植物生态学报 ›› 2022, Vol. 46 ›› Issue (10): 1151-1166.DOI: 10.17521/cjpe.2022.0223
所属专题： 遥感生态学
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., |
| [1] |
Asner GP. (1998). Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment, 64, 234-253.
DOI URL |
| [2] | Asner GP, Knapp DE, Anderson CB, Martin RE, Vaughn N. (2016). Large-scale climatic and geophysical controls on the leaf economics spectrum. Proceedings of the National Academy of Sciences of the United States of America, 113, E4043-E4051. |
| [3] |
Asner GP, Martin RE. (2008). Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels. Remote Sensing of Environment, 112, 3958-3970.
DOI URL |
| [4] |
Asner GP, Martin RE, Anderson CB, Knapp DE. (2015). Quantifying forest canopy traits: imaging spectroscopy versus field survey. Remote Sensing of Environment, 158, 15-27.
DOI URL |
| [5] |
Asner GP, Martin RE, Carranza-Jiménez L, Sinca F, Tupayachi R, Anderson CB, Martinez P. (2014). Functional and biological diversity of foliar spectra in tree canopies throughout the Andes to Amazon region. New Phytologist, 204, 127-139.
DOI PMID |
| [6] |
Asner GP, Martin RE, Ford AJ, Metcalfe DJ, Liddell MJ. (2009). Leaf chemical and spectral diversity in Australian tropical forests. Ecological Applications, 19, 236-253.
DOI URL |
| [7] |
Asner GP, Martin RE, Knapp DE, Tupayachi R, Anderson CB, Sinca F, Vaughn NR, Llactayo W. (2017). Airborne laser- guided imaging spectroscopy to map forest trait diversity and guide conservation. Science, 355, 385-389.
DOI PMID |
| [8] |
Asner GP, Martin RE, Suhaili AB. (2012). Sources of canopy chemical and spectral diversity in lowland bornean forest. Ecosystems, 15, 504-517.
DOI URL |
| [9] |
Asner GP, Martin RE, Tupayachi R, Emerson R, Martinez P, Sinca F, Powell GVN, Wright SJ, Lugo AE. (2011). Taxonomy and remote sensing of leaf mass per area (LMA) in humid tropical forests. Ecological Applications, 21, 85-98.
DOI URL |
| [10] |
Bayat B, van der Tol C, Verhoef W. (2016). Remote sensing of grass response to drought stress using spectroscopic techniques and canopy reflectance model inversion. Remote Sensing, 8, 557. DOI: 10.3390/rs8070557.
DOI |
| [11] |
Berger K, Verrelst J, Féret JB, Wang ZH, Wocher M, Strathmann M, Danner M, Mauser W, Hank T. (2020). Crop nitrogen monitoring: recent progress and principal developments in the context of imaging spectroscopy missions. Remote Sensing of Environment, 242, 111758. DOI: 10.1016/j.rse.2020.111758.
DOI |
| [12] |
Berzaghi F, Wright IJ, Kramer K, Oddou-Muratorio S, Bohn FJ, Reyer CPO, Sabaté S, Sanders TGM, Hartig F. (2020). Towards a new generation of trait-flexible vegetation models. Trends in Ecology & Evolution, 35, 191-205.
DOI URL |
| [13] | Bongers FJ, Schmid B, Bruelheide H, Bongers F, Li S, von Oheimb G, Li Y, Cheng A, Ma K, Liu X. (2021). Functional diversity effects on productivity increase with age in a forest biodiversity experiment. Nature Ecology & Evolution, 5, 1594-1603. |
| [14] |
Burnett AC, Anderson J, Davidson KJ, Ely KS, Lamour J, Li Q, Morrison BD, Yang D, Rogers A, Serbin SP. (2021a). A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression. Journal of Experimental Botany, 72, 6175-6189.
DOI URL |
| [15] | Burnett AC, Serbin SP, Rogers A. (2021b). Source: sink imbalance detected with leaf-and canopy-level spectroscopy in a field-grown crop. Plant, Cell & Environment, 44, 2466-2479. |
| [16] |
Camino C, Gonzalez-Dugo V, Hernandez P, Zarco-Tejada PJ. (2019). Radiative transfer Vcmax estimation from hyperspectral imagery and SIF retrievals to assess photosynthetic performance in rainfed and irrigated plant phenotyping trials. Remote Sensing of Environment, 231, 111186. DOI: 10.1016/j.rse.2019.05.005.
DOI |
| [17] | Cavender-Bares J, Gamon JA, Townsend PA. (2020). Remote Sensing of Plant Biodiversity. Springer, Cham, Switzerland. |
| [18] | Cavender-Bares J, Schneider FD, Santos MJ, Armstrong A, Carnaval A, Dahlin KM, Fatoyinbo L, Hurtt GC, Schimel D, Townsend PA, Ustin SL, Wang Z, Wilson AM. (2022). Integrating remote sensing with ecology and evolution to advance biodiversity conservation. Nature Ecology & Evolution, 6, 506-519. |
| [19] |
Chavana-Bryant C, Malhi Y, Wu J, Asner GP, Anastasiou A, Enquist BJ, Cosio Caravasi EG, Doughty CE, Saleska SR, Martin RE, Gerard FF. (2017). Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements. New Phytologist, 214, 1049-1063.
