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Crop monitoring over large areas with high accuracy is of great significance in precision agriculture. The study is an attempt to assess the potential of open-source high-resolution satellite datasets and open-source digital platforms in combination with AI/ML algorithms for near-real-time crop monitoring and yield estimation at the farm level. In this research, we used Sentinel-1 and Sentinel-2 datasets, by processing them on the Google Earth Engine platform and developed several crop-based indicators to assess crop phenology as well as the distinction between a well-managed field (demo plots) vs a normal farmers' practice-managed crop (control plots) using Sentinel-1 satellite data. Further, crop yields were estimated before the harvesting of the crop by using Sentinel-1 and Sentinel-2 data with machine learning algorithms. The findings demonstrate that the effect of an improved package of practices on rice was significantly different from the farmer's practice. Among the statistical yield models developed for yield estimation, the gradient tree boosting model performed better than other models. This study proposes a novel method of near-real-time remote crop monitoring right from sowing to harvest time to estimate crop yields with an accuracy of 77 percent. There is potential in using open-source satellite data for monitoring farm fields in the future.