India had announced the longest ever lockdown from 24 March 2020 to 14 April 2020 amid COVID-19 pandemic. It was reported that the water quality of the Ganga River has improved as compared to regular during this country-wide lockdown. In the present study, an attempt has been made to study the change in water quality of the river in terms of turbidity purely through remote sensing data, in the absence of ground observations, especially during this time period. The change in spectral reflectance of water along the river in the visible region has been analyzed using the Sentinel-2 multispectral remote sensing data at Haridwar, Kanpur, Prayagraj, and Varanasi stretches of the river. In the present study, it was found that the red and NIR bands are most sensitive, and can be used to estimate the turbidity. Further, the temporal variation in turbidity was also analyzed through normalized difference turbidity index at each location. It was observed that the turbidity in the river has reduced drastically at each stretch of the river. The study elicited that the remote sensing approach can be used to make qualitative estimates on turbidity, even in the absence of field observations.

Rapid urban growth processes give rise to impervious surfaces and are regarded as the primary cause of urban flooding or waterlogging in urban areas. The high rate of urbanization has caused waterlogging and urban flooding in many parts of Dhaka city. Therefore, the study is undertaken to quantify the changes in land use/land cover (LULC) and urban runoff extent based on the Natural Resources Conservation Service (NRCS) Curve Number (CN) during 1978–2018. The five-decadal LULC has been analyzed using three-generation Landsat time-series data considering six different classes, namely agriculture, built-up, wetland, open land, green spaces, and water bodies for the years 1978, 1988, 1998, 2007, and 2018. Significant changes in LULC for the study area from 1978–2018 are observed as 13.1%, 4.8%, and 7.8% reduction in agricultural land, green spaces, and water bodies, respectively, and a 22.1% increase in the built-up area is estimated. Within Dhaka city, 14.6%, 16.0%, and 12.3% reduction in agricultural land, green spaces, and water bodies, respectively, and a radical increase of 41.9% in built-up area are reckoned. The decadal runoff assessment has been carried out using the NRCS-CN method, considering an extreme rainfall event of 341 mm/day (13 September 2004). The catchment area under very high runoff category is observed as 159.5 km² (1978) and 318.3 km² (2018), whereas, for Dhaka city, the setting is dynamic as the area under the very high runoff category has increased from 74.24 km² (24.44%) to 174.23 km² (57.36%) in years 1978 and 2018, respectively, and, mostly, the very high runoff potential areas correspond to the dense built-up surfaces.

Conventional methods and models available for estimation of reservoir sedimentation process differ greatly in terms of complexity, inputs, and other requirements. An artificial neural network (ANN) model was used to estimate the volume of sediment retained in a reservoir. Annual rainfall, annual inflow, and capacity of the reservoir were chosen as inputs. Thirty Two years of data pertaining to Gobindsagar Reservoir on the Satluj River in India, were used in this study (23 years for training and 9 years for testing). The pattern of the sediment volume retained in this reservoir was well captured by the Multi-Layer Perceptron (3–5-1) ANN model using the back propagation algorithm. Based on several performance indices, it was found that the ANN model estimated the volume of sediment retained in the reservoir with better accuracy and less effort as compared to conventional regression analysis.