. The relationship between them is then obtained, and the angular information threshold for high-precision inversion is -3.5. Secondly, the optimal observation plane and the least angles required in BRDF inversion were found out by analyzing the angular information content of combinations in each observation plane. It shows that the optimal plane is the principal plane, and the minimum number angles is 5 while the recommended number is 6 and 7. Thirdly, the optimal angle combinations under different solar zenith angles are found as relatively regularly distributed in the forward and backward scattering, and there should be two angles around the hot spot within ±10°. The validation finally proves that the optimal angle combinations are suitable for most land surface cover types except the snow/ice case and especially good for sparse vegetation, with RRMSE (Relative Root Mean Squared Error) of 0.14 in red band, and 0.046 in Nir band. The results of this study are useful for multi-angular satellite sensor design, multi-angular reflectance observation experiment and angular weights assigning in BRDF inversion.

Key words: BRDF    Kernel-driven BRDF model    Remote sensing inversion    Angular sampling
收稿日期: 2022-02-18 出版日期: 2023-04-12
ZTFLH:  TP75  
基金资助: 中国科学院战略性先导专项 A类项目(XDA15012102);国家自然科学基金项目(41971316);高分辨率对地观测系统重大专项(21?Y20B01?9001?19/22)
通讯作者: 游冬琴     E-mail: zhangteng192@mails.ucas.ac.cn;youdq@aircas.ac.cn
作者简介: 张 腾(1997-),男,山东潍坊人,硕士研究生,主要从事BRDF反演研究。E?mail:zhangteng192@mails.ucas.ac.cn
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引用本文:

张腾,游冬琴,闻建光,唐勇. 二向反射遥感反演最优角度采样方法研究[J]. 遥感技术与应用, 2023, 38(1): 66-77.

Teng ZHANG,Dongqin YOU,Jianguang WEN,Yong TANG. The Optimal Angular Sampling for Bi-directional Reflectance Distribution Function Retrieval. Remote Sensing Technology and Application, 2023, 38(1): 66-77.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2023.1.0066        http://www.rsta.ac.cn/CN/Y2023/V38/I1/66

参数输入
叶片生化参数LOPEX93数据集
叶倾角分布喜平型,喜直型,倾斜型,球面型,均匀型
叶面积指数0~4,间隔为0.5;4~8,间隔为1
波段红(645 nm)、近红外(858 nm)
太阳天顶角0°~60°,间隔为10°
观测天顶角0°~60°,间隔为5°
相对方位角0°~360°,间隔为10°
表1  PROSAIL模型输入参数
图1  PROSAIL模拟的红波段反射率(玉米场景,SZA = 0°)
图2  各类地表在太阳主平面上的反射率
图3  角度采样研究技术路线图
图4  未添加噪声的模拟数据对应的统计关系
图5  模拟数据与反演结果在主平面上的比较(SZA=20°,LAI=1的玉米场景)
图6  添加噪声的模拟数据对应的统计关系
图7  POLDER数据对应的统计关系
图8  观测平面上不同角度个数组合对应的平均信息量
图9  不同角度个数的组合对应的信息量
SZA组合
-60°、-45°、-5°、15°、55°
10°-60°、-20°、0°、20°、60°
20°-60°、-30°、-10°、25°、55°
30°-60°、-40°、-15°、25°、55°
40°-60°、-45°、-25°、15°、60°
50°-60°、-40°、-10°、20°、60°
60°-50°、-20°、0°、15°、55°
表2  角度个数为5的优选组合
SZA组合
-50°、-15°、-5°、5°、15°、50°
10°-60°、-45°、-20°、0°、25°、60°
20°-60°、-30°、-10°、5°、25°、55°
30°-60°、-40°、-20°、0°、25°、55°
40°-60°、-45°、-30°、0°、35°、55°
50°-60°、-45°、-30°、10°、40°、60°
60°-50°、-20°、0°、15°、40°、55°
表3  角度个数为6的优选组合
SZA组合
-60°、-45°、-25°、-5°、5°、25°、55°
10°-60°、-45°、-20°、0°、15°、30°、60°
20°-60°、-45°、-30°、-10°、15°、30°、55°
30°-60°、-40°、-20°、-5°、20°、40°、60°
40°-60°、-45°、-25°、-10°、25°、45°、60°
50°-60°、-40°、-25°、0°、20°、45°、60°
60°-50°、-30°、-10°、10°、25°、40°、55°
表4  角度个数为7的优选组合
图10  6角度最优角度采样(玉米场景,LAI=1)
图11  有无热点参与反演的结果比较(LAI=1,玉米冠层,SZA=30°,红波段)
图12  添加噪声和未添加噪声的模拟数据的反演误差
波段REDNIR
角度个数567567
沙漠草地0.041 70.040 40.039 20.038 90.035 80.034 9
玉米0.131 50.091 30.083 40.046 00.043 10.035 9
土壤0.045 80.044 20.034 40.044 40.037 60.035 9
冰雪0.134 30.114 90.093 30.135 60.117 50.098 9
表5  不同地表类型对应的反演误差(RRMSE)均值
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