scholarly journals Correcting non-photochemical quenching of Saildrone chlorophyll-a fluorescence for evaluation of satellite ocean color retrievals

2020 ◽  
Vol 28 (3) ◽  
pp. 4274 ◽  
Author(s):  
Joel P. Scott ◽  
Scout Crooke ◽  
Ivona Cetinić ◽  
Carlos E. Del Castillo ◽  
Chelle L. Gentemann
Keyword(s):  
Chlorophyll A ◽  
Chlorophyll A Fluorescence ◽  
Ocean Color ◽  
Photochemical Quenching ◽  
Non Photochemical Quenching ◽  
Satellite Ocean Color

Photoprotective mechanisms during leaf ontogeny: cuticular development and anthocyanin deposition in two morphs of Agave striata that differ in leaf coloration

Botany ◽  
2009 ◽  
Vol 87 (12) ◽  
pp. 1186-1197 ◽  
Author(s):  
Nicolas Y. Fondom ◽  
Sergio Castro-Nava ◽  
Alfredo J. Huerta
Keyword(s):  
Chlorophyll A ◽  
Chlorophyll A Fluorescence ◽  
Xanthophyll Cycle ◽  
Photochemical Quenching ◽  
Delayed Response ◽  
Anthocyanin Content ◽  
Leaf Ontogeny ◽  
Leaf Coloration ◽  
Young Leaves ◽  
Non Photochemical Quenching

Our objectives were to test whether in Agave striata Zucc., a plant with crassulacean acid metabolism (CAM plant), leaf wax development is a delayed response to sunlight exposure following cutin development, and whether energy dissipation shifts from non-photochemical quenching to photochemical quenching during leaf ontogeny. Under field conditions, photosynthesis, cuticular development, and anthocyanin deposition were studied in two morphs of A. striata that differ in leaf coloration (green vs. red). We quantified leaf anthocyanin, wax, and cutin content, and also measured chlorophyll a fluorescence and leaf surface temperature. In addition, using three leaf reflectance indices, we measured relative chlorophyll and anthocyanin content, and also xanthophyll-cycle de-epoxidation state (xanthophyll conversion). Our results revealed that the main components of cuticle (wax and cutin) in leaves of A. striata are deposited during different developmental windows, which are similar to leaves of monocots such as grasses. Exposure to sunlight was found to be the most likely candidate to affect wax and anthocyanin deposition. Chlorophyll a fluorescence data revealed that the sunlight conditions experienced by both morphs predisposed the young leaves of the green morph and old leaves of both morphs to photoinhibition. Our results also revealed that old leaves of the red morph, which contain a reduced level of chlorophyll and anthocyanin, had additional photoprotection via xanthophyll conversion. The results presented here support the photoprotective function of leaf anthocyanins and wax accumulation during leaf ontogeny, indicating that their presence may compensate for the reduced dependence of non-photochemical quenching and the xanthophyll-cycle pigment conversion.

scholarly journals A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration

2021 ◽  
Vol 13 (10) ◽  
pp. 2003
Author(s):  
Daeyong Jin ◽  
Eojin Lee ◽  
Kyonghwan Kwon ◽  
Taeyun Kim
Keyword(s):  
Deep Learning ◽  
Chlorophyll A ◽  
Hydrodynamic Model ◽  
Ocean Color ◽  
Learning Model ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Temporal And Spatial Distribution ◽  
Deep Learning Model ◽  
Satellite Ocean Color

In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R2) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a.

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