DOI PMID |
| [20] |
Chen L, Zhang Y, Nunes MH, Stoddart J, Khoury S, Chan AHY, Coomes DA. (2022). Predicting leaf traits of temperate broadleaf deciduous trees from hyperspectral reflectance: Can a general model be applied across a growing season? Remote Sensing of Environment, 269, 112767. DOI: 10.1016/j.rse.2019.05.005.
DOI |
| [21] |
Chlus A, Townsend PA. (2022). Characterizing seasonal variation in foliar biochemistry with airborne imaging spectroscopy. Remote Sensing of Environment, 275, 113023. DOI: 10.1016/j.rse.2022.113023.
DOI |
| [22] | Coast O, Shah S, Ivakov A, Gaju O, Wilson PB, Posch BC, Bryant CJ, Negrini ACA, Evans JR, Condon AG, Silva-Pérez V, Reynolds MP, Pogson BJ, Millar AH, Furbank RT, Atkin OK. (2019). Predicting dark respiration rates of wheat leaves from hyperspectral reflectance. Plant, Cell & Environment, 42, 2133-2150. |
| [23] | Cocks T, Jensen R, Stewart A, Wilson I, Shields T. (1998). The HyMap airborne hyperspectral sensor: the system, calibration and performance//Schaepman M, Schläpfer D, Itten KI. Proceedings of 1st EARSeL Workshop on Imaging Spectroscopy, Zurich, Switzerland. 37-42. |
| [24] |
Colgan MS, Baldeck CA, Féret J-B, Asner GP. (2012). Mapping savanna tree species at ecosystem scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data. Remote Sensing, 4, 3462-3480.
DOI URL |
| [25] |
Coops NC, Smith ML, Martin ME, Ollinger SV. (2003). Prediction of eucalypt foliage nitrogen content from satellite- derived hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 41, 1338-1346.
DOI URL |
| [26] |
Cotrozzi L, Peron R, Tuinstra MR, Mickelbart MV, Couture JJ. (2020). Spectral phenotyping of physiological and anatomical leaf traits related with maize water status. Plant Physiology, 184, 1363-1377.
DOI PMID |
| [27] |
Cotrozzi L, Townsend PA, Pellegrini E, Nali C, Couture JJ. (2018). Reflectance spectroscopy: a novel approach to better understand and monitor the impact of air pollution on Mediterranean plants. Environmental Science and Pollution Research, 25, 8249-8267.
DOI URL |
| [28] |
Curran PJ. (1989). Remote sensing of foliar chemistry. Remote Sensing of Environment, 30, 271-278.
DOI URL |
| [29] |
Dechant B, Cuntz M, Vohland M, Schulz E, Doktor D. (2017). Estimation of photosynthesis traits from leaf reflectance spectra: correlation to nitrogen content as the dominant mechanism. Remote Sensing of Environment, 196, 279-292.
DOI URL |
| [30] |
Díaz S, Kattge J, Cornelissen JHC, Wright IJ, Lavorel S, Dray S, Reu B, Kleyer M, Wirth C, Colin Prentice I, Garnier E, Bönisch G, Westoby M, Poorter H, Reich PB, et al. (2016). The global spectrum of plant form and function. Nature, 529, 167-171.
DOI URL |
| [31] |
Dong N, Prentice IC, Wright IJ, Evans BJ, Togashi HF, Caddy-Retalic S, McInerney FA, Sparrow B, Leitch E, Lowe AJ. (2020). Components of leaf-trait variation along environmental gradients. New Phytologist, 228, 82-94.
DOI URL |
| [32] |
Durán SM, Martin RE, Díaz S, Maitner BS, Malhi Y, Salinas N, Shenkin A, Silman MR, Wieczynski DJ, Asner GP, Bentley LP, Savage VM, Enquist BJ. (2019). Informing trait-based ecology by assessing remotely sensed functional diversity across a broad tropical temperature gradient. Science Advances, 5, eaaw8114. DOI: 10.1126/sciadv.aaw8114.
DOI |
| [33] | Eckardt A, Horack J, Lehmann F, Krutz D, Drescher J, Whorton M, Soutullo M. (2015). Desis (dlr earth sensing imaging spectrometer for the iss-muses platform)//2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy. 1457-1459. |
| [34] |
Elvidge CD. (1990). Visible and near infrared reflectance characteristics of dry plant materials. International Journal of Remote Sensing, 11, 1775-1795.
DOI URL |
| [35] |
Ely KS, Burnett AC, Lieberman-Cribbin W, Serbin SP, Rogers A. (2019). Spectroscopy can predict key leaf traits associated with source-sink balance and carbon-nitrogen status. Journal of Experimental Botany, 70, 1789-1799.
