Extracting Red Edge Position Parameters from Ground- and Space-Based Hyperspectral Data for Estimation of Canopy Leaf Nitrogen Concentration in Rice

Tian, Yongchao; Yao, Xia; Yang, Jie; Cao, Weixing; Zhu, Yan

doi:10.1626/pps.14.270

Cited by 21 publications

(2 citation statements)

References 42 publications

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“…Therefore, REP-related methods are typically used to derive canopy chlorophyll content, being the product of LAI and leaf chlorophyll content (Clevers and Kooistra 2012;Li et al 2017). Many mathematical approaches have been proposed to exploit this region as a sensitive indicator, including: (1) high-order curve fitting (Broge and Leblanc 2001;Clevers et al 2004;(2) inverted Gaussian models (Cho and Skidmore 2006;Cho et al 2008;Miller et al 1990; (3) linear interpolation and extrapolation methods (Cho et al 2008;Tian et al 2011;4) Lagrangian interpolation (Dawson et al 1998;Pu et al 2003;(5) rational function application (Baranoski and Rokne 2005); and, more recently, (6) a wavelet-based technique (Li et al 2017). -Derivative-based indices.…”

Section: Parametric Approaches Based On Spectral Shapes and Spectral mentioning

confidence: 99%

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

et al. 2018

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An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2)

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Section: Parametric Approaches Based On Spectral Shapes and Spectral mentioning

confidence: 99%

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

et al. 2018

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“…In agricultural systems and freshwater wetlands, remote sensing has been used to detect various plant properties such as foliar N content (e.g., [37][38][39][40]). Vegetation indices have also been used to detect leaf water content and the effects of porewater salinity and redox potential on plant distribution and growth in a salt marsh dominated by Salicornia [41] and waterlogging stress in terrestrial plant species [42,43].…”

Section: Introductionmentioning

confidence: 99%

Assessing Salt Marsh Vulnerability Using High-Resolution Hyperspectral Imagery

et al. 2020

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Change in the coastal zone is accelerating with external forcing by sea-level rise, nutrient loading, drought, and over-harvest, leading to significant stress on the foundation plant species of coastal salt marshes. The rapid evolution of marsh state induced by these drivers makes the ability to detect stressors prior to marsh loss important. However, field work in coastal salt marshes can be challenging due to limited access and their fragile nature. Thus, remote sensing approaches hold promise for rapid and accurate determination of marsh state across multiple spatial scales. In this study, we evaluated the use of remote sensing tools to detect three dominant stressors on Spartina alterniflora. We took advantage of a barrier island salt marsh chronosequence in Virginia, USA, where marshes of different ages and level of stressor exist side by side. We collected hyperspectral imagery of plants along with salinity, sediment redox potential, and foliar nitrogen content in the field. We also conducted a greenhouse study where we manipulated environmental conditions. We found that models developed for stressors based on plant spectral response correlated well with salinity and foliar nitrogen within the greenhouse and field data, but were not transferable from lab to field, likely due to the limited range of conditions explored within the greenhouse experiments and the coincidence of multiple stressors in the field. This study is an important step towards the development of a remote sensing tool for tracking of ecosystem development, marsh health, and future ecosystem services.

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Local detrended fluctuation analysis for spectral red-edge parameters extraction

et al. 2018

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Extracting Red Edge Position Parameters from Ground- and Space-Based Hyperspectral Data for Estimation of Canopy Leaf Nitrogen Concentration in Rice

Cited by 21 publications

References 42 publications

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

Assessing Salt Marsh Vulnerability Using High-Resolution Hyperspectral Imagery

Local detrended fluctuation analysis for spectral red-edge parameters extraction

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