Land surface soil moisture (SSM) is a critical parameter in agriculture, hydrology, and climate studies. Microwave remote sensing technology, which is extensively used for acquiring SSM information at the regional scale, faces accuracy limitations due to influencing factors such as vegetation coverage. This study first comprehensively assessed the accuracy of SSM products derived from the Microwave Radiation Imager (MWRI) carried by Chinese Fengyun-3 (FY-3) satellites. The results revealed a clear decline in correlation and an increase in unbiased root-mean-square error (ubRMSE) with increasing vegetation density, particularly when the normalized difference vegetation index (NDVI) exceeded 0.5. To address this, a piecewise artificial neural network (pANN) model was developed by integrating FY-3 MWRI multi-frequency brightness temperature (BT), FY-3 Medium Resolution Spectral Imager (MERSI) NDVI, and auxiliary data (land cover, soil texture, and date information). This approach significantly improved SSM estimation accuracy across diverse vegetation conditions by effectively capturing the complex interactions between vegetation, soil, and the atmosphere. The pANN model enhanced the correlation coefficient (R) with ground measurements from 0.240 to 0.517 in sparsely vegetated areas (NDVI 0.0–0.3) and from −0.050 to 0.346 in densely vegetated areas (NDVI > 0.5). Furthermore, the pANN-based product demonstrated competitive performance against the international soil moisture active passive (SMAP) level-3 (L3) soil moisture product and substantially increased spatial coverage compared to the original FY-3 data. The refined SSM dataset proves to be a reliable resource for applications in drought and flood monitoring, as well as weather and climate modeling.

Accurate estimation of regional-scale crop yield under drought conditions allows farmers and agricultural agencies to make well-informed decisions and guide agronomic management. However, few studies have focused on using the crop model data assimilation (CMDA) method for regional-scale winter wheat yield estimation under drought stress and partial-irrigation conditions. In this study, we developed a CMDA framework to integrate remotely sensed water stress factor (MOD16 ET PET⁻¹) with the WOFOST model using an ensemble Kalman filter (EnKF) for winter wheat yield estimation at the regional scale in the North China Plain (NCP) during 2008–2018. According to our results, integration of MOD16 ET PET⁻¹ with the WOFOST model produced more accurate estimates of regional winter wheat yield than open-loop simulation. The correlation coefficient of simulated yield with statistical yield increased for each year and error decreased in most years, with r ranging from 0.28 to 0.65 and RMSE ranging from 700.08 to 1966.12 kg ha⁻¹. Yield estimation using the CMDA method was more suitable in drought years (r = 0.47, RMSE = 919.04 kg ha⁻¹) than in normal years (r = 0.30, RMSE = 1215.51 kg ha⁻¹). Our approach performed better in yield estimation under drought conditions than the conventional empirical correlation method using vegetation condition index (VCI). This research highlighted the potential of assimilating remotely sensed water stress factor, which can account for irrigation benefit, into crop model for improving the accuracy of winter wheat yield estimation at the regional scale especially under drought conditions, and this approach can be easily adapted to other regions and crops.