DOI PMID |
| [36] |
Féret J-B, Berger K, de Boissieu F, Malenovský Z. (2021). PROSPECT-PRO for estimating content of nitrogen- containing leaf proteins and other carbon-based constituents. Remote Sensing of Environment, 252, 112173. DOI: 10.1016/j.rse.2020.112173.
DOI |
| [37] |
Féret J-B, François C, Asner GP, Gitelson AA, Martin RE, Bidel LPR, Ustin SL, Le Maire G, Jacquemoud S. (2008). PROSPECT-4 and 5: advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment, 112, 3030-3043.
DOI URL |
| [38] |
Féret J-B, François C, Gitelson A, Asner GP, Barry KM, Panigada C, Richardson AD, Jacquemoud S. (2011). Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sensing of Environment, 115, 2742-2750.
DOI URL |
| [39] |
Féret J-B, Gitelson AA, Noble SD, Jacquemoud S. (2017). PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle. Remote Sensing of Environment, 193, 204-215.
DOI URL |
| [40] |
Féret J-B le Maire G, Jay S, Berveiller D, Bendoula R, Hmimina G, Cheraiet A, Oliveira JC, Ponzoni FJ, Solanki T, de Boissieu F, Chave J, Nouvellon Y, Porcar-Castell A, Proisy C, et al. (2019). Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning. Remote Sensing of Environment, 231, 110959. DOI: 10.1016/j.rse.2018.11.002.
DOI |
| [41] |
Fisher RA, Koven CD, Anderegg WRL, Christoffersen BO, Dietze MC, Farrior CE, Holm JA, Hurtt GC, Knox RG, Lawrence PJ, Lichstein JW, Longo M, Matheny AM, Medvigy D, Muller-Landau HC, et al. (2018). Vegetation demographics in Earth System Models: a review of progress and priorities. Global Change Biology, 24, 35-54.
DOI PMID |
| [42] |
Fu P, Meacham-Hensold K, Guan K, Bernacchi CJ. (2019). Hyperspectral leaf reflectance as proxy for photosynthetic capacities: an ensemble approach based on multiple machine learning algorithms. Frontiers in Plant Science, 10, 730. DOI: 10.3389/fpls.2019.00730.
DOI |
| [43] |
Fu P, Montes CM, Siebers MH, Gomez-Casanovas N, McGrath JM, Ainsworth EA, Bernacchi CJ. (2022). Advances in field-based high-throughput photosynthetic phenotyping. Journal of Experimental Botany, 73, 3157-3172.
DOI PMID |
| [44] |
Gates DM, Keegan HJ, Schleter JC, Weidner VR. (1965). Spectral properties of plants. Applied Optics, 4, 11-20.
DOI URL |
| [45] |
Gökkaya K, Thomas V, Noland T, McCaughey H, Morrison I, Treitz P. (2015). Mapping continuous forest type variation by means of correlating remotely sensed metrics to canopy N:P ratio in a boreal mixedwood forest. Applied Vegetation Science, 18, 143-157.
DOI URL |
| [46] |
Guanter L, Kaufmann H, Segl K, Foerster S, Rogass C, Chabrillat S, Kuester T, Hollstein A, Rossner G, Chlebek C, Straif C, Fischer S, Schrader S, Storch T, Heiden U, et al. (2015). The EnMAP spaceborne imaging spectroscopy mission for earth observation. Remote Sensing, 7, 8830-8857.
DOI URL |
| [47] |
He L, Chen JM, Liu J, Zheng T, Wang R, Joiner J, Chou S, Chen B, Liu Y, Liu R, Rogers C. (2019a). Diverse photosynthetic capacity of global ecosystems mapped by satellite chlorophyll fluorescence measurements. Remote Sensing of Environment, 232, 111344. DOI: 10.1016/j.rse.2019.111344.
DOI |
| [48] |
He N, Liu C, Piao S, Sack L, Xu L, Luo Y, He J, Han X, Zhou G, Zhou X, Lin Y, Yu Q, Liu S, Sun W, Niu S, et al. (2019b). Ecosystem traits linking functional traits to macroecology. Trends in Ecology & Evolution, 34, 200-210. DOI: 10.1016/j.tree.2018.11.004.
DOI URL |
| [49] |
Helfenstein IS, Schneider FD, Schaepman ME, Morsdorf F. (2022). Assessing biodiversity from space: impact of spatial and spectral resolution on trait-based functional diversity. Remote Sensing of Environment, 275, 113024. DOI: 10.1016/j.rse.2022.113024.
DOI |
| [50] |
Hill J, Buddenbaum H, Townsend PA. (2019). Imaging spectroscopy of forest ecosystems: perspectives for the use of space-borne hyperspectral earth observation systems. Surveys in Geophysics, 40, 553-588.
DOI URL |
| [51] |
Jacquemoud S, Baret F. (1990). PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment, 34, 75-91.
DOI URL |
| [52] | Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada PJ, Asner GP, François C, Ustin SL. (2009). PROSPECT+ SAIL models: a review of use for vegetation characterization. Remote Sensing of Environment, 113, S56-S66. |
| [53] |
Jay S, Gorretta N, Morel J, Maupas F, Bendoula R, Rabatel G, Dutartre D, Comar A, Baret F. (2017). Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery. Remote Sensing of Environment, 198, 173-186.
