Abstract:
This study presents a comparative evaluation of three real-time imaging-based approaches for the prediction of optically active
water constituents as chlorophyll-
a
(Chl-
a
), turbidity, suspended particulate matter (SPM), and reservoir water colour. The
imaging models comprise of Landsat ETM+-visible and NIR (VNIR) data and EyeOnWater and HydroColor Smartphone
sensor apps. To estimate the selected water quality parameters (WQP) from Landsat ETM+-VNIR, predictive models based on
empirical relationships were developed. From the in situ measurements and the Landsat regression models, the results from the
remote re
fl
ectances of ETM+ green, blue, and NIR independently yielded the best
fi
ts for the respective predictions of Chl-
a
,
turbidity, and SPM. The concentration of Chl-
a
was derived from the Landsat ETM+ and HydroColor with respective Pearson
correlation coe
ffi
cients
r
of 0.8977 and 0.8310. The degree of turbidity was determined from Landsat, EyeOnWater, and
HydroColor with respective
r
values of 0.9628, 0.819, and 0.8405. From the same models, the retrieved SPM was regressed with
the laboratory measurements with
r
value results of 0.6808, 0.7315, and 0.8637, respectively, from Landsat ETM+, EyeOnWater,
and HydroColor. The empirical study results showed that the imaging models can be e
ff
ectively applied in the estimation of the
physical WQP.
