It is necessary to classify crops throughout the Kharif season for food security and sustainable agriculture. During this time, there are a lot of clouds and rain patterns that are hard to predict, which makes optical satellite images are cloudy. This means that weather-independent monitoring systems are needed. This study utilized automated phenology-based temporal signatures that adapt according to regional crop calendars and monsoon variability. It integrated polarimetric features with rainfall data to enhance differentiation between water-sensitive crops such as rice and maize, and dryland crops like cotton. Additionally, it employed cloud-scalable machine learning algorithms, specifically Random Forest and Support Vector Machine classifiers, optimized for SAR-specific characteristics, and implemented cross-polarization ratio analysis (VH/VV) to monitor structural changes in crop canopies across various growth stages. The framework makes maps of different sorts of crops that are always right by using data from Sentinel-1 across a number of years. It also uses Start-of-Season detection algorithms to keep track of how well the planting is going. The technology also has the ability to analyze trends to find changes in cropping patterns and send out early warnings when planting is delayed because of monsoon abnormalities. The methodology includes field-level confidence mapping, which gives farmers and policymakers estimates of how reliable crop predictions are by showing how uncertain each classified pixel is and this gives them a complete picture of agricultural intelligence that can help them adapt to climate change and use sustainable farming practices.