# Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA
## Materials and Methods
### Determination of Start and End of Season Determination Using TIMESAT and Derivative Analysis  
TIMESAT allows the user to specify SOS and EOS dates as a user-defined fraction of the seasonal amplitude. We derived SOS and EOS using different thresholds (fractions of seasonal amplitude), such as 5%, 10%, 15%, and 20%. We did not test thresholds below five percent, as it might be influenced by the noise from the non-growing season [55]. Phenology studies using TIMESAT have mostly preferred to use thresholds above 10% (usually 20%; sometimes 50%) for deriving SOS and EOS dates using a NIR wavelength-based vegetation index time series. Most of these thresholds are usually determined based on expert opinions or in-field observations of the SOS and EOS [85; 86; 87]. However,the phenology of the salt marsh ecosystem has neither been studied in detail, nor did we have any in-field monitoring stations observing real-time SOS and EOS dates that could have been the basis for choosing or validating appropriate thresholds of SOS and EOS for TIMESAT. Temperature variations at the start and end of the growing season may be used to determine the SOS and EOS; however, plant phenology and seasonality parameters are not only influenced by temperature, but also by other environmental variables or local environmental disturbances and dynamics. Further, the NDVI time series or thresholds could not be utilized for deriving SOS and EOS, as NDVI or any NIR-based indices are prone to insensitivity in salt marsh settings [12]. GBM was derived from MODIS 500 m eight-day surface reflectance composites for LA marshes using the VARI [75], which had been shown to be more sensitive than NIR-based indices, especially for salt marshes with perpetually saturated soil [12]. Therefore, we tested the performances of different thresholds by matching the SOS and EOS dates derived using derivative analysis against dates derived using different thresholds in TIMESAT, as described in the following paragraph.  
Unfortunately, there is no specific universal threshold that can be applied to estimate SOS and EOS dates; and calibration is necessary before the selection of any specific threshold. We investigated the derivatives of smoothed GBM time-series data, which were derived using both AG and DL methods from ~400 random pixels. We estimated the third-order derivative from the smoothed time series, and derived local maxima and minima [83]. The third-order derivative has been demonstrated as a better indicator for identifying SOS and EOS than the second order, as the latter represents the timing when the majority of vegetation within a pixel is turning green, rather than the timing when the change of green-up rate or brown-down rate is the greatest [83]. For the SOS and EOS, we matched the local maxima and local minima, respectively, with the SOS and EOS dates derived using different thresholds in TIMESAT. We did this using a smoothed time series derived by employing both AG and DL smoothing functions in TIMESAT. We did not analyze derivatives for peak of season (POS) date estimations, as POS is a function of the maximum value encountered in the middle of the growing season, and it does not depend on the thresholds chosen for SOS and EOS. Once we matched SOS and EOS dates derived from derivative analysis against TIMESAT thresholds, we estimated the error between the observations using root mean square error (RMSE), and also analyzed residual plots for any specific trends. We selected the TIMESAT SOS and EOS thresholds that illustrated the greatest agreement with the dates derived from derivative analysis. After that, we derived the SOS and EOS for the entire salt marsh landscape using the best SOS and EOS thresholds and the best smoothing technique in TIMESAT, for further analysis and validation.