DOI URL |
| [54] |
Jetz W, Cavender-Bares J, Pavlick R, Schimel D, Davis FW, Asner GP, Guralnick R, Kattge J, Latimer AM, Moorcroft P, Schaepman ME, Schildhauer MP, Schneider FD, Schrodt F, Stahl U, Ustin SL. (2016). Monitoring plant functional diversity from space. Nature Plants, 2, 16024. DOI: 10.1038/nplants.2016.24.
DOI |
| [55] | Joswig JS, Wirth C, Schuman MC, Kattge J, Reu B, Wright IJ, Sippel SD, Rüger N, Richter R, Schaepman ME, van Bodegom PM, Cornelissen JHC, Díaz S, Hattingh WN, Kramer K, et al. (2022). Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation. Nature Ecology & Evolution, 6, 36-50. |
| [56] |
Kampe TU, Johnson BR, Kuester MA, Keller M. (2010). NEON: the first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure. Journal of Applied Remote Sensing, 4, 043510. DOI: 10.1117/1.3361375.
DOI |
| [57] | Kokaly RF, Asner GP, Ollinger SV, Martin ME, Wessman CA. (2009). Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sensing of Environment, 113, S78-S91. |
| [58] |
Lamour J, Davidson KJ, Ely KS, Anderson JA, Rogers A, Wu J, Serbin SP. (2021). Rapid estimation of photosynthetic leaf traits of tropical plants in diverse environmental conditions using reflectance spectroscopy. PLOS ONE, 16, e0258791. DOI: 10.1371/journal.pone.0258791.
DOI |
| [59] |
Lee CM, Glenn NF, Stavros EN, Luvall J, Yuen K, Hain C, Schollaert Uz S. (2022). Systematic integration of applications into the Surface Biology and Geology (SBG) earth mission architecture study. Journal of Geophysical Research: Biogeosciences, 127, e2021JG006720. DOI: 10.1029/2021JG006720.
DOI |
| [60] |
Lee H, Wang J, Leblon B. (2020). Using linear regression, random forests, and support vector machine with unmanned aerial vehicle multispectral images to predict canopy nitrogen weight in corn. Remote Sensing, 12, 2071. DOI: 10.3390/rs12132071.
DOI |
| [61] |
Lepine LC, Ollinger SV, Ouimette AP, Martin ME. (2016). Examining spectral reflectance features related to foliar nitrogen in forests: implications for broad-scale nitrogen mapping. Remote Sensing of Environment, 173, 174-186.
DOI URL |
| [62] | Li N, Dong XF, Gan FP, Yan BK. (2020). Application evaluation of ZY-1-02D satellite hyperspectral data in geological survey. Spacecraft Engineering, 29, 186-191. |
| [李娜, 董新丰, 甘甫平, 闫柏琨 (2020). 资源一号02D卫星高光谱数据地质调查应用评价. 航天器工程, 29, 186-191.] | |
| [63] | Li XY, Fan HS, Pan SL, Jiang XH, Wu JQ. (2019). Overview of Zhuhai-1 hyperspectral satellite data and application. Satellite Application, (8), 12-18. |
| [李先怡, 范海生, 潘申林, 蒋晓华, 吴佳奇 (2019). 珠海一号高光谱卫星数据及应用概况. 卫星应用, (8), 12-18.] | |
| [64] | Li ZW, Liu LY, Zhang H, Zhang JX, Li XZ. (2013). Radiometric calibration and validation of TG-1 hyper-spectral imager. Remote Sensing Technology and Application, 28, 850-857. |
| [李振旺, 刘良云, 张浩, 张九星, 李绪志 (2013). 天宫一号高光谱成像仪在轨辐射定标与验证. 遥感技术与应用, 28, 850-857.] | |
| [65] | Liang SL, Li XW, Wang JD. (2017). Quantitative Remote Sensing: Theory and Algorithm. Science Press, Beijing. |
| [梁顺林, 李小文, 王锦地 (2017). 定量遥感: 理念与算法. 科学出版社, 北京.] | |
| [66] |
Liu N, Townsend PA, Naber MR, Bethke PC, Hills WB, Wang Y. (2021). Hyperspectral imagery to monitor crop nutrient status within and across growing seasons. Remote Sensing of Environment, 255, 112303. DOI: 10.1016/j.rse.2021. 112303.
DOI |
| [67] |
Locherer M, Hank T, Danner M, Mauser W. (2015). Retrieval of seasonal leaf area index from simulated EnMAP Data through optimized LUT-based inversion of the PROSAIL model. Remote Sensing, 7, 10321-10346.
DOI URL |
| [68] | Loizzo R, Daraio M, Guarini R, Longo F, Lorusso R, Dini L, Lopinto E. (2019). Prisma mission status and perspective// 2019 IEEE International Geoscience and Remote Sensing Symposium. IGARSS 2019, Yokohama, Japan. 4503-4506. |
| [69] |
Lu J, Li W, Yu M, Zhang X, Ma Y, Su X, Yao X, Cheng T, Zhu Y, Cao W, Tian Y. (2021). Estimation of rice plant potassium accumulation based on non-negative matrix factorization using hyperspectral reflectance. Precision Agriculture, 22, 51-74. DOI: 10.1007/s11119-020-09729-z.
