# Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
## 3 Results
### Historical Trends  
Spatial averaging of pixel time series within endmember regions can enhance the detection of interannual trends over the duration of the satellite image archive. Because time series within each endmember region are selected on the basis of their statistical cosimilarity (and their dissimilarity from pixels in the other endmember regions), the benefits of averaging coherent time series are maximized, while the confounding effects of introducing time series with phase differences into the average are  
Figure 7: Full 18-year Jabl Al Qamar EM time series. Each curve is the spatial average of the 101 pixels that comprise each EM region. As with Jabl Al Qara, substantial variability is observed among and within EMs. Eastern forests are generally greener than western forests. Post-monsoon regreening appears more frequent and more intense in Jabl Al Qamar than Jabl Al Qara. As in Jabl Al Qara, years with both strong and weak greening of Jabl Al Qamar are present at the beginning and end of the time series. Again, no clear trend in any EM time series is evident. Minor but consistent differences are present between the eastern and western Nejd, but the values of NDVI involved are so low that we do not attempt to ascribe physical meaning to these differences. minimized. This approach simultaneously 1) reduces the noise present in single pixel time series, and 2) allows for the preservation of geographic differences in phenology. Segregating time series by phenolic phase is particularly important for analysis of historical trends, because relative phase differences can bias estimation of interannual trends if averaged together. We approach the question of historical trend detection using anniversary images (collected on the same Julian day each year) from dates with the fewest number of cloudy acquisitions across the 17-year time series. We do this for three 30-day windows after the end of the monsoon (Figure 8). Anniversary images are required due to the natural interannual variability in timing and amplitude of the monsoon-phenological system seen in Figures 4-7. For an interannual trend to be detected, its magnitude must be at least as large as this natural variability. Introducing even small changes in sampling date has the potential to bias multiyear statistical analysis. Year-to-year changes in sampling date are unavoidable with products using the 8- or 16-day MODIS compositing process. We choose the months after the monsoon in order to optimize the trade-off between amplitude of NDVI signal and lower frequency of cloud cover. However, to avoid introducing bias by choosing an unrepresentative Julian Day, we also show full calendar plots for each endmember region. Every cell of these 17 x 366 matrices represents one day of the time series. Each row is a year and each column is a Julian Day. Spatial average NDVI of each set of endmember pixels is represented as grayscale brightness. Brighter cells have higher NDVI. These calendar plots have the advantage of displaying the entire image time series for each endmember region. Obvious trends in monsoon or vegetation phenology should be apparent as vertical gradations in the lightness of these calendar plots. Less obvious trends may emerge from statistical analysis of the anniversary date time series. The anniversary plots that are shown to the left of the calendar plots are simply column profiles of the calendar plots with the fewest data drop-outs or cloud cover. The subregional analyses reveal no consistent trends in the onset or duration of the monsoon, nor any interannual trend in overall vegetation abundance. Both parametric linear regression and non-parametric Mann-Kendall tests were applied to each time series. Of the 21 anniversary date--endmember region combinations examined here, only one shows a trend significant at the 90% or 95% confidence levels, and none show significant trends at the 99% confidence level.