Accurate description of surface soil moisture (SSM) in vegetation-covered areas is of great significance to water resource management and drought monitoring. To remove the effect of vegetation on SSM estimation, the vegetation index obtained from Sentinel-2 (S2) was applied for vegetation water content (VWC) estimation. The VWC model was substituted into the water cloud model (WCM), and thus, the SSM estimation model was developed based on the WCM. The methodology was tested at Daxing, Beijing, and Gu’an, Hebei, in which training and validation data of SSM were acquired by in situ measurements. The results can be described as follows: (1) For the vegetation-covered areas, the Modified Chlorophyll Absorption Ratio Index (MCARI) obtained from the B3, B4, and B5 bands of S2 was the most suitable for removing the influence of vegetation on SSM estimation; (2) Compared to Sentinel-1 (S1) vertical–horizontal (VH) polarization, vertical–vertical (VV) polarization was more suitable for SSM estimation and achieved higher accuracy; (3) The developed model could be used to estimate SSM under crop cover with high accuracy, which indicated the correlation coefficients (R2) between in situ measured and estimated SSM were 0.867, the root mean square error (RMSE) was 0.028 cm3/cm3, and the MAE was 0.023 cm3/cm3. Thus, this methodology has the potential for SSM estimation in vegetated areas.

The timely and accurate estimation of soil water content (SWC) and evapotranspiration (ET) is of great significance in drought estimation, irrigation management, and water resources comprehensive utilization. The unsupervised classification was used to identify the crops in the region. Based on MOD16A2 and the meteorological data, a SEBS model was used to estimate the ET in the Jiefangzha Irrigation Field from 2011 to 2015. Based on the crop water stress index (CWSI), the SWC in 2014 was retrieved and verified with the measured SWC on different underlying surfaces (sunflower, corn, wheat, and pepper). The results showed that: (1) The positional accuracy of maize, sunflower, wheat, and pepper are 0.81, 0.80, 0.90, and 0.82, respectively; (2) The annual ET from 2011 to 2015 presented well the spatial distribution of the ET within the field; (3) The validation results of the estimated SWC on the underlying surface of wheat and sunflower showed a good robustness, the R2 was 0.748 and 0.357, respectively, the RMSE was 2.61% and 2.309%, respectively, and the MAE was 2.249% and 1.975%, respectively. However, for maize and pepper with more irrigation times, the SWC estimation results, based on the CWSI were poor, indicating that the method was more sensitive to soil drought and suitable for the crop SWC estimation with less irrigation and drought tolerance. The results can provide a reference for the agricultural water resources management and the irrigation forecast at a regional scale.

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