Intermittent Rivers and Ephemeral Streams (IRES) are the major sources of flowing water on Earth. Yet, their dynamics are challenging for optical and radar satellites to monitor due to the heavy cloud cover and narrow water surfaces. The significant backscattering mechanism change and image mismatch further hinder the joint use of optical-SAR images in IRES monitoring. Here, a D eep data fusion-based R econstruction of the wide accepted Modified Normalized Difference Water Index (MNDWI) time series is conducted for I RES M onitoring (DRIM). The study utilizes 3 categories of explanatory variables, i.e., the cross-orbits Sentinel-1 SAR for the continuous IRES observation, anchor data for the implicit co-registration, and auxiliary data that reflects the dynamics of IRES. A tight-coupled CNN-RNN architecture is designed to achieve pixel-level SAR-to-optical reconstruction under significant backscattering mechanism changes. The 10 m MNDWI time series with a 12-day interval is effectively regressed, R2 > 0.80, on the experimental catchment. The comparison with the RF, RNN, and CNN methods affirms the advantage of the tight-coupled CNN-RNN system in the SAR-to-optical regression with the R2 increasing by 0.68 at least. The ablation test highlights the contributions of the Sentinel-1 to the precise MNDWI time series reconstruction, and the anchor and auxiliary data to the effective multi-source data fusion, respectively. The reconstructions highly match the observations of IRES with river widths ranging from m to 300 m. Furthermore, the DRIM method shows excellent applicability, i.e., average R2 of 0.77, in IRES under polar, temperate, tropical, and arid climates. In conclusion, the proposed method is powerful in reconstructing the MNDWI time series of sub-pixel to multi-pixel scale IRES under the problem of backscattering mechanism change and image mismatch. The reconstructed MNDWI time series are essential for exploring the hydrological processes of IRES dynamics and optimizing water resource management at the basin scale.