Remote Sensing of Photosynthetic Parameters

Relation of Photochemical Reflectance Indices Based on Different Wavelengths to the Parameters of Light Reactions in Photosystems I and II in Pea Plants

Remote Sensing ◽

10.3390/rs12081312 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1312

Author(s):

Ekaterina Sukhova ◽

Vladimir Sukhov

Keyword(s):

Remote Sensing ◽

Pulse Amplitude ◽

Ecological Monitoring ◽

Photochemical Quenching ◽

Photosynthetic Parameters ◽

Pam Fluorometer ◽

Physiological Processes ◽

Non Photochemical Quenching ◽

Induced Changes ◽

Reflectance Indices

Measurement and analysis of the numerous reflectance indices of plants is an effective approach for the remote sensing of plant physiological processes in agriculture and ecological monitoring. A photochemical reflectance index (PRI) plays an important role in this kind of remote sensing because it can be related to early changes in photosynthetic processes under the action of stressors (excess light, changes in temperature, drought, etc.). In particular, we previously showed that light-induced changes in PRIs could be strongly related to the energy-dependent component of the non-photochemical quenching in photosystem II. The aim of the present work was to undertake comparative analysis of the efficiency of using light-induced changes in PRIs (ΔPRIs) based on different wavelengths for the estimation of the parameters of photosynthetic light reactions (including the parameters of photosystem I). Pea plants were used in the investigation; the photosynthetic parameters were measured using the pulse-amplitude-modulated (PAM) fluorometer Dual-PAM-100 and the intensities of the reflected light were measured using the spectrometer S100. The ΔPRIs were calculated as ΔPRI(band,570), where the band was 531 nm for the typical PRI and 515, 525, 535, 545, or 555 nm for modified PRIs; 570 nm was the reference wavelength for all PRIs. There were several important results: (1) ∆PRI(525,570), ∆PRI(531,570), ∆PRI(535,570), and ∆PRI(545,570) could be used for estimation of most of the photosynthetic parameters under light only or under dark only conditions. (2) The combination of dark and light conditions decreased the efficiency of ∆PRIs for the estimation of the photosynthetic parameters; ∆PRI(535,570) and ∆PRI(545,570) had maximal efficiency under these conditions. (3) ∆PRI(515,570) and ∆PRI(525,570) mainly included the slow-relaxing component of PRI; in contrast, ∆PRI(531,570), ∆PRI(535,570), ∆PRI(545,570), and ∆PRI(555,570) mainly included the fast-relaxing component of PRI. These components were probably caused by different mechanisms.

Download Full-text

Phytoplankton photosynthetic parameters off Baja California: A tool to estimate primary production with remote sensing data

Ciencias Marinas ◽

10.7773/cm.v43i3.2746 ◽

2017 ◽

Vol 43 (3) ◽

pp. 157-172 ◽

Cited By ~ 1

Author(s):

Ramón Sosa-Ávalos

Keyword(s):

Remote Sensing ◽

Primary Production ◽

Baja California ◽

Remote Sensing Data ◽

Photosynthetic Parameters ◽

Sensing Data

Download Full-text

Assessing the effect of extra nitrogen on Kandelia obovata growth under cadmium stress using high-resolution thermal infrared remote sensing and the three-temperature model

Functional Plant Biology ◽

10.1071/fp17295 ◽

2018 ◽

Vol 45 (11) ◽

pp. 1162

Author(s):

Xiaoxue Shen ◽

Ruili Li ◽

Minwei Chai ◽

Ke Yu ◽

Qijie Zan ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Thermal Infrared ◽

Photosynthetic Parameters ◽

N Addition ◽

Growth Responses ◽

Cd Stress ◽

Kandelia Obovata ◽

Thermal Infrared Remote Sensing ◽

Infrared Remote Sensing

Mangrove forests provide many ecological services and are among the most productive intertidal ecosystems on earth. Currently, these forests frequently face significant heavy metal pollution as well as eutrophication. The present study assessed the response of Kandelia obovata Sheue, H.Y. Liu & J. Yong to combined NH4+–N addition and Cd stress based on a three-temperature (3T) model using high-resolution thermal infrared remote sensing. The results show that leaf surface temperature (Tc) and the plant transpiration transfer coefficient (hat) became larger with increasing NH4+–N concentrations in the same Cd treatment, especially under high NH4+–N (50 and 100 mg·L−1) and Cd stress. The thermal bioindicators, growth responses and photosynthetic parameters changed in a consistent fashion, indicating that combined high NH4+–N addition and Cd stress led to stomatal closure, reduced the cooling effect of transpiration, and increased Tc and hat values. Furthermore, appropriate NH4+–N supply reduced stomatal conductance (gs) and the transpiration rate (Tr), which were increased by Cd stress, and then maintained Tc and hat at normal levels. The normalised hat helped to reduce the influence of environmental variation during the diagnosis of mangrove plant health. This indicated that the 3T model with high-resolution thermal infrared remote sensing provides an effective technique for determining the health status of mangrove plants under stress.

