International Journal of Environment and Climate Change

Aims: The study aimed to delineate rice accurately (Oryza sativa L.) cultivation areas in the Kuttanad region, Kerala, during the Puncha season of 2023-24 using medium- to high-resolution optical satellite data, particularly Landsat 8 Operational Land Imager (OLI), to aid in preharvest prediction of agricultural production and policy making.

Study Design: This study used a remote sensing-based approach for rice area estimation, focusing on supervised classification methods.

Place and Duration of Study: The study was conducted in Kuttanad, Kerala, a low-lying agroecosystem, during the Puncha season of 2023-24.

Methodology: The study utilised two cloud-free Landsat 8 OLI images to delineate rice-growing areas. The images were pre-processed, mosaicked, and analysed using ArcGIS software. A supervised classification approach was employed using the Maximum Likelihood Classification algorithm. The study area was classified into five categories: rice fields, other crops, low vegetation, built-up areas, and water bodies. Ground-truth data was used to validate the classification accuracy.

Results: The total rice area delineated during the Puncha season was 43,550.28 hectares. The classification achieved an accuracy of 93.33%, with a kappa coefficient of 0.87, indicating high reliability.

Conclusion: The accurate delineation of rice-growing areas using satellite imagery provides valuable information for assessing production levels and planning for food security. This methodology can aid in agricultural planning and contingency strategies, particularly in regions like Kuttanad, which face challenges such as flooding and soil toxicity.