NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error <0.04 m3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the ESA Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio-temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

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.