AIMC Topic: Chlorophyll

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A novel hybrid machine learning approach for accurate retrieval of ocean surface chlorophyll-a across oligotrophic to eutrophic waters.

Environmental research Aug 15, 2025
Accurate assessment of chlorophyll a (Chla) concentration distribution and variations is significant for environmental monitoring and ecological research. However, the inversion of Chla in different optical types of water bodies can only be achieved ...
Algorithms Eutrophication Machine Learning Chlorophyll Chlorophyll A Oceans and Seas Environmental Monitoring Seawater
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Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region.

Optics express Apr 22, 2024
Chlorophyll a (Chl-a) in lakes serves as an effective marker for assessing algal biomass and the nutritional level of lakes, and its observation is feasible through remote sensing methods. HJ-1 (Huanjing-1) satellite, deployed in 2008, incorporates a...
Satellite Imagery Lakes Environmental Monitoring Chlorophyll Chlorophyll A Remote Sensing Technology Machine Learning Algorithms
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Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning.

Journal of experimental botany Nov 2, 2022
A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. To address this problem, this research investigated a conceptual framework by integrating a crop g...
Water Plant Leaves Triticum Deep Learning Chlorophyll Plant Breeding
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A system of coordinated autonomous robots for Lagrangian studies of microbes in the oceanic deep chlorophyll maximum.

Science robotics Jan 13, 2021
The deep chlorophyll maximum (DCM) layer is an ecologically important feature of the open ocean. The DCM cannot be observed using aerial or satellite remote sensing; thus, in situ observations are essential. Further, understanding the responses of mi...
Chlorophyll Oceanography Seawater Pacific Ocean Oceans and Seas Microbiota Acoustics Plankton Satellite Communications Environmental Monitoring Robotics Humans Ecosystem
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Combined multivariate analysis and machine learning reveals a predictive module of metabolic stress response in Arabidopsis thaliana.

Molecular omics Dec 3, 2018
Abiotic stress exposure of plants induces metabolic reprogramming which is tightly regulated by signalling cascades connecting transcriptional with translational and metabolic regulation. Complexity of such interconnected metabolic networks impedes t...
Machine Learning Sucrose Computational Biology Cell Line Multivariate Analysis Arabidopsis Chlorophyll Stress, Physiological Mutation Metabolomics Analysis of Variance Phosphoglucomutase
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[A hyperspectral assessment model for leaf chlorophyll content of Pinus massoniana based on neural network].

Ying yong sheng tai xue bao = The journal of applied ecology Apr 18, 2017
The relationships between the leaf chlorophyll content (LCC) of Pinus massoniana at different growth stages and their chlorophyll content were analyzed. 7 of 36 red edge-based parameters were finally selected as the typical spectral response paramete...
Pinus Spectrum Analysis Chlorophyll Neural Networks, Computer Plant Leaves
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[Research on Modeling Method for Chlorophyll Content Fine Measurement Based on Neural Network].

Guang pu xue yu guang pu fen xi = Guang pu Sep 1, 2015
Aiming at SPAD values of living plant leaf chlorophyll content affected easily by the blade thickness, water content, etc, a fine retrieval method of chlorophyll content based on multiple parameters of neural network model is presented. The SPAD valu...
Neural Networks, Computer Light Spectrophotometry Chlorophyll Water Plant Leaves
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[Adaptive "3R" De-Noising Algorithm Based on Near Infrared Bi-Spectrum].

Guang pu xue yu guang pu fen xi = Guang pu Apr 1, 2015
Adaptive de-noising algorithm is proposed based on transmission spectrum and absorption spectrum of near infrared. Near infrared transmission spectrum and absorption spectrum collected synchronously are decomposed into intrinsic mode functions by ens...
Chlorophyll Neural Networks, Computer Algorithms Models, Theoretical
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[Monitoring of the Moskva River Water Using Microbiological Parameters and Chlorophyll a Fluorescence].

Mikrobiologiia Jan 1, 2015
The results of investigations of three Moskva River sites with different degree of pollution using a complex of microbiological characteristics and the parameters of chlorophyll a fluorescence are presented. We determined that the bacterioplankton se...
Russia Chlorophyll Chlorophyll A Rivers Water Microbiology Environmental Monitoring Spectrometry, Fluorescence
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