BACKGROUND The present work estimates the area and corresponding wheat crop production in the study area, which comprises the Etah region of Uttar Pradesh, India. For this purpose, multispectral images of multiple sensors, namely Sentinel‐2, Landsat‐8 and Landsat‐9 during the preharvest period, i.e. March for the years 2021 and 2022, were used. A multispectral information fusion approach was proposed, involving image classification as well as vegetation index‐based information extraction. For imposing information fusion, appropriate image bands were identified with the help of separability analysis followed by land cover classification for wheat crop class extraction. Support vector machine (SVM), artificial neural network (ANN) and maximum likelihood (ML) were used for classification, whereas normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) were used for index‐based crop area extraction. RESULTS A maximum accuracy of 98.34% was achieved for Sentinel‐2 data using ANN, whereas a minimum accuracy of 80.21% was achieved for Landsat‐9 using the ML classifier. The estimated area for Sentinel‐2 data for the year 2021 was 260 540 ha using ANN and 203 240 ha using ML, which is close to the reference data, i.e. 238 600 ha. SVM also showed good performance and calculated least error in estimated crop area for the year 2022 on Sentinel‐2 data. It calculated 8 408 490 tons of wheat for the same year. CONCLUSION The proposed method utilizes a single image per year for extraction of information supported by the ground truth data, which makes it a novel approach to information extraction for crop production monitoring. © 2023 Society of Chemical Industry.