Irrigation is not well represented in land surface, hydrological, and climate models. One way to account for irrigation is by assimilating satellite soil moisture data that contains irrigation signal with land surface models. In this study, the irrigation detection ability of SMAP enhanced 9 km and SMAP-Sentinel 1 (SMAP-S1), 3 km and 1 km soil moisture products are evaluated using the first moment (mean) and the second moment (variability) of soil moisture data. The SMAP enhanced 9 km soil moisture product lacks irrigation signals in an irrigated plain south of Urmia Lake, whereas SMAP-S1 products record irrigation signal in soil moisture variability. Despite observing higher variability over irrigated areas, there are only small and inconsistent wet biases observed over irrigated pixels relative to nearby nonirrigated pixels during the irrigation season. This is partly attributable to the climatology vegetation water content used in the SMAP-S1 soil moisture retrieval algorithm that is not accounting for crop rotation and land management. Thus, in the second part of this study, we updated the retrieval algorithm to use dynamic vegetation water content. The update increased vegetation water content up to 1 kg/m ² which corresponds with a 0.05 cm ³ /cm ³ increase in soil moisture during irrigation season. The update does not notably change soil moisture retrievals off season. This study shows that irrigation signals are present in both the first and second moment of soil moisture time series, and employing dynamic vegetation water content in the SMAP-S1 algorithm can enhance the irrigation signal over agricultural regions.

Irrigation is the largest human intervention in the water cycle that can modulate climate extremes, yet irrigation water use (IWU) remains largely unknown in most regions. Microwave remote sensing offers a practical way to quantify IWU by monitoring changes in soil moisture caused by irrigation. However, high‐resolution satellite soil moisture data is typically infrequent (e.g., 6–12 days) and thus may miss irrigation events. This study evaluates the ability to quantify IWU by assimilating high‐resolution (1 km) SMAP‐Sentinel 1 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 experiments show that unbiased simulations with known irrigation timing can produce an accurate irrigation estimate with a mean annual bias of 0.45% and a mean R² of 0.97, relative to observed IWU. Unknown irrigation timing can significantly deteriorate the model performance, resulting in an increased mean annual bias to 23% and decreased mean R² to 0.36. Adding random noise to synthetic observations does not significantly decrease model performance except for the experiments with low observation frequency (>12 days). In real‐world experiments, the PBS data assimilation approach underestimates observed IWU by 18.6% when the timing of IWU is known. IWU estimates are consistently significantly higher over irrigated pixels compared to the non‐irrigated pixels, indicating data assimilation skillfully conveys irrigation signals to the LSM.

Considering variations in surface soil moisture (SSM) is essential in improving crop yield and irrigation scheduling. Today, most remotely sensed soil moisture products have difficulties in resolving irrigation signals at the plot scale. This study aims to use Sentinel-1 radar backscatter and Sentinel-2 multispectral imagery to estimate SSM at high spatial (10 m) and temporal resolution (at least 5 days) over an agricultural domain. Three supervised machine learning algorithms, multilayer perceptron (MLP), a convolutional neural network (CNN), and linear regression models, were trained to estimate changes in SSM based on the variation in surface reflectance and backscatter over five different crops. Results showed that CNN is the best algorithm as it understands spatial relations and better represents two-dimensional images. Estimated values for SSM were in agreement with in-situ measurements regardless of the crop type, with $RMSE=0.0292 ({\mathrm{cm}}^3/{\mathrm{cm}}^3)$ and $R^2=0.92$ for the Sentinel-2 derived SSM and $RMSE=0.0317 ({\mathrm{cm}}^3/{\mathrm{cm}}^3)$ and $R^2=0.84$ for the Sentinel-1 soil moisture data. Moreover, a time series of estimated SSM based on Sentinel-1 (SSM-S1), Sentinel-2 (SSM-S2), and SSM derived from SMAP-Sentinel1 was compared. The developed SSM data showed a significantly higher mean SSM state over irrigated agriculture relative to the rainfed cropland area during the irrigation season. The multiple comparisons (fisher LSD) were tested and found that these two groups are different ($p_{value}=0.035$ in 95% confidence interval). Therefore, by employing the maximum likelihood classification on the SSM data, we managed to map the irrigated agriculture. The overall accuracy of this unsupervised classification is 77%, with a kappa coefficient of 65%.