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Data interoperability and statistics

Outcomes

Jupyter Notebook

Description

Develop a notebook that accesses data from 3 missions (S1, S2 and Landsat). Develop some statistical results to show the impact of clouds on selected regions and the impact of multi-mission data interoperability.

  • User selects a region and time period
  • Calculate max gap, mean gap and min gap between clear views for all pixels (box-and-whisker) with individual missions and combinations of missions.
  • Calculate the % cloud cover for individual optical missions. Report the min, max, median and mean for the time series.
  • Report any days where there are simultaneous views from multiple missions. These represent opportunities for Cal-Val campaigns and data comparison.
  • Create a 365-day Pie-Chart with days-of-year around the pie and solid shading representing when data is available from single missions and combination of missions.
  • Produce a “heat map” of the number of clear views per pixel with any mission combination (single missions and combined missions)