Predicting leaf traits of temperate broadleaf deciduous trees from hyperspectral reflectance: can a general model be applied across a growing season?

Chen, Litong; Zhang, Yi; Nunes, Matheus Henrique; Stoddart, Jaz; Khoury, Sacha; Chan, Aland H. Y.; Coomes, David A.

doi:10.1016/j.rse.2021.112767

Cited by 27 publications

(15 citation statements)

References 89 publications

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“…The timing at which chlorophyll, woody water content, and tree crown structure vary (in response to crop development or weather events) may not perfectly match, causing differences in the synchronicity of GCC, NDVI and CR time series [31]. Unlike the chlorophyll content which increased rapidly with the growth of young leaves [58], the woody water content decreased slowly after leaf-out [51], thus explaining why CR time series change slower than GCC and NDVI time series. Our study can deepen the understanding of the response of SAR data to deciduous forests growth at the present stage, but also provide a new avenue for future research on the phenology based on SAR data.…”

Section: B Comparisons Between Sos Derived From Gcc Cr and Ndvimentioning

confidence: 99%

Extracting Deciduous Forests Spring Phenology From Sentinel-1 Cross Ratio Index

Yang

Wang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Deciduous forests spring phenology plays a major role in balancing carbon cycle. The cloud cover affects images acquired from optical sensor and reduces its performance in monitoring phenology. Synthetic Aperture Radar (SAR) can regularly acquire images day and night independent of weather conditions, which offers more frequent observations of vegetation phenology compared to optical sensors. However, it remains unclear how SAR data-derived indices vary across different growth stages of forests. Here we explored the relationship between cross ratio (CR) index derived from Sentinel-1 data and deciduous forest growth process. We proposed a deciduous forests spring phenology extraction method using CR and compared the extracted start of growing season (SOS) with those extracted using Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 optical satellite data and Green Chromatic Coordinate (GCC) derived from ground PhenoCam data. We extracted the SOS of 41 PhenoCam sites over Continental United States in 2018 using dynamic threshold method. Our results showed that the variations of CR time series are closely related to the phenological processes of deciduous forests. The SOS extracted using CR data showed high consistency with those extracted using GCC (R 2 =0.46), with slightly lower accuracy compared with NDVI derived results (R 2 =0.62). Our study illustrates the value and mechanism of deciduous forests spring phenology extraction using SAR data, and provides reference for using SAR data to improve forest phenology extraction in addition to using optical remote sensing data, especially in rainy and cloudy regions.

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Section: B Comparisons Between Sos Derived From Gcc Cr and Ndvimentioning

confidence: 99%

Extracting Deciduous Forests Spring Phenology From Sentinel-1 Cross Ratio Index

Yang

Wang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The 'Blue' band covers the wavelengths from 450 − 510 nm; the 'Green' band 530 − 590 nm; the 'Red' band 640 − 670 nm; the 'Near Infrared' (NIR) band 850 − 880nm; the 'Shortwave Infrared 1' (SWIR1) band 1570 − 1650 nm; the 'Shortwave Infrared 2' (SWIR2) band 2110 − 2290 nm. These spectral band values were chosen because they were shown to capture the δ 15 N at leaf and canopy scales [Wang et al, 2007;Serbin et al, 2014;Hellmann et al, 2015;Chen et al, 2022]. We also obtained the quality assessment channel (QA) and removed data with cloud or cloud shadow.…”

Section: Landsat Spectramentioning

confidence: 99%

“…Recent developments in spectrometry allow the estimation of δ 15 N from the spectral re ectance of plant materials [Wang et al, 2007;Serbin et al, 2014;Hellmann et al, 2015;Chen et al, 2022]. Reassuringly, remotely-sensed foliage spectral re ectance has been successfully used as indicators of plant physiological traits [Ma et al, 2019;Fernández-Habas et al, 2021;Williams et al, 2021;Wan et al, 2022].…”

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Trajectories of plant nitrogen availability globally during 1984-2022 uncovered by satellite-derived nitrogen stable isotope ratio

Yang

Zhang

Guo

et al. 2023

Preprint

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Nitrogen (N) availability regulates the productivity of terrestrial plants and the ecological services they provide. There is evidence for both increasing and decreasing plant N availability in different biomes, but the data are fragmentary. How plant N availability responds to climate change, N deposition and increasing atmospheric CO2 concentration remains a major uncertainty in the projection of the terrestrial carbon sink. The foliar N stable isotope ratio (δ15N) is an indicator of plant N availability but its usefulness to infer long-term global patterns has been limited by data scarcity. Combining ground-based δ15N and Landsat spectra, we derived annual global maps of Landsat-based foliar δ15N as estimates of plant N availability during 1984-2022. We found significant decreases in plant N availability for 44% and increases in 16% of vegetated Earth’s surface with large spatial heterogeneity. Plant N availability declined in woody-dominated ecosystems but increased in herbaceous-dominated ones. These δ15N trends were consistently and negatively correlated with the trends of Normalised-Difference-Vegetation-Index as they varied across ecosystems, suggesting increasing plant cover could have led to decreasing plant N availability. Our results indicate possible future reductions in plant N availability in many terrestrial ecosystems and provide a useful way to monitor those changes globally.