DOI URL |
| [70] |
Martin ME, Plourde LC, Ollinger SV, Smith ML, McNeil BE. (2008). A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems. Remote Sensing of Environment, 112, 3511-3519.
DOI URL |
| [71] |
Meacham-Hensold K, Montes CM, Wu J, Guan K, Fu P, Ainsworth EA, Pederson T, Moore CE, Brown KL, Raines C, Bernacchi CJ. (2019). High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity. Remote Sensing of Environment, 231, 111176. DOI: 10.1016/j.rse.2019.04.029.
DOI |
| [72] |
Moharana S, Dutta S. (2016). Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 122, 17-29.
DOI URL |
| [73] |
Nakaji T, Oguma H, Nakamura M, Kachina P, Asanok L, Marod D, Aiba M, Kurokawa H, Kosugi Y, Kassim AR, Hiura T. (2019). Estimation of six leaf traits of East Asian forest tree species by leaf spectroscopy and partial least square regression. Remote Sensing of Environment, 233, 111381. DOI: 10.1016/j.rse.2019.111381.
DOI |
| [74] |
Norris KH, Barnes RF, Moore JE, Shenk JS. (1976). Predicting forage quality by infrared reflectance spectroscopy. Journal of Animal Science, 43, 889-897.
DOI URL |
| [75] |
Norris KH, Hart JR. (1966). Direct spectrophotometric determination of moisture content of grain and seeds. Journal of Near Infrared Spectroscopy, 4, 23-30.
DOI URL |
| [76] |
Ollinger SV. (2011). Sources of variability in canopy reflectance and the convergent properties of plants. New Phytologist, 189, 375-394.
DOI PMID |
| [77] |
Omari K, White HP, Staenz K, King DJ. (2013). Retrieval of forest canopy parameters by inversion of the PROFLAIR leaf-canopy reflectance model using the LUT approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 715-723.
DOI URL |
| [78] |
Pearlman JS, Barry PS, Segal CC, Shepanski J, Beiso D, Carman SL. (2003). Hyperion, a space-based imaging spectrometer. IEEE Transactions on Geoscience and Remote Sensing, 41, 1160-1173.
DOI URL |
| [79] |
Pullanagari RR, Dehghan-Shoar M, Yule IJ, Bhatia N. (2021). Field spectroscopy of canopy nitrogen concentration in temperate grasslands using a convolutional neural network. Remote Sensing of Environment, 257, 112353. DOI: 10.1016/j.rse.2021.112353.
DOI |
| [80] |
Rabideau GS, French CS, Holt AS. (1946). The absorption and reflection spectra of leaves, chloroplast suspensions, and chloroplast fragments as measured in an Ulbricht sphere. American Journal of Botany, 33, 769-777.
DOI URL |
| [81] | Rast M, Ananasso C, Bach H, Ben-Dor E, Chabrillat S, Colombo R, del Bello U, Feret J, Giardino C, Green RO, Guanter L, Marsh S, Nieke J, Ong CCH, Rum G, et al. (2019). Copernicus hyperspectral imaging mission for the environment: mission requirements document//Mission Requirements Document (MRD). European Space Agency, Noordwijk, the Netherlands. |
| [82] | Roberts DA, Ustin SL, Ogunjemiyo S, Greenberg J, Dobrowski SZ, Chen J, Hinckley TM. (2004). Spectral and structural measures of northwest forest vegetation at leaf to landscape scales. Ecosystems, 7, 545-562. |
| [83] |
Schimel D, Pavlick R, Fisher JB, Asner GP, Saatchi S, Townsend P, Miller C, Frankenberg C, Hibbard K, Cox P. (2015). Observing terrestrial ecosystems and the carbon cycle from space. Global Change Biology, 21, 1762-1776.
DOI PMID |
| [84] | Schimel D, Townsend PA, Pavlick R. (2020). Prospects and pitfalls for spectroscopic remote sensing of biodiversity at the global scale//Cavender-Bares J, Gamon JA, Townsend PA. Remote Sensing of Plant Biodiversity. Springer, Cham, Switzerland. 503-518. |
| [85] |
Schneider FD, Morsdorf F, Schmid B, Petchey OL, Hueni A, Schimel DS, Schaepman ME. (2017). Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nature Communications, 8, 1441. DOI: 10.1038/s41467-017-01530-3.
DOI |
| [86] | Schweiger AK, Cavender-Bares J, Townsend PA, Hobbie SE, Madritch MD, Wang R, Tilman D, Gamon JA. (2018). Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function. Nature Ecology & Evolution, 2, 976-982. |
| [87] |
Schweiger AK, Laliberté E. (2022). Plant beta-diversity across biomes captured by imaging spectroscopy. Nature Communications, 13, 2767. DOI: 10.1038/s41467-022-30369-6.
