植物生态学报 ›› 2022, Vol. 46 ›› Issue (10): 1151-1166.DOI: 10.17521/cjpe.2022.0223
所属专题： 生态学研究的方法和技术； 生态遥感及应用； 植物功能性状
严正兵, 刘树文, 吴锦*(
)
收稿日期:2022-06-01
接受日期:2022-09-05
出版日期:2022-10-20
发布日期:2022-09-28
通讯作者:
* 吴锦(jinwu@hku.hk)
基金资助:
YAN Zheng-Bing, LIU Shu-Wen, WU Jin*()
Received:2022-06-01
Accepted:2022-09-05
Online:2022-10-20
Published:2022-09-28
Contact:
* WU Jin(jinwu@hku.hk)
Supported by:摘要：
植物功能性状作为指示植物对环境适应和进化的可量度特征, 其变异格局和驱动机制是植物生态学和地球系统建模的重要研究内容。传统野外测定方法费时费力费钱, 且通常关注生长季和优势物种, 使得功能性状的尺度延展和时空覆盖存在极大挑战。近些年兴起的多尺度高光谱遥感技术为解决当前植物功能性状数据时空覆盖度差的问题提供了新的思路和方法。该文在概述高光谱遥感技术监测植物功能性状的基本原理和发展简史的基础上, 详细介绍了当前光谱-性状关系的主要建模方法, 即经验或半经验方法、物理模型反演方法, 其中以经验统计模型方法中的偏最小二乘回归使用最为广泛。进一步结合实例, 重点讨论了高光谱遥感技术在叶片、群落和景观尺度监测植物功能性状的应用及存在的主要问题。最后, 为促进高光谱技术在植物功能性状及其多样性监测方面的研究, 提出以下4个未来应该重点关注的方向: 1)检验光谱-性状模型的普适性, 并解析其背后的调控机理; 2)发展光谱-性状关系尺度延展的方法体系; 3)解析植物功能性状及其多样性的时空变异和调控机制; 4)探究环境-植物功能多样性-生物多样性-生态系统功能间的关联。
严正兵, 刘树文, 吴锦. 高光谱遥感技术在植物功能性状监测中的应用与展望. 植物生态学报, 2022, 46(10): 1151-1166. DOI: 10.17521/cjpe.2022.0223
YAN Zheng-Bing, LIU Shu-Wen, WU Jin. Hyperspectral remote sensing of plant functional traits: monitoring techniques and future advances. Chinese Journal of Plant Ecology, 2022, 46(10): 1151-1166. DOI: 10.17521/cjpe.2022.0223
图1 植物绿色成熟叶片光谱反射率的基本特征(改自Serbin和Townsend (2020))。植物叶片在不同波长光的反射率的差异与它们的化学成分、细胞结构和生理特性紧密关联。
Fig. 1 Essential characteristics of mature green-leaf hyperspectral reflectance in plants (adapted from Serbin & Townsend (2020)). Differences in the reflectance of plant leaves at different wavelengths are tightly connected with their chemical composition, cell structure, and physiological properties.
