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