DOI |
| [88] |
Serbin SP, Dillaway DN, Kruger EL, Townsend PA. (2012). Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature. Journal of Experimental Botany, 63, 489-502.
DOI PMID |
| [89] |
Serbin SP, Singh A, Desai AR, Dubois SG, Jablonski AD, Kingdon CC, Kruger EL, Townsend PA. (2015). Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy. Remote Sensing of Environment, 167, 78-87.
DOI URL |
| [90] |
Serbin SP, Singh A, McNeil BE, Kingdon CC, Townsend PA. (2014). Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. Ecological Applications, 24, 1651-1669.
DOI URL |
| [91] | Serbin SP, Townsend PA. (2020). Scaling functional traits from leaves to canopies//Cavender-Bares J, Gamon JA, Townsend PA. Remote Sensing of Plant Biodiversity. Springer, Cham, Switzerland. 43-82. |
| [92] |
Serbin SP, Wu J, Ely KS, Kruger EL, Townsend PA, Meng R, Wolfe BT, Chlus A, Wang Z, Rogers A. (2019). From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance. New Phytologist, 224, 1557-1568.
DOI PMID |
| [93] |
Sexton T, Sankaran S, Cousins AB. (2021). Predicting photosynthetic capacity in tobacco using shortwave infrared spectral reflectance. Journal of Experimental Botany, 72, 4373-4383.
DOI URL |
| [94] |
Shiklomanov AN, Bond-Lamberty B, Atkins JW, Gough CM. (2020). Structure and parameter uncertainty in centennial projections of forest community structure and carbon cycling. Global Change Biology, 26, 6080-6096.
DOI URL |
| [95] |
Silva-Perez V, Molero G, Serbin SP, Condon AG, Reynolds MP, Furbank RT, Evans JR. (2018). Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat. Journal of Experimental Botany, 69, 483-496.
DOI PMID |
| [96] |
Singh A, Serbin SP, McNeil BE, Kingdon CC, Townsend PA. (2015). Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties. Ecological Applications, 25, 2180-2197.
DOI URL |
| [97] |
Skidmore AK, Ferwerda JG, Mutanga O, van Wieren SE, Peel M, Grant RC, Prins HHT, Balcik FB, Venus V. (2010). Forage quality of savannas—Simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery. Remote Sensing of Environment, 114, 64-72.
DOI URL |
| [98] |
Suarez L, González-Dugo V, Camino C, Hornero A, Zarco- Tejada PJ (2021). Physical model inversion of the green spectral region to track assimilation rate in almond trees with an airborne nano-hyperspectral imager. Remote Sensing of Environment, 252, 112147. DOI: 10.1016/j.rse.2020. 112147.
DOI |
| [99] |
Swinfield T, Both S, Riutta T, Bongalov B, Elias D, Majalap- Lee N, Ostle N, Svátek M, Kvasnica J, Milodowski D, Jucker T, Ewers RM, Zhang Y, Johnson D, Teh YA, et al. (2020). Imaging spectroscopy reveals the effects of topography and logging on the leaf chemistry of tropical forest canopy trees. Global Change Biology, 26, 989-1002.
DOI PMID |
| [100] |
Tagliabue G, Boschetti M, Bramati G, Candiani G, Colombo R, Nutini F, Pompilio L, Rivera-Caicedo JP, Rossi M, Rossini M, Verrelst J, Panigada C. (2022). Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 187, 362-377.
DOI URL |
| [101] | Tang ZY, Xu WT, Zhou GY, Bai YF, Li JX, Tang XL, Chen DM, Liu Q, Ma WH, Xiong GM, He HL, He NP, Guo YP, Guo Q, Zhu JL, et al. (2018). Patterns of plant carbon, nitrogen, and phosphorus concentration in relation to productivity in China’s terrestrial ecosystems. Proceedings of the National Academy of Sciences of the United States of America, 115, 4033-4038. |
| [102] | Thenkabail PS, Lyon JG, Huete A. (2018). Biophysical and Biochemical Characterization and Plant Species Studies. CRC Press. |
| [103] |
Thompson DR, Natraj V, Green RO, Helmlinger MC, Gao BC, Eastwood ML. (2018). Optimal estimation for imaging spectrometer atmospheric correction. Remote Sensing of Environment, 216, 355-373.
DOI URL |
| [104] |
Thomson E, Malhi Y, Bartholomeus H, Oliveras I, Gvozdevaite A, Peprah T, Suomalainen J, Quansah J, Seidu J, Adonteng C, Abraham A, Herold M, Adu-Bredu S, Doughty C. (2018). Mapping the leaf economic spectrum across west African tropical forests using UAV-Acquired hyperspectral imagery. Remote Sensing, 10, 1532. DOI: 10.3390/rs10101532.
