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Hydrosat leverages land surface temperature measured by thermal infrared (TIR) satellite technology to help growers save water and increase yields. Key agronomical parameters, e.g., soil moisture, crop development, and crop water demand, are monitored daily over arbitrarily large areas by solving the surface energy balance. Coupled to a soil water balance model based on meteorological data, remote sensing algorithms also estimate the amount of irrigation water applied by farmers and generate irrigation recommendations optimizing water productivity, i.e., maximizing crop yield while minimizing irrigation water consumption.IrriWatch is Hydrosat’s irrigation management decision support system, which allows growers to track water demand and growth progress of their crops down to individual 10x10 m² pixels, daily, in near-real-time. With governments becoming more conscious about conserving their water reserves, applying high-resolution remote sensing algorithms over large irrigation districts - and potentially even whole nations - is becoming increasingly relevant. Compared to small proof-of-concept models, this requires careful balancing of complex steps, including automated field delineation and crop identification at scale early in the growing season, data fusion and sharpening to obtain high-fidelity daily TIR data at a spatial resolution compatible with detecting in-field variations, as well as energy and water balance modelling capable to handle diverse environmental conditions and soil or crop types without any local data or farm management information available. To effectively help governments preserve water while increasing farmers’ crop yields, the immense amount of data generated by our models must be condensed to clear actionable indicators that are intuitive to an audience not necessarily familiar with remote sensing concepts.We will present an overview of an operational processing pipeline to support both field-level precision agriculture applications and large-scale water productivity monitoring and optimization. Leveraging daily high-resolution land surface temperature, both from Hydrosat’s own satellite constellation and from a novel thermal sharpening algorithm, allows to track water productivity over tens of thousands of square kilometers. We find that high spatio-temporal resolution is critical to accurately monitor crop development even at regional or seasonal scale, as insufficient resolution introduces substantial errors in actual evapotranspiration estimates. In addition, correcting for geomorphological factors, e.g., microclimate or effect of elevation or slope on surface temperature, becomes increasingly important over large spatial scales.Statistical analysis of field-scale results over large areas reveals spatial patterns of conditions responsible for yield losses or excessive water consumption. We will demonstrate how such insights support automatic identification of root causes for low water productivity, forming the basis for efficiently implementing data-driven mitigation actions.