Remote sensing-based site suitability assessment and selection of Rabi crop cultivation areas following flood events

Assessing the impact of floods on crop cultivation is critical for ensuring food security and resilience in flood-prone regions. This study relied on cloud-free imagery from Landsat to construct six vegetation indices. These indices were used to conduct a comparative analysis of the flood disaster of 2022 in Pakistan. A random forest classifier was employed on pre-flood images to identify agricultural zones. A model was developed using two indices, Brightness (DBSI) and Wetness (TCW), along with the vegetative index of crop vigor (MSAVI), during the pre-flood months of 2021. The model’s accuracy was validated and exhibited an RMSE of 0.03 in predicting MSAVI values. The model was used to forecast MSAVI during post-flood periods. Spatial analysis identified approximately 30-47% of the study area as ‘Suitable’ and 46-64% as 'Highly suitable’ for early recultivation of Rabi Crops. The approach helps in planning for fast agricultural recovery in the aftermath of floods.