Irrigation is the largest human intervention in the water cycle that can modulate climate extremes, yet global irrigation water use (IWU) remains largely unknown. Microwave remote sensing offers a practical way to quantify IWU by monitoring changes in soil moisture caused by irrigation. This study evaluates the ability to quantify IWU by assimilating high-resolution (1km) SMAP-Sentinel 1 (SMAP-S1) remotely sensed soil moisture with a physically-based land surface model (LSM) using a particle batch smoother (PBS). A suite of synthetic experiments is devised to evaluate different error sources. Results from the synthetic experimentation show that unbiased simulations with known irrigation timing can produce an accurate irrigation estimate with a mean annual bias of 0.45% and the mean R² of 96.5%, relative to observed IWU. Unknown irrigation timing can significantly deteriorate the model performance by increasing the mean annual bias to 23% and decreasing the mean R² to 36%. In real-world experiments, the PBS data assimilation approach provides a mean bias of -18.6% when the timing of irrigation water use is known. This underestimation is possibly attributable to missing part of the irrigation signal. Yet, significantly higher irrigation was estimated over the irrigated pixels compared to the non-irrigated pixels, indicating that data assimilation can skillfully convey irrigation signals to LSMs. LSM calibration provides a 10% improvement to soil moistrue RMSE relative to the open-loop simulation. PBS data assimilation provides an additional 50% improvement to simulated soil moisture RMSE by correcting the model state and superimposing the optimal (unmodeled) irrigation on precipitation forcing.