Long-term (1973–2021) shoreline displacement, rate of change, and temporal pattern were examined using multi-date Landsat data and Digital Shoreline Analysis System (DSAS) along the 200 km coast of Hooghly estuary. Orthogonal transects of 100 m apart were casted for calculation of End Point Rate and Weighted Linear Regression rate on different temporal scales for seven analysis zones. The shoreline change pattern was established using Hierarchical agglomerative clustering. The study reveals that almost 43.45% of the beachfront has eroded and 56.55% has accreted during the past four decades. The average erosion rate varies between −0.01 and −13.71 m yr.−1 and accretion of −0.01 to −22.30 m yr.−1. The littoral drift resulted in a maximum seaward aggression by 1096.89 m in the zone 1. Landward movement was maximum (−602.96 m) in the zone 4. Although west bank is prograding @ 3.47 m yr.−1 (±5.83), the east bank is eroding @ 1.30 m yr.−1 (±4.08). Based on the cluster analysis about –1.87% of the shoreline exhibits consistent erosion over all the intervals, whereas trend was evident in 4.73% of the coastline. The portions of coastlines, which exhibit high erosion rate and consistent erosion need immediate attention and policy intervention.

The lack of availability of two shortwave-infrared (SWIR) bands in most of the satellite sensors restricts the utilization of shortwave angle slope index (SASI). The potential of Resourcesat-2 Advanced Wide-Field Sensor (AWiFS) with a single SWIR band was explored to generate surface moisture information comparable to SASI over Haryana, India. The fractional values of several vegetation indices, viz. normalized difference water index (NDWI), SASI and angle-based drought index (ABDI) derived from moderate resolution imaging spectroradiometer (MODIS) were compared. At different phenological stages, ABDI was found to be a better index than NDWI while representing SASI values using regression analysis and Mahalonobis distance measurement. A very high coefficient of regression value (R² = 0.8) was observed while analyzing the agreement between AWiFS-derived ABDI and MODIS-derived SASI for the year 2012. The regression parameters were deployed on AWiFS-derived ABDI to generate improved moisture images for the year 2013 and were compared with actual SASI images from MODIS data.