# Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA  
[PERSON]  
1 Centre for Geospatial Research, University of Georgia, Athens, GA 30602, USA; [EMAIL_ADDRESS] Centre for Geospatial Research, University of Georgia, Athens, GA 30602, USA; dmishra@uga. rise [9; 10]. This reduced productivity of C\({}_{4}\) plants puts them at an adaptive disadvantage, where they might be out-competed by C\({}_{3}\) species [11], causing massive shifts in biogeochemistry and ecosystem functioning. The continuous monitoring of the phenology of salt marsh ecosystems for the detection and analysis of any such trends is of utmost priority for the successful implementation of conservation and restoration measures, both at site-specific and landscape levels [12]. One way to study and understand the productive health of any vegetation ecosystem is through analyzing long-term spatiotemporal trends in phenology in relation to their environment [10]. Phenological measurements such as the timing of budburst and flowering are now widely used to detect and study the effect of climate variability on vegetation health, both at the site-specific anc broader landscape scale [13; 14; 15]. Further, phenology has been shown to be an effective indicator of forest and crop management practices [16; 17], urban heat islands [18; 19], the El Nino Southern Oscillation [20; 21; 22], and even the impact of allergies on human health [23]. Moreover, the accurate monitoring of vegetation phenology is crucial for the development of global surface energy and water flux models [24], and the global carbon cycle [25; 26]. Therefore, phenological properties or seasonality parameters, such as the timing of the start and end of the season, the rate of green-up and brown-down, amplitude, and maximum growth level have become emerging indicators of global environmental changes [27; 28]. Satellite-derived biophysical properties have been frequently utilized to understand vegetation phenology such as the onset of leaf greenness in the spring and leaf coloring in autumn [29; 30]. Remote sensing-based phenology began with the Advanced Very High Resolution Radiometer (AVHRR) [31], and has been significantly improved with the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites [32]. Both AVHRR and MODIS are currently operational, but MODIS provides images with improved spatial resolution (250 m to 1 km versus 1 km), spectral resolution (36 spectral bands versus six), and geolocation accuracy [33]. Studies have utilized MODIS images for monitoring ecosystem phenology at both site-specific and landscape scales [34; 35; 36; 37; 38]. Developing algorithms to automatically retrieve land surface phenology metrics from satellite data has been a popular research topic [39; 40; 41]. However, the nature of satellite data makes it difficult to directly extract phenological metrics [38; 42]. Satellite derived time-series data inevitably contain disturbances caused by cloud presence [43], atmospheric variability [44], and aerosol scattering [45], which degrade the data quality and hinder analysis. Therefore, satellite-derived time-series data are commonly quality-screened and/or smoothed to minimize noise and compensate for the absence of data before phenological metrics can be estimated [46]. Several methods based on the interpolation of time-series data have been proposed to remove such noise and reconstruct high-quality time-series products [47; 48; 49; 50; 51; 52]. The MATLAB-based program TIMESAT [53] allows the user to test different smoothing techniques, along with various statistical filters, for increasing the signal-to-noise ratio and generating noise-free phenology that can be further utilized for generating seasonality parameters [48; 54].