Evaluation of river basins requires land-use and land-cover (LULC) change detection to
determine hydrological and ecological conditions for sustainable use of their resources. This
study investigates the changes in cropping patterns, classification accuracy, and land use
patterns during the kharif and rabi seasons of 2018-19. The supervised classification,
employing the maximum likelihood classifier method, was used to generate the classified
LULC maps in the ERDAS Imagine. The classified images produced by this technique were
evaluated for accuracy through matrix union using the statistical kappa coefficient and overall
accuracy measures. Change detection for the periods 2018-19 was conducted using matrix
union (intersection) to identify apparent changes in various LULC classes. The analysis
shows a significant shift in cropping practices, particularly a notable transition from rice to
wheat during the rabi season, with wheat cultivation increasing by 75.53%. Other crops such
as mustard, vegetable pea, and sugarcane also saw significant changes in acreage, reflecting
farmer’s responses to market and climatic conditions. Soybean, traditionally grown during the
kharif season, shifted to wheat in the rabi season. The classification accuracy for both kharif
and rabi crops was high, with overall accuracies of 92.95% and 94.02%, respectively, and
Kappa coefficients of 89.98% and 92.81%, indicating reliable classification results. Key
challenges included resolving spectral confusion between crops (e.g., wheat vs. mustard) and
addressing cloud cover limitations in kharif-season imagery. The study’s contributions
include: (1) a robust framework for high-resolution crop monitoring in heterogeneous
landscapes, (2) quantification of rapid cropping system transitions, and (3) demonstration of
Sentinel-2’s operational utility for precision agriculture. Results support evidence-based
policymaking for sustainable water and land use in monsoon-dependent systems.