Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation  
[PERSON]  
1 Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL. 32816, USA; [EMAIL_ADDRESS] (S.T. ); [EMAIL_ADDRESS] (A.S.)  
[PERSON]  
2 Department of Civil Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL. 32114, USA 2*Correspondence: [EMAIL_ADDRESS] 2*Correspondence: medeiros@erau. an inherent disruption of harmonizing information due to differences in ground resolution, spectral ranges, and spectral properties, such as band number, position, and width [9]. Therefore, using satellite sensors of similar spatial and spectral properties, especially for monthly CW analyses, is vital for the coherent fusion of multi-sensor satellite data. Landsat is generally recognized to have the longest publicly available data record beginning in 1972. Landsat 5 has been producing imagery since 1984 and has been used extensively for local and global monitoring. Landsat 8 (L8), launched in 2013, is the latest generation space vehicle in the Landsat Data Continuity mission. The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are both currently in operation aboard L8 with an orbital revisit time of 16 days [1]. The historical record of Landsat imagery is hindered by the cloud and shadow obscurity that affects all passive optical satellite sensors. Periods of obscured imagery in the time series record of L8 requires complementary data to make it suitable for monthly CW monitoring in applications such as long-term salt marsh change and mapping [10; 11], forest degradation [9], rapid phenology changes [11], and CW degradation [12; 13]. Normalized Difference Vegetation Index (NDVI) is calculated from multispectral optical remote sensors and characterizes the reflective and absorptive characteristics of the ground surface in the red and near infrared (NIR) bands. A consistent investigation of NDVI over time at local to regional scales can indicate phenological and event-driven changes in coastal marsh vegetation [11; 13; 14]. Current optical sensors have enough spatial and temporal resolution for NDVI production, but images may be obscured by clouds thereby masking critical areas of change over time. These obscured pixels are one of the principal barriers to effective satellite image interpretation from optical sensors. However, panchromatic and multispectral sensors such as Landsat, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel are the only freely available (to the public) sources of reflectance data [15] with others being commercial and not publicly available except at significant cost. Hyperspectral sensors on satellites such as MERIS/ENVISAT-1 could provide additional useful information about the composition of coastal vegetation however their relatively coarse resolution is not suitable for a local ecosystem change identification and the narrow swath width results in low availability of data for coastal areas, both of which prevent their efficient and widespread utilization for many coastal applications. Previous research has reported many successful applications of sensor fusion among Landsat, Moderate Resolution Imaging Spectroradiometer, (MODIS), and Sentinel [16; 17]. Additionally, satellite sensors including Landsat, Advanced Very High Resolution Radiometer (AVHRR), ASTER, Sentinel, and MODIS have been used for NDVI mapping at local or global scales [18; 19]. Despite the availability of NDVI data from multiple sources, an inherent inconsistency hinders the synergistic use of multi-source NDVI. Previous researchers such as [PERSON] and [PERSON] [20] have also presented comprehensive literature reviews on spectral consistency issues in NDVI data. Since NDVI is computed using reflectance values in the red and NIR bands, any spectral band inconsistencies directly impact the NDVI calculations.