DOI |
| [105] | Tong QX, Xue YQ, Wang JN, Zhang LF, Fang JY, Yang YD, Liu X, Qi HX, Zheng LF, Huang CP. (2010). Development and application of the field imaging spectrometer system. Journal of Remote Sensing, 14, 409-422. |
| [童庆禧, 薛永祺, 王晋年, 张立福, 方俊永, 杨一德, 刘学, 亓洪兴, 郑兰芬, 黄长平 (2010). 地面成像光谱辐射测量系统及其应用. 遥感学报, 14, 409-422.] | |
| [106] |
Tong QX, Xue YQ, Zhang LF. (2014). Progress in hyperspectral remote sensing science and technology in China over the past three decades. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 70-91.
DOI URL |
| [107] | Tong QX, Zhang B, Zhang LF. (2016). Current progress of hyperspectral remote sensing in China. Journal of Remote Sensing, 20, 689-707. |
| [童庆禧, 张兵, 张立福 (2016). 中国高光谱遥感的前沿进展. 遥感学报, 20, 689-707.] | |
| [108] | Tong QX, Zhang B, Zheng LF. (2006). Hyperspectral Remote Sensing-principle, Technology and Applications. Higher Education Press, Beijing. |
| [童庆禧, 张兵, 郑兰芬 (2006). 高光谱遥感-原理、技术与应用. 高等教育出版社, 北京.] | |
| [109] |
Townsend PA, Foster JR, Chastain RA, Currie WS. (2003). Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using Hyperion and AVIRIS. IEEE Transactions on Geoscience and Remote Sensing, 41, 1347-1354.
DOI URL |
| [110] | Ustin SL, Gitelson AA, Jacquemoud S, Schaepman M, Asner GP, Gamon JA, Zarco-Tejada P. (2009). Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sensing of Environment, 113, S67-S77. |
| [111] |
Verrelst J, Malenovský Z, van der Tol C, Camps-Valls G, Gastellu-Etchegorry JP, Lewis P, North P, Moreno J. (2019). Quantifying vegetation biophysical variables from imaging spectroscopy data: a review on retrieval methods. Surveys in Geophysics, 40, 589-629.
DOI |
| [112] |
Verrelst J, Rivera-Caicedo JP, Reyes-Muñoz P, Morata M, Amin E, Tagliabue G, Panigada C, Hank T, Berger K. (2021). Mapping landscape canopy nitrogen content from space using PRISMA data. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 382-395.
DOI URL |
| [113] |
Violle C, Navas M-L, Vile D, Kazakou E, Fortunel C, Hummel I, Garnier E. (2007). Let the concept of trait be functional! Oikos, 116, 882-892.
DOI URL |
| [114] | Wang Q, Wu CQ, Li Q. (2010). Environment satellite 1 and its application in environmental monitoring. Journal of Remote Sensing, 14, 104-121. |
| [王桥, 吴传庆, 厉青 (2010). 环境一号卫星及其在环境监测中的应用. 遥感学报, 14, 104-121.] | |
| [115] |
Wang R, Gamon JA, Moore R, Zygielbaum AI, Arkebauer TJ, Perk R, Leavitt B, Cogliati S, Wardlow B, Qi Y. (2021). Errors associated with atmospheric correction methods for airborne imaging spectroscopy: implications for vegetation indices and plant traits. Remote Sensing of Environment, 265, 112663. DOI: 10.1016/j.rse.2021.112663.
DOI |
| [116] |
Wang Z, Chlus A, Geygan R, Ye Z, Zheng T, Singh A, Couture JJ, Cavender-Bares J, Kruger EL, Townsend PA. (2020). Foliar functional traits from imaging spectroscopy across biomes in eastern North America. New Phytologist, 228, 494-511.
DOI URL |
| [117] | Wang Z, Skidmore AK, Darvishzadeh R, Wang T. (2018). Mapping forest canopy nitrogen content by inversion of coupled leaf-canopy radiative transfer models from airborne hyperspectral imagery. Agricultural and Forest Meteorology, 253- 254, 247-260. |
| [118] |
Wang Z, Townsend PA, Kruger EL. (2022). Leaf spectroscopy reveals divergent inter- and intra-species foliar trait covariation and trait-environment relationships across NEON domains. New Phytologist, 235, 923-938.
DOI PMID |
| [119] |
Wang Z, Townsend PA, Schweiger AK, Couture JJ, Singh A, Hobbie SE, Cavender-Bares J. (2019). Mapping foliar functional traits and their uncertainties across three years in a grassland experiment. Remote Sensing of Environment, 221, 405-416.
DOI URL |
| [120] |
Wessman CA, Aber JD, Peterson DL. (1989). An evaluation of imaging spectrometry for estimating forest canopy chemistry. International Journal of Remote Sensing, 10, 1293-1316.
DOI URL |
| [121] |
Wessman CA, Aber JD, Peterson DL, Melillo JM. (1988). Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems. Nature, 335, 154-156.
DOI URL |
| [122] |
White JC, Coops NC, Hilker T, Wulder MA, Carroll AL. (2007). Detecting mountain pine beetle red attack damage with EO-1 Hyperion moisture indices. International Journal of Remote Sensing, 28, 2111-2121.
DOI URL |
| [123] |
Widlowski JL, Mio C, Disney M, Adams J, Andredakis I, Atzberger C, Brennan J, Busetto L, Chelle M, Ceccherini G, Colombo R, Côté JF, Eenmäe A, Essery R, Gastellu- Etchegorry JP, et al. (2015). The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: actual canopy scenarios and conformity testing. Remote Sensing of Environment, 169, 418-437.
