Monitoring and validating spatio-temporal dynamics of biogeochemical properties in Mersin Bay (Turkey) using Landsat ETM+

Abstract

The objective of this study was to devise and validate simple models for estimating spatio-temporal dynamics of seven optically (in)active biogeochemical properties in Mersin Bay using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data and GIS. Spatio-temporal dynamics of Secchi depth (S (depth)), dissolved oxygen (DO), nitrite nitrogen (NO(2)-N), nitrate nitrogen (NO₃-N), silicate (SiO₄), 5-day biological oxygen demand (BOD5), and chlorophyll-a (Chl-a) were estimated using best-fit multiple linear regression (MLR) models as a function of Landsat 7 ETM+ and ground data in 2007 and 2008, latitude, longitude, and day of year. Validation of the MLR models against Landsat and ground data in 2005 led to r values ranging from 0.39 for NO₂-N (P = 0.008) to 0.79 for S (depth) (P < 0.001). Parsimonious MLR models built in this study appear to be promising for monitoring and predicting spatio-temporal dynamics of optically (in)active water quality characteristics in Mersin Bay.

Spatial–temporal dynamics of NDVI and Chl-a concentration from 1998 to 2009 in the East coastal zone of China: integrating terrestrial and oceanic components

“…With regard to issues closely related to findings in this study, such as the reasons of the dynamics of NDVI and Chl-a concentration and the reasons of the differences between them, substantial evidence reported in many academic articles (Song and Ma 2007;Han et al 2009;Sun et al 2011;Kim et al 2009;Hong et al 2011;Karakaya and Evrendilek 2011) prove that human activities, especially rapid urban sprawl, exploitation of natural resources, and extensive land use changes, are most probably the main driving forces of NDVI variations on decadal time scale, although climate changes, such as variations of temperature and precipitation have great impacts on it also, and variations of sea surface temperature, suspended sediment concentration, nutrient inputs from land, and lots of land-based human activities have great impacts on spatial-temporal dynamics of Chl-a concentration directly or indirectly. Of course, there are more complicated driving forces and more a b Fig.…”

Hybrid forward-selection method-based water-quality estimation via combining Landsat TM, ETM+, and OLI/TIRS images and ancillary environmental data

“…Radiance data from the thermal bands (band 6 of Landsat TM and ETM+, and band 10 of Landsat TIRS) were converted to water surface temperature. As discussed earlier, water temperature has been found to be related to phytoplankton concentration [ 23 , 24 ], and thus, related to water quality [ 63 ]. However, in this study, most of the satellite image dates differ by several days compared with the closest corresponding actual water-quality sampling date; thus, the water surface temperature derived from the satellite images does not represent the actual water temperature at the time of water sampling.…”

“…Nevertheless, many studies divide their analyses by season [ 20 , 21 ] due to systemic seasonal differences in factors such as concentrations of color-producing substances (including phytoplankton), atmospheric disturbances [ 21 ], and solar zenith angle [ 22 ]. Some studies have shown that the predictive power of equations created without distinguishing by season is lower than it would otherwise be [ 23 , 24 ].…”

Study on Spatiotemporal Variability of Water Quality Parameters in Florida Bay Using Remote Sensing

“…Their result showed the reflectance peaks at 404 and 477 nm, and phosphorus at 350 nm, for nitrogen and phosphorous, respectively, and developed a quantitative retrieval model for these two parameters. Karakaya and Evrendilek [35] applied Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data to measure the concentration of nitrite nitrogen (NO 2 -N) and nitrate nitrogen (NO 3 -N) using best-fit The normal probability-probability (P-P) plots of regression standardized residuals for chl-a and turbidity in dry and wet seasons. multiple linear regression (MLR) models as a function of Landsat 7 ETM+ and ground data in Mersin Bay, Turkey.…”