Fine-scale leaf chlorophyll distribution across a deciduous forest through two-step model inversion from Sentinel-2 data

Li, Yingjie; Ma, Qingmiao; Chen, Jing M.; Croft, Holly; Luo, Xiangzhong; Zheng, Ting; Rogers, Cheryl; Liu, Jane

doi:10.1016/j.rse.2021.112618

Cited by 25 publications

(12 citation statements)

References 70 publications

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“…In the characterization of the foliar biochemical traits, it was observed that H. brasiletto, and P. dulce presented higher contents of Chl a , Chl b , Chl total , and C than P. guamacho, which may be a possible cause of the better performance in quantum efficiency in these two species since chlorophyll is key to light harvesting and biochemical energy production in the photosynthetic process [29,68,69], and carotenoids play a special role in protecting PSII from photooxidative damage, allowing the dissipation of PSII energy as heat [28,29,70]. For photosynthetic pigments, a positive correlation has been observed between chlorophyll content and chlorophyll a fluorescence parameters [71].…”

Section: Discussionmentioning

confidence: 99%

Foliar Functional Traits of Resource Island-Forming Nurse Tree Species from a Semi-Arid Ecosystem of La Guajira, Colombia

Toro-Tobón¹,

Alvarez-Flórez²,

Mariño-Blanco³

et al. 2022

Plants

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Semi-arid environments characterized by low rainfall are subject to soil desertification processes. These environments have heterogeneous landscapes with patches of vegetation known as resource islands that are generated by nurse species that delay the desertification process because they increase the availability of water and nutrients in the soil. The study aimed to characterize some foliar physiological, biochemical, and anatomical traits of three nurse tree species that form resource islands in the semi-arid environment of La Guajira, Colombia, i.e., Haematoxylum brasiletto, Pithecellobium dulce, and Pereskia guamacho. The results showed that H. brasiletto and P. dulce have sclerophyllous strategies, are thin (0.2 and 0.23 mm, respectively), and have a high leaf dry matter content (364.8 and 437.47 mg/g). Moreover, both species have a high photochemical performance, reaching Fv/Fm values of 0.84 and 0.82 and PIABS values of 5.84 and 4.42, respectively. These results agree with the OJIP curves and JIP parameters. Both species had a compact leaf with a similar dorsiventral mesophyll. On the other hand, P. guamacho has a typical succulent, equifacial leaf with a 97.78% relative water content and 0.81 mm thickness. This species had the lowest Fv/Fm (0.73) and PIABS (1.16) values and OJIP curve but had the highest energy dissipation value (DIo/RC).

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Section: Discussionmentioning

confidence: 99%

Foliar Functional Traits of Resource Island-Forming Nurse Tree Species from a Semi-Arid Ecosystem of La Guajira, Colombia

Toro-Tobón¹,

Alvarez-Flórez²,

Mariño-Blanco³

et al. 2022

Plants

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“…In the processing of inversion using a physically-based model, the inversion results are not always unique, 170 as different combinations of canopy and leaf parameters may produce almost similar spectra (Si et al, 2012). It has been indicated that using prior knowledge is an effective way to deal with this problem and facilitate LCC retrieval (Sun et al, 2021a;Li et al, 2021). The DBF LUT for retrieving DBF and MF LCC from MERIS/OLCI reflectance was optimized based on sun-view geometry that could be available from each satellite image.…”

Section: Canopy Reflectance Modeling -Prospect-d and 4-scale Modelmentioning

confidence: 99%

“…Therefore, quantifying photosynthesis on a global scale is fundamental to understanding the global carbon cycle (Ryu et al, 2019). Chlorophyll, as the primary photosynthetic pigment, facilitates the harvesting of energy from light, and the conversion of the light energy into stored chemical energy and thus plays a vital role in the exchange of matter and energy fluxes (Gitelson et al, 2003;Li et al, 2021). Currently, many studies have indicated that leaf chlorophyll content (LCC) is closely related to plant photosynthetic capacity parameter (Vcmax) (Croft et al, 2017;Qian 40 et al, 2021;Lu et al, 2020;Li et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…CC BY 4.0 License. vegetation leaf and canopy spectra simulations, that integrates a variety of confounding scene information, and is therefore considered ideal for global LCC mapping (Li et al, 2021). To deal with the ill-posed problems in the retrieval of crop LCC caused by the lack of prior information, a look-up-table (LUT) method that combines multiple types of canopy structure and soil background was proposed in our previous study (Qian and Liu, 2020).…”

