草地学报

草地学报 ›› 2025, Vol. 33 ›› Issue (1): 250-261.DOI: 10.11733/j.issn.1007-0435.2025.01.028

• 研究论文 • 上一篇    

基于机器学习的梭梭同化枝光合色素含量高光谱反演估算

黄轲盼1, 杨雪梅2,3, 刘志飞1, 张忠3, 王景瑞3, 徐浩杰1,4   

  1. 1. 兰州大学草种创新与草地农业生态系统全国重点实验室/兰州大学农业农村部草牧业创新重点实验室/兰州大学草地农业教育部工程研究中心/兰州大学草地农业科技学院, 甘肃 兰州 730020;
    2. 兰州文理学院, 甘肃 兰州 730010;
    3. 甘肃省治沙研究所, 甘肃 兰州 733000;
    4. 兰州大学寒旱区生态环境遥感研究中心, 甘肃 兰州 730000
  • 收稿日期:2024-04-11 修回日期:2024-07-05 发布日期:2025-01-22
  • 通讯作者: 徐浩杰,E-mail：xuhaojie@lzu.edu.cn
  • 作者简介:黄轲盼（2001-）,男,汉族,河南三门峡人,硕士研究生,主要从事基于高光谱遥感的人工梭梭林水分胁迫监测,E-mail：huangkp2023@lzu.edu.cn
  • 基金资助:
    国家重点研发计划项目（2020YFA0608401）；国家自然科学基金项目（32060373,41901113）；甘肃省自然科学基金项目（23JRRA1048,22JR5RA766）；中央高校基本科研业务费专项资金项目（lzujbky-2022-27）资助

Estimation of the Photosynthetic Pigment Content in Assimilated Branches of Haloxylon ammodendron Based on Hyperspectral Data and Machine Learning Methods

HUANG Ke-pan1, YANG Xue-mei2,3, LIU Zhi-fei1, ZHANG Zhong3, WANG Jing-rui3, XU Hao-jie1,4   

  1. 1. State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems/Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs/Engineering Research Center of Grassland Industry, Ministry of Education/College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu Province 730020, China;
    2. Tourism School, Lanzhou University of Arts and Science, Lanzhou, Gansu Province 730010, China;
    3. Gansu Institute of desertification Control, Lanzhou, Gansu Province 733000, China;
    4. Center for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou, Gansu Province 730000, China
  • Received:2024-04-11 Revised:2024-07-05 Published:2025-01-22

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摘要： 基于民勤干旱荒漠区地面实测的人工梭梭同化枝高光谱反射率与光合色素数据,经光谱曲线处理和特征参数提取后,采用随机森林（RF）、反向传递人工神经网络（BP-ANN）、支持向量机（SVM）三种常见的机器学习模型模拟叶绿素a、叶绿素b和类胡萝卜素含量,确定梭梭同化枝光合色素含量的最优估算模型,揭示影响光合色素含量的光谱关键变量。结果表明,RF模型在模拟精度上表现最优,对三种光合色素的R2值介于0.73至0.77,RMSE介于0.023至0.302 mg·g-1。其中,RF对叶绿素b的预测精度最高,R2为0.77,RMSE为0.023 mg·g-1。不同光合色素对干旱胁迫的响应各异,导致影响它们的光谱变量也不同。叶绿素a与叶绿素b的最优模型受到吸收位置（Absorption position,AP）、红边位置（Red-edge position,REP）和红边拐点（Red-edge inflection point,REIP）的影响较多,而类胡萝卜素的最优模型更容易受REIP、Vogelmann红边指数（Vogelmann red-edge index 2,VOG2）与水波段指数（Water band index,WBI）的影响。该研究为基于高光谱遥感与机器学习模型反演梭梭同化枝光合色素含量提供依据,服务于人工梭梭林的旱情监测与抚育管理。

关键词: 梭梭, 光合色素, 高光谱, 反射特性, 模型模拟, 变量重要性

Abstract: Based on the ground-measured hyperspectral reflectance and photosynthetic pigment data of assimilated branches of Haloxylon ammodendron in the Minqin desert area, the hyperspectral data were processed with noise reduction and feature parameter extraction. Three common machine learning models, including random forest （RF）, back propagation-artificial neural networks, and support vector machine, were used to simulate chlorophyll a, chlorophyll b, and carotenoid contents. The optimal estimation model for photosynthetic pigment content in the assimilated branch of Haloxylon ammodendron was determined, and the key spectral variables affecting the photosynthetic pigment content were revealed.The results showed that the RF model had the best simulation accuracy, with R2 values ranging from 0.73 to 0.77 and RMSE ranging from 0.023 to 0.302 mg·g-1 for the three photosynthetic pigments. Among them, RF had the highest prediction accuracy for chlorophyll b with R2 value of 0.77 and 0.023 mg·g-1 RMSE. Different photosynthetic pigments had different responses to drought stress, resulting in different spectral variables affecting them. The optimal models of chlorophyll a and chlorophyll b were mostly affected by Absorption position （AP）, Red-edge position （REP） and Red-edge inflection point （REIP）； while the optimal model of carotenoids was more susceptible to the influence of REIP, Vogelmann red-edge index 2 （VOG2） and Water band index （WBI）. This study established a foundation for retrieving photosynthetic pigment content in assimilated branches of Haloxylon ammodendron using hyperspectral remote sensing and machine learning models. It also contributed to the drought monitoring and rearing management of artificial Haloxylon ammodendron forests.

Key words: Haloxylon ammodendron, Photosynthetic pigment, Hyperspectral, Reflection characteristics, Model simulation, Variable importance

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