For example, satellite observations are post-processed to rectify various spatial [21], temporal [22], radiometric [23], and spectral factors [24]. Much of this work is focused on facilitating sensor fusion at the surface reflectance level, rather than at the indexed data product level (NDVI for example). Specifically, recent work by [PERSON] et al. fused L8 OLI with Sentinel-2A MultiSpectral Instrument (MSI) surface reflectances to radiometrically correct blue, green, red, NIR, short-wave infrared (SWIR) 1, and SWIR 2 bands [25]. [PERSON] et al. also harmonized L8 OLI and S2A MSI into a virtual constellation to produce a surface reflectance dataset [26]. While it is crucial to investigate spectral differences, extending the analysis to derived products such as NDVI is important for assessing the applicability of sensor fusion to meet the long term monitoring needs of the scientific community [7; 27]. Differences in spatial resolution can add bias to fused NDVI data since it is scale dependent [28]. To reduce overall uncertainty, multiple NDVI intercalation studiesagree that data from the subject and reference sensors must be spatially co-registered and resampled enabling the comparison of data at the pixel level [29]. Subsequently, any physical quantities can be computed via accurate sensor calibration. In cases of spectral and spatial similarity, multi-sensor data can be used interchangeably [1; 30]. In other cases, the multi-sensor data are compared prior to being combined [31]. However, research gaps remain in the body of knowledge around sensor fusion. [PERSON] [32] compared Sentinel-2A (S2A) MSI and L8 OLI data but did not account for mis-registration between sensors. While previous research had focused on terrestrial areas (desert) only [33] and used simulated reflectance data [34], their models considered only limited amounts of data from the spatio-temporal domain. These models were based on the fusion of multi-sensor data for pre-selected dates only, thus rendering the model inapplicable to other seasons [35]. However, [PERSON] et al. did investigate sensor fusion across seasons for both individual bands and vegetation indices and concluded that absent systematic errors in data retrieval or random errors in atmospheric interference, L8 OLI and S2A MSI can be used for long term monitoring [36]. Other studies into sensor fusion did not consider more than two satellite systems of similar spectral and spatial features [37] or focused on categorically different land cover types such as urban areas [38]. The aforementioned research gaps are perhaps most prominent in multiple sensor fusion models that are applicable to any season or any area. Therefore, we propose a virtual constellation model for NDVI that integrates compatible sensors and addresses the sensor inequality issues by utilizing the coincident imagery from a selected four-year time series. The technique uses S2A MSI and ASTER synergistically with L8 OLI. The free access to L8, S2A, and ASTER, the similar wavelength for the bands relevant to NDVI, and similar geographic coordinate systems [39] provide a viable opportunity to combine these three satellite systems for more continuous monthly monitoring of CW areas. ASTER has been used in conjunction with Landsat 5 (L5) to compare two vegetation indices generated by these two sensors. Both NDVI and soil-adjusted vegetation index (SAVI) showed lower spectral vegetation index measurements for ASTER compared to L5 Enhanced Thematic Mapper Plus (ETM+) for the same target, but still showed a strong positive linear relationship [40]. Several simulation studies have also shown the potential of combining S2A and L8 [41; 42] as well as L8 and ASTER [40]. Here, we build on this previous work to demonstrate the benefit of using of L8, ASTER and S2A synergistically for long term continuous monthly monitoring of NDVI in coastal wetlands. [PERSON] [43], [PERSON] [44], and [PERSON] et al.