scholarly journals Estimation of chlorophyll-a concentration of caseⅡwaters from hyperspectral remote sensing data in NDCI method

2013 ◽  
Vol 25 (3) ◽  
pp. 437-444 ◽  
Author(s):  
AN Ru ◽  
◽  
LIU Yingying ◽  
QU Chunmei ◽  
QUAYE-BALLARD JA ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Chlorophyll A Concentration ◽  
Sensing Data

scholarly journals Spatiotemporal Evolution of Chlorophyll-a Concentration From MODIS Data Inversion in the Middle and Lower Reaches of the Hanjiang River, China

2021 ◽  
Author(s):  
Zhuo Chen ◽  
Ming Dou ◽  
Rui Xia ◽  
Guiqiu Li ◽  
Lisha Shen
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Remote Sensing Data ◽  
Observation Data ◽  
Model Errors ◽  
Spatiotemporal Evolution ◽  
Chlorophyll A Concentration ◽  
Sensing Data ◽  
Hanjiang River ◽  
Water Blooms

Abstract The global construction of water projects has led to a clear trend of river and lake reservoir formation, spurring increasingly serious ecological environmental deterioration, especially that caused by the frequent occurrence of water blooms. Because of monitoring technology limitations, monitoring the algae content index in water has lagged behind the conventional water quality index, which makes sample monitoring too sparse in many rivers and the monitoring data incoherent, so it cannot truly reflect the evolution of water eutrophication. With moderate resolution imaging spectroradiometer (MODIS) remote sensing data monitoring, continuous chlorophyll-a observation data can be collected effectively. This method has important guiding significance for the early warning and control of water blooms. This study considers the middle and lower reaches of the Hanjiang River in China, based on the current remote sensing communication technology, MODIS remote sensing data, and statistical methods and measured chlorophyll-a concentration correlation analysis. Through the use of the trial and error method to establish the band ratio model and BP neural network model, two types of model errors were compared to determine the optimal algorithm settings for the middle and lower reaches of the Hanjiang River chlorophyll-a inversion. Subsequently, the algorithm model for 2000 to 2011 in the middle and lower reaches of the Hanjiang River chlorophyll-a concentration inversion and the results of the inversion analysis of spatiotemporal evolution characteristics we used to determine the influence of various environmental factors on the chlorophyll-a concentration change.

scholarly journals Spatiotemporal evolution of chlorophyll-a concentration from MODIS data inversion in the middle and lower reaches of the Hanjiang River, China

2021 ◽  
Author(s):  
zhuo Chen ◽  
ming Dou ◽  
Rui Xia ◽  
Guiqiu Li ◽  
Lisha Shen
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Remote Sensing Data ◽  
Observation Data ◽  
Model Errors ◽  
Spatiotemporal Evolution ◽  
Chlorophyll A Concentration ◽  
Sensing Data ◽  
Hanjiang River ◽  
Water Blooms

The global construction of water projects has led to a clear trend of river and lake reservoir formation, spurring increasingly serious ecological environmental deterioration, especially that caused by the frequent occurrence of water blooms. Because of monitoring technology limitations, monitoring the algae content index in water has lagged behind the conventional water quality index, which makes sample monitoring too sparse in many rivers and the monitoring data incoherent, so it cannot truly reflect the evolution of water eutrophication. With moderate resolution imaging spectroradiometer (MODIS) remote sensing data monitoring, continuous chlorophyll-a observation data can be collected effectively. This method has important guiding significance for the early warning and control of water blooms. This study considers the middle and lower reaches of the Hanjiang River in China, based on the current remote sensing communication technology, MODIS remote sensing data, and statistical methods and measured chlorophyll-a concentration correlation analysis. Through the use of the trial and error method to establish the band ratio model and BP neural network model, two types of model errors were compared to determine the optimal algorithm settings for the middle and lower reaches of the Hanjiang River chlorophyll-a inversion. Subsequently, the algorithm model for 2000 to 2011 in the middle and lower reaches of the Hanjiang River chlorophyll-a concentration inversion and the results of the inversion analysis of spatiotemporal evolution characteristics we used to determine the influence of various environmental factors on the chlorophyll-a concentration change.

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