Spatio-temporal evolution of chlorophyll-a in the Bay of Bengal: a remote sensing and bio-argo perspective

2016 ◽  
Author(s):  
T. V. S. Udaya Bhaskar ◽  
Chiranjivi Jayaram ◽  
P. Rama Rao E. ◽  
K. H. Rao
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Bay Of Bengal ◽  
Temporal Evolution ◽  
Spatio Temporal

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

Modeling the Spatio-Temporal Evolution of Chlorophyll-a in Three Tropical Rivers Comoé, Bandama, and Bia Rivers (Côte d’Ivoire) by Artificial Neural Network

Wetlands ◽  
2020 ◽  
Vol 40 (5) ◽  
pp. 939-956
Author(s):  
Maley-Pacôme Soro ◽  
Koffi Marcellin Yao ◽  
N’Guessan Louis Berenger Kouassi ◽  
Ahmed Abauriet Ouattara ◽  
Thomas Diaco
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chlorophyll A ◽  
Cote D'ivoire ◽  
Côte D’Ivoire ◽  
Temporal Evolution ◽  
Tropical Rivers ◽  
Cote D’Ivoire ◽  
Artificial Neural ◽  
Spatio Temporal

New dimensions of urban landscapes: The spatio-temporal evolution from a polynuclei area to a mega-region based on remote sensing data

2014 ◽  
Vol 47 ◽  
pp. 137-153 ◽  
Author(s):  
H. Taubenböck ◽  
M. Wiesner ◽  
A. Felbier ◽  
M. Marconcini ◽  
T. Esch ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Temporal Evolution ◽  
Remote Sensing Data ◽  
Urban Landscapes ◽  
Sensing Data ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Assessment of "Chlorophyll a" in Banco Chinchorro using Remote Sensing

2018 ◽  
Vol 147 (12) ◽  
pp. 213-223 ◽  
Author(s):  
Hugo E. Lazcano-Hernandez ◽  
Javier Arellano-Verdejo ◽  
Héctor A. Hernandez-Arana ◽  
M. Susana Alvarado-Barrientos
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Spatio Temporal ◽  
Temporal Assessment
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