Accurate mapping and monitoring of paddy crop-growing areas are crucial for food security because of ever-increasing population. The cultivated paddy field is often exposed to the risks associated with extreme weather conditions leading to the need for crop insurance. The recognized scheme for crop insurance ensures a guaranteed fraction of normal yield in an insured region. It is, however, failing in its efficacy due to the lack of reliable yield data. The present work focuses on deriving a substitute to yield data known as the crop health index in the crop season of 2022. The map of paddy crop is generated by utilizing the images of the Sentinel satellite, weather data, and ground observation. Also, the health indicators viz. NDVI, LSWI, FAPAR, and backscatter are analyzed for the same year. By utilizing the min–max method for data normalization and entropy-based weighting for parameter analysis, crop health factor (CHF) varying between 0 and 1, is calculated for the year of study. Variations of CHF and yield demonstrated a good correlation throughout the year. The yield data can be substituted effectively by CHF data for compensation and pay-out considerations in the year 2022, as advised by the authorities in advance. The analytical hierarchy process (AHP) process systematically prioritizes the relative importance of indicators, providing a robust foundation for decision-making. This research not only contributes to early production estimation but also paves the way for innovative, remote sensing-based agricultural risk management strategies, thereby enhancing the resilience of agricultural systems.