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“…1)通常只关注生长季中期的植物功能性状, 忽略了它们本身也随个体发育、叶龄和季节气候动态而变化这一事实, 而这些动态变化对解析生态系统过程非常重要 (Wu et al, 2016(Wu et al, , 2017Yang et al, 2016;Chavana-Bryant et al, 2017;Chen et al, 2022); 2)大 Wu et al, 2017;Serbin et al, 2019;Yan et al, 2021) 生产力监测提供了新途径 (Zhang et al, 2021), 也为与光合作用相关的功能性状(如叶片最大羧化速率、叶绿素含量和氮含量)监测提供了新方法 (Camino et al, 2019;He et al, 2019a;Berger et al, 2020) et al, 1988, 1989)。AIS是机载可见光/近红外成像光谱仪(AVIRS)的前身。此后, 陆续出现了许多其他类型的机载成像光谱仪系统, 包括HyMap (Cocks et al, 1998), et al, 2012), AVIRIS-Next Generation (Thompson et al, 2018) www.plant-ecology.com 功能多样性图 (Zhao et al, 2018;Zheng et al, 2021) 度上的适用性 (Yang et al, 2016;Wu et al, 2017Wu et al, , 2019Chen et al, 2022),…”

Section: 植物功能性状是指植物对环境适应和进化所表unclassified

“…©植物生态学报 Chinese Journal of Plant Ecology 严正兵等: 高光谱遥感技术在植物功能性状监测中的应用与展望 1155 DOI: 10.17521/cjpe.2022.0223PROSPECT-5 (Féret et al, 2008) 和花青素的 PROSPECT-D(Féret et al, 2017), 再到增加了氮含量的PROSPECT-PRO(Féret et al, 2021)。在冠层辐代数值优化方法旨在通过不断改变输入变量直到模型模拟的冠层光谱与实际观测的冠层光谱之间的差异(即成本函数)达到最小(Bayat et al, 2016)。基于查找表的方法是利用辐射传输模型, 根据合理的输入参数组合, 先大量模拟冠层光谱, 然后从模拟的光谱库中寻找与观测光谱最相似的结果 (Lochereret al, 2015)。使用辐射传输模型反演方法优点在于节的叶片生物化学和形态结构性状(Yang et al, 2016;Chen et al, 2022); 刻画了南美热带雨林的叶片年龄、生化特性和光合作用潜力, 并反演了光合作用潜力随叶片年龄的动态变化规律 (Chavana-Bryant et al, 2017; Wu et al, 2017, 2019)。与此同时, 陆续有研究将高光谱遥感技术运用到农作物表型监测中, 发现该技术可以准确预测作物因基因型、生长阶段、水分胁迫、养分胁迫等因素引起的性状变化, 这为作物高通量表型分析和育种提供了重要的技术支撑(Yendrek et al, 2017; Silva-Perez et al, 2018; Ely et al, 2019; Meacham-Hensold et al, 2019; Burnett et al, 2021b)。 www.plant-ecology.com 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 Asneret al, 2014;Ely et al, 2019; Féret et al, 2019; Burnett et al, 2021b;Yan et al, 2021; 2021; Chen et al, 2022 钾含量 Potassium content 0.54-0.61 PLSR, SVM Asner et al, 2014; Féret et al, 2019; Chen et al, 2022 PLSR Chavana-Bryant et al, 2017; Wu et al, 2017 ANN, 人工神经网络; GPR, 高斯过程回归; PLSR, 偏最小二乘回归; RTM, 辐射传输模型; SVM, 支持向量机; VI, 植被指数。 ANN, artificial neural network; GPR, Gaussian processes regression; PLSR, partial least squares regression; RTM, radiative transfer model; SVM, support vector machine; VI, vegetation index. 严正兵等: 高光谱遥感技术在植物功能性状监测中的应用与展望 1157 DOI: 10.17521/cjpe.2022.0223 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…”

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Hyperspectral remote sensing of plant functional traits: monitoring techniques and future advances

YAN¹,

LIU²,

Wu³

2022

Chinese Journal of Plant Ecology

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Plant functional traits are the measurable characteristics that indicates plant adaptation to the environment, and understanding the patterns of certain characteristics, and their drivers is an essential component of plant ecology and earth system modeling research. Traditional field-based approaches for characterizing plant functional traits are time-consuming, labor-intensive and expensive, and usually focus on the traits of peak growing season and dominant species, making the scaling extension and spatiotemporal coverage of plant functional traits a great challenge. In contrast, newly emerging multi-scale hyperspectral remote sensing techniques potentially provide new avenues to easily identify and characterize functional traits. Here we first overview the principles and brief history of hyperspectral remote sensing technology for plant functional traits monitoring. Then, we detailed the principal methods for modelling the spectral-trait relationships, including empirical and semi-empirical statistical methods and inversion methods relying on physical-based modelling, among which the statistical partial least squares regression is widely used. We then used case studies to demonstrate the application while illustrating the remaining problems of plant functional traits monitoring using the hyperspectral remote sensing techniques respectively at leaf, community and landscape scales. Finally, we highlight four important future directions to advance hyperspectral remote sensing of plant functional traits, including: 1) exploring the generalizability and underlying mechanisms of spectral-trait modelling; 2) developing novel, transparent methodology that scales the spectral-trait relationships from leaf, canopy to satellite levels; 3) elucidating the pattern and drivers of remotely sensed plant functional traits and diversity across various spatiotemporal scales; and 4) investigating the linkage among environment, plant functional diversity, biodiversity and ecosystem functioning.

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Predicting leaf traits of temperate broadleaf deciduous trees from hyperspectral reflectance: can a general model be applied across a growing season?

Cited by 27 publications

References 89 publications

Extracting Deciduous Forests Spring Phenology From Sentinel-1 Cross Ratio Index

Extracting Deciduous Forests Spring Phenology From Sentinel-1 Cross Ratio Index

Trajectories of plant nitrogen availability globally during 1984-2022 uncovered by satellite-derived nitrogen stable isotope ratio

Hyperspectral remote sensing of plant functional traits: monitoring techniques and future advances

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