Download Full-text

Use of leaf hyperspectral data and different regression models to estimate photosynthetic parameters (Vcmax and Jmax) in three different row crops

10.5194/egusphere-egu2020-18251 ◽

2020 ◽

Author(s):

Maria Luisa Buchaillot ◽

David Soba ◽

Tianchu Shu ◽

Liu Juan ◽

José Luis Araus ◽

...

Keyword(s):

Remote Sensing ◽

Maximum Rate ◽

Regression Models ◽

Vegetation Index ◽

Phenotypic Variability ◽

Vegetation Indices ◽

Hyperspectral Data ◽

Spatial And Temporal Variation ◽

Photosynthetic Parameters ◽

Row Crops

By 2050 future global food demand is projected to require a doubling of agricultural output, and climate change will exacerbate this challenge by intensifying the exposure of field crops to abiotic stress conditions, including rising temperature, increased drought, and increased CO2 concentration ([CO2]). One of the keys to improving crop yield under different stresses is studying is photosynthesis. Photosynthetic parameters, such as the maximum rate of carboxylation of RuBP (Vc,max), and the maximum rate of electron transport driving RuBP regeneration (Jmax) vary in response to climate conditions and have been identified as a target for improvement. However, the techniques used to measure these physiological parameters are very time consuming, ranging from 30 to 70 min per measurement and require specialized personnel. Therefore, breeding or genetic mapping for these traits under these conditions is prohibitively time-consuming. Spatial and temporal variation in plant photosynthesis can be estimated using remote sensing-derived spectral vegetation indices. Spectral estimates of green vegetation biomass and vigor, including vegetation indices such as the Normalized Difference Vegetation Index (NDVI), are widely used to estimate vegetation productivity across spatial and temporal scales but are unable to provide assessments of specific photosynthetic parameters. For that reason, hyperspectral remote sensing shows promise for predicting photosynthetic capacity based on more detailed leaf optical properties. In this study, we developed and assessed estimates of Vcmax and Jmax through four different advanced regression models: PLS, BR, ARDR, and LASSO based on leaf reflectance metrics measured with an ASD FieldSpec4 Hi-RES of different crops under different environmental conditions such as (1) different varieties of soybean under high [CO2] and high temperature, (2) different varieties of peanut under drought stress and (3) 20 varieties of cotton diverse origin and grown under field conditions. Both phenotypic variability and varying levels of stress were employed with each crop to ensure adequate ranges of responses. Model sensitivities were assessed for each crop and treatment separately and in combination in order to better understand the strengths and weaknesses of each model in all the different conditions. For the combination of three species, all the models suggest a robust prediction of Vcmax around R2:0.67 and the same for the Jmax R2: 0.55.

Download Full-text

Influence of Local Burning on Difference Reflectance Indices Based on 400–700 nm Wavelengths in Leaves of Pea Seedlings

Plants ◽

10.3390/plants10050878 ◽

2021 ◽

Vol 10 (5) ◽

pp. 878

Author(s):

Ekaterina Sukhova ◽

Lyubov Yudina ◽

Ekaterina Gromova ◽

Anastasiia Ryabkova ◽

Vladimir Vodeneev ◽

...

Keyword(s):

Remote Sensing ◽

Complex Analysis ◽

Higher Plants ◽

Photosynthetic Parameters ◽

Local Damage ◽

Physiological Changes ◽

Pea Seedlings ◽

Reflectance Indices ◽

Systemic Responses ◽

Local Burning

Local damage (e.g., burning) induces a variation potential (VP), which is an important electrical signal in higher plants. A VP propagates into undamaged parts of the plant and influences numerous physiological processes, including photosynthesis. Rapidly increasing plant tolerance to stressors is likely to be a result of the physiological changes. Thus, developing methods of revealing VP-induced physiological changes can be used for the remote sensing of plant systemic responses to local damage. Previously, we showed that burning-induced VP influenced a photochemical reflectance index in pea leaves, but the influence of the electrical signals on other reflectance indices was not investigated. In this study, we performed a complex analysis of the influence of VP induction by local burning on difference reflectance indices based on 400–700 nm wavelengths in leaves of pea seedlings. Heat maps of the significance of local burning-induced changes in the reflectance indices and their correlations with photosynthetic parameters were constructed. Large spectral regions with significant changes in these indices after VP induction were revealed. Most changes were strongly correlated to photosynthetic parameters. Some indices, which can be potentially effective for revealing local burning-induced photosynthetic changes, are separately shown. Our results show that difference reflectance indices based on 400–700 nm wavelengths can potentially be used for the remote sensing of plant systemic responses induced by local damages and subsequent propagation of VPs.

Download Full-text