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.
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