| 性状类型 Trait type | 性状 Trait | 模型精度 Model accuracy (R2) | 建模方法 Modelling method | 参考文献 Reference |
|---|---|---|---|---|
| 生理性状 Physiological trait | 暗呼吸速率 Dark respiration rate | 0.50-0.74 | PLSR | Coast et al., |
| 气孔导度 Stomatal conductance | 0.34 | PLSR | Silva-Perez et al., | |
| 最大电子传递速率 Maximum electron transport rate | 0.45-0.93 | PLSR, VI, ANN, SVM | Serbin et al., | |
| 最大羧化速率 Maximum carboxylation rate | 0.50-0.89 | PLSR, VI, ANN, SVM, GPR | Serbin et al., | |
| 生物化学性状 Biochemical trait | 氨基酸含量 Amino acid content | 0.56-0.58 | PLSR | Ely et al., |
| 半纤维素含量 Hemicellulose content | 0.63-0.64 | PLSR | Asner et al., | |
| 单宁含量 Tannin content | 0.59-0.86 | PLSR | Asner et al., | |
| 蛋白质含量 Protein content | 0.73-0.85 | PLSR | Ely et al., | |
| 15N自然丰度 Natural abundance of 15N (δ15N) | 0.60-0.85 | PLSR | Serbin et al., | |
| 氮含量 Nitrogen content | 0.59-0.97 | VI, PLSR, SVM, RTM | Asner et al., | |
| 淀粉含量 Starch content | 0.80-0.93 | PLSR | Ely et al., | |
| 非结构性碳含量 Nonstructural carbon content | 0.70-0.93 | PLSR | Ely et al., | |
| 酚类含量 Phenolic content | 0.72-0.79 | PLSR | Asner et al., | |
| 钙含量 Calcium content | 0.76-0.78 | PLSR | Asner et al., | |
| 果糖含量 Fructose content | 0.44-0.55 | PLSR | Ely et al., | |
| 含水量 Water content | 0.69-0.93 | PLSR, SVM | Asner et al., | |
| 钾含量 Potassium content | 0.54-0.61 | PLSR, SVM | Asner et al., | |
| 可溶性碳含量 Soluble carbon content | 0.53-0.66 | PLSR | Asner et al., | |
| 类胡萝卜素含量 Carotenoid content | 0.71-0.93 | PLSR | Asner et al., | |
| 磷含量 Phosphorus content | 0.44-0.70 | PLSR, SVM | Asner et al., | |
| 镁含量 Magnesium content | 0.41-0.70 | PLSR | Asner et al., | |
| 锰含量 Manganese content | 0.34-0.62 | PLSR | Asner et al., | |
| 木质素含量 Lignin content | 0.38-0.72 | PLSR | Asner et al., | |
| 硼含量 Boron content | 0.32-0.39 | PLSR | Asner et al., | |
| 脯氨酸 Proline content | 0.84 | PLSR | Burnett et al., | |
| 葡萄糖含量 Glucose content | 0.59-0.63 | PLSR | Ely et al., | |
| 13C自然丰度 Natural abundance of 13C (δ13C) | 0.68 | PLSR | Chen et al., | |
| 碳氮比 C:N | 0.92 | PLSR | Ely et al., | |
| 碳含量 Carbon content | 0.58-0.95 | PLSR | Serbin et al., | |
| 铁含量 Iron content | 0.58-0.74 | PLSR | Asner et al., | |
| 脱落酸含量 Abscisic acid content | 0.50 | PLSR | Burnett et al., | |
| 纤维素含量 Cellulose content | 0.52-0.81 | PLSR | Asner et al., | |
| 锌含量 Zinc content | 0.67 | PLSR | Chen et al., | |
| 叶绿素含量 Chlorophyll content | 0.77-0.95 | VI, PLSR, RTM | Asner et al., | |
| 蔗糖含量 Sucrose content | 0.62-0.76 | PLSR | Yendrek et al., | |
| 形态结构性状 Morphological trait | 比叶质量 Leaf mass per area | 0.75-0.95 | PLSR | Asner et al., |
| 气孔密度 Stomatal density | 0.45 | PLSR | Cotrozzi et al., | |
| 物候性状 Phenological trait | 叶龄 Leaf age | 0.79-0.86 | PLSR | Chavana-Bryant et al., |
表1 高光谱遥感技术在叶片尺度植物功能性状监测中的应用案例
Table 1 Application case of hyperspectral remote sensing technology in monitoring plant functional traits at leaf scale
