Rice cultivation supplies half the world's population with staple food, while also being a major driver of freshwater depletion—consuming roughly a quarter of global freshwater—and accounting for $\sim$48\% of greenhouse gas emissions from croplands. In regions like Punjab, India, where groundwater levels are plummeting at 41.6 cm/year, adopting water-saving rice farming practices is critical. Direct-Seeded Rice (DSR) and Alternate Wetting and Drying (AWD) can cut irrigation water use by 20–40\% without hurting yields, yet lack of spatial data on adoption impedes effective adaptation policy and climate action. We present a machine learning framework to bridge this data gap by monitoring sustainable rice farming at scale. In collaboration with agronomy experts and a large-scale farmer training program, we obtained ground-truth data from $\sim$1,400 fields across Punjab. Leveraging this partnership, we developed a novel dimensional classification approach that decouples sowing and irrigation practices, achieving F1 scores of 0.772 and 0.806 respectively, solely employing Sentinel-1 satellite imagery. Explainability analysis reveals that DSR classification is reliable while AWD classification depends primarily on planting schedule differences, as Sentinel-1's 12-day revisit frequency cannot capture the higher frequency irrigation cycles characteristic of AWD practices. Applying this model across 3 million fields reveals spatial inequities in adoption at the state level, highlighting gaps and opportunities for policy targeting. Our district-level adoption rates correlate strongly (Pearson=0.87) with government estimates. This study provides policymakers and sustainability programs a powerful tool to track practice adoption, inform targeted interventions, and drive data-driven policies for water conservation and climate mitigation at regional scale. We release all code and models at: \url{https://github.com/anonymous-repo} to support reproducibility and future research.