Abstract:
The present study evaluates the potential of Ku-band Scatterometer
Satellite-1 (SCATSAT-1) for quantification of spatiotemporal variability
in snow cover area (SCA) over Himalayas (Himachal Pradesh) India. The
SCA has been measured using dual-polarized (HH and VV) backscattered SCATSAT-1 data. Two classification approaches, i.e., Linear Mixer
Model (LMM) and Artificial Neural Network (ANN) model have been
used for the present study. Both available backscatter coefficients
sigma-naught σ0 and gamma-naught γ0 have been considered for
the estimation of SCA. To compute the seasonal snow cover trends
for winter (2016‒2017 and 2017‒2018), a post-classification comparison (PCC) based change detection approach has been demonstrated
on the classified dataset (LMM and ANN). The SCA maps have been
validated using reference snow cover maps generated from the
Moderate-resolution Imaging Spectroradiometer (MODIS) sensor. The
final change-category maps have effectively mapped the snow cover
variations with accuracy in between 83.01% and 95.33%. The results
indicate the suitability of SCATSAT-1 for estimating the magnitude of
snow extent over the Himalayas.
