Monitoring Suaeda salsa Spectral Response to Salt Conditions in Coastal Wetlands: A Case Study in Dafeng Elk National Nature Reserve, China

Xia, Lu; Zhang, Sen; Tian, Yanqin; Li, Yurong; Wen, Rui; Tsou, Jin Yeu; Zhang, Yuanzhi

doi:10.3390/rs12172700

Cited by 9 publications

(6 citation statements)

References 63 publications

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“…However, when we measured the reflectance of the canopy spectrum, we found that the reflectance of the canopy spectrum may also be related to light conditions, soil types, and crop varieties [41]. More studies are needed to verify our findings [42]. (2) We constructed thirteen new vegetation indices.…”

Section: Discussionmentioning

confidence: 81%

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

Zheng¹,

Xia²,

Li³

et al. 2021

Remote Sensing

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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.

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Section: Discussionmentioning

confidence: 81%

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

Zheng¹,

Xia²,

Li³

et al. 2021

Remote Sensing

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“…The Yancheng Dafeng Elk Nature Reserve (Longitude: 120°47′~120°53′ E, Latitude: 32°59′~33°03′ N) is located in the eastern part of Jiangsu province, near to the Yellow Sea, and occurs at the transition zone between a subtropical and a warm temperate zone ( Figure 1 ). Covering an area of 2666 ha, it is the largest elk nature reserve worldwide with the largest number of wild elk populations [ 34 ]. In January 2002, the reserve was included in the list of important international wetlands by the United Nations Wetland Conservation Organization as a permanent protected area.…”

Section: Methodsmentioning

confidence: 99%

Spartina alterniflora Leaf and Soil Eco-Stoichiometry in the Yancheng Coastal Wetland

Zuo

Wei

Lei

et al. 2020

Plants

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Carbon, nitrogen, and phosphorus—nutrient and restrictive elements for plant growth and important components of the plant body—are mainly transferred and exchanged between plants and the soil environment. Changes in the carbon, nitrogen, and phosphorus eco-stoichiometry greatly impact the growth and expansion of Spartina alterniflora, and understanding these changes can reveal the nutrient coordination mechanism among ecosystem components. To explore the relationship between leaf and soil eco-stoichiometry and determine the key soil factors that affect leaf eco-stoichiometry, we collected leaf and soil samples of S. alterniflora at different tidal levels (i.e., 1, 3, and 5 km away from the coastline) in a coastal wetland in the Yancheng Elk Nature Reserve, Jiangsu province. We measured the leaf and soil carbon, nitrogen, and phosphorus contents and ratios, as well as the soil salinity and soil organic carbon. The results revealed the following. (1) The leaf stoichiometric characteristics and soil properties of S. alterniflora differed significantly between tidal levels; for example, total carbon, nitrogen, soil organic carbon were detected at their highest levels at 3 km and lowest levels at 5 km. (2) Significant correlations were detected between the leaf stoichiometric characteristics and soil characteristics. Additionally, nitrogen limitation was evident in the study area, as indicated by the nitrogen–phosphorus ratio being less than 14 and the soil nitrogen–phosphorus ratio being less than 1. (3) Soil salinity and the soil carbon–nitrogen ratio were shown to be the key factors that affect the eco-stoichiometric characteristics of S. alterniflora. These findings furthered our understanding of the nutrient distribution mechanisms and invasion strategy of S. alterniflora and can thus be used to guide S. alterniflora control policies formulated by government management departments in China.

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“…Besides, multivariate data analysis can help uncover useful information hidden within it ( Maione et al, 2019 ), especially for massive datasets from sensors. Machine learning methods showed the excellent data mining ability in hyperspectral data mining ( Yang et al, 2020 ; Najafabadi, 2021 ; Weng et al, 2021 ), and the combination between them can be exploited as a competent tool in plant science ( Greener et al, 2022 ) such as early stress detection ( Gu et al, 2019 ; Lu et al, 2020 ; Zheng et al, 2020 ), unsound kernel identification ( Liang et al, 2020 ; Zhang et al, 2021a ), and the evaluation of nutrition content ( Zhang et al, 2020a , b ; Najafabadi, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

Tao

Bai

et al. 2022

Front. Plant Sci.

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Glyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cultivars. This work took maize seedlings as the experimental object, and the spectral indices of leaves were calculated to construct a model with good robustness that could be used in different experiments. Compared with no transfer strategies, transferability of support vector machine learning model was improved by randomly selecting 14% of source domain from target domain to train and applying transfer component analysis algorithm, the accuracy on target domain reached 83% (increased by 71%), recall increased from 10 to 100%, and F1-score increased from 0.17 to 0.86. The overall results showed that both transfer component analysis algorithm and updating source domain could improve the transferability of model among experiments, and these two transfer strategies could complement each other’s advantages to achieve the best classification performance. Therefore, this work is beneficial to timely understanding of the physiological status of plants, identifying glyphosate resistant cultivars, and ultimately provides theoretical basis and technical support for new cultivar creation and high-throughput selection.

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Monitoring Suaeda salsa Spectral Response to Salt Conditions in Coastal Wetlands: A Case Study in Dafeng Elk National Nature Reserve, China

Cited by 9 publications

References 63 publications

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

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

Spartina alterniflora Leaf and Soil Eco-Stoichiometry in the Yancheng Coastal Wetland

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

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