Spectral response and the retrieval of canopy chlorophyll content under interspecific competition in wetlands — case study of wetlands in the Yangtze River Estuary

Zhuo, Wei; Shi, Runhe; Wu, Nan; Zhang, Chao; Tian, Bo

doi:10.1007/s12145-021-00630-5

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…How to measure the chlorophyll content of leaves accurately and quickly is important for monitoring the growth of plants and guiding agricultural production. Numerous studies have shown that spectroscopic techniques are effective methods for the rapid detection of chlorophyll content in leaves, obviating the disadvantages of inefficient traditional chemical analysis methods and helping achieve real-time collection and analysis of data, and uninterrupted monitoring [5][6][7][8] .…”

Section: Introduction mentioning

confidence: 99%

Estimation of chlorophyll content in pepper leaves using spectral transmittance red-edge parameters

Huang¹,

You²,

Wang³

et al. 2022

International Journal of Agricultural and Biological Engineering

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The objective of this work was to monitor the growth status of pepper and provide precise guidance on fertilization through non-destructive detection methods for chlorophyll content based on spectral transmittance. The analysis of the narrower red-edge spectral region (680-760 nm) reduced the requirements for light sources and light detection sensors, and provided a simpler and more accurate method of data acquisition for the process of developing instruments for estimating chlorophyll content in leaves. The red-edge region of spectral transmittance was demonstrated to be closely related to chlorophyll content. Regression models for estimating chlorophyll content with seven different methods were developed using the four red-edge parameters extracted from the red-edge region. The problems of multicollinearity of red-edge parameters and errors in model coefficients were solved by the ridge regression method in the process of building a multivariate regression model. The results indicated that the ridge regression method reduces the errors of the model coefficients and constant terms while improving the detection accuracy, thus the ridge regression model could estimate the leaf chlorophyll content more accurately and repeatedly.

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Section: Introduction mentioning

confidence: 99%

Estimation of chlorophyll content in pepper leaves using spectral transmittance red-edge parameters

Huang¹,

You²,

Wang³

et al. 2022

International Journal of Agricultural and Biological Engineering

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“…Remote Sens. 2021, 13, 4671 2 of 16 Remote sensing images have been used in previous studies for mapping chlorophyll content in different ecosystems, such as farmlands [3,[9][10][11], forests [12][13][14], grasslands [15][16][17], and wetlands [18,19]. Among these ecosystems, the farmlands and forests are more homogeneous in terms of species composition.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impacts of Species Composition on the Accuracy of Mapping Chlorophyll Content in Heterogeneous Ecosystems

2021

Remote Sensing

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Chlorophyll is an essential vegetation pigment influencing plant photosynthesis rate and growth conditions. Remote sensing images have been widely used for mapping vegetation chlorophyll content in different ecosystems (e.g., farmlands, forests, grasslands, and wetlands) for evaluating vegetation growth status and productivity of these ecosystems. Compared to farmlands and forests that are more homogeneous in terms of species composition, grasslands and wetlands are more heterogeneous with highly mixed species (e.g., various grass, forb, and shrub species). Different species contribute differently to the ecosystem services, thus, monitoring species-specific chlorophyll content is critical for better understanding their growth status, evaluating ecosystem functions, and supporting ecosystem management (e.g., control invasive species). However, previous studies in mapping chlorophyll content in heterogeneous ecosystems have rarely estimated species-specific chlorophyll content, which was partially due to the limited spatial resolution of remote sensing images commonly used in the past few decades for recognizing different species. In addition, many previous studies have used one universal model built with data of all species for mapping chlorophyll of the entire study area, which did not fully consider the impacts of species composition on the accuracy of chlorophyll estimation (i.e., establishing species-specific chlorophyll estimation models may generate higher accuracy). In this study, helicopter-acquired high-spatial resolution hyperspectral images were acquired for species classification and species-specific chlorophyll content estimation. Four estimation models, including a universal linear regression (LR) model (i.e., built with data of all species), species-specific LR models (i.e., built with data of each species, respectively), a universal random forest regression (RFR) model, and species-specific RFR models, were compared to determine their performance in mapping chlorophyll and to evaluate the impacts of species composition. The results show that species-specific models performed better than the universal models, especially for species with fewer samples in the dataset. The best performed species-specific models were then used to generate species-specific chlorophyll content maps using the species classification results. Impacts of species composition on the retrieval of chlorophyll content were further assessed to support future chlorophyll mapping in heterogeneous ecosystems and ecosystem management.

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Estimation of Aboveground Vegetation Carbon Storage in Coastal Salt Marshes based on ZhuHai-1 Hyperspectral Satellite Images

Zou

Lin³

et al. 2023

Proceedings of the 2023 12th International Conference on Informatics, Environment, Energy and Applications

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Spectral response and the retrieval of canopy chlorophyll content under interspecific competition in wetlands — case study of wetlands in the Yangtze River Estuary

Cited by 5 publications

References 41 publications

Estimation of chlorophyll content in pepper leaves using spectral transmittance red-edge parameters

Estimation of chlorophyll content in pepper leaves using spectral transmittance red-edge parameters

Assessing the Impacts of Species Composition on the Accuracy of Mapping Chlorophyll Content in Heterogeneous Ecosystems

Estimation of Aboveground Vegetation Carbon Storage in Coastal Salt Marshes based on ZhuHai-1 Hyperspectral Satellite Images

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