| 性状类型 Trait type | 性状 Trait | 模型精度 Model accuracy (R2) | 建模方法 Modelling method | 参考文献 Reference |
|---|---|---|---|---|
| 生理性状 Physiological trait | 暗呼吸速率 Dark respiration rate | 0.50-0.74 | PLSR | Coast et al., |
| 气孔导度 Stomatal conductance | 0.34 | PLSR | Silva-Perez et al., | |
| 最大电子传递速率 Maximum electron transport rate | 0.45-0.93 | PLSR, VI, ANN, SVM | Serbin et al., | |
| 最大羧化速率 Maximum carboxylation rate | 0.50-0.89 | PLSR, VI, ANN, SVM, GPR | Serbin et al., | |
| 生物化学性状 Biochemical trait | 氨基酸含量 Amino acid content | 0.56-0.58 | PLSR | Ely et al., |
| 半纤维素含量 Hemicellulose content | 0.63-0.64 | PLSR | Asner et al., | |
| 单宁含量 Tannin content | 0.59-0.86 | PLSR | Asner et al., | |
| 蛋白质含量 Protein content | 0.73-0.85 | PLSR | Ely et al., | |
| 15N自然丰度 Natural abundance of 15N (δ15N) | 0.60-0.85 | PLSR | Serbin et al., | |
| 氮含量 Nitrogen content | 0.59-0.97 | VI, PLSR, SVM, RTM | Asner et al., | |
| 淀粉含量 Starch content | 0.80-0.93 | PLSR | Ely et al., | |
| 非结构性碳含量 Nonstructural carbon content | 0.70-0.93 | PLSR | Ely et al., | |
| 酚类含量 Phenolic content | 0.72-0.79 | PLSR | Asner et al., | |
| 钙含量 Calcium content | 0.76-0.78 | PLSR | Asner et al., | |
| 果糖含量 Fructose content | 0.44-0.55 | PLSR | Ely et al., | |
| 含水量 Water content | 0.69-0.93 | PLSR, SVM | Asner et al., | |
| 钾含量 Potassium content | 0.54-0.61 | PLSR, SVM | Asner et al., | |
| 可溶性碳含量 Soluble carbon content | 0.53-0.66 | PLSR | Asner et al., | |
| 类胡萝卜素含量 Carotenoid content | 0.71-0.93 | PLSR | Asner et al., | |
| 磷含量 Phosphorus content | 0.44-0.70 | PLSR, SVM | Asner et al., | |
| 镁含量 Magnesium content | 0.41-0.70 | PLSR | Asner et al., | |
| 锰含量 Manganese content | 0.34-0.62 | PLSR | Asner et al., | |
| 木质素含量 Lignin content | 0.38-0.72 | PLSR | Asner et al., | |
| 硼含量 Boron content | 0.32-0.39 | PLSR | Asner et al., | |
| 脯氨酸 Proline content | 0.84 | PLSR | Burnett et al., | |
| 葡萄糖含量 Glucose content | 0.59-0.63 | PLSR | Ely et al., | |
| 13C自然丰度 Natural abundance of 13C (δ13C) | 0.68 | PLSR | Chen et al., | |
| 碳氮比 C:N | 0.92 | PLSR | Ely et al., | |
| 碳含量 Carbon content | 0.58-0.95 | PLSR | Serbin et al., | |
| 铁含量 Iron content | 0.58-0.74 | PLSR | Asner et al., | |
| 脱落酸含量 Abscisic acid content | 0.50 | PLSR | Burnett et al., | |
| 纤维素含量 Cellulose content | 0.52-0.81 | PLSR | Asner et al., | |
| 锌含量 Zinc content | 0.67 | PLSR | Chen et al., | |
| 叶绿素含量 Chlorophyll content | 0.77-0.95 | VI, PLSR, RTM | Asner et al., | |
| 蔗糖含量 Sucrose content | 0.62-0.76 | PLSR | Yendrek et al., | |
| 形态结构性状 Morphological trait | 比叶质量 Leaf mass per area | 0.75-0.95 | PLSR | Asner et al., |
| 气孔密度 Stomatal density | 0.45 | PLSR | Cotrozzi et al., | |
| 物候性状 Phenological trait | 叶龄 Leaf age | 0.79-0.86 | PLSR | Chavana-Bryant et al., |
| 性状类型 Trait type | 性状 Trait | 观测平台 Platform | 模型精度 Model accuracy (R2) | 建模方法 Modelling method | 参考文献 Reference |