DOI URL |
| [124] |
Wold S, Sjöström M, Eriksson L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109-130.
DOI URL |
| [125] |
Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, Cavender-Bares J, Chapin T, Cornelissen JHC, Diemer M, Flexas J, Garnier E, Groom PK, Gulias J, Hikosaka K, et al. (2004). The worldwide leaf economics spectrum. Nature, 428, 821-827.
DOI URL |
| [126] |
Wu J, Albert LP, Lopes AP, Restrepo-Coupe N, Hayek M, Wiedemann KT, Guan K, Stark SC, Christoffersen B, Prohaska N, Tavares JV, Marostica S, Kobayashi H, Ferreira ML, Campos KS, et al. (2016). Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science, 351, 972-976.
DOI PMID |
| [127] |
Wu J, Chavana-Bryant C, Prohaska N, Serbin SP, Guan K, Albert LP, Yang X, Garnello AJ, Martins G, Malhi Y, Gerard F, Oliviera RC, Saleska SR. (2017). Convergence in relationships between leaf traits, spectra and age across diverse canopy environments and two contrasting tropical forests. New Phytologist, 214, 1033-1048.
DOI PMID |
| [128] |
Wu J, Kobayashi H, Stark SC, Meng R, Guan K, Tran NN, Gao S, Yang W, Restrepo-Coupe N, Miura T, Oliviera Jr RC, Rogers A, Dye DG, Nelson BW, Serbin SP, et al. (2018). Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest. New Phytologist, 217, 1507-1520.
DOI PMID |
| [129] |
Wu J, Rogers A, Albert LP, Ely K, Prohaska N, Wolfe BT, Oliveira RC, Saleska SR, Serbin SP. (2019). Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests. New Phytologist, 224, 663-674.
DOI PMID |
| [130] |
Yan Z, Guo Z, Serbin SP, Song G, Zhao Y, Chen Y, Wu S, Wang J, Wang X, Li J, Wang B, Wu Y, Su Y, Wang H, Rogers A, et al. (2021). Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types. New Phytologist, 232, 134-147.
DOI URL |
| [131] |
Yang X, Tang J, Mustard JF, Wu J, Zhao K, Serbin S, Lee JE. (2016). Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests. Remote Sensing of Environment, 179, 1-12.
DOI URL |
| [132] |
Yang Y, Wang H, Harrison SP, Prentice IC, Wright IJ, Peng C, Lin G. (2019). Quantifying leaf-trait covariation and its controls across climates and biomes. New Phytologist, 221, 155-168.
DOI PMID |
| [133] |
Yendrek CR, Tomaz T, Montes CM, Cao Y, Morse AM, Brown PJ, McIntyre LM, Leakey ADB, Ainsworth EA. (2017). High-throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance. Plant Physiology, 173, 614-626.
DOI PMID |
| [134] |
Zald HSJ, Wulder MA, White JC, Hilker T, Hermosilla T, Hobart GW, Coops NC. (2016). Integrating Landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada. Remote Sensing of Environment, 176, 188-201.
DOI URL |
| [135] |
Zarco-Tejada PJ, González-Dugo V, Berni JAJ. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment, 117, 322-337.
DOI URL |
| [136] |
Zhang Y, Migliavacca M, Peñuelas J, Ju W. (2021). Advances in hyperspectral remote sensing of vegetation traits and functions. Remote Sensing of Environment, 252, 112121. DOI: 10.1016/j.rse.2020.112121.
DOI |
| [137] |
Zhang YW, Guo YP, Tang R, Tang ZY. (2022). Progress and trends of application of hyperspectral remote sensing in plant diversity research. National Remote Sensing Bulletin. DOI: 10.11834/jrs.20211120.
DOI |
|
[张艺伟, 郭焱培, 唐荣, 唐志尧 (2022). 高光谱遥感在植物多样性研究中的应用: 进展与趋势. 遥感学报. DOI: 10.11834/jrs.20211120.]
DOI |
|
| [138] |
Zhao YJ, Sun YH, Lu XM, Zhao XZ, Yang L, Sun ZY, Bai YF. (2021). Hyperspectral retrieval of leaf physiological traits and their links to ecosystem productivity in grassland monocultures. Ecological Indicators, 122, 107267. DOI: 10.1016/j.ecolind.2020.107267.
DOI |
| [139] |
Zhao YJ, Zeng Y, Zheng ZJ, Dong WX, Zhao D, Wu BF, Zhao QJ. (2018). Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China. Remote Sensing of Environment, 213, 104-114.
DOI URL |
| [140] |
Zheng Z, Zeng Y, Schneider FD, Zhao Y, Zhao D, Schmid B, Schaepman ME, Morsdorf F. (2021). Mapping functional diversity using individual tree-based morphological and physiological traits in a subtropical forest. Remote Sensing of Environment, 252, 112170. DOI: 10.1016/j.rse.2020. 112170.
DOI |
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