Hyperspectral Identification of Chlorophyll Fluorescence Parameters of Suaeda salsa in Coastal Wetlands

Abstract

The stomata of Suaeda salsa are closed and the photosynthetic efficiency is decreased under conditions of water–salt imbalance, with the change to photosynthesis closely related to the chlorophyll fluorescence parameters of the photosystem PSII. Accordingly, chlorophyll fluorescence parameters were selected to monitor the growth status of Suaeda salsa in coastal wetlands under conditions of water and salt. Taking Suaeda salsa in coastal wetlands as the research object, we set up five groundwater levels (0 cm, –5 cm, –10 cm, –20 cm, and –30 cm) and six NaCl salt concentrations (0%, 0.5%, 1 %, 1.5%, 2%, and 2.5%) to carry out independent tests of Suaeda salsa potted plants and measured the canopy reflectance spectrum and chlorophyll fluorescence parameters of Suaeda salsa. A polynomial regression method was used to carry out hyperspectral identification of Suaeda salsa chlorophyll fluorescence parameters under water and salt stress. The results indicated that the chlorophyll fluorescence parameters Fv/Fm, Fm', and ΦPSII of Suaeda salsa showed significant relationships with vegetation index under water and salt conditions. The sensitive canopy band ranges of Suaeda salsa under water and salt conditions were 680–750 nm, 480–560 nm, 950–1000 nm, 1800–1850 nm, and 1890–1910 nm. Based on the spectrum and the first-order differential spectrum, the spectral ratio of A/B was constructed to analyze the correlation between it and the chlorophyll fluorescence parameters of Suaeda salsa. We constructed thirteen new vegetation indices. In addition, we discovered that the hyperspectral vegetation index D690/D1320 retrieved Suaeda chlorophyll fluorescence parameter Fv/Fm with the highest accuracy, with a multiple determination coefficient R2 of 0.813 and an RMSE of 0.042, and that D725/D1284 retrieved Suaeda chlorophyll fluorescence parameter ΦPSII model with the highest accuracy, with a multiple determination coefficient R2 of 0.848 and an RMSE of 0.096. The hyperspectral vegetation index can be used to retrieve the chlorophyll fluorescence parameters of Suaeda salsa in coastal wetlands under water and salt conditions, providing theoretical and technical support for future large-scale remote sensing inversion of chlorophyll fluorescence parameters.

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach

“…Although the coefficients of determination and correlation for Chl a and Chl a+b were moderate (with R 2 values of 0.041 and 0.057), they were still lower than those reported in other studies [22,68,69]. These authors reported that components of the epidermis contributed to the poor quality of the generated models, including the effects of moisture, surface modifications, waxes, epicuticular layers, as well as the presence of trichomes and stomata [22,[68][69][70][71]. The distribution and organization of concentration pigments in leaves may also influence the extinction or attenuation coefficients of light, which could help explain the observed correlation [72,73].…”

“…Most previous studies have only focused on reflectance curves, limiting their ability to fully comprehend the spectral interactions that lead to accurate pigment estimation. In this way, the method provides a more efficient and cost-effective alternative to laboratory methods, which can be both time-consuming and potentially harmful to the environment [22,[68][69][70][71]. The use of two sensors and PLSR analysis represents a significant advancement in the field, enabling a quick and accurate estimation of carotenoids, anthocyanins, flavonoids, and phenolic compounds in tobacco and lettuce, plants with varying pigment contents, and leaf structures [75].…”

Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm

“…The chlorophyll fluorescence signal peaks are weak in the blue-green and near-infrared regions, making it difficult to extract spectral features ( Mohammed et al., 2019 ). Currently, many studies eliminate background interference and improve the correlations between crop physiological components and spectra by spectral transformation ( Zheng et al., 2021 ). Continuous wavelet transformation (CWT) can capture subtle changes in reflectance and highlight the weak spectral features of crops ( Liu et al., 2020 ; Zhang et al., 2020 ).…”

Geospatial modeling and assessment of Island ecosystem health after restoration projects: a case study in Qinshan Island, Jiangsu Province, China

“…Chinese researchers mainly focused on the review of theoretical methods of ecosystem health assessment during 1994-2001, [5][6][7][8] whereas they focused on indicator systems and some case studies of methodology application in wetland, watershed, forest, grassland, marine, and other ecosystems from 2002 to the present. [9][10][11][12][13][14][15][16] In terms of island ecosystem health, many studies have been conducted in the last 30 years. Song 17 constructed an island ecosystem health assessment indicator system based on vitality, organization, heterogeneity, and coordination, creating a comprehensive assessment method and applying it to the islands of South Changshan, North Changshan, and Daheishan.…”

“…He stated that “ecosystem health refers to the stability and sustainability of an ecosystem, which means the ability to maintain its organization, self-regulation, and resilience to stress over time.” Later, Costanza 4 summarized the concept of ecosystem health as follows: “health is a stable phenomenon within the ecology; health is the absence of disease, health is diversity or complexity, health is stability or recoverability, health is space with vitality or growth, and health is the balance between system elements.” Chinese researchers mainly focused on the review of theoretical methods of ecosystem health assessment during 1994–2001, 5 – 8 whereas they focused on indicator systems and some case studies of methodology application in wetland, watershed, forest, grassland, marine, and other ecosystems from 2002 to the present 9 – 16 …”