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Abstract
For river water quality monitoring at 30m × 1-day spatio-temporal scales, a spatial
and temporal adaptive reflectance fusion model (STARFM) is developed for estimating
turbidity (Tu), total suspended solid (TSS), and six heavy metals (HV) of iron, zinc,
copper, chromium, lead and cadmium, by blending the Moderate-Resolution Imaging Spectroradiometer
(MODIS) and Landsat (Ls) spectral bands. A combination of regression analysis and
genetic algorithm (GA) techniques are applied to develop spectral relationships between
Tu-Ls, TSS-Tu, and each HV-TSS. The STARFM algorithm and all the developed relationship
models are evaluated satisfactorily by various performance evaluation measures to
develop heavy metal pollution index-based vulnerability maps at 1-km resolution in
the Brahmani River in eastern India. The Monte-Carlo simulation based analysis of
the developed formulations reveals that the uncertainty in estimating Zn and Cd is
the minimum (1.04%) and the maximum (5.05%), respectively. Hence, the remote sensing
based approach developed herein can effectively be used in many world rivers for real-time
monitoring of heavy metal pollution.