mentioning

confidence: 99%

“…In addition, LAI parameters, which can represent vegetation cover to some extent, should also be highly considered. Numerous studies have shown that due to the interference of soil background, the retrieval accuracy of LCC is low under sparse vegetation (Zarco-Tejada et al, 2019;Xu et al, 2019;Croft et al, 2020;Li et al, 2021). How to eliminate the influence of soil background 295 and improve the retrieval accuracy of LCC under sparse vegetation by remote sensing is an urgent scientific question worthy of deep investigation.…”

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confidence: 99%

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The global leaf chlorophyll content dataset over 2003–2012 and 2018–2020 derived from MERIS/OLCI satellite data (GLCC): algorithm and validation

Qian

Liu²,

Chen³

et al. 2022

Preprint

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Abstract. Leaf chlorophyll content (LCC), a prominent plant physiological trait and a proxy for leaf photosynthetic capacity, plays a crucial role in the monitoring of agriculture and carbon cycle modeling. In this study, global 500 m LCC weekly dataset (GLCC) for the period 2003–2012 to 2018–2020 were produced from ENVISAT MERIS and Sentinel-3 OLCI satellite data using a physically-based radiative transfer modeling approach. Firstly, five look-up-tables (LUTs) were generated using PROSAIL-D and PROSPECT-D+4-Scale models for woody and non-woody plants, respectively. For the four LUTs applicable to woody plants, each LUT contains three sub-LUTs corresponding to three types of crown height. For the one LUT applicable to non-woody vegetation type, it includes 25 sub-LUTs corresponding to five kinds of canopy structure and five kinds of soil background. The average of the LCC inversion results of all sub-LUTs for each plant function type (PFT) was considered as the retrieval. The LUT algorithm was validated using the synthetic dataset, which gave an R2 value higher than 0.79 and an RMSE value lower than 10.5 μg cm−2. Then, the GLCC dataset was generated using the MERIS/OLCI multispectral data over 2003–2012 and 2018–2020 at a spatial resolution of 500 m and temporal resolution of one week. The GLCC dataset was validated using 161 field measurements, covering six PFTs. The validation results yielded an overall accuracy of R2 = 0.41 and RMSE = 8.94 μg cm−2. Finally, the GLCC dataset presented acceptable consistency with the existing MERIS LCC dataset developed by Croft et al. (2020). OLCI, as the successor to MERIS data, was used for the first time to co-produce LCC data from 2003–2012 to 2018–2020 in conjunction with MERIS data. This new GLCC dataset spanning nearly 20 years will provide a valuable opportunity for the monitoring of vegetation growth and terrestrial carbon cycle modeling. The GLCC dataset is available at https://doi.org/10.25452/figshare.plus.20439351 (Qian et al., 2022b).

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Effects of Soil, Water and Air Pollution with Heavy Metal Ions Around Lead and Zinc Mining and Processing Factories

Sharifi,

Zaeimdar,

Jozi

et al. 2023

Water Air Soil Pollut

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Fine-scale leaf chlorophyll distribution across a deciduous forest through two-step model inversion from Sentinel-2 data

Cited by 25 publications

References 70 publications

Foliar Functional Traits of Resource Island-Forming Nurse Tree Species from a Semi-Arid Ecosystem of La Guajira, Colombia

Foliar Functional Traits of Resource Island-Forming Nurse Tree Species from a Semi-Arid Ecosystem of La Guajira, Colombia

The global leaf chlorophyll content dataset over 2003–2012 and 2018–2020 derived from MERIS/OLCI satellite data (GLCC): algorithm and validation

Effects of Soil, Water and Air Pollution with Heavy Metal Ions Around Lead and Zinc Mining and Processing Factories

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