|---|---|---|---|---|---|
| 生理性状 Physiological trait | 气孔导度 Stomatal conductance | 无人机 Unmanned aerial vehicle | 0.62 | VI | Zarco-Tejada et al., |
| 最大羧化速率 Maximum carboxylation rate | 航空 Airborne | 0.77-0.94 | PLSR, RTM | Serbin et al., | |
| 近地面 Close range | 0.84 | PLSR | Fu et al., | ||
| 最大电子传递速率 Maximum electron transport rate | 近地面 Close range | 0.80 | PLSR | Fu et al., | |
| 生物化学 性状 Biochemical trait | 叶绿素含量 Chlorophyll content | 航空 Airborne | 0.33-0.86 | PLSR, VI | Asner et al., |
| 无人机 Unmanned aerial vehicle | 0.87 | PLSR | Zhao et al., | ||
| 近地面 Close range | 0.83 | VI | Jay et al., | ||
| 类胡萝卜素含量 Carotenoid content | 航空 Airborne | 0.39-0.63 | PLSR, VI | Asner et al., | |
| 无人机 Unmanned aerial vehicle | 0.97 | PLSR | Zhao et al., | ||
| 氮含量 Nitrogen content | 航空 Airborne | 0.54-0.86 | PLSR, SMR, RTM | Wessman et al., | |
| 无人机 Unmanned aerial vehicle | 0.22-0.83 | PLSR | Thomson et al., | ||
| 近地面 Close range | 0.84 | CNN | Pullanagari et al., | ||
| 磷含量 Phosphorus content | 航空 Airborne | 0.41-0.71 | PLSR | Asner et al., | |
| 无人机 Unmanned aerial vehicle | 0.21-0.25 | PLSR | Thomson et al., | ||
| 含水量 Water content | 航空 Airborne | 0.46-0.62 | PLSR | Asner et al., | |
| 可溶性碳含量 Soluble carbon content | 航空 Airborne | 0.49 | PLSR | Asner et al., | |
| 酚类含量 Phenolic content | 航空 Airborne | 0.33-0.86 | PLSR | Asner et al., | |
| 木质素含量 Lignin content | 航空 Airborne | 0.51-0.74 | PLSR | Asner et al., | |
| 无人机 Unmanned aerial vehicle | 0.53 | PLSR | Zhao et al., | ||
| 纤维素含量 Cellulose content | 航空 Airborne | 0.38-0.49 | PLSR | Asner et al., | |
| 无人机 Unmanned aerial vehicle | 0.72 | PLSR | Zhao et al., | ||
| 碳含量 Carbon content | 航空 Airborne | 0.69 | PLSR | Asner et al., | |
| 钙含量 Calcium content | 航空 Airborne | 0.25-0.79 | PLSR | Asner et al., | |
| 硼含量 Boron content | 航空 Airborne | 0.52-0.53 | PLSR | Asner et al., | |
| 铁含量 Iron content | 航空 Airborne | 0.56 | PLSR | Asner et al., | |
| 钾含量 Potassium content | 航空 Airborne | 0.42-0.65 | PLSR | Asner et al., | |
| 无人机 Unmanned aerial vehicle | 0.76 | PLSR | Lu et al., | ||
| 镁含量 Magnesium content | 航空 Airborne | 0.34-0.37 | PLSR | Asner et al., | |
| 铜含量 Copper content | 航空 Airborne | 0.51 | PLSR | Wang et al., | |
| 锰含量 Manganese content | 航空 Airborne | 0.37 | PLSR | Wang et al., | |
| 非结构性碳含量 Nonstructural carbon content | 航空 Airborne | 0.72 | PLSR | Wang et al., | |
| 淀粉含量 Starch content | 航空 Airborne | 0.64 | PLSR | Wang et al., | |
| 15N自然丰度 Natural abundance of 15N (δ15N) | 航空 Airborne | 0.48 | PLSR | Singh et al., | |
| 形态结构 性状 Morphological trait | 比叶质量 Leaf mass per area | 航空 Airborne | 0.58-0.88 | PLSR | Asner et al., |
| 无人机 Unmanned aerial vehicle | 0.25-0.87 | PLSR | Thomson et al., |
表2 高光谱遥感技术在群落尺度植物功能性状监测中的应用案例
Table 2 Application case of hyperspectral remote sensing technology in monitoring plant functional traits at community scale
| 性状类型 Trait type | 性状 Trait | 观测平台 Platform | 模型精度 Model accuracy (R2) | 建模方法 Modelling method | 参考文献 Reference |
|---|---|---|---|---|---|
| 生理性状 Physiological trait | 气孔导度 Stomatal conductance | 无人机 Unmanned aerial vehicle | 0.62 | VI | Zarco-Tejada et al., |
| 最大羧化速率 Maximum carboxylation rate | 航空 Airborne | 0.77-0.94 | PLSR, RTM | Serbin et al., | |
| 近地面 Close range | 0.84 | PLSR | Fu et al., | ||
| 最大电子传递速率 Maximum electron transport rate | 近地面 Close range | 0.80 | PLSR | Fu et al., | |
| 生物化学 性状 Biochemical trait | 叶绿素含量 Chlorophyll content | 航空 Airborne | 0.33-0.86 | PLSR, VI | Asner et al., |
| 无人机 Unmanned aerial vehicle | 0.87 | PLSR | Zhao et al., | ||
| 近地面 Close range | 0.83 | VI | Jay et al., | ||
| 类胡萝卜素含量 Carotenoid content | 航空 Airborne | 0.39-0.63 | PLSR, VI | Asner et al., | |
| 无人机 Unmanned aerial vehicle | 0.97 | PLSR | Zhao et al., | ||
| 氮含量 Nitrogen content | 航空 Airborne | 0.54-0.86 | PLSR, SMR, RTM | Wessman et al., | |
| 无人机 Unmanned aerial vehicle | 0.22-0.83 | PLSR | Thomson et al., | ||
| 近地面 Close range | 0.84 | CNN | Pullanagari et al., | ||
| 磷含量 Phosphorus content | 航空 Airborne | 0.41-0.71 | PLSR | Asner et al., | |
| 无人机 Unmanned aerial vehicle | 0.21-0.25 | PLSR | Thomson et al., | ||
| 含水量 Water content | 航空 Airborne | 0.46-0.62 | PLSR | Asner et al., | |
| 可溶性碳含量 Soluble carbon content | 航空 Airborne | 0.49 | PLSR | Asner et al., | |
| 酚类含量 Phenolic content | 航空 Airborne | 0.33-0.86 | PLSR | Asner et al., | |
| 木质素含量 Lignin content | 航空 Airborne | 0.51-0.74 | PLSR | Asner et al., | |
| 无人机 Unmanned aerial vehicle | 0.53 | PLSR | Zhao et al., | ||
| 纤维素含量 Cellulose content | 航空 Airborne | 0.38-0.49 | PLSR | Asner et al., | |
| 无人机 Unmanned aerial vehicle | 0.72 | PLSR | Zhao et al., | ||
| 碳含量 Carbon content | 航空 Airborne | 0.69 | PLSR | Asner et al., | |
| 钙含量 Calcium content | 航空 Airborne | 0.25-0.79 | PLSR | Asner et al., | |
| 硼含量 Boron content | 航空 Airborne | 0.52-0.53 | PLSR | Asner et al., | |
| 铁含量 Iron content | 航空 Airborne | 0.56 | PLSR | Asner et al., | |
| 钾含量 Potassium content | 航空 Airborne | 0.42-0.65 | PLSR | Asner et al., | |
| 无人机 Unmanned aerial vehicle | 0.76 | PLSR | Lu et al., | ||
| 镁含量 Magnesium content | 航空 Airborne | 0.34-0.37 | PLSR | Asner et al., | |
| 铜含量 Copper content | 航空 Airborne | 0.51 | PLSR | Wang et al., | |
| 锰含量 Manganese content | 航空 Airborne | 0.37 | PLSR | Wang et al., | |
| 非结构性碳含量 Nonstructural carbon content | 航空 Airborne | 0.72 | PLSR | Wang et al., | |
| 淀粉含量 Starch content | 航空 Airborne | 0.64 | PLSR | Wang et al., | |
| 15N自然丰度 Natural abundance of 15N (δ15N) | 航空 Airborne | 0.48 | PLSR | Singh et al., | |
| 形态结构 性状 Morphological trait | 比叶质量 Leaf mass per area | 航空 Airborne | 0.58-0.88 | PLSR | Asner et al., |
| 无人机 Unmanned aerial vehicle | 0.25-0.87 | PLSR | Thomson et al., |
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