# Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA
## Materials and Methods
### Time-Series Composites  
The MODIS Land Science Team provides several data products derived from MODIS observations to the public, including the eight-day composite Land Surface Reflectance (MOD09A1). The MOD09A1 datasets include seven spectral bands at a spatial resolution of 500 m corrected for the effects of atmospheric gases, aerosols, and thin cirrus clouds [(74)]. We generated time-series composites for green biomass (GBM) for the salt marshes of LA for a 16-year period (2000-2015) using MODIS eight-day surface reflectance data (500 m). We generated subsets of LA salt marshes using the most recent vector boundaries obtained from the National Wetlands Inventory (NWI) database [(63)]. We generated GBM composites using the relationship between GBM and the Visible Atmospheric Resistant Index (VARI) [(75)] described in [PERSON] et al. [(12)], as follows:  
\[\textbf{GBM (grams per square meter) = 3617.104 \times VARI+543.3514} \tag{1}\]  
The GBM biophysical model was developed using MODIS surface reflectance and in situ estimates of GBM from 200 study plots across the northern Gulf of Mexico. The model produced a R\({}^{2}\) of 0.93 and an inherent percent normalized root mean square error (RMSE) of 17.34% in GBM estimation. [PERSON] et al. [(12)] demonstrated the superior performance of VARI for mapping salt marsh GBM using the MODIS 500 m images compared with the near infrared (NIR) based indices such as NDVI; although studies have utilized NIR-based indices to study salt marsh phenology with finer resolution sensors [(12; 62; 76)]. The poor performance of the traditional NIR-based indices was attributed to the severe interference of background moisture and tidal signals to NIR wavelengths, whereas the sensitivity of visible bands was argued to be less susceptible to background water signals. While generating GBM composites, we utilized MODIS quality assurance bands to retain only the best quality pixels in the time-series composites [(74; 12)]. This enabled us to assign equal weight to all of the salt marsh pixels for the extraction of phenology, rather than assigning weights based on quality pixels in TIMESAT. However, tidal correction was not performed, as it is not possible to determine the level of the contribution of daily MODIS images to the eight-day surface reflectance imagery, which would otherwise had helped correct for tidal signals using daily tidal fluctuation data [(12)].  
Since the MODIS surface reflectance products have a temporal resolution of eight days, 46 GBM composites were generated per year for the LA salt marshes; thus, for the 16-year study period, 736 composites were generated in total. We analyzed the salt marshes of the southeast and southwest climatic zones separately in order to avoid any discrepancy due to climate variability. We created subsets of salt marsh GBM composites for individual climatic zones, using the LA climate zone boundaries obtained from the National Climatic Data Centre (NCDC) archives [(65)]. Once subsets were generated for the salt marshes for individual climatic zones, we converted the raw GBM composites to 16-bit signed generic binary format for running TIMESAT [(53)]. We also compiled temperature, precipitation, and Palmer Drought Severity Index (PDSI) [(77)] data for the individual climatic zones from the NCDC archives, to investigate any significant temperature, precipitation, and PDSI anomalies during the 16-year period that might potentially contribute to